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Managing Geographic Data as an Asset: A Case Study in Large Scale Data Management by Clay Smithers A thesis submitted in partial fulfillment of the requirements for the degree of Master of Arts Department of Geography College of Arts and Sciences University of South Florida Major Professor: Steven Reader, Ph.D. Committee Member: Jayaji t Chakraborty, Ph.D. Committee Member: Hyun Kim, Ph.D. Date of Approval: November 21, 2008 Keywords: GIS, Asset Management, Metadata, Return on Investment, Spatial Data Infrastructure Copyright 2008 Clay Smithers
i Table of Contents List of Figures ............................................................................................................... ................iii Abstract .................................................................................................................... ............iv Chapter 1: In troduction........................................................................................................ .............1 Chapter 2: Lite rature Review................................................................................................... ........3 2.1 Introdu ction............................................................................................................... .............3 2.2 Defining G eographic Data................................................................................................... ..3 2.3 Providing Context for Geographi c Data.................................................................................7 2.4 Defining Assets and Asset M anagement .............................................................................12 2.5 Defining a Data As set Managemen t System.......................................................................21 2.6 Conclusion................................................................................................................. ..........27 Chapter 3: Case Study Overview................................................................................................. ..29 Chapter 4: Defining Geographi c Data as an Asse t........................................................................35 4.1 Introdu ction............................................................................................................... ...........35 4.2 What is G eographic Da ta?..................................................................................................3 7 4.3 Organizing G eographic Data...............................................................................................43 4.4 What is an Asset?.......................................................................................................... ......44 4.5 Applications of Asset M anagement for Geog raphic Da ta....................................................61 4.6 Conclusion................................................................................................................. ..........70 Chapter 5: Defining a Geographic Data Asset Management System...........................................72 5.1 Introdu ction............................................................................................................... ...........72 5.2 Requirements for a Geographic Data Asset Management System....................................72 5.3 Conclusion................................................................................................................. ..........83 Chapter 6: Conclusion.......................................................................................................... ..........85
ii List of References............................................................................................................. .............90 Appendix A ................................................................................................................... ...........93 Appendix B ................................................................................................................... ...........98
iii List of Figures Figure 1: Deferred Main tenance over Time...................................................................................18 Figure 2: Proposed C entral Florida Comm uter Rail Map...............................................................30 Figure 3: Commuter Rail Sampling Plan Downtown Orlando.....................................................32 Figure 4: Potential Contaminated Sites......................................................................................... 33 Figure 5: Data-Informati on-Asset Context.....................................................................................36 Figure 6: Maintenance Assistant Screen shot Geographic Da ta Inventory..................................48 Figure 7: Aerial Imagery Comparison 19 99-2006 ..........................................................................50 Figure 8: Return on Investment (ROI ) Calculation Se lect Datasets............................................53 Figure 9: Aerial Imagery Resolution Co mparison..........................................................................57 Figure 10: Maintenance A ssistant Scre enshots............................................................................68 Figure 11: Summary of Geographic Data A sset Management System Requireme nts.................83
iv Managing Geographic Data as an Asset: A Case Study in Large Scale Data Management Clay Smithers ABSTRACT Geographic data is a hallowed element within the Geographic Information Systems (GIS) discipline. As geographic data faces increased usage in distributed and mobile environments, the ability to access and maintain that data can become cha llenging. Traditional methods of data management through the use of file storage, databases, and data catalog software are valuable in their ability to organize data, but provide little information about how the data was collected, how often the data is upd ated, and what value the data holds for an organization. By defining geographic data as an asset it becomes a valuable resource that requires acquisition, maintenance and sometimes retirement during its lifetime. To further understand why geographic data is different than other types of data, we must look at the many components of geographic data and specifically how that data is gathered and organized. To best align geographic data to the asset m anagement discipline, this thesis will focus on six key dimensions, established through the wo rk of Vanier (2000, 2001), which seek to evaluate asset management systems. Using a conc eptual narrative linked to an environmental analysis case study, this research seeks to info rm as to the strategies for efficiently managing geospatial data resources. Thes e resources gain value through the context applied by the inclusion of a standard structure and methodologies from the asset management field. The result of this thesis is the determination of the ex tent to which geographic data can be considered an asset, what asset management strategies are applicable to geographic data, and what are the requirements for geographic data asset management systems.
1 Chapter 1: Introduction Geographic data is a fundamental element with in the realm of Geographic Information Systems (GIS) technology and practice. Hardware and softwar e purchases may constitute the majority of a GIS organizations spending, but the successf ul utilization of a GIS ultimately depends on access to data. As geographic data faces in creased usage in distributed and mobile environments, the ability to access and maintain that data can become cha llenging. Traditional methods of data management through the use of file storage, databases, and data catalog software are valuable in their ability to organize data, but provide little information about how the data was collected, how often the data is upd ated, and what value the data holds for an organization. By defining geographic data as an asset it becomes a valuable resource that requires acquisition, maintenance and sometimes retirement during its lifetime. To further understand why geographic data is different than other types of data, we must look at the many components of geographic data, and sp ecifically how that data is gathered and organized. Asset management strategies and systems used by the facilities management, construction, and information technology industries provide a fram ework that enables us to consider whether such systems might be able to better organize data for GIS use. Managing geographic data as an asset includes an understanding of geographic information systems, data management techniques, and asse t management strategies. GIS practitioners generally focus on the display and analysis of data, rather than the specific organization of their data. In the information technology field, dat a management is frequently discussed within the realm of database management systems to or ganize and distribute data. Meanwhile, asset management is primarily considered as a method for monitoring the distribution and use of
2 physical materials or facility management. The aim of this thesis is to attempt to investigate the potential of an asset management approach to organize and manage geographic data. To do this, the thesis will use six key dimensions of data related to asset management, established through the work of Vanier (2000, 2001). The six questions he defines provide an investigative framework for this research and are a guide to the design of an asset management system. The six dimensions are: What do you own? What is it worth? What is the deferred maintenance? What is its condition? What is the remaining service life? What do you fix first? Using these six dimensions as a framework, t he following research questions will be addressed: 1. To what extent can geographic data be considered an asset? 2. Can existing asset management techniques, strategies, and solutions be successfully applied to geographic data? 3. What are the requirements for a geographic data-focused asset management system? The first question relates the attributes of geographi c data (a non-physical entity) to those of the physical asset, typically managed by an asset management system. The second question extends the first by relating an asset and its various management techniques to the management of geographic data. These first two questions se rve as the schema for the data organization and system architecture necessary for an object -oriented approach to software analysis and design. The final question of the thesis endeav ors to define the necessary requirements for a geographic data-centered asset management system Each of these research questions will be discussed in terms of Vanier's six dimensions an d will draw upon the experiences of a case study linked to an environmental analysis project for a commuter rail corridor in Orlando, FL.
3 Chapter 2: Literature Review 2.1 Introduction The concept of combining asset management systems and geographic data is only tangentially addressed in the scholarly literatur e. To develop this topic further it has been necessary to draw upon literature in many related, but disparate genres of thought. The core of this review is focused on the organization, context, and use of data, specifically data which is used by geographic information systems. As geographic data becomes increasingly available through the academic, scientific, and corporate communities a number of complexities enter into its organization and classification. The following literature review aspires to delve into the intricate issues that may arise, focusing specifically on the ability to manage geographic data as an asset as a method to address these complexities. 2.2 Defining Geographic Data The review begins by laying out the foundation of geographic data and the distinctions between data and information. It continues by further dev eloping these definitions through the works of authors who acknowledge the value of viewing data as an asset. At its most basic level, data is anything recorded about an object or phenomenon. This simple definition belies a need for further context and identification for the collected data to become useful. In this thesis, data will be framed in a geographic context. Geographic da ta is somewhat differentiated by involving both a spatial and temporal location, as well as subject specific data (Lo and Yeung 2002). The complexity inherent in the simple recording of a single data point location on a map becomes apparent through this description, as the person collecting the data must record the spatial
4 location, the time the data was recorded, and potentially numerous other associated data attributes based on the needs of the end user. Lo and Yeung (2002) discuss the nature of geogr aphic data by breaking it down into its component parts. Recording geographic data in computer systems is based on the geographic matrix, developed by Berry (1964). This spread sheet-like matrix is commonly utilized by many GIS applications today. The structure of this matrix records data both two and three dimensionally by using the column to record the location and the row to record attribute information about that location. The third dimension is temporal, allowing for attributes to be recorded at various times for the same spatial location. Through this model, a cell becomes a single piece of geographic data. Lo and Yeung (2002) use this model to break the recording of real-life elements into representations of objects and phenomena. Obje cts are discrete, definable locations on the earth's surface, such as a mountain, a lake, or a roadway. These definable objects are represented using point, line, and polygon vector graphics in GIS. Phenomena are elements which are distributed continuously across a given landscape. These representations are often raster-based, meaning they include a single cell representation of some value, such as temperature, rainfall, or population density. T he recording of these real-world objects form the basis for the representations, analysis and inform ation provided by the GIS discipline. The data definitions, common terms, and analysis met hods, provided by Lo and Yeung (2002), can be beneficial when organizing data collected from vari ous sources. Oversight groups, such as the Open GIS Consortium, are mandating the creation of data standards and semantic translators, which codify data based on several criteria. The further development of context around geogr aphic data through identification, aggregation and selection, allows for the creation of information. Barr and Masser (1997) discuss the
5 perceived nature of geographic information and base their article on the technological revolutions transforming the use and dissemination of geogr aphic information. They delve into the differences between data, information and knowledge. It is their contention that data, by itself, is of no use without context or analysis. The interp retation of the data in some form provides a platform for the creation of information that c an then be utilized. Through further examination and understanding of the information, one can make the more existential leap to knowledge. However, as knowledge can only be understood on an individual basis, Barr and Masser (1997) focus on information as a format that can be disc ussed in many forms based on the context in which it is placed. The ability to use this in formation lies in the repr esentative abilities of geographic information systems. However, GIS is lacking in its ability to maintain information about the context of data. To gain better info rmation about the structure and use of geographic data, several authors have begun to utilize new methods of identifying data. These new identification methods provide the context for geographic data, by framing it in terms of who creates the data, who ow ns the data, and how can data be standardized for common use. Barr and Masser (1997) discuss this in terms norm ally attributed to the realm of physical entities: resources, commodities, infrastructures, and assets. As a resource, geographic data has qualities that give it an advantage over other econo mic resources, since it is not bound to rules attributed to a physical entity. Cleveland (1985) provides six differences between information resources and physical resources. 1. Information is expandable, it increases with use. 2. Information is compressible, able to be summarized, integrated, etc. 3. Information can substitute for other re sources, e.g. replac ing physical facilities 4. Information is transportab le virtually instantaneously 5. Information is diffusive, tending to leak from the straightjacket of secrecy and control, and the more it leaks the more there is.
6 6. Information is shareable, not exchangeabl e; it can be given away and retained at the same time. (Cleveland, cited in Barr and Masser, 1997) Given that data is a resource that can be creat ed and used, it is inherent in a market economy that it will be commodified. Through the ability to buy and sell data it gains monetary value; however, its sale as a commodity is problematic as the ownership of electronic data is rarely mutually exclusive. This issue extends into the c onflict of data as part of the public infrastructure or the private marketplace. Barr and Masser (199 7) contend that much of the existing geographic data has been created as part of gov ernment efforts to better understand various interests of that nation. If a public entity creates the informat ion, then ownership falls to the people who make up that government. The question then is whether geog raphic information that is created by a public entity can be considered a commodity. To better ac count for the issues of information ownership Barr and Masser (1997) consider the definition of information as an asset Barr and Massers (1997) definitions of geogr aphic information as an asset goes beyond simple descriptions of ownership to tie more directly in to managing data for greatest ease of use. Given the difficulties of exclusive ownership of inform ation and the frequent lack of need for an entire dataset, they state that it is more reasonable to consider sharing or licensing subsets of the information. The ability to distribute portions of the entire set of information within the owners purview creates the option of securing sensitive data or improving technological response times by releasing only the information that the user re quires. The selection of data requires a level of background be established around the data el ement. However, these details are frequently unavailable, as dataset development is systemica lly lacking in detail. These details often go unused, not because they are not useful, but bec ause they do not exist. Better definitions of dataset improve ones ability to identify the data that is most directly applicable to a project.
7 2.3 Providing Context for Geographic Data The development of context is the necessary st ep to transform geographic data into geographic information. Spatial data infrastructures and metadata are being developed as methods for standardizing and applying this context. These effo rts have the potential to describe data as an asset through the inclusion of attributes which are not necessarily relevant to its everyday use. This information becomes useful to the asset m anager by providing the context of organizational structure, data quality measuremen t, and potentially quantifiable value for a piece of stored data. The organization of geographic data attributes initially requires data accessibility, an understanding of the quality of the data represen tation, and a standard for organizing the data that can best promote reusability. In defini ng the lifecycle of an asset, software developer TechTrack defines the first step as acquisition (2007). Collecting the data to be managed as an asset requires that users must be able to firs t access the data necessa ry for developing their organization's GIS. Barr and Masser (1997) ac knowledge that more effort needs to be spent providing an infrastructure that allows users to access geographic data assets. The association of asset management to geographic information within the US Federal Government's National Spatial Data Infrastructure (NSDI) is a solid step within the public realm; however, their efforts to date have been inconsistent in their ability to pr ovide easy access to data. Fortunately, the private sector is beginning to see the value in the distribution of data as an asset, even if the availability requires the purchase of a license. Environment al Services Rese arch Institute's (ESRI) provides standard geographic data sets th rough its ArcGIS Server technology which is licensed to the users of their software. ESRI m anages this data by distributing select sets of information and allows users to include finite geographic elements, without ever touching the underlying file structure containi ng the data (2007). This seeming conflict between public data that may be difficult to access and private info rmation that comes at a hefty financial cost remains a roadblock in the ability to truly defi ne the accessibility of geographic information.
8 Understanding the origins of and updates to geog raphic data is determinative step in quantifying the quality of the systems and outpu ts that are used by organizations utilizing GIS. Gunther and Voisard (1997) speak of the value of metadata, or additional data used to describe useable data, by describing methods for collecting data, modeling data, and detailing the collection of international standards available for organizing meta data. Metadata is itself data that has been collected during the processes of data capture, data aggregation, data storage and data analysis, each of which the authors describe in detail. T hese items create additional attributes to define the initial data set for a user who may not be familiar with the collection methods, accuracy requirements or manipulations by the creating organization. Metadata is also useful in its ability to solidify such data standards as naming conventions and relationship definitions. The authors describe two forms of metadata denotative and annotative. Denotative metadata provides the logical structure of the dataset, much a like t he schema of a geodatabase. Annotative metadata is focused on the context of the data, detaili ng who, what, when, where and how the data was collected. The combination of these levels of data information provides the user with a picture of the relevance and accuracy requirements to thei r own line of work. To take advantage of metadata, the authors have ident ified past commercial products such as Geolineus and GeoChange which attempt to organize and manage metadata for geographic use by providing visual maps or navigation tools. Others, such as the commonly used ESRI ArcGIS suite of products, provide simplified options for stora ge of metadata but little other functionality. The development of geographic data al so benefits from standardization. This topic is one of wide discussion in the geographic data community and is centered on the previously mentioned spatial data infrastructure (SDI). "With increasing frequency, countries throughout the world are developing SDI to better manage and utilize their s patial data assets", states Rajabifard (2001). The value of standardization, through the SD I, is the ability to both promote reusability and organize spatial data across municipalities, regions, and nations. Rajabifard provides a description of the necessary components and object ives of a SDI, along with a set of examples
9 that define their current strong points and setbacks. The nee d for a SDI stems from the vast amounts of data required to fulfill the geographi c information needs of an ever growing constituency. To support this need, the development of a SDI aims to improve the ability to share data through efficient organization and quoting Coleman and McLaughlin (1998), Rajabifard (2001) states that an SDI encompasses "t he policies, technologies, standards & human resources necessary for the effective collection, management, access, delivery, and utilization of geospatial data in a global community". Rajabi fard describes both the opportunities and the shortcomings of current efforts, as most ar e soundly focused on the technical components of policy, organization and standardization, but miss an important component. The key, beyond these structural aspects, is the human element of the SDI, as the ability efficiently access information improves the decision making capabilities of people within an organization. It is the purpose of a geographic information system to prov ide the right information to the right people at the right times. United States' National Spatial Data Infrastructure (NSDI) is a set of standards is managed by the Federal Geographic Data Committee (FGDC) and includes components which focus on organizing both data and metadata (Gunther and Voisar d 1997). The effort was initiated as an effort to reduce redundant data storage across governmental departments and agencies, while providing a common data format to ease the transfer and use of data. These standards were mandated by Presidential Order 12906 in 1994. The NSDI is made up of two different standards, one focused on the transfer of data, the other focused on the requirement and structure of metadata. The three part Spatial Data Transf er Standard (SDTS) requires that geographic data include a logical specification describing the dat a model and accuracy, the data content registry which describes relevant attributes and entities and the physical data structure. For each element of data, the FGDC requires metadata described in using t he common Content Standards for Digital Geospatial Metadata.
10 The CSDGM is structured around seven major compound elements that are required for each geographic dataset held and distributed by the US Government. Each of these compound elements is made up of additional compound elemen ts that define various aspects of the data set. Built in to the CSDGM is a level of flexibilit y, which allows the user to enter all or some of the requested information. The optional natur e of CSDGM elements has both advantages and disadvantages as users may only include certai n required elements while leaving out others to speed the creation of metadata. However, l eaving out the optional information reduces the quality of the metadata and the ability for others to fully understand the data if the original source is not available. Identification Information is one of two required elements for metadata. This first section is made up of up to 14 sub-elements whic h aim to define the source, purpose, existence and availability of the geographic dataset. Fulf illing the other required element is Metadata Reference Information, which provides the user with details about the creation and maintenance of the metadata itself. This information is very similar in nature to the identification information that is required for the geographic data, such as creation date, creator name, access capability and usage constraints (FGDC 1998). Each of these provides a starting point for users who are working to identify a geographic dataset and verify its validity and source. The other five CSDGM components are optional, ye t provide meaningful details for one who is wishing to distribute or integrate a data product into their organization's efforts. Data Quality Information is provides the user with an unders tanding of the accuracy and usability of the data set. This quality information is valuable for us ers who have specific requi rements for the use of data, such as high-resolution ortho-imagery or su rvey grade spatial data for use in construction or engineering efforts. Quality assessment also ext ends to the accuracy to which attributes about the spatial data were collected. This section may include sampling equipment tolerances or laboratory testing procedures which can attest to the accuracy of the data attributes. Defining the technical nature of the geographic data set is t he Spatial Data Organization Information. Both direct and indirect spatial details are included de fined by the point, vector and raster data used
11 to represent the real-world object and the descriptive subject of the data, respectively. Further defining the location, the fourth element Spatial Reference Information identifies the coordinate system, projection, and geographic extents of the dataset. This information is required for replicating the proper display formats used wh en the data was collected. Once the spatial components are defined, the FGDC makes space fo r the Entity and Attribute Information, providing the additional details created or co llected about the geographical data element. Details about entities and attributes include descriptions, data types, data source, and other details about the creation of the attribute data. The final, opt ional element of the CSDGM provides distribution information detailing how the information can be used, the methods for transferring the data and options for acquiring the data through purchase, licensing or other methods. The full FGDC Content Standard for Digital Geospatial Metadata is listed in table form in Appendix A. The Federal Geographic Data Committee is the c oordinating agency for the U.S. Governments NSDI and provides, respectively, an insight of both its current state and future directions. Armstrong (2006) provides a diagram displaying geo graphic data at the core of the NSDI, with metadata and the organization framework extending ou t from this core. All of these items are encompassed by a set of common standards. Si tting on top of all of these components is the clearinghouse which provides linkages to partnerships, whose importance Armstrong emphasizes. The partnerships span the private sector, academia and all levels of government and are made up of both data suppliers and data consumers. In this regard, each of the partners utilizes the standard framework for m anaging, creating and sharing data with others. Armstrong (2006) espouses the transformational natur e of the NSDI as it aims to "designate nationally significant data as a Federal-wide, common capital asset and manage them as a portfolio, instead of discrete data sets." This desc ription provides a look into the future of the NSDI and the linkage between its organizational structures and the existing definitions and strategies for the management of assets. The NSDI organizational struct ure is currently based
12 on the alignment of data sets to themes. Themes represent a variety of categories from elevation to hydrography, transportation to government units. These categories are constantly being revised and improved by a FGDC team focus ed on standards development. Additional teams are specifically focused on communication, training and partnership, based on the recognition that this information is only being shared by a finite group. Across the use of project, company and global in formation structures there is a call for the standardization of a data assets to improve t he reliability of systems, improve communication between applications and ensure data consistency (Kyle, et al. 2002). The efforts to coordinate data structures have gained the stamp of approval from heads of state (Executive Order 12906) and have been the focus of entire university departm ents (University of Melbourne, Centre for Spatial Data Infrastructures and Land Administra tion). The work performed in this area is valuable in both the provision of a set of attr ibutes for a data organization system, but also through the improvements found in data distributio n. The common transfer of this standard, within the geographic data realm, will be through the use of metadata. Translating data into the asset management world requires a strong understanding of the sour ce of the data, how it has been maintained and modified, and its methods of access. Through the FGDC's Content Standard for Digital Geospatial Metadata we gain a comprehensive and commonly used standard that identifies these attributes for geographi c data. It is through an established systemic approach that context can be effectively develop ed, providing the information necessary for managing data as an asset. 2.4 Defining Assets and Asset Management To best understand the commonalities between data and assets, it is worthwhile to delve deeper into the makeup of an asset and how it is defined by industry experts. Common definitions of an asset include an implication of ownership or cust odianship, with the ability to apply a quantifiable value to the asset (American Heritage Dictionary 2008). Others describe an asset more simply,
13 as any object which is discrete and definable (L o and Yeung 2002). Literature from the asset management discipline is focused primarily on cataloging, valuing, tracking and maintaining data through various methodologies and systems. Vanier (2000, 2001) has developed a well establis hed pattern of scholarly research on asset management in the construction and facilities management industries through his work for the National Research Council of Canada. He asks six key questions to classify and understand assets, which have been discussed in terms of t he definition of asset management (Vanier 2000), planning of municipal infrastructures (Vanie r and Danylo 1998), descriptions of asset management software (Kyle et al 2002a), along with citations by other authors in the asset management discipline The further development of each of Vaniers six asset management dimensions attempts to allow an organization to id entify, appraise the value, assess the condition, and validate the useful life of an asset. While mainly focusing on the construction and facility management industries, his critique of solutions av ailable to this market is potentially applicable to the management of all types of assets. His work centers on the following six dimensions to determine the worth of an asset management system to an organization: 1. What do you own? 2. What is it worth? 3. What is the deferred maintenance? 4. What is its condition? 5. What is the remaining service life? 6. What do you fix first? The selection of this framework for the assessme nt of geographic data as an asset is based on its established place in asset management literature, as well as the comprehensive nature in which it accounts for all stages of the asset lifecycle. De termining what is owned focuses on the inception of an asset or its initial inclusion into a manag ement system, while determining what to fix first looks at the end of an assets lif e by prioritizing repair and replacement decisions. The limitations
14 to the six dimensions exist in the fact that t hey only provide a framework and not the descriptive measures necessary for answering each question. For these answers, Vanier relies on the work of asset-specific techniques, software solution s, and other scholarly efforts. To answer the research questions of this thesis, each of Vaniers six dimensions and their associated solutions will be discussed in terms of their applicability to geographic data. What do you own? The ability to inventory assets which are to be managed answers the first question, "What do you own? This essential first question from V anier (2000) drives the creation of the asset management portfolio. This full accounting of asse ts can be an extensive effort, relying on both manual and automated systems to determine the curr ent state of each asset under the purview of the asset manager. Kyle, et al. (2002) provide for the use of design documents, maintenance records, purchase orders, contracts, and other tran saction-based devices to account for all assets in an organization. Vanier (2000) trends toward the information system approach to define and track the inventory catalog. Ironically, this increasingly common approach to asset management portfolio development includes the use of GIS software. This technique approaches the questions asked in this thesis from an opposite direction by applying geographic data as an attribute for assets. This concept remains valuab le through its ability to catalog assets that are stored in different locations as is often the case for geographic data collected through government agency websites or stored on comp uters in remote locations. However, the approach of this thesis does not pursue the use of location as an attribute for a data asset. What is it worth? Once the inventory of assets is established, V aniers (2000, 2001) second question focuses six different values that must be taken into acco unt to calculate the worth of an asset to an organization. These values are: Historical Value
15 Appreciated Historical Value Current Replacement Value Market Value Performance in Use Value Deprival Cost The first looks at the original value, or in this case, cost of the asset when it was acquired. This value is represented by the original acquisition cost, or historical value. The second value, appreciated historical value, represents the worth of the asset in current terms; however it is not able to represent the third value, current replacem ent value, or the cost of the item if replaced today's market. The comparison between these two values provides a snapshot of the return on the investment of the asset. Similar to the curr ent replacement value, the fourth value provides detail on the current worth of the asset in the ma rket by representing t he price that the asset could be sold for today. The final two costs are not directly associated with the price of the asset, but more closely the internal value to the organization. The "performance in use" value, coined by Lemer (1998) and the deprival cost allow t he asset manager to understand the value placed on the asset by its users and, respectively, the cost to the organization if they did not have use of the asset. While most organizations only reco rd the initial cost of an item for accounting purposes, the additional value calculations provide decision making capabilities to asset managers. The value of an asset is significant when an as set manager is determining the best course of action for an asset in their organization. The val uation of an asset is of primary importance in the commercial sector; however the public sector is not immune from asset valuation, as acquisition, maintenance and other decisions are frequently ba sed on the costs associated to an asset. To describe the value of data to the public and priv ate sectors, Branscomb (1995) offers the following example of the worth of an asset to a community. She describes the value of a prized
16 fishing location as part of the public good for a tribal society versus the private good of a commercial fisherman's most lucrative fishing hol e. This example describes the conflict between the value of public and private ownership of data. Data collected and distributed for the public good is valued differently than data collected by a private organization for internal use or sale. Every facet of data development requires some form of economic resource that contributes to the valuation of the data, from the human capital involv ed in its collection to the financial cost of its recording, archival, and distribution. Discussing public versus private data is a pertine nt undertaking at this point, as ownership is one of the hallmarks in the identification of an asset. The ownership of data is a bit nebulous as it does not adhere to the same concepts of scarcit y that exist for physical assets. As data collection is described in the first asset managem ent dimension, it becomes clear that there are costs associated with its creation through the need for expensive hardware, software and skilled labor. Branscomb (1995) states that "An inform ation economy is based upon the premise that information has an economic value and requires and information marketplace in which such value can be exchanged." This valuation of informat ion is further enhanced when the dispersion of data is restricted by its owners. The benefi ts of data ownership come through increased management capabilities, ensuring data integrity and performance of regular data maintenance. These abilities allow for t he licensing of data, which involves providing access to the data without transferring or distributing the individual files. Both public and private organizations participate in the licensing of geographic data (Barr and Masser 1997). ESRI, one of the largest providers of GIS software, provides aerial, topographical, st reet, and feature raster data to its customers through its ArcGIS Server technology. In t he public realm, the United Kingdom and the United States are both working on licensing models for their spatial data infrastructures, because of the increased value produced by ownership.
17 While data has no individual desire to acquire va lue, those investing the financial resources to create information gain ownership of a final product termed intellectual property. Branscomb describes patronage, procurement, and property as the three forms of capital used in creating intellectual property. Patronage and procurement produce data that has been paid for by the organization which will manage the information. In the public realm, government entities will provide the funding to either create the informati on themselves or procure it from some outside source. This intellectual property is most oft en disseminated to the masses, as the initial funding source was taxes levied for such services. The third funding model, property, relies on some third party which produces the information in the hopes that others will require and eventually purchase the intellectual asset they created. The constant strugg le in the production of value through intellectual property is the difficulty inher ent in protecting a product that is so easily reproducible. The author describes many form s of protection, but c oncedes that government intervention and regulation of data dissemination, while limited, are the only checks on an asset that is so easily transferable. Hence, the val uation of data is often contingent on the source of that data and the costs associated with accessin g data from public sources or purchasing from private sources. What is the deferred maintenance? The words maintenance and repair are frequently se en as an extension of t he other to the extent that they could be considered interchangeable in conversation. However, maintenance is a preventative measure that should be performed on an asset throughout its life, but is not required for continued use of the asset. Repair, on the other hand, is a required, one-time measure necessary to return an asset to service because of some sort of failure. The value of maintenance over repair becomes evident throug h the discussion of deferred maintenance. According to Vanier (2001), deferred maintenance is the cost to bring an asset up to its current value, if maintenance that has not been completed on a regular basis. This notion is based on
18 the understanding that an organization has not or can not perform regular maintenance on an asset. Vanier relates the costs associated with deferred maintenance to DeSitter's Law of Fives, which reads that repairs will cost five times the amount of maintenance if it is not performed on a regular basis and replacement of the asset will cost five times the cost of the repair. The below graph provides some insight into the deferred maintenance costs based on the investment in maintenance from year to year by displaying t he exponential cost to return an asset to its full potential when maintenance investments may or may not be completed. Figure 1: Deferred Maintenance over Time (Vanier, 2001) What is the condition? Similar to the value of the asset, the condition of the asset is a necessary, measurement of the asset's significance to the organization. The condition of the asset determines whether repair or replacement is necessary for the organization to continue successful operation of the asset. Two common systems for measuring the condition of assets are the Facility Condition Index (FCI), developed by the National Association of College and University Business Officers (NACUBO), and the Condition Assessment Survey (CAS), developed by the US Department of Energy. The FCI, as described by Teicholz and Edga r (2001), is a ratio of the cost of the assets deficiencies to the cost of replacing the asset. The lower the ratio, the better condition the asset is considered to be. In the instance of facilit y management, a ratio of less than .05 is considered
19 to be good, .05 to .10 fair, and over .10 the asset is considered in poor condition as the deficiencies make up 10% of the replacement va lue. The deficiencies described by Teicholz and Edgar are what Vanier refers to as deferred maintenance costs. The CAS, on the other hand, is a standard ev aluation approach developed and used by the US Department of Energy for facility and asset insp ections (CAS 2008). This approach was instituted to support funding requests by the department to congress by providing a common basis for facility evaluation. CAS is both a set of r equirements and a methodology followed by trained inspectors who review assets divided into 12 cate gories. Each category is a subset of a whole building or other inspected item, which is linked to a database providing standard cost information for estimating repair or replacement of assets. Through the standardized inspection process and a common web-based, cost estimate database, the department is better able to benchmark the condition of facilities or assets in disparate locations. This deter mination of condition is not only useful as a measure of the current functionality of the asset, but also as a predictive device for determining its future capabilities. What is the remaining service life? The unit of measurement of the future capabilitie s of an asset is the subject of question five, remaining service life, which the Canadian Standa rds Association (CSA) defines as the actual period of time during which [the asset] or any of its components performs without unforeseen costs of disruption for maintenance and repair (1995). This assessment provides the manager with a time block that can be used to estimate the planned maintenance costs, potential repair costs, and future retirement date of a particular asset. The service life can be looked at as either a technical service life or an economic service lif e (Vanier 2001). The technical service life is the useful life in comparison to other assets in the marketplace. As assets age they not only deteriorate in terms of their useful condition but face extinction in the marketplace as new innovations come on line. This situation is exem plified in the computer industry through the
20 circular innovations required by both hardware an d software advances. The technical service life is drastically reduced for assets which are require d to operate at levels for which they were not originally conceived. In the same regard, the economic service life of an asset is driven by the costs to maintain and repair an asset. At some gi ven point for each asset, the economic service life is reduced to the point that it is no l onger feasible for the asset to be repaired and replacement should be considered. Kyle, et al. (2002) describes a method of service life asset management set which involves constant evaluat ion of the usefulness of an asset during the course of its life. This method derives its calculat ion from the remaining service life in conjunction with the value placed on that asset by its managing organization. What do you fix first? Organizations can frequently count their assets by the thousands and it becomes the task of the asset manager to review the condition, value and life of each to make informed decisions to answer Vanier's final question, what to fi x first. The decision will be made regardless; however, "good decisions can only be made from g ood data," according to Kyle, et al. (2002). The value of asset information management plays a st rong role in providing this information, as the values mentioned previously (inventory, val ue, condition, service lif e) can be examined by asset managers or decision support systems to create a prioritization of asset repair and replacement or retirement. In the definition of an asset, repair is considered to be any nonplanned cost that must be incurred in order for t he asset to remain in use. Renewal is the process of taking an asset out of service by eit her retiring it from organizational use or replacing an asset that can no longer be repaired in a financially-viable manner. Maintenance is not considered at this point in the equation as it is an ongoing process and would be budgeted separately from repair and replacement costs. The prioritization of repair or replacement represents both a level of importance of an asset to an organization, as well as a representation of the available resources that can be allocated to the particular asset. This final step question provides closure for an asset that had followed the lif ecycle from initial owne rship to renewal.
21 The establishment a method for identifying and u nderstanding assets is an important foundation for the determination of the extent to which data can be considered an asset. Vanier's expertise in the field of asset management allows for a strong comparative basis on which to determine whether an object can be considered an asset. The six dimensions focus an asset manager's efforts beyond those of organizational ownership, into the realm of condition, value and usable life. Associating these questions with the concur rent time lines of the asset lifecycle provides additional identifying features for each asset thr ough the ability to continually monitor the value of an asset to an organization through identificati on, measurement of effectiveness, and retirement from use. Vanier (2000) laments that there is not currently an asset management tool that can meet all of these needs for all industries. Each industry has its own set of systems and techniques, but none of these have been fully in tegrated into a single system. However, the questions he has developed provide a strong fou ndation for asset managers wishing to embrace all facets of asset assessment. Each of thes e questions will be further examined during this thesis into their applicability to geographic data asset management. 2.5 Defining a Data Asset Management System The structure of the remaining ch apters of this thesis is focuse d on the possibility of creating a viable geographic data asset management system. This system is based on a set of attributes and methods derived from both the geographic data and asset management fields. Through the definitions provided above on bot h of these topics, additional literature has been reviewed to develop the organizational structur e for the system's attributes and the necessary methods used to support an asset managers decision making process. The options for attribute organization described in the following paragraphs were selected not only for the completeness of which they cover geographic data structure, but also for their standardization across national and international lines. Utilizing standard attribut es and organizational structures for system data definition will allow for the widest range of use for both public and private users. The selection of
22 asset management methods for use in a data asse t management system is a bit more abstract, as methods for asset valuation and condition asse ssment are frequently specific in nature to the particular type of asset being reviewed. For in stance, the US Army Construction Engineering Research Laboratory (CERL) has created PAVER, ROOFER, BUILDER, and RAILER systems which contain specific methods for analyzing the condition of roadway, roof, building, and railway assets respectively. The methods described below are those asset management methods which can be integrated into or described in terms of ge ographic data. Fortunatel y, the work of system design is rarely started from scratch. T here are a number of existing asset management, inventory management, and data cataloging system s that exist that can be adapted with the attributes and methods described below. Their development and design efforts are illustrated to provide a framework in which existing literature can be related to the system described in the conclusions of this study. Methods most frequently associated with eit her geographic data or asset management are subject specific and rarely translate across medium s. The following set of methods tie in with Vanier's six asset assessment dimensions by pr oviding some form of additional analysis that can be performed to answer the respective question. The data attribute contributions, consisting of the standards defined by spatial data infrastructu res (SDI) and metadata, serve as the answer to the first question (What do you own?) by providing the inputs to the asset portfolio. The second question focuses on calculating the value of the asset. Using common financial calculations the owners of data or information can begin to det ermine these economies by examining the costs and benefits of the data creation and usage. Joffe (2007) has spoken on the determination of the return on investment for GIS applications. Data development and usage are a major component of any GIS program and his exposition of ROI fo r applications serves to provide a basis for creating an ROI for data as well. Joffe encourages any model used for these calculations be experience-based, include transparent calculat ions, explicitly state assumptions and most importantly allow for flexibility. The ROI calculat ion consists of the cumulative year-to-year net
23 benefit provided by the GIS applicat ion benefits minus its implementa tion costs. For descriptive purposes, the ROI becomes the time required to recover the initial investment, based on the cumulative benefit of the product. Once the cost s are considered, the organization must be able to account for the benefits of efficiency, dec ision making, cost avoidance, and increased revenue. The offset between the costs and benefit s will provide a return on the investment of data that is being managed. Data development incurs many of the same cost s as application development analysis of what is needed, design of attributes and other pertinent information, collection of data through various forms, quality control and ongoing maintenance. This ongoing maintenance is a portion of the descriptive language that allows for the calcul ation of deferred maintenance, Vanier's third question. The value to this effort comes through the notation of the 5% of data cost allocated to both quality control and metadata development, as each of these ensures understanding and integrity of data. Secondly, a full 10% of the cost of the data should be attributed to future maintenance (Joffe 2007). As Vanier discusses abov e, an asset which is not maintained will require more expensive repair and eventual r eplacement. Maintaining data can be a difficult concept to wrap one's head around, as data frequently exists without need for change or update. However, additional perspectives include the ab ility for data to be continually improved through continual verification of the existing data attri butes or the improvement of the data through aggregation and analysis. To assess the condition of geographic data asse ts we can turn to the Life Cycle Asset Management (LCAM) methodology, which is defined by Sawers (2000), as a "practical tool developed to identify, quantify, and prioritize def erred maintenance and component renewal". It was developed as a tool for assessing the conditi on of buildings and their various systems, but its practices can be adapted for use in analyzing the co ndition of a data asset as well. In Sawers scenario, the inventory is already understood, but the condition is in question. The methodology
24 proposes a four step plan which includes inspection of the asset by experts, an estimation of the maintenance and repair needs/costs, modeling fundi ng alternatives for any corrective action and the development of an implementation plan. This lifecycle of condition assessment and update provides a sense of how data could be broken down and analyzed by experts and a formula for determining the funding necessary to repair or renew the data. Sawers concludes with a call for updating these manual or database-driven processes into a computer-based application which could perform the decision-support tasks invo lved in this area of asset management. Kyle, Vanier, Kosovac, and Froese (2002), have developed a system of facility management around the fifth question, focused on determining se rvice life. This method requires a continuous examination of the current condition and performan ce, as well as re-evaluation of initial design requirements & long term plans. The need for this method of asset management stems from inevitable changes from client expectations of the asset, advances in technology or changes in regulations associated with the asset. Determi nation of service life is based on the current performance of the asset, its life expectancy, the required service life of the asset and the significance to the overall system. The final question, determining what to fix first was not able to be associated to the existing body of literature when relating asset management and g eographic data. Options for fulfilling this method are perhaps a combination of the priorities collected through the conclusions drawn in the previous questions. There are, perhaps, other methods for fulfilling the needs of asset identification and assessment, but these will suffice for the initial development efforts of this system. By establishing a set of methods that answer the questions that have been proven useful by Vanier, we can be better assured of the applicability to system development. Vanier (2000) and Sawers (2000) both lament the la ck of existence of a single tool to meet all of the needs required fully asses an asset. However, several tools can be combined together to
25 manage the assets of a specific industry or discip line. The difficulty with a single solution lies in the various attributes necessary to determine the value, condition, and other classifications of an asset. In regards to geographic information, we must consider how each of Vanier's questions pertains to the management of data. A proposed solution may include a computerized maintenance management system (CMMS) to ca talog the data, a GIS for maintenance and representation of the data, along with additional algorithms for determining the value and condition of the data. This process must begi n with the alignment of geographic data to that of other physical assets and an examination of how to best manage the additional value that geographic data contains. Within the software development framework, Goodc hild, et al., (1992) focus on four overlapping options which they describe as useful for int egrating data and geographic information systems. Each option gradually increases the amount of integration and coordination between the application and the GIS. The initial method consis ts of coordinating the outputs of two separate applications which have no direct connection. This option allows for both the data management application and the GIS to be mutually exclusive in design and development. In practice, however, this method requires additi onal processes to merge the ou tputs of the two systems into a meaningful result, which could be potentially tr oublesome if third party software is not designed to be compatible or an additional translation progra m is required. In the current marketplace, the most frequently available option described by the authors is software that is loosely coupled through open development or creating outputs t hat can be utilized by other software. Open source code and output formats, such as XML, offer simple access for integration with outside applications. In Goodchild's scenario, the out puts of the analysis software could be integrated into the GIS with minimal work by adhering to the input formats required by the system. Close coupling between an application and a GIS is the most useful possibility that can be expected from two separate software development effo rts. To achieve this standard, applications
26 must be initially designed to work together by pr oviding specific transfer formats and protocols between applications. The software industry part icipates in this model most often through licensing of intellectual property to outside devel opers who wish to take advantage of well-known or frequently used technologies. While the resu lting products would provide a more seamless solution between the GIS and the analytical applic ation, one or both applications may not be able to achieve the full possible potential if operating on its own. Finally, full integration between an application and a GIS can be expected when the concept has been proven valuable and necessary in the marketplace. Primary provid ers of GIS software or mainstream application developers would develop systems which include both forms of functionality as a single package for all users. The downsides to full integration include an increase in production time, increased costs and the possibility that the additional t ools may not be necessary for all users. The advantage of this option, according to the Goodch ild, is the full coordination between analytical tools and GIS. As the market begins to see valu e in additional analytical options the tools and ensuing results could become standard operati ng procedure across the industry. Much like the case of spatial data analysis, the integration of asset management software with a GIS offers new options for understanding the use of geographic data within a system. The similarities between the two technologies lie in their capacity to provide additional information about the data being used by the system and their ability to improve the GIS user's experience through enhanced results. Acceptance by t he marketplace of asset management techniques gives a boost to understanding the potential val ue the technology may hold when integrated with a GIS. Even so, the combination of these two co ncepts is still unproven and thus the software life cycle would most likely begin with the developmen t of an application external to a GIS. More recent advances in GIS technology allow for more options to loosely or closely couple applications together through the use of softwar e development kits (SDK) or data dictionaries which provide insight for third party application de velopers to create tools that will more easily integrate with a specific software package. Also worthy of future consideration, but perhaps out
27 of the scope of this study, is the capability of asset management techniques to provide additional analysis of spatial data. Insights along this line of questioning may focus on how information stored about the cost and benefits of the use of data could provi de insight into its commercial feasibility. 2.6 Conclusion While asset management is not a new concept, the availability of software to support new interpretations of assets is nearly non-exist ent. Vanier (2001), Sawers (2000) and others espouse specific methodologies for the measurement and classification of assets, yet bemoan the lack of capable software to fully implement t he methodology. Vanier's six dimensions provide an evaluation tool for existing asset management appl ications that are currently available in the marketplace. The potential for the successful implementation or development of a decision support system is dependent on the extent to which geographic data is able to be defined as an asset Special consideration should continue to be pl aced on geographic data as the consistent use, financial viability, and realm of ownership are impo rtant to those who are both creating and using data. Standardization of data provides many benef its, particularly in the ability to efficiently organize and share data without additional translation. However, as information remains readily distributable, the public versus private conf lict will continue as the market-based economy requires payment for products produced. The ability to account for the costs and benefits of geographic data will help both public and private entities value the data t hat they own and share through the allocation of each across a gi ven tax base or on a user-by-user basis. This translation of geographic information terminology to fit into the asset management discipline has been of central to this review. This understan ding comes in the form of its detailed definition, the right of ownership to that asset, and the circ umstances around its use. In terms of geographic
28 data and information, we can us e these existing concepts to attempt to create both a viable framework for analyzing our data assets and t he requirements for asset management software which accounts for the concerns of those wi shing to better organize and understand their geographic data.
29 Chapter 3: Case Study Overview The case study providing the background for the t hesis investigation is an environmental analysis of a rail corridor in Orlando, Florida. The Cent ral Florida Commuter Rail Transit (CFCRT) project is aimed at improving the commute for thousa nds in the Orlando metropolitan area through the conversion of existing CSX Transportation freight rail lines for use by commuter trains, operated by the Florida Department of Transportation (FDO T). The concept for this GIS data management effort stemmed from the project team needs for the examination of the 61-mile rail corridor. FDOT is converting the line from a mainly freight -use railway to a commuter railway in order to accommodate present and future transportation needs and enhance mobility throughout the region. Proposed improvements include construction of new stations, updates to existing stations and platforms, installation of a second track section, and installation of a new signal system. The project will begin at the Deland Amtrak station in Volusia County, traverse through downtown Orlando and will extend south to Poinciana in Osce ola County. Each of the proposed changes along the rail corridor require extensive environ mental analysis to ensure that contaminants, existing at acceptable levels for freight rail, can be remediated to levels acceptable for daily commuter use. The case study has grown out of a need to organize the wealth of data that has been accumulated throughout the many projects t hat make up the CFCRT conversion effort.
30 Figure 2: Proposed Central Florida Commuter Rail Map
31 In late 2006, a Contamination Screening Evaluat ion Report (CSER) was commissioned to identify sites along the CFCRT in various risk classifications. These sites were further refined based upon the following: vicinity to the study area, the potential contaminants of concern, and potential impacts to the track. The presence of so il, groundwater, surface water and/or sediment contamination or the existence of petroleum products or hazardous substances within the project study area can have a significant negative impac t on the cost and schedule to complete this transit project. FDOT contracted with three env ironmental engineering firms to perform the field sampling effort. The case study is centered on the managing firms efforts to develop the sampling plan, setup the mobile GPS data collection units, retrieve and organize the location data and provide reports and analysis based on the combination of location and laboratory data. During the Level I project, field teams were required to take multiple soil borings every 1/10th mile for the entire duration of the commuter rail corridor.
32 Figure 3: Commuter Rail Sampling Plan Downtown Orlando The rail corridor was separated into 10 major sections, labeled A(1) through J(10), from north to south, respectively. The sampling was complet ed by three environmental engineering firms by taking four to five core samples horizontally acro ss the track at the specified interval. Along with each soil boring a GPS point was plotted to ancho r the geological and chemical data collected at that specific location as seen in the above graphi c. As the core sample results were returned by each of two laboratories, they were joined with t he location data to provide visual representations of the results along the corridor. These results, depicting chemical exceedances at each sample location were further analyzed to determine which sites required additional sampling and/or remediation.
33 During July and August, contamination impact assessment activities commenced to further investigate the presence or absence of potential contamination within the proposed commuter rail right-of-way. This assessment was conducted to further verify or refute contamination resulting from adjacent properties indicated as having contam ination potential. Results of this report will be used to alert the FDOT of any potential haz ards along the commuter rail corridor, help coordinate remediation of contamination prior to construction, and provide property valuation information. Based on lab analysis of the soil samples, 89 specific sites required additional examination. The project team was again required to develop a sampling plan for each potentially contaminated site, i dentifying potential sampling locations along the railroad right-ofway, within the parcel of the site. Again, field teams sampled the soil at these specific sites and recorded the GPS coordinates for analysis and repo rting. Based on the reported findings from these sample locations, FDOT can determine whether construction can proceed or whether further mitigation is required. Figure 4: Potential Contaminated Sites The two projects described above are the first of many additional, smaller analyses conducted along the rail line. The subsequent projects each require their own sampling plans, data collection, and representations. To successfully organize the data for the environmental analysis, we must look at the various types of geographic data that were accumulated, created, and utilized throughout. CSX provided proprietary data on railroad lines and track right-of-way (ROW).
34 Government entities, such as the FDOT, countie s, and municipalities pr ovided the study area, site and parcel information. Third party pr oviders developed many of the engineering and construction drawings, as well as commissioned the flight of high-resolution aerial imagery of the corridor. Much of this geographic data was pr ovided free of charge as the project team was contracted to perform analysis, however, some third party data required purchase. This purchased data was collected by both contracted and subcontracted firms throughout the project. There are a number of difficulties inherent to integrating data from ten field teams from three coordinated companies using five GPS units and providing samples to two laboratories. GPS data must be gathered from each team and uploaded to a single location on an established schedule. Data schemas and results from each la b must be coordinated to ensure that standard formats are utilized and results are recorded on a time ly basis. Once data is retrieved, it must be organized into a struct ure that can be utilized by distribut ed GIS teams who are each completing various sections of the analysis and final reports. While the organization of this effort may be a daunting task, it is a problem that has been approached before by organizations that are responsible for managing large amounts of equipm ent that may be spread amongst many jobs or unavailable for extended periods of time. Fr om a business perspective, the management of project data becomes a matter of costs and benefits. Techniques are necessary to determine the ongoing value of data, as well as its quality and co ndition for continued project use. Accountability requirements must also be taken into account, as each government entity utilizing the final deliverables has legislative mandat es for data quality and detail.
35 Chapter 4: Defining Geographic Data as an Asset 4.1 Introduction Data is the basic building block on which info rmation, and thus nearly any decision support system, is based. As the inform ation age progresses, the ability to examine how data is created, aggregated, managed, and accessed allows organiza tions to make decisions on how employ the best management strategies to the advantage of their constituents. Current data management practices, in the information technology discip line, are focused on providing efficient storage and access to data; however these sy stems rarely provide details to why the data was created or how it can be made more useful. Other industries require this information when focusing on physical assets particularly in those indu stries where equipment utilization is a driving factor in revenue production. Industries, like construction and facilities management, have turned to asset management techniques, strategies, and solutions to best understand who, what, where, why and how their assets are being used in order to track its usabl e life and value. To determine whether GIS and asset management are compatible, this chapter focuses on the first two research questions of this thesis. The first seeks to determine the ex tent to which geographic data can be considered an asset. The second examines whether exis ting asset management techniques, strategies, and solutions can be applied to geographic data. Thr ough these questions a critique of similarities and differences can be weighed to determine whet her a picture of a geographic data asset can be developed. The evaluation efforts will adhere to the six asset management dimensions posed by Vanier (2000, 2001). This common ground will allow for methods that incorporate validation, condition assessment, valuation and lifecycle management of a geographic data asset.
36 Figure 5: Data-Information-Asset Context The significance of the development of data as an asset comes through the creation of context about geographic data. The above figure provides a vi sual description of the transformations that occur as context increases throughout its useful life of an asset, from inventory efforts to replacement needs. To become informati on, data gains context through the metadata, documenting spatial extent, temporal location, and other pertinent details that allow for proper representation. Through the implementation of standards, specifically those from the asset management field, information is provided with context that allows for additional management capabilities. As context is applied, an element of data continually gains value as it is transformed first into information and then into an asset. The process of adding context to the geographic data used in the case study is the focus of the fi nal section of this chapter, describing the various organizational efforts attempted to both track data th rough its useful life, as well as the increase in value of the data to the managing organization.
37 4.2 What is Geographic Data? Driving the ever accelerating information age is a vast amount of data that is recorded about every known subject. The difficulty of defining data is not dissimilar to the dilemma faced by one trying to define a word by using the same word in the definition. For the purpose of this thesis, data is a collection of recorded attribut es that may be used to describe an object or a phenomenon. From this simplistic definition, descriptions of data become a great deal more complex, as data structures are used to describe ot her data structures. Thus, to be truly useful to a particular discipline data must be translated into a specific lexicon to allow the element to effectively describe the object or phenomenon for which is was recorded. As this section aims to develop the set of geogr aphic data attributes and methods necessary for developing a strong data management system, we will specifically focus on those encountered during the course of the case study. Special attention will be paid to spatial data initiatives (SDI) and metadata as each works to provide a co mmon context for those utilizing geographic information systems. Geographic data can be eas ily explored in this manner because it is explicitly defined, has been the subject of global or ganizational efforts, and has a definable set of attributes that can be used as a comparative tool. Through a review of data flow structures it is possible to define how data is acquired and processed for use as an asset within an organiza tion (Gunther & Voisard 1997). The first step in this flow is data creation, which involves the collection of raw data. Once acquired, the data flow structure requires the selection of a data storage medium, often a file or database in a computerbased infrastructure, which provides the most effi cient access to the data. Data aggregation is the process of turning the raw data into informatio n that is useful for the system or project of which the data will be a part. Finally, the analysis phase provides for opportunities to relate the
38 created information in a meaningful way to the constituency for which it was intended. The following paragraphs delve into the data flow structure and its relations to geographic data. In the business world, data creation is a by-pr oduct of sales, manufacturing, or financial activities. In scientific research, data is a recorded observation of some phenomenon. Geographic data, however, requires methods of production through various efforts such as rectifying aerial imagery, recording GPS points or manually digitizing the rocks, trees, and streams of an observed area. This production me thod may include the business or scientific results and their relationship to a real-world location. As discussed in chapter two, geographic data must contain a spatial component, attributes about that location, and optionally a temporal component (Lo & Yeung 2002). The attributes t hat are recorded provide the context to the associated spatial location, while the time freezes the data at that moment in history. In many instances the user or manager of the dat a is only responsible for acquiring the previously created data that is necessary for the project or process being undertaken. Geographic data that is made available by government and other pu blic sources is commonly deployed to a wide variety of users. An example of such a facilit y is the website of the Florida Geographic Data Library (FGDL 2008). This website provides an online forum for downloading municipal boundary information, environmental data, aerial image ry, and other geographic data that has been collected for those performing mapping and other spatia l analysis in the state of Florida. This data has been collected and processed through tax-payer funded mandates which provide for its free distribution to the general public. Publicly-available data often provides a solid basi s for the development of a GIS; however, it is rarely able to tell the full story for a researcher or commercial interest. Global positioning satellite (GPS) technology, surveying, remote sensing of ae rial imagery are some of the options that have become increasingly common through the increas ed capabilities of technology available to the
39 field. These data production methods provide for the collection of data specified for the needs of the project which may not have been collected previo usly. In the commuter rail case study, data was both acquired and collected as necessary. The Florida Department of Transportation and other public sources provided much of t he municipal boundary, roadway, and common environmental features for the study corridor. T he project team was responsible for collecting soil samples along the project railroad corridor. The sampling locations were recorded using mobile GPS units which would allow the teams to create a reference for each of the sampled locations on a map. This data creation illustration uses t he combination of acquired data to provide the base level of the map using common information and the customization of the map through the use of collected data. Once data has been created and provided a contex t through which results can be reviewed, the data should be stored in a manner that provides easy and efficient access by those who will be its primary customers. There are many options fo r the storage and management of data within the information technology arena. These options, in the GIS discipline, fall to two distinct management methods those stored as individual files and those stored in a database. File options provide for a simple structure and employ folders to organize data. The organization of the folder structure is managed by the user and does not have any specific rules that govern where or how data is arranged in the file system Database options provide a more in-depth structure to the data, but include additional overhead that does not exis t in the file structure. The GIS field has the opportunity to take advantage of geodatabases, which, along with providing a data storage medium, afford the user spatial location management, rules and constraints based on location in the form of topologies, and stri cter formats for data storage and retrieval. While both of these formats can be managed on a personal computer, the industry is moving to take advantage of network-based, disperse d systems of data management. GIS software providers are developing server-based technologie s that allow data managers to provide data
40 from a single source to authorized users. Technologies such as image servers provide efficient methods for storing, spatially organizing and distri buting large amounts of raster data to users who many not have the need or desire to acquire this amount of data. While these advantages exist, disadvantages also exist in the form of regulation of data, as described in the management of privately-held data and through the high costs in both technology and skill required to set up these distributed systems. The data for the commuter rail project was acquired from a variety of sources, including field data collection, outside consultants, and governmen t agencies. File-based storage was utilized for both raster based data and one-off datasets that had little-to-no relation with other collected data. Raster data, which primarily consists of aerial im age files, were stored in a file-based system on a central server computer that allowed the project team to include the same high-resolution imagery in maps created throughout the project. Other dat a sets, stored as individual files, were either created for one time use or did not required asso ciation to other datasets across the project. These files will be revisited later in the chapter, as they posed additional problems as the project expanded and progressed. Geodatabases represented the bulk of the data storage options for the CFCRT project. The majority makeup of these struct ures was vector data, represent ing sample locations, potential stations, railroad tracks, parcels, and other dat a collected and acquired for the project. Each geodatabase contained all of the associated data for a specific track study section or sub-project. Individual projects and sub-projects include an alysis of the full project corridor, specific maintenance yards, future double track locations a nd train stations. This organizational structure allowed the project team to take advantage of the processing and validation functionality of the geodatabase, as well as reduced data redundancy by st oring all of the data for each study area in its own specified structure.
41 The final set of data used in the project was data licensed from ESRI as part of the ArcGIS Server technology. Through the software license agreement, the team was granted access to data owned and managed by ESRI, but made available thr ough secure access to a variety of data sets stored on an image server. These datasets prov ide frequently-used raster data for use in a variety of projects. The CFCRT project was abl e to take advantage of street maps, aerial imagery, and topographical maps during each phase of the project. The licensed data has the advantage of providing easy access to common data sets ; however there is no ability to manipulate the data if it has not been updated recent ly or is reporting inaccurate information. Each of the above data storage options has advant ages and disadvantages, but collectively they provide a set of options that serve projects and organizations with varying levels of technology resources. With the use of num erous data sources, a great deal of time is spent managing the data to ensure its integrity. The need for an organizing premise for the vast amounts and varying locations of data storage is the driving force behi nd the question asked by this chapter. As this organization is achieved, the final steps in the data flow proces s can be pursued. Research projects often have a story which cannot be fully told by individual points displayed on a map. Gunther and Voisard (1997) speak of the opportunities provided through aggregation and analysis to develop information from the collected geographic data. Through this compilation, data is able to take on the representative forms requi red by a specific project. This final phase in the data flow process drives the purpose for the collection of data. Through aggregation, spatial locations are matched with phenomena recorded at that location at that time. This combination allows the user to create a virtual representation of a real world event at that recorded location. The analysis component works to extrapolate info rmation from the aggregated data by looking at changes at the real-world location from one time period to the next or by viewing the relationship between multiple geographic datasets. This abilit y to draw further understanding from geographic data is driven by complex models which are developed for the analysis being performed.
42 Advances in spatial analysis can be helpful in ortho-rectification, data assessment, sampling plan development, and developing conclusions based on collected data (Goodchild 1992). While not the prime target of this thesis, cases of aggregation and analysis were prevalent in the commuter rail project. The story to be told is driven by the samples taken at regular intervals along the project corridor. While the location of these samples is recorded through the use of GPS-based, handheld data collectors, the maps created would represent very little beyond the path taken by the collection teams. It is throu gh the aggregation of this data with the samples analyzed by laboratories that map-value can be ac hieved. Thus, illustrations can be developed to best represent the results of the sampling process in this case displaying where specific analyte exceedances exist. From these aggregations, addi tional project analysis can be undertaken by agencies utilizing the maps and reports. Stat istical analysis allows for extrapolation of contamination trends for plume identification or for the planning of cleanup efforts. Simulations are also planned, based on the results, for t he remediation of future double track and commuter rail station locations. From the analysis and aggregation of data, to it s identification and stor age, geographic data is a key component of geographic information systems that must be managed to ensure it is properly created, cared for and used during its useful life. This data flow process is similar to the process followed for data collected in the business world (Gunther & Voisard 1997); however, geographical data contains the spatial component th at differentiates it from other forms of data. For the purpose of the commuter rail case study, the data collected and used for the project becomes the translation method between field observations and future design and implementation decisions for the whole of t he project. Along with laboratory data and other details inferred from analysis efforts, inform ation can be developed to drive decision making. While the development of this information is va luable for the success of the CFCRT project, this
43 thesis requires us to take a step back to look at the organizational efforts being undertaken in the geographic data structure. 4.3 Organizing Geographic Data There are a number of organizational elements that exist beyond hardware and software tools which can, if implemented properly, provide as much organizational capability to geographic data as an efficient file system. These additional elem ents are contained within the configuration of the data and are being driven by standardization e fforts that are being carried out across the globe. This thesis focuses on the Federal G eographic Data Committees (FGDC) Content Standard for Digital Geospatial Metadata (CSDGM), as its structure is readily available and is directly applicable to the project. This standard protocol is implemented through the use of metadata, which possesses the unique ability to describe data while also being represented and managed as a piece of data (Hohl 1998). While not exclusive to geographic data, metadata provides a common framework for data identific ation and a set structure for attributes of geographic data. Throughout the commuter rail case study, met adata was both reviewed by and created for the project. Through the ESRI ArcCatalog product, metada ta provided early on in the project by the Department of Transportation and other consulting organizations could be reviewed. This review provided the geographic extent of the study ar ea, the standard coordinat e system that would be used throughout the project and a number of ot her data quality standards t hat would be replicated through the team's data collection and acquisition e fforts. Some of this data, particularly that which is available from larger government agen cies, followed the standards set forth in the FGDC, providing all of the required elements and many of the optional elements. However, data from outside consulting firms did not always in clude a full set of metadata. This lack of documentation about the dataset often introduced di fficulties in processing data for use in the project through inconsistencies in coordinate syst ems or accuracy issues. One example of this
44 was in mismatched parcel data for Seminole and Orange Counties. This data was not validated before distribution and required updates on the part of all organizations utilizing the data to ensure that parcels were not only continuous from county to county, but also matched the highresolution aerial imagery that was to be used throughout the project. In the collection and creation of geographic data the project team also utilized the FGDC's content standard to create metadata. However, t he team's approach to created comprehensive metadata was based on the potential distribution fo r the collected data. For data that was to be used internally, only the identification information was included. Optional information, including data quality, spatial organization and attribute info rmation was included for data to be shared with other organizations working on the commuter rail project. Decisions determining whether optional metadata details would be included were made based on the labor costs required to create the metadata. When data would only be used intern ally, extensive met adata creation was not required. Again, the value of this optional data is in the ability for acquiring organizations to understand and apply the same conditions within their GIS for proper use and display of the data. The metadata created by the team early in the project provided a basis for the interpretation of the geographic data as an asset, which will be furt her discussed later in the chapter. 4.4 What is an Asset? The development of asset management has grown ou t of various industries involved in tracking physical resources that may be distributed across geographic locations or personnel. The basis for their development stems from the difficult ies in understanding where resources exist, what their current status is, and what value they continue to hold for the organization. The strategies for maintaining accountability of our assets are not necessarily software dependent, although using computer-based t ools can greatly reduce the requi red effort. The identification strategies used in this effort include methods for discussing Vaniers (2000, 2001) six asset management dimensions. Secondly, the process will take into account the specific nature of the
45 asset at hand. Determining the condition of a bulld ozer is a far cry from determining the condition of geographic data collected on a rail corridor. Aski ng these questions specific to the nature of the asset provides a process framework to acco unt for those assets with which an organization works. The nature of data is of special consideration when attempting to view it as an asset, a description most commonly attributed to a physical ob ject. While it could be argued that data is a physical item, as it is recorded in some physica l medium, data has many differences that set it apart from a physical asset. The first is its ability to be infinitely duplicated at little to no cost to the creator, as no raw materials are consumed in its creation. Secondly, data does not adhere to the same rules of appreciation and depreciation that affect a physical asset. At some point in its life, a physical asset can no longer provide the same value that it exhibited at the beginning of its life. Conversely, the value of dat a is contingent only on its ability to remain relevant in the context for which it was collected. The data itself never degrades to the point which it cannot be used. Geographic data is a special form of data, as discussed in chapter two, which requires the inclusion of a spatial component that must be coll ected. This additional step in its manufacture provides geographic data with a ttributes that allow for its examination as an asset. The following six dimensions delve further into these attri butes in an attempt to evaluate this capability What do you own? The first question, asking "What do you own?" re fers to the development of an inventory of objects which makeup the assets of an organization. In this regard, we are cataloging those datasets which are relevant to the project or or ganization, or as Kyle et al. (2002) describe, complete, up-to-date, set of digital data reflecti ng the current state of the asset. The simplest method of cataloging assets is to take a cens us of everything under the purview of the asset manager; however, the desire by a project team for a tracking system suggests that there are an overwhelming number of assets to be managed. Va nier specifically recommends using existing
46 software tools to define an inventory of assets For assets which are mobile in nature, he recommends the use of GIS software which can be used to inventory assets based on their spatial location. For facility management, the us e of computer aided drafting and design (CADD) software can be useful in the interpretation of pl ans and drawings of fac ilities. These drawings provide a visual representation of the machinery, structures, and other stationary assets that are to be managed (Vanier 2000). Unfortunately, nei ther of these suggested methods are directly applicable to geographic data, as it is stored with in the confines of a desktop computer, network server, or some other technol ogy-oriented storage device. The marketplace includes a great number of intelligent tools for co llecting the hardware, software, and data inventories of a system of networked co mputers. Solutions such as these could be valuable to the data asset manager if applied in the proper manner which would include the ability to catalog all managed data on the variety of hardware systems on which they are stored. This solution would prove useful in the CFCRT en vironmental analysis project if the project data was unknown or if all of the data sources fell under the management of the project team. However, the data for the case study was collecte d from a number of disparate sources, including those managed by other organizations, while data stored internally was often located on nonnetworked computers and mobile collection units. Although this software inventory solution was rendered ineffective for the case study, the t eam had the advantage of a strong data recording process throughout the project first through the use of a spreadsheet, then through the use of a CMMS. Vanier (2000) specifically recommends the us e of the Computerized Maintenance Management System (CMMS) as it is an established genre of software, and has the ability to provide inventory control, manage asset changes and updates, as well as provide a historical look at the assets under management. During the CFCRT projec t, a CMMS application was utilized as a data cataloging tool because of its ab ility to inventory all of the data assets for the project. The
47 benefits and detriments of using this application as an asset management tool will be further in the last section of this chapter. The CMMS se lected for the case study is a free product called Maintenance Assistant 2.0.10 (http ://www.maintenanceassistant.com). This product is network enabled, which allows team members in different locations to access and update the data inventory. The tool is also able to record a great deal of other information pertaining to the ownership, use, and maintenance of the project dat a. While, additional functionality and pitfalls to this solution will be discussed in the coming sections and chapters, the CMMS provides a software-based strategy for initially recording a nd maintaining the inventory of what is owned. Creating the inventory of data assets for the commuter rail project involved more than just identifying the individual datasets that were to be used on the project. Geographic data, as described above, contains spatial, temporal and metadata components. A CMMS was implemented as the project increased in complexi ty. The initial spreadsheet tracked data by identifying the name of the dataset and where that da taset was located, either in a computer file structure, a geodatabase, or on a mobile data collecti on unit. This solution quickly lost favor with the project team, as it was not easily accessible by all members of the team and provided little information about the creation, update and use of the project data. As the CMMS software that was not designed to account for the custom fiel ds necessary to store geographic data, it was necessary to take advantage of the ESRI ArcCatal og tool, which has a storage method for spatial metadata. In this scenario, geographic data was recorded for the project inventory and management in the CMMS and the additional metada ta was recorded within the dataset in the separate ArcCatalog tool. While this method created some difficulties with information being stored in multiple locations, co nsistency was maintained through th e ability for all of the project team to manage the data through the CMMS, ev en as metadata changes were being recorded within the dataset itself. This dual-software solu tion, while not ideal, was able to fully account for both the datasets in use in the project as well as the associated data components of spatial data, temporal data, and metadata.
48 Figure 6: Maintenance Assistant Screenshot Geographic Data Inventory What is it worth? Items affecting the bottom line of any organization are rarely overlooked. Budgets are driven by both the costs of assets, as well as the labor ne cessary to use and maintain those assets. To ensure continued investment in an asset, an organi zation must be able to determine the value of the asset. The measurement of value is a comple x task, requiring that multiple factors be taken into account. This multitude of factors gives birth to variety of methodologies available for determining the value of an asset. A consistent determination of asset value remains elusive because the nature of each disciplines assets varies greatly. The calculation of value is further complicated as external, market and internal, organization conditions constantly change the perceived value of an asset. There are select sy stems that have been created to calculate value
49 based on concrete asset attributes for the constr uction and facility management fields, but these are based on complicated algorithms using size, shape, and raw material costs. The CMMS, as described above, frequently does not incorporate val ue calculations as it is more focused on cataloging and tracking assets, not calculating the value of the asset to the organization. To attempt to calculate the value of the geographic data collected throughout the case study, beyond the historical value reported in figure two of chapter four, it was necessary to look at the nature of geographic data. Much like a hardware asset, geographic data is discrete and identifiable, as noted by Lo and Yeung (2002) in their Object-Based Model. However this data may only be in its described extent for a limited amou nt of time. Data can appreciate in value as accuracy improves or additional attributes are co llected for a given spatial point. In the same regard, the value of the data begins to depreciate as the physical nature of the location inevitably changes. A common example of this loss of val ue is visible in aerial imagery of rapid growth, such as the below neighborhood, comparing images taken in 1999 to those taken in 2006. Of specific note in the figure is the former orange grove that has been converted into a golf course neighborhood on the east side and the other smaller neighborhood under construction on the west side. Therefore, while historic data may still be useful in certain applications, to maintain the accuracy of the current geogra phy, data must be constantly updated or discarded once its ability to represent the current state has diminished.
50 1999 Pasco County (Florida Department of Environmental Protection Land Boundary Information System) 2006 Pasco County (Southwest Florida Water Management District) Figure 7: Aerial Imagery Comparison 1999-2006 The ability to continually represent current or ma rket value of geographic data is an incarnation of the ability to understand whether it is still a useful asset. To do this we must be able to define those factors which contribute to the value of geog raphic data. Bruce Joffe of GIS Consultants, a San Francisco Bay Area GIS consulting firm, has presented on the difficulties in analyzing the costs and benefits of GIS implementation projects His efforts include the quantification of the return on the investment in geographic data and determination of future benefits based on the initial investment. The standard calculation of t he percent Return on Investment (ROI) is the net benefit divided by the costs times 100 or: ROI = ((Benefits Costs) / Costs) 100 To determine the worth of this equation we mu st better define what benefits and costs pertain specifically to geographic data. According to Joffe (2007), this determination is a function of the data workflow. Specifically, we must determine who gets the data, who changes the data and the sequence of events required. Breaking down this sequence, we can analyze each element based on the asset management lifecycle. In term s of acquisition costs, we must include the price paid for external data, the labor and equipm ent costs used to collect data and the hardware and systems costs allocated to this project. Co sts in the sequence of tracking and maintaining
51 our data include additional storage space for new ve rsions of data, labor costs for administration and quality assurance, and the time to compile the updated data. The financial benefits of geographic data remain di fficult to quantify, as their presence does not directly appear as an acquisition or labor cost in our financial calculations. However, they can be found in the efficiencies created through reduced effort and redundancy, improved analysis capability, and improved financial results (Joffe 2007). Using asset management tools, a better understanding can be gained of the location of the data and data re-creation can be reduced by first verifying its existence. Reporting and calculation tools available with certain asset management packages can be utilized to determine how frequently a dataset is being used by project teams. This utilization can be quantified throu gh comparison of the initial cost versus the time x labor rate of the user of the data The cumulative use, or benefit, of the data continually allows for calculation of the return on investment through the equation above. While this is the simplest method of calculating a quant ifiable benefit, it is beneficial to continue to look beyond this low hanging fruit to additional opport unities provided by improved organization and data tracking, decision making capabilities and the tr ue cost/benefit ratio of data value. Following through the ROI sequence with the project case study, the first step is calculating the costs of acquisition. Much of the project data was provided by the depart ment of transportation and was considered a cost of doing business, instea d of a specific asset cost. Additional costs were incurred through purchase (i.e. high-resolut ion imagery) and creation by project staff. Costs incurred by the team were recorded as a time x labor rate charge. Tracking costs were applied as an administrative labor cost to the project. The cost of maintenance was limited to those datasets that required update durin g the duration of this project. These costs, along with any additional acquisition and tracking costs will cont inue to accumulate throughout the duration of the project. Asset disposal costs are also bei ng incurred based on the administrative nature of determining the value of data and any necessary archival. Calculation of benefits were made
52 based on the assignment of data to specific pr ojects and the understanding that redundant data collection efforts would not be required, as diffe rent project teams would experience economies through use of previously collected geographic data. Future costs would be limited to the collection of additional data attributes at pre-collected locations along the project corridor. The organizational system put in place utilizing an asset management structure also provided additional benefits to the GIS team involved in report production. The team was able to better define which data elements were used for each proj ect, providing further efficiencies in project finalization efforts. Figure eight provides an overview of the return on investment process utilized through the case study project. The first table represents the acqui sition and maintenance costs for a subset of the project data. Acquisition represents both the costs to purchase specific sets of data, such as high-resolution aerial imagery, and the costs incu rred when data collection was performed. The data collection costs are associated with the b illing rates of those who performed the associated tasks times the number of hours required by the task. Maintenance costs involved a similar calculation; with the amount of time repr esenting the number of hours spent performing maintenance tasks for a specific dataset. Benefit calculations are derived from the relevance of a dataset to a specific project. The relevant percentage to a project was estimated for each data set and utilized the following benefit calculation: Benefits = ((Sum of Project Use) Initial Project Use) Data Set Cost This calculation is based on the association of total costs incurred by the dataset to the total project usage of the data across all projects. If the dataset is only relevant to a single project, the ROI is 0%. If it is fully relev ant on two projects, the ROI is 100%. The each project described is a sub-project that makes up a portion of the entire environmental analysis effort. Initial Environmental Assessments review a large area such as the full corridor or a specific site to determine where contamination may exist. Contam ination assessments delve further into these hotspots to evaluate the level of remedi ation that may be required for the site.
53 Calculation of Return on Investment. Cost = Sum of Acquisition and Maintenance Costs Benefit = Total Cost Usage on Each Project ROI = (Benefits Costs) / Costs | Value indi cates percentage return on initial Investment Costs Datafiles: Labor Time Acquisition Cost Labor Time Maintenance Cost Total Proposed Soil Borings $71 120 $8,520 $71 10 $710 $9,230 GPS Sample Locations $52 2000 $104,000 $52 36 $1,872 $105,872 Risk Potential Sites $52 300 $15,600 $0 $15,600 Historic $71 40 $2,840 $0 $2,840 High Resolution $7,000 $0 $7,000 Laboratory Results $12,000 $71 100 $7,100 $19,100 FDOT Study Area $33 2 $66 $33 1 $33 $99 CSX Right-of-Way $33 2 $66 $33 1 $33 $99 CSX Station Line $33 2 $66 $33 1 $33 $99 Property Parcels $71 12 $852 $71 40 $2,840 $3,692 Land Use $71 10 $710 $33 1 $33 $743 Soils $33 2 $66 $33 6 $198 $264 Total Cost: $164,638 Project Benefits of Use ROI Project Descriptions: Full Corridor Initial Environmental Assessment Full Corridor Contaminated Site Assessment Right-of-Way Contamination Assessment 10-Acre Site Environmental Assessment 10-Acre Site Contamination Assessment Total Proposed Soil Borings 1 0.2 $1,846 -80.00% GPS Sample Locations 1 0.2 0.1 0.1 $42,349 -60.00% Risk Potential Sites 1 1 $15,600 0.00% Historic 1 1 0.1 $3,124 10.00% High Resolution 1 1 1 $14,000 100.00% Laboratory Results 1 0.2 $3,820 -80.00% FDOT Study Area 1 1 1 0.1 0.1 $218 120.00% CSX Right-of-Way 1 1 1 0.1 0.1 $218 120.00% CSX Station Line 1 1 1 0.1 0.1 $218 120.00% Property Parcels 1 1 1 0.1 0.1 $8,122 120.00% Land Use 1 1 0.1 $817 10.00% Soils 1 1 0.1 $290 10.00% Total Benefits $90,622 Note: Results do not represent ROI for entire CF CRT Project Effort. Calculations are based on a snapshot taken 03/12/2008 Figure 8: Return on Investment (ROI) Calculation Select Datasets
54 What is the deferred maintenance? Developing the attribute set for deferred main tenance for geographic data requires the asset manager to create both the maintenance schedule and assess the current maintenance state of the assets under management. The deferred main tenance, as illustrated in chapter two, is the maintenance cost necessary to br ing an asset up to its original potential. Maintenance differs from repair in that it is an ongoing process that should be included as a portion of the budget for asset management. If regular maintenance is perfo rmed on all assets, it is possible that deferred maintenance would never need to be calculated ye t in real-world examples this opportunity is rarely realized. First is the det ermination of whether maintenance is necessary. As part of the CMMS, the ability to regularly schedule maintenance is part of the structure of the software. If the schedule is not followed, either for business reasons or by mistake, the asset becomes a candidate for this calculation. The calculat ion involves the normal maintenance costs, the possible repair costs and the replacement cost s of the asset. The replacement costs are determined as part of the valuation of the asset. As a decision support tool, the ability to identify what the growing costs of a non-maintained asset ve rsus the costs of repair or replacement allow the manager to delve further into question six, determining what to fix first. Little is provided from the asset management discipline on the best methods for determining deferred maintenance, aside from pointing to DeSi tter's Law of Fives, which state that repair costs are typically five times that of maintenance and replacement costs are typically five times the cost of repair, as described by Vanier (2 001). Applying either the concept of deferred maintenance or the Law of Fives is difficult in the scope of the case study project. As the application of asset management software and concepts has been ongoing for approximately two years, cycles of maintenance, repair, and replacement have been limited. From a planning perspective, deferred maintenance finds greater va lue when paired with the Law of Fives. As
55 ongoing maintenance is planned for each dataset that continues to be managed through the project life cycle, it is reviewed once per quar ter to determine whether updates are required. Maintenance estimates, in terms of labor costs, are required for several categories of geographic data acquired for the project. The subsequent re pair and replacement costs are calculated as five times the cost of maintenance and five times the cost of repair, respectively. Deferred maintenance remained a minor need during the course of this case study as the project is of a significantly short duration and much of the data collected will not have a useful life beyond the scope of the project. The concept is valuable when viewed from the level of the organization. Geographic data that is collected on the or ganizational level can be meaningful for multiple projects. It is for geographic data with this long term potential that deferred maintenance gains credence. When viewed as a long term investmen t, regular maintenance is necessary to ensure that the data maintains the value and condition needs required for project use. The calculation of deferred maintenance in this scenario provid es a planning tool for use when determining maintenance schedules and future cost potential when the work is not performed. What is its condition? Assessing the condition of geographic data faces the same difficulties as the calculation of deferred maintenance. However, unlike deferr ed maintenance, standard methods and tools for determining asset condition exist in the marketplac e. The downfall of each of these is in the specificity of the asset to which they offer t he greatest benefit, as none currently exists for geographic data. However, the metadata cont ent standard includes a section devoted to the documentation of data condition, through the notation of data quality information. This section includes documentation of attribute accuracy, logical consistency, completeness, position and lineage (FGDC 1998). However, these attributes are specific to a data set and do not describe a consistent process that could be applied to dat a sets across a project or organization.
56 While it is out of the scope of this project to develop and test a consistent methodology for assessing the condition of geographic data, it is possible to deconstruct the several existing methods described in the literature review to gather useful methods that can be applied to geographic data on a generic level. The facility condition index (FCI) allows for the creation of a ratio between the costs of an assets deficiencies to the cost of replacing the asset. If the ratio is over .10, or the cost to repair is greater than 10% of the cost of replacement, the asset is listed in poor condition (Teicholz and Edgar 2001). In the case study project, this methodology is limited as much of the data was required for use by the FDOT and created data wa s limited to the scope of the project. The life cycle asset managem ent model (LCAM) prescribes a four step methodology requiring inspection by a subject matter expert, estimation of the maintenance needs, modeling of funding alternatives and dev elopment of an implementation plan (Sawers 2000). While the FCI calculation can be applied to long-term use geographic data through the determination of maintenance and replacement cost s, the LCAM model offers little outside of a general approach for condition evaluation. Accepting that there is not currently a direct assessment model for geographic data, the case study attempted to measure condition on two fronts, accuracy and resolution. These two elements are key to the spatial nature of geographi c data, much as the size, shape, and age of an asset are important in the FCI and LCAM models. The accuracy standard is significant for the vector data collected throughout the project. Du ring post-processing of the GPS location data, the project team was able to calculate the accu racy of each dataset. Datasets deemed outside the standards of accuracy for the project were either classified as poor quality and were resampled by field teams, when necessary. The standards for each condition assessment are flexible depending on the needs of the project for which they are being used. For example, in the below images previously disc ussed in chapter three the imagery on the left is of is a higher resolution than that on the right. The high resolution, black and white imagery used
57 was necessary to ensure that those reviewing t he deliverable could visually pinpoint where each sample location occurred. In this instance, using the low resolution imagery to display the sample location detail would result in a visually in decipherable representation, causing difficulty determining whether a sample was located next to a building or a road. High Resolution Aerial Imagery (Earthtech, 2006) Low Resolution Aerial Imagery (Florida Department of Environmental Protection Land Boundary Information System, 2004) Figure 9: Aerial Imagery Resolution Comparison In another example, certain sample locations required replacement after the project team determined that the GPS data for several datasets were collected at a much lower resolution, due to technical difficulties in the field. The condition of this dataset was suspect and was subsequently replaced. If additional field sampling had not been feasible, the condition of the dataset would have been listed as poor provid ing future users with an understanding that sample points were collected, but may only be accurate within 20 feet, instead of the requisite 3 feet. While these measures were acceptable fo r the case study, the condition measurement of geographic data will remain a subjective measur e until a repeatable method for assessment can be developed. What is the remaining service life? The life of a particular geographic data set is depende nt on the needs of the organization that is managing that asset. Much of the data collected sp ecifically for the CFCRT effort has a service life only as long as the project itself, as this data is only relevant to the remediation and
58 conversion of the rail line. This relatively shor t life span requires those who oversee the data to plan for maintenance and review of the data for t he estimated length of the project. Other data for the project may have a lifespan that is significant ly shorter or longer lifespan than the project, based on the needs of its final deliverables. Aerial imagery is a prime example of data exhibiting both of these characteristics, based on the tempor al component of geographic data. The highresolution imagery collected for the project was ph otographed during the early part of 2006. This data is to be used throughout the project; however its service life is questionable because it is only truly correct on the date that it was flown. As long as the project is content to reference data that could be two or more years old, the servic e life has not expired. However, once new aerial imagery is flown the service life of this dataset is finished, with one exception. Historical aerial imagery is another valuable resource to environm ental analysis projects, su ch as this one. The service life of historical imagery is potentially in finite, as its place in the geographic data hierarchy is one of being a snapshot in time for a particular location. The high-resolution data, whose service life as a current representation has ended, ye t its service life as historical imagery has just begun. Other datasets, such as street maps, parcel data and envi ronmental features are subject to changes over time and must consistently be main tained to ensure that they meet the levels of quality required by the managing organization. The calculation of service life for a common physical asset is associated with standard depreciation techniques that exist in financial acco unting methods. Geographic data, as it is rarely viewed in the same light as a physica l asset, can not apply these same standards to the development of a service life. Differentiating between technical and economic service life, discussed in the literature review, remains benef icial in the discussion of geographic data. Calculating this service life is less methodical than iterativ e, as each dataset requires an estimation of the length of service the user expe cts, based on the project or organizations needs for that dataset. The commuter rail datasets focu s primarily on the technical service life, as the project team planned their data storage techniques based on the perceived storage life of each
59 dataset. Data that was collected specifically for a single project or for the duration of the entire CFCRT effort were stored in project-specific ge odatabases and folder structures, whereas data with a service life beyond the project timeline was stored in department-wide databases and folders for use on outside projects. Economic servic e life calculations, on the other hand, take into account the estimated amount of time that is required for the use of an asset, based on the value and condition, and combine it with the costs that would be incurred through the life of that asset for maintenance, repair and renewal. The value to this economic service life calculation is in the additional decision support it provides to the as set manager through its ability to forecast the costs associated with a data asset that is being maintained over the long term. What do you fix first? The final question is a prioritization of what sh ould be repaired and replaced within the list of assets. While no physical asset is designed to last forever, geographic data can have a longterm statute of use if it is of temporal import ance; however, much geographic data is designed to represent real-time data. This data, while va luable in showing a phenomenon at a certain time period will require cha nges or replacement in order to contin ue providing the same value it did at its inception. Determining what to fix first is a function of the value of the asset to the organization, the condition of the asset, the cont inuing costs to maintain the asset and the time period of use for the asset. Each of the first four elements is answered through the previous four questions. The time period of the asset is defined in sect ion 1.3 of the CSDGM, essentially defining the opening and expiration date for the validity of the dat aset. If the dataset reaches this expiration point, it becomes a candidate for replacement, as its representative value has diminished. The notion that a dataset must eventually be replaced, as is the case with most physical assets, is not always accurate when speaking of geographic dat a. Considerations must be made based on the service life of that dataset. When data is determined to have an ext ended service life, the
60 questions of fixing that data fall along the lines of the amount of effort necessary to repair or replace the specific dataset. The option of capital renewal analysis is one such method which provides an accounting of the replacement or renewal costs of an asset spre ad equally over the numbe r of years until its expiration date (Vanier 2000). The capital renewal option does provide the basis for financial calculations for those geographic datasets which re quire a larger degree of time and expense to collect, such as the field sampling data for the ra ilroad corridor. Thus, questions of condition are necessary for the decision maker to determine whether it is less expensive to repair the dataset, than to require the field team to re-sample, or mo re specifically replace, the entire dataset. When considering at the entire set of data for the pr oject, only a limited few datasets require such a great level of effort to repair or renew. During the case study, the question of what to fix first or in this regard, whether to repair or replace arose on several occasions. In one instance a repair decision was made for a maintenance yard rail section provided by a th ird party vendor. It was determined that the dataset provided did not correctly line up with t he high resolution imagery that was to be used for the project's deliverables. The decision to repair, instead of replace, was made as the labor costs and turnaround time were far less than the potential time and cost of returning the work to the vendor for repair or replacement. The methods for these decisions, while not directly tied to an industry standard are a pplicable through the use of decisi on making capabilities put forward through the knowledge of each of the previous questions. This section has aimed to answer each of Vani er's asset management questions by delving into the set of methods that have been applied through the course of the case study project. It is important to note that there are a number of difficulties faced in the ability to apply asset management methods to a geographic data asset. Ot her industries have the opportunity to take
61 advantage of time-tested condition measuremen t, valuation, and maintenance calculation techniques which assist in decision support. When viewing geographic data as an asset we can begin to apply these existing methods in certain circumstances. To fully qualify a geographic data asset management system, these methods will requ ire additional vetting to ensure that they are applicable to all forms of geographic data and over a quantifiable amount of time. Despite the lack of established methodologies av ailable for application to geographic data, the above attempts to define Vanier's six dimensio ns prove useful throughout the case study example. Certain elements, specifically determi ning what is owned and what the value is are more readily available than deferred maintenanc e and service life. Future research into applicable methods is necessary if data is to es tablish itself as commonly recognized asset, as discussed in the literature review. For this purpo ses of this study, certain assumptions must be accepted as to their validity in order to atte mpt to integrate the aforementioned strategies and techniques into the final requirements for the geographic data asset management system. 4.5 Applications of Asset Management for Geographic Data There are a great number of asset management so lutions available in today's marketplace. Software, strategies and solutions are abundant fo r specific disciplines, from the monitoring and assessment of buildings to construction equipment fleet tracking and maintenance. Application selection is a process undertaken by asset managers based on the needs of the objects to be monitored, the capabilities of the systems to provide decision support, and the resources available to acquire or develop a system. The co sts of systems vary greatly, with many basic applications freely available for download, such as the Maintenance Assi stant CMMS application used throughout the case study project. On the higher end, IBM offers their Maximo Spatial Asset Management system as a $25,000 add-on to ESRI's ArcGIS suite of products, which provides asset management functionality with spatia l location capability. Through the following section, a number of systems were reviewed in terms of their potential to support geographic data
62 as an asset. The applicability of each option showed great variation with some asset management options showing little to no value fo r data and others having great potential with only a few minor adjustments. Geographic information systems are the curr ent standard for the use and organization of geographic data, allowing users to support its creation, storage, and update through a number of established, commercially-available software pack ages. Applications such as ESRI's ArcGIS, Pitney Bowes MapInfo, and others provide a suit e of options to delve into location-based information. While many of these packages ar e expensive, they are crucial pieces of the geographic data management process as the display medium in a mapping or spatial analysis project. Throughout the case study project, t he team utilized the ESRI ArcCatalog program to create and manage geodatabases, access data sets in their respective storage locations, and perform data check-out/check-in processes with the mobile data collection units. The application was also the primary tool for the creation and management of metadata for each data set; however the editor provided is very basic in nature. A number of freeware and commercial metadata editors, many of which are compliant with the standards put forth in the Content Standard for Digital Geospatial Metadata (CSDGM), are documented by the FDGC ( http://www.fgdc.gov/metadata/ geospatial-metadata-tools) Geographic information system software remains th e highest priority component with the project team's toolbox; however, a set of limitati ons exist that caused the need for additional organizational and tracking capability, which GIS so ftware could not provide. Specifically, the GIS software does not provide the capability to track who is worki ng with a dataset at any given time. As is visible in Appendi x A, there are more than 100 datasets, which were in use by members of the project team in various locations, as dictated by the priority of the project work being completed. While it was possible to k eep track of the data usi ng the file management capabilities of the GIS, mistakes were made by the project team which caused updated datasets
63 to be overwritten with older data and incorrect data to be included in deliverables. While these mistakes were eventually caught, the implications of including incorrect information in a final deliverable for the department of transportation caused potential liab ilities for the project team. The need for a new organizational method was driven by both the large amount of data allotted to the project and the need to be able to track wher e that data was being used at any given time. It quickly became apparent that a simple, operatingsystem file organization would be unacceptable as a tracking method for the numerous project data sets. The constraints of the project required that a potential solution be found fairly quickly and be inexpensive to implement. The project team initially turned to the concept of version control to track data as it was created and modified for each phase of the project. As data was ac quired, the file name for the data was appended with the date of acquisition and a version number. This file was noted on a spreadsheet listing all of the files that would be used and maintained du ring the project. Each subsequent update to the file would require the original file to be copied and the date and version number to be updated. Major changes to the file, such as appending field data for a new section of railway, would require a new version number. For minor updates to a file, such as label changes or spatial point adjustments, a decimal notati on would be added to the file name along with the date of the change. As the project progressed, the viability of this manual version control option drastically decreased as the number of team s and individuals requiring access to the data increased. Project management applications were the first so ftware considerations that were approached for organizing the data. Both Microsoft Projec t and the open-source Open Workbench solutions were reviewed to determine their usefulness to t he project needs. To associate geographic data to the application, each dataset was created as a task in the application and was assigned to resources for various lengths of time. These reso urces could be the various computers that were used for processing, data collect ors for field work, or individuals who may be working with the
64 data. This option was able to record all of t he datasets and offered the ability to both track where data was being used and offer planning capabilities inherent in their project management background. The disassociation between a project management task and an asset was the downfall for project management software as a viable data organization method. There was little to no option for recording data storage location, doc umenting changes to individual attributes, or customizing fields without radical changes to t he working structure of the application. While neither version control nor project management so ftware was ideal for providing the level of data organization and tracking that was necessary for t he case study, put together they offer basic opportunities to manage, assign, and track the use of any type of data through the use of readily available software options. As the limitations of each of the above alternativ es become evident, it was necessary to look at more robust alternatives to attempt the managem ent of data that is distributed amongst several users and storage locations. The introduction to the asset management genre of software and strategies came through articles and program demonstrations for the IBM Maximo product described at the beginning of this chapter. While not looking for a spatial solution to the problem, the abilities of the asset management system to record, store and provide an ongoing assessment of the value, quality, and life of the geographic data for the project provided opportunities that the previous solutions coul d not. There are many advantages to be gained through the implementation of existing asset m anagement software. Asset management tools are prevalent in the marketplace and many, fully-f unctioning tools are freely available for business use. They provide a standard set of elements for tracking and maintenance including the ability to identify a specific facility, us er or manufacturer. Tools frequently include reporting options to display utilization, maintenance records, or inv entory control lists. Additional benefit of asset management software include simplified distribu tion of information about the production and dissemination of geographic data and if informatio n that can be used to determine condition or calculate the return on investment.
65 Currently, there is little customization availabl e with out-of-the-box asset management tools, which reduces its compatibility with geographic data. It is through their organizational capabilities that its power is drawn. We can take advantage of the attributes that are standard from dataset to dataset, such as spatial location, coordinate systems, and file types to develop a translation between the GIS and asset management lexicon. The ability to purposefully track which elements within an organization are utilizing data helps to ensure the integrity of the data and examine how it can be more efficiently distributed amongst proj ects. The downsides to existing tools include tight specialization to the type of asset they ar e meant to manage (Vanier 2000, Sawers 2000). For a geographic asset, this lack of customizability in available tool s requires translations to be made to match the geographic data. Another disadvan tage is the lack of quality control built into systems. The adage garbage in, garbage out hold s true with these solutions and requires that procedures be put in place to ensure that data is both correctly entered and maintained within the system. One of the asset management and condition asse ssment systems that Vanier uses as an example through several of his articles (Kyle et al 2002a/2002b, Vanier 2000) is the BELCAM Visualizer. BELCAM, or the Building Life Cycle Asset Management project, was an effort by various Canadian government agencies to integrate asset management, maintenance scheduling, life cycle economics, service life prediction, and risk analysis. While the application contains maintenance costs and condition assess ment measurements, the true benefit of this system is believed to be through its visual repres entation of the assets, in this case buildings, which are being managed. Capabilities to vi sually represent an asset "have already been developed within GIS and these types of vi sualization should be extracted, adapted and incorporated into asset management tools." (Kyle et al 2002a) However, the BELCAM Visualizer application is itself of little use for managing geogra phic data, as its initial use was for that of lowslope roofing systems. Visualization capabilities, when viewi ng data, are frequently limited to the
66 operating system or file management software of the data storage device. Benefits could be found by spatially depicting the lo cation of the storage device; however this would be rarely relevant considering the speed and access to remotely stored data through ever improving network speeds. As discussed previously, one of the commonly us ed forms of asset management software is the Computerized Maintenance Management System (CMMS). While primarily focused on the tracking and maintenance of hardware assets, a CMMS can provide a strong beginning for the tracking of geographic data. Based on our underst anding of what a CMMS could offer it was determined that it would provide the most accurate understanding of the status of the project data, without having to re-purpose the software. Using the Maintenance Assistant CMMS within this framework, all of the pertinent project data wa s cataloged as an asset in the software. This information is separated into a two categories those specifically pertaining to the asset and those that may pertain to a group of assets. Speaking in geographic data terms, asset-specific items may include name, spatial reference, f ile size, collection date and acquisition costs. Attributes that span multiple datasets would include associated projects, data suppliers and hardware used to make necessary updates. As th is was not a purpose-built system, the project was bound by the jargon native to the CMMS tool. Therefore, field scientists and GIS specialists became technicians, hardware resources such as GPS units, servers and desktop computers became facilities and projects became maintenance work orders.
67 Technicians Represents the Personnel interacting with the Data Facilities Represents the Hard ware used for Data Storage
68 Data Suppliers Represents the Data Acquisition Sources Maintenance Represents the Updates and Scheduled Maintenance on the Data Figure 10: Maintenance Assistant Screenshots
69 As the data was recorded in the asset managem ent system, tracking and maintaining the data became a function of administration within the tool. To maintain or update data within the system, it would be assigned to a piece of hardware and a project team or technician. Once the data collection process was completed, datasets woul d be assigned to GIS resources within the organization for analysis and deliverable developm ent. Tracking data becomes greatly simplified by way of assignment to a project and/or device in the CMMS. Additionally, the software provides report creation capabilities, which provide usage data, asset updates and maintenance, and historical information which provide quantifi able results about the geographic data assets to decision makers. These organization, tracking and data comparison assessments and statistics provided a strong solution for the needs of the case study. Data, created and stored in the GIS system, was able to be recorded and its status view ed by all members of the project team, which reduced the number of errors pertaining to its daily use. The organization managing the whole of the commuter rail project was able to validate t he usage statistics for the data, thus gaining an understanding of the value of the investment in ti me and purchase cost for the data assets that were recorded throughout. Despite the benefits of the applic ation of the CMMS, it remains only half of the solution for the geographic data management issues in our case study. The lack of ability for the CMMS software to be customized for geographic data requires a secondary system to record the pertinent data attributes. While a synergy between applications is not required, as is noted by Goodchild's (1992) first step of application int egration, interactions extending beyond the mere ability to automate inventory development would prov e useful through the reduction of errors and the efficiencies created by closely coupled system s. The combination of CMMS and GIS has the potential to both represent data and successf ully manage the data in a form that is organizationally sustainable.
70 4.6 Conclusion Geographic data has the unique ability to represent real-world phenomena taking place at a single, spatial location and time. While still not a commonly accepted practice, researchers like Rajabifard (2001), Branscomb (1995), Barr and Masser (1997) are beginning the discussion of data as an asset. In this definition, the devel opment of context around a data asset provides a level of ownership and responsibility to proper ly manage data. Through this management, value can be derived from the acquisition and collect ion of data and projects can be improved by measuring the condition and ensuring maintenance is performed on a timely basis. Each of Vaniers asset management dimensions are associated with elements defined as part of the national spatial data infrastructure, through t he FGDC Content Standard for Digital Geospatial Metadata. This metadata defines all of t he common elements that a geographic data set should contain when created and shared amongst US Government agencies. To define whether existing asset management te chniques, strategies, and solutions can be successfully applied to geographic data, it is neces sary to review the level to which each can be applied to the case study. The concepts of in ventory assessment, data valuation, maintenance scheduling, and determination of repair or replac ement decisions are able to be directly applied either through functionality that is implemented via existing applications such as the inventory capabilities and maintenance scheduling functi onality of the CMMS or through the use of proven methods for valuation and assessment, such as those use for the calculation of the ROI for an asset. Established techniques for assessing condition and service life cannot be directly applied to geographic data, as they are primarily asset-type specific. Wh ile subjective, projectspecific assessments were developed for these dimensions through the case study, further research is necessary to dete rmine whether these are viable methods for assessing condition and service life with the same level of quality avail able to other industries. Therefore, while not all of the six dimensions can be applied to geographic data in their current state, a level of successful implementation can be confirmed for those techniques which can be directly applied to
71 the case study project. However, this succes s should be tempered with the consideration that future efforts should be undertaken to confirm th ese results through examination of other large scale geographic data management efforts. As a potential system development effort, this commo n structure and set of established attributes and methods from the asset management discipline combine to form two valuable conclusions. The first is the ability to define geographic data as an asset and second, a foundation can established based on existing asset and data management applications. Version control practices continue to be a worthwhile through the notation of each iteration of a data set; however, it does create an additional load on data st orage devices as each version begins with a copy of the previous version. From the projec t management discipline, assignment of assets to specific resources, as well as methodologies for resource and asset scheduling, assist in future asset and resource planning needs. Visual t ools, such as that provided by BELCAM could provide a clearer picture of the data storage structure used by a project team, but little in the way of value and condition assessment that can be provided for physical asse ts. Finally, GIS systems provide the geographic data st ructure that is necessary to account for the attributes necessary to assure understanding of the dataset s being used. As none of these systems is a catchall for fully managing geographic data, it is worthwhile to l ook beyond existing systems to the possibility of developing an entirely new system for managing data as an asset.
72 Chapter 5: Defining a Geographic Data Asset Management System 5.1 Introduction The final product of this thesis is dedicated to the discussion of the design for an ideal system for the management of geographic data as an asset. Th is chapter will focus on coupling the benefits of existing asset management systems to the attributes and methods that have been found valuable for managing geographic data in the previo us chapter, in order to define the necessary requirements for a combined system for managi ng data as an asset. These requirements are a prescription for the design of key functionality that was found to be missing through the evaluation of existing asset management applicat ions during the case study. The market for asset management solutions has much to offer in terms of techniques, condition assessment and organization; howev er, none currently address the needs of data. Through the chapter, we will address many of the available options, looking spec ifically at those discussed as potential answers to Vanier's six asset manag ement dimensions, which have been a central theme to this thesis. The combination of the requirements gathered for a geographic data asset management system lie the groundwork for the ev entual design and development of such a tool for the GIS market. As in previous chapters, we return to the environmental analysis case study as a bench mark for the applicability of these requirements for a geographic data asset management system. 5.2 Requirements for a Geographic Data Asset Management System The development of a new application requires a brief description of the software development life cycle (SDLC). This life cycle is made up of a five step process which includes analysis,
73 design, development, testing and production. This se ction will discuss the analysis portion of the life cycle, with a focus on requirements gatheri ng for a potential asset management system for geographic data. If the system proves to be vi able, future efforts would include the design and construction of the software application. Thr oughout the case study, two mutually exclusive applications are used to fulfill the roles of data manager and asset manager. The proposed system aims to combine the functionality of t hese two systems together by creating a looselycoupled system which is composed of two applicat ions using a common data source or transfer protocol (Goodchild 1992). The benefit to this format, over of a single, fully-coupled application, is the ability to utilize the strengths of estab lished GIS software applications through software development kits (SDKs). The SDK provides a back door, of sorts, to allow another application to access a set of data structures and methods whic h provide interaction, without the need for creating an entirely new GIS system. This ne w, loosely-coupled application does not require extensive analysis and shifts the focus to those attributes and methods that assist in answering the questions established by Vanier for creating a viable asset management system. What do you own? Inventory assessment and contro l is one of the primary features of any good asset management system and is a solution to the question of w hat do you own. This requirement for an asset management system is also the primary questi on asked by the project team during the case study. Vanier (2000) recommends the use of softw are applications to catalog the assets that are to be a part of the system. For traditional, physi cal assets, GIS and CAD systems offer the ability to locate and show integrations between various assets. For a computer-based asset, file and database schema catalog software is a potential so lution for creating the inventory of what is owned. Performing this task sh ould take into account the abilit y to reach out to interconnected data storage devices to ensure that all project dat a is visible within the system, as well as the ability to report the location of the data within t he device. This full accounting of the data assets for a project or organization has several advantag es. Primarily it reduces the amount of time and
74 effort by project team members to compile all of the necessary data for a project. Secondly, it improves the data integrity for the asset managem ent application. Automated cataloging would create an exact replica of the data that currently exists within the data storage devices for a project, providing a single view of all of a project' s data assets. This avoids the potential pitfalls that exist when human error is introduced throug h data entry errors or duplication of effort. Within the requirements of a system for geograph ic data, there lie a set of deeper needs which are inherent to their structure as an asset. This requirement for additional customization reflects the dynamic nature of the ability of data to not only reflect the spatial location and temporal nature of a represented area, but also the complement ary information that is collected about that location. At its base level, the application shou ld be able to account for that data that has been defined in the Federal Geographic Data Committee's Content Standard for Digital Geospatial Metadata (CSDGM). The inclusion of this sta ndard provides many of the data attributes necessary to answer the following five questions, and thus the remaining requirements for the system. These elements will desc ribe the nature of the data as ve ctor or raster, the spatial location and coordinate system information, temporal information, and a number of other elements defining additional attributes, as we ll as usage and distribution information. The application of this standard will allow for ease of in teraction with geographic datasets provided by US Government agencies, along with other organizations utilizing this common standard. While this standard information can be collected th rough the same process as the data inventory, the asset management system should also be able to store custom fields that extend beyond those of the stored metadata. The lack of field customization was one of the major disadvantages found during the application assess ment process of the case study, in both the project management solution and particularly in t he use of the Maintenance Assistant application. Each organization has its own method of conducting project work, whether requiring specific project numbers or including a standard set of fi elds in every geographic dataset. The ability to
75 customize fields within a system provides a le vel of familiarity for users of the data set and reduces the need for translation and thus potential confusion, as was experienced in the use of the CMMS application during the case study. The combination of both standard and custom fields provides a strong set of attributes for t he application, which along with the integration of cataloging capabilities work together to allow an organization to understand what geographic data assets it owns. What is it worth? The calculation of geographic data value in te rms of an asset management system returns to focus on the six forms described by Vanier: Historical Value Appreciated Historical Value Current Replacement Cost Market Value Performance-in-Use value Deprival Cost Each of these values plays a part in understanding the total worth of an asset to the organization. From an accounting standpoint, the historical and ap preciated historical values are the simplest to calculate and represent in an application. The hist orical value is the original cost of the data asset, whether purchased or created, and is a common field in most asset management applications, including Maintenance Assistant. The appreciated historical value requires a basic calculation to determine the inflation adjusted cost of the original value. Determining the value of current replacement costs and market value is a bit more vague. For an application to keep track of the current replacement cost it will require t he ability to consistently update costs pertaining to the acquisition of the asset. These costs co uld be calculated through links to outside organizations through which data was originally pur chased or, in the case of data collected by an organization, could take into account the amount of time required for acquisition times current
76 labor rates. Market value, on the other hand, is based on the value that one would pay for the asset that is under management. This value wo uld be determined on a varying scale that takes into account the appreciated historical value of the data, the perceived rari ty of the data, profit margins for commercial producers, and any adjus tments based on market conditions. The subjective nature of the market place does not prov ide for a standard calculation of value at this time. The final two measurements of value, Performancein-Use and deprival cost are the final keys in the decision support capability of the asset manage ment system. These two costs are based on the calculation of the return on the investment of the asset. This calculation ROI = ( ((Benefits Costs) / Costs) 100 ) relates the costs of the asset to the organization, including acquisition, maintenance, storage, and other pertinent costs, to the benefits gained by its use. The final value is the worth to the organization or Performa nce-in-Use value. This value can also be used to determine the final value measurement, depr ival cost, which represents the costs the organization would occur if the benefits of owne rship were not realized. Data asset benefits remain difficult to quantify, as their presence does not directly appear as an acquisition or labor cost in our financial calculations. However, t hey can be found in the efficiencies created through reduced effort and redundancy, improved analysis c apability, and improved financial results (Joffe 2007). To determine benefits, a valuable additi on to the asset management system would be utilization reporting capabilities. The ability to r apidly assess how each data is being utilized from project to project improves the ability to asse ss the benefits of ongoing maintenance of that asset. Vanier (2000) does not require that all of t he assessments of worth be documented for a valid asset management system, but instead presents t hem as building blocks for fully assessing the value of an asset portfolio. The ability to represent each of these perceptio ns of the value of an asset provides a valuable decision making tool for those managing the data. In the following
77 section, we will delve further into maintenance capabilities, but accounting for their costs and benefits provides visibility for thos e deciding whether to continue t he use of a particular asset by providing a quantifiable measurement of its wort h to an organization. The inclusion of the common return on investment calculation also offers an essential parallel when comparing the investment in a data asset to the investment in ot her assets of an organization. Such tools exist for representing this aggregation of data in the form of executive dashboards, which provide a quick view of pertinent information for decision makers. What is the deferred maintenance? To answer the question of the deferred maintenance of an asse t, one should first ascertain its scheduled maintenance. Data has the distinct adv antage of not depreciating in the same manner as physical assets when regular maintenance is no t performed. That is not to say that regular maintenance is not necessary. Much of the geog raphic data in the case study was reviewed on a quarterly basis to ensure that it still accurately depicted the spatial locations and attributes for which it was created. The maintenance ta sks performed include reviewing the data in accordance with other data that has been collecte d to ensure that the latest data is being used by field teams and for deliverable creation. If updates are necessary, they are made to the original file and distributed to the various locations t hat utilize that dataset. The scheduling of maintenance is one of the ideal uses for the Ma intenance Assistant application, which includes specified functionality not only creating these sc hedules but notifying the personnel who should perform the maintenance. The deferred maintenance, as described in previo us chapters, is the cost of bringing the asset back up to its original value to the organization, when regular maintenance has not been performed. The costs fo r this maintenance will vary between data sets, but the effects can be exponential, depending on the daily usage of t he data and the maintenance necessary. These exponential costs stem from the scenarios in wh ich the data is used. For example, a dataset
78 depicting contaminated locations could be used as a representative layer in five deliverable maps and as part of the sampling plan on three data collection devices. If changes are made to the data but the changes are not distributed to other instances of use, sc heduled maintenance could rectify the problem. If this does not occur, t he incorrect data set will remain in use by the organization, perpetuating the errors contained in the original dataset. The calculation required for use in the asset management tool is the ab ility to represent the regular maintenance costs, contrasted with the potential costs of repair or r eplacement in this case going back and fixing or replacing the dataset in potentially numerous inst ances throughout the case study project. These costs can be generically calculated by using DeSitter's Law of Fives, as described in the previous chapter as repair being five times the cost of maintenance and replacement being five times the cost of repair. These exponential costs not onl y highlight the need for regular maintenance, but also provide decision making capability for an orga nization which does not have the resources to continually maintain assets. What is its condition? More so than Vaniers other classification questio ns, it can be challenging to directly asses the condition of data as it does not degrade in the same way as a physical item. Accepted metrics that are applied to assess the cond ition of physical assets are unable to be applied directly to data. For geographic data, quality assurance proc ess can be the initiation of the determination of an assets condition. The simple process of upl oading data can be one of great difficulty, if not properly enforced. In our project, we had several teams uploading data on a frequent basis. To improve this process, a simple quality assurance (QA) method was put in place. This effort documented how to verify that data was in its co rrect location, contained a ll necessary attributes, and was properly named. This validation enhanced the data acquisition phase of the lifecycle by ensuring that new data would not conflict with pr eviously collected data or data collected by other teams. This information is recorded in Section 2 Data Quality Information, as part of the CSDGM and helps a user understand the initial quality of the data asset.
79 The ongoing condition of data can be measured by examining its relevance to the project, the frequency in which the asset is used and the benefits that the asset continues to provide. Each of these characteristics plays to the continuing good c ondition of a piece of data, as it is considered useful. However, these are subjective measures and it would be difficult to create a quantifiable measure that could be replicated form data set to data set, let alone from organization to organization. Condition assessment remains o ne of the missing links in assessing the asset discipline's ability to manage geographic data. To fully account for the capabilities provided by this question, further research should be conducted into solid methodologies for the assessment of geographic data asset condition. What is the service life? The service life, while including the subjective meas ure of condition, is a bit more concrete in its ability to display the useful life remaining for a gi ven asset. Initially, this value can be pulled from standard metadata, which records the Time Period of Content a list of the currentness of the data set reference. This representation of the se rvice life of the asset is useful for a specific amount of time, although the value should be co ntinually evaluated when maintenance is performed. Within the case study, data collect ed in the field was giv en a service life that extended from the sampling date to the date of plann ed remediation for the specified site. This span of time equates to the service life for the va lues recorded at that location, as no additional sampling is planned before remediation. An except ion to this rule occurred after a tropical storm event in which a portion of the commuter rail trac k flooded and a section of the track had to be resampled and the data set amended. In this instance, the initial data set was edited to remove the re-sampled locations and a new dataset was creat ed to represent the re-sample locations. Decisions around the determination of service life also include the answers provided to the above questions of value, condition and maintenance. As described in chapter four, each of these
80 elements provides details into t he costs that are incurred by a dat a set during its life, as well as the ongoing value of the asset. While a dataset ma y remain current in the spatial locations or attribute information it depicts, the upkeep of the data may shorten its service life for the company. Understanding the utilization, maintenan ce costs, and return on investment that is generated by asset allows managers to make deci sions of whether to continue the use of the asset. What do you fix first? Vanier's final question is a prioritization of the repair and or replacement of a data asset that is no longer functioning properly for an organization. As described in the section on deferred maintenance, these potentially expensive choice s to make based on the relevance of a data asset project to project. To create the priority list for repair and replacement, each of the above questions must be answered. The performance in use value represents how much the asset is currently worth to the organization through its representation of benefits to costs. An asset with a high value in this column is crucial to the organiza tion on a daily basis. Assets which either have a low or no calculated performance in use value can turn to condition measurements or deferred maintenance costs to determine whether action shoul d be taken. If a data is of low condition already or has a high deferred maintenance cos t, it may be financially beneficial to plan for the disposal of the asset, especially if dat a incurs licensing or storage costs. CMMS systems, such as Maintenance Assistant, contain functionality to facilitate the repair of assets through the assignment of work orders. To assist in determining when such functionality would be required, the use of customized fields w ould allow for the establishment of alert levels for value, condition, and maintenance costs on ce each reaches a certain level a work order could be auto-generated to begin the repair or replacement of an asset. The system requirements to fulfill this functionality are grounde d in the ability to answer each of the previous questions and the financial and technical resources available to the asset manager. The benefit
81 of a decision support system, such as a geograph ic data asset management system, is to provide collective this insight to organizations that may have difficulty assessing large amounts of geographic data. By structuring the requirements of this syst em around the Vanier's six asset management dimensions a firm foundation is created with whic h we can further explore software solutions for geographic data. These requirements, bolstered by the attributes and methods created in the previous two chapters establish the analysis portion of the so ftware development life cycle for a geographic data asset management system. Answer ing what do we own, the ability to automate the inventory process of data improves the us ability of any application through processing speed and the assurance of quality through avoidance of hu man error. The ability to customize the data stored around each geographic data set further im proves the understanding ownership through the inclusion of all related information about a spec ific asset. In assessing value, the requirement to account for each form of asset value helps an organization ensure that investments in data are prudent and appraise the potential value of an asset as it ages. These valuations require that the system take both costs and benefits into account which can be accomplished by recording the monetary and labor costs and examining the benefits of utilization, efficiency of reuse and lack of redundancy caused by the re-collection of a data set. Existing CMMS applications have the capability to schedule maintenance for assets and this should be carried forward into the proposed asset management system. The need to constantly review data ensures that it remains current and accurately reflects the spatial information it represents. Along with the ability to schedule maintenance, the system should also be able to record if maintenance does not occur at the sc heduled time to ensure that deferred maintenance costs are taken into account. These costs, if ma intenance is not performed can affect the state of the asset in ways covered by the following requ irements. Condition assessment, while not able to take advantage of an existing methodology can ma ke use of subjective measures, put in place
82 by the asset manager, based on the understood quality and continued utilization of the data asset. If the system is capable of documenting custom fields, the subjective notation of this requirement can be developed by the asset manage r or project team that is performing the assessment. The final two questions create requ irements that document the service life and repair/replacement priorities for assets. Both the technical and economic service life should be documented the former through notation in the metadata and the latter through the determination of continued asset value. This remaining service life, along with the previous requirements, answers our final question by setting the priorities of repair and replacement. The requirement of what to fix first is not necessarily a technical requirement of the system, but more an element of functionality that compiles other resu lts to assist the asset manager in questions of repair or replacement. Each of these requirements has been defined at a high level. The next step in the SDLC, design, requires the interaction of subject matter experts to establish the specifics necessary to create the system. For example, the requirement for t he automation of inventory cataloging requires specific functions and protocols be reviewed to determine whic h will be programmed into the final technology solution. Certain elements will be readily defined, such as those that can take advantage of existing program functionality, like inventory cataloging and maintenance scheduling. Others, such as condition assessment and economic service life, will require an additional amount of research to create methodologies that are applicable to multiple organizations. This initial work has been fo cused on determining whether a set of requirements could be developed that would exemplify the spir it of Vanier's six dimensions for asset classification, and while supplemental requirement s could be included in the final system this initial set answers the necessary questions to be gin the creation of a viable system for managing geographic data.
83 What do you own? Automate the creation of the Geographic Data Catalog Account for all elements of Content Standard for Digital Geospatial Metadata Allow for the Customization of additional data fi elds, specific to the needs of the project What is it worth? Calculate the Appreciated Historical Val ue, from the Original, Historic Value Links to data sources for current repl acement and market value calculations Continuous monitoring of Return on Investm ent, through tracking of use by projects Development of Executive Dashboard for ongoing view into asset portfolio value What is the Deferred Maintenance? Documentation of maintenance schedule fo r each data set both planned and executed Representation of Maintenance Costs, Repair Costs and Replacement Costs, per DeSitter's Law of Fives What is the Condition? Quality Assurance methodology and standards for geographic data managed by the system Assessment of data condition, in terms of re presentative capability for the project at hand What is the Remaining Service Life? Documentation of the recommended Technical & Ec onomic Service Life of the geographic data set Documentation of the potential ongoing value of data set as historical representation tool Ongoing maintenance and potential repair/replac ement costs incurred over life of asset What do you Fix First? Prioritization of data set repair and replacemen t, based on value, condition, and service life Figure 11: Summary of Geographic Data Asset Management System Requirements 5.3 Conclusion Measuring the success of a geographic data as set management system is much like measuring the benefits of the use of geographic data. The system should be able to meet the requirements of both an asset management system, effectivel y cataloging, tracking, and organizing the maintenance of data, while including geographic data specific attributes, tracking processes, and condition assessment capabilities. Quantifiable results of such a system may include market adoption rates, compatibility with existing GIS so ftware, interface customization and scalability. The ability to develop such a system using es tablished principles of asset management, a
84 common set of metadata attributes sanctioned by the federal government, and utilizing tested software and methods will speed the acceptance of a system that proposes to manage geographic data in this manner. Further improving this process are innovations that are becoming pervas ive throughout the GIS community. Beyond direct integration with existi ng GIS platforms, the ability to access data in real-time either through server-based GIS or from the field using wireless access will allow for users to maintain data in real time, reducing erro rs and improving data tracking. Technology will continue to improve the data asset management process through organizational techniques, reporting, and value calculations but it remains the responsibilit y of those using any system to ensure that data, and its integrity, remains the focu s throughout its entire lif e cycle. The existing application benefits described throughout this chapter and the requirements that have been created to answer the key asset management quest ions work to assure the data integrity and continued worth to the organization to which it bel ongs. Through closer examination of the asset management and geographic information system fields future solutions will provide stronger data organization capabilities to all geographic data manager
85 Chapter 6: Conclusion Through the eyes of our commuter rail project, this thesis has focused on how geographic data can be viewed and managed much like any other phy sical asset by an organization. It is through the lifecycle of ownership that data elements can be tracked, providi ng users with a better understanding of the value of the data within their realm of ownership. While no current tool exists to specifically manage geographic data as an asset, existing asset management software can be used to accurately track the status of geogra phic data elements as they are created, updated and assigned to technicians, mobile data collectors, laboratories a nd projects. The ability to track the use of data throughout its lifecycle provides t he information necessary for asset managers to perform preventative maintenance and determine renewal and replacement guidelines required for defining the life of an asset. Throughout the project, we have examined the pot ential use of asset management technology in conjunction with geographic data thr ough the environmental analysis of a commuter rail corridor in Orlando, Florida. This effort, made up of multip le, smaller projects, required the organization of numerous sets of geographic data which were ac quired through acquisition, purchase and data collection. The difficulty faced in the daily management of this data encouraged further research into alternatives for data management. While several established forms of organizing software were reviewed, the asset management discipline offe red the strongest parallel with the tasks that were being undertaken during the case study. T he product selected for use was a computerized maintenance management system (CMMS), proper ly named Maintenance Assistant. Through a combination of this application and the data manipulation capabilities of existing geographic information system (GIS) software, the project team was able to successfully track and maintain
86 data that was distributed amongst several data storage devices. The applied solution improved data integrity through the documentation of ut ilization and allowed for the assessment of data value, condition and useful life to the both t he project and organization. Even though the solution proved to be a successful organizational met hod for this project a number of drawbacks remained. The two primary drawbacks were the lack of customization possible for naming data elements and describing product functionality, wh ich caused a great deal of confusion for new users of the technology, as well as the lack of technology bridge between the asset and GIS systems which forced the manual entry of information from one system to the next. The successes and drawbacks of this two applicat ion approach to organizing data led to a desire to ascertain the validity of such a solution. In research on the asset management field, the work of Vanier was consistently cited, along with his six question approach for assessing asset management systems. Further research into t hese questions What do you own? What is it worth? What is the deferred maintenance? What is its condition? What is the remaining service life? and What do you fix first? offered a framework and a set of comparable solutions from the construction and facilities management industries. Through evaluation efforts conducted within the case study, Vaniers six dimensions offered a feasible solution for the development of the data inventory, determining the value of a data asset, understanding the schedules and costs of data maintenance and defining whether data repair or replacement should occur. However, the six dimensions fell short in the assessment of dat a condition and the definition of service life. Each of these items required subjective form s of measurement, based on the needs of the case study project. Improvements to the use of these six dimensions as a data asset assessment framework would include the addition of data-specif ic techniques that can be time-tested and proven over a large number of projects, as exists with many of the physical assets that Vanier (2000) describes.
87 Using the case study as a reference tool, further research was conducted into these questions in an attempt to define the requirements for a geographic data management system. The development of an object-oriented program involves analysis to define a set of requirements. These requirements are made up of a set of attributes which represent real-world objects and a set of methods which represent phenomena. The work completed on the commuter rail project provided a sample of commonly used geographic data sets, from which a common set of attributes was drawn. These attributes are stru ctured using both the requirements set forth in Vanier's questions and the Federal Geographic Data Committee's Content Standard for Digital Geospatial Metadata. The methods used to devel op the requirements for the system were drawn from research developed by Vanier and others who have created solutions for measuring value, condition, maintenance, and service life of asse ts. The combination of attributes and methods work in concert to define the initial stages of a system for managing geographic data as an asset. The development of this system is reliant on the abili ty of the industry to view data as an asset. While this is not a commonly used moniker when speaking of data, several authors (Branscomb 1995, Barr and Masser 1997, Rajabifard 2001) have begun to describe geographic data in these terms. The advantages to viewing geographic dat a in this manner are in the ability to manage data as one would a physical asset. Traditional data management is technology dependent, relying on file and database structur es to efficiently monitor the location of data, however there is little provision for documenting the use of data. Asset management focuses on the daily use of the asset, understanding its utilizatio n, ensuring that it remains in working order, and measuring its value to the organization. In the case study project, each of these situations was occurring, but existing organizational systems could not ac count for them. Through the use of asset management methods, the utilization of data could be tracked to a specific project or projects, maintenance could be scheduled to ensure t hat data remained current, and the organization could determine the value of the data. This va lue calculation is a growing need for commercial organizations that are often required to purchas e data from third parties or collect the data
88 themselves, often at great cost. Through acqu isition and ownership of data, the value of the investment in data can be quantified. The appl ication of these asset management capabilities improve the ability for geographic data to be better managed when it is applied in multiple locations or projects. Managing data as an asset may not yet prove to be the best solution for managing all geographic data. First, the process for managing data as an asset introduces an additional level of bureaucracy into a GIS project, through the inclus ion of additional attributes and methods that are not normally recorded for geographic data. For sma ll projects, the additional level of detail about the value, use and condition of data may prove to be cumbersome in both the time and effort required to collect this additional information. The asset management solution is better suited to projects which require either the management of large amounts of data over long periods of time or require the additional context provided by asset management tools. Second, while geographic data has many parallels to physical assets, data is not a physical matter and cannot truly be measured as one. The construction and facility management industries have well established methods for calculating the value, condition and maintenance costs of their assets. While methods can be adapted for data in many instances, there are no established methods for assessing the condition of geographic data. Further research is necessary to determine if existing condition assessment methods can be accommodat e geographic data or if new methods should be created. Time-testing and quantifiable measur ement is also necessary to ensure that methods for calculating value, determining service life and making repair and replacement decisions are applicable in other large scale implementations. The future of this work will be based on the further development of research into the application and assessment of geographic data as an asset. Th rough this research, design efforts can begin to delve fully into the requirements for a geographic data asset management system, as described through chapter six. The strengths a nd weaknesses of this application of asset
89 management will be further discovered as the techniques are applied to other large-scale geographic data management efforts. Advances in technology will further improve the future environment for a system of this type, as we ll. Location-based technology remains a growing industry and the proliferation of global positioni ng systems is encouraging an increase in the number of geographic data sets to be managed an d represented. Although there remains work to be done to validate asset assessment met hods, the conception of a geographic data asset management system is a viable option to ensure the integrity and organization of large amounts of spatial data.
90 List of References 1. Armstrong, Leslie. 2006, Geospatial is Special Environmental Information Symposium 2006, 6 Dec. 2006, Environmental Protection Agency. 2. Barr, Robert, and Ian Masser. 1997, "Geogra phic Information: a Resource, a Commodity, an Asset or an Infrastructu re?" Innovations in GIS 4 Ed. Zarine Kemp. London: Taylor & Francis, Ltd., 1997. 234-248. 3. Berry, B. J. L. 1964, "Approaches to R egional Analysis: A Synthesis." Annals of the Association of American Geographers 54 (1964): 2-11. 4. Branscomb, Anne W. 1995, "Public and Priv ate Domains of Information: Defining the Legal Boundaries." Bulletin of the American Society for Information Science 21 (1995): 14-18. 5. Brynjolfsson, Erik. 1994, "Informati on Assets, Technology, and Organization." Management Science 40 (1994): 1645-662. 6. "CAS Homepage." Condition Assessment Survey 2008. US Department of Energy.
91 15. Federal Geographic Data Committee. FGDC -STD-001-1998. Content standard for digital geospatial metadata (revised June 1998). Federal Geographic Data Committee. Washington, D.C. 16. Florida Geographic Data Library and Map Server Mar. 2008. GeoPlan Center, University of Florida.
92 Annals of the Associatio n of American Geographers 87 (1997): 363-372. 30. Rajabifard, Abbas, and Ian P. Williamson. 2001. Spatial Data Infrastructures: Concept, SDI Hierarchy and Future Directions . GEOMATICS'80 Conference, 2001. 31. Robinson, Milo. 2005, NSDI Future Directions Initiative: Towards a National Geospatial Strategy and Implementation Plan. Federal Geographic Data Committee. Reston, VA, 2005.
93 Appendix A Central Florida Commuter Rail Transit Project Data Set File Names, Locations and Sources Central Florida Commuter Rail Project Geographic And Supplemental Data Sets Filename Description Type Location Source All Projects Reference Shapes Database Common files used by all Project Consultants Geodatabase Milepost Mileposts along CSX Line Point Reference Shapes Database FDOT ROW CSX Right of Way Line Reference Shapes Database FDOT Station Station/Rail Line Line Reference Shapes Database FDOT Sheet Groupings for Mapbook Layout Polygon Reference Shapes Database Internal HIRES 04708_01 High Resolution, Black & White Aerial Imagery of Project Corridor Raster File Storage System FDOT HIRES 04708_n High Resolution, Black & White Aerial Imagery of Project Corridor Raster File Storage System FDOT HIRES 04708_40 High Resolution, Black & White Aerial Imagery of Project Corridor Raster File Storage System FDOT Various Historic Aerials for Corridor, by Decade since 1940 Raster File Storage System FDOT / UF PALMM Various Historic Topographica l Maps Raster File Storage System FDEP ESRI_ShadedRelie f_World ESRI Geographical Feature Map Raster Licensed from ESRI ESRI I3_Imagery_Prime_ World 1-Ft True Color Aerial Imagery Raster Licensed from ESRI ESRI NGS_Topo_US_2D National Geographic Society Topographical Maps Raster Licensed from ESRI ESRI ESRI_StreetMap_ World ESRI Street Maps Raster Licensed from ESRI ESRI Project 206131 Level 1 Environmental Assessment 206131 Database Shapefile database for project 206131 Geodatabase Internal FieldDataMaster Master Shapefile of all collected Arsenic Sample Locations Point Level I Database Internal SegmentA1 Arsenic Sample Locations for Corridor Segment A1 Point Level I Database Internal
94SegmentB2 Arsenic Sample Locations for Corridor Segment B2 Point Level I Database Internal SegmentC3 Arsenic Sample Locations for Corridor Segment C3 Point Level I Database Internal SegmentD4 Arsenic Sample Locations for Corridor Segment D4 Point Level I Database Internal SegmentE5 Arsenic Sample Locations for Corridor Segment E5 Point Level I Database Internal SegmentF6 Arsenic Sample Locations for Corridor Segment F6 Point Level I Database Internal SegmentG7 Arsenic Sample Locations for Corridor Segment G7 Point Level I Database Internal SegmentH8 Arsenic Sample Locations for Corridor Segment H8 Point Level I Database Internal SegmentI9 Arsenic Sample Locations for Corridor Segment I9 Point Level I Database Internal SegmentJ10 Arsenic Sample Locations for Corridor Segment J10 Point Level I Database Internal LandUse Existing Land Use Polygon Level I Database Internal LineMeasure Sample Location Planning To l Line Level I Database Internal MissingFieldDataM anualPlots Manually Created Data Points, based on Field Observations Point Level I Database Internal G7 Plume Contamination Plume for G7 Area Polygon Level I Database Internal SpellmanPlume Contamination Plume for Spellman Site Polygon Level I Database Internal FAMPlume Contamination Plume for FAM Site Polygon Level I Database Internal GassificationPlume Contamination Plume for Gassification Plant Site Polygon Level I Database Internal SentinelPlume Contaminat ion Plume for Sentinel Site Polygon Level I Database Internal FloodingResample Locations Re-Sampled Locations affected by Tropical Storm Flooding Point Level I Database Internal ScreenExtent Figure Production Template Polygon Level I Database Internal SectionInset Figure Production Templa te Polygon Level I Database Internal SectionLines Figure Production Templa te Line Level I Database Internal StudyAreaBoundar y Figure Production Template Poly gon Level I Database Internal GPSCollectionA1 GPS Proposed Sample Locations for Corridor Segment A1 Point Mobile Data Collection Unit Internal GPSCollectionB2 GPS Proposed Sample Locations for Corridor Segment B2 Point Mobile Data Collection Unit Internal GPSCollectionC3 GPS Proposed Sample Locations for Corridor Segment C3 Point Mobile Data Collection Unit Internal GPSCollectionD4 GPS Proposed Sample Locations for Corridor Segment D4 Point Mobile Data Collection Unit Internal GPSCollectionE5 GPS Proposed Sample Locations for Corridor Segment E5 Point Mobile Data Collection Unit Internal GPSCollectionF6 GPS Proposed Sample Locations for Corridor Segment F6 Point Mobile Data Collection Unit Internal GPSCollectionG7 GPS Proposed Sample Locations for Corridor Segment G7 Point Mobile Data Collection Unit Internal GPSCollectionH8 GPS Proposed Sample Locations for Corridor Segment H8 Point Mobile Data Collection Unit Internal GPSCollectionI9 GPS Proposed Sample Locations for Corridor Segment I9 Point Mobile Data Collection Unit Internal GPSCollectionJ10 GPS Proposed Sample Locations for Corridor Segment J10 Point Mobile Data Collection Unit Internal
95 ResultsA1 Lab Results for Corridor Segment A1 Samples Table File Storage System Laboratory ResultsB2 Lab Results for Corridor Segment B2 Samples Table File Storage System Laboratory ResultsC3 Lab Results for Corridor Segment C3 Samples Table File Storage System Laboratory ResultsD4 Lab Results for Corridor Segment D4 Samples Table File Storage System Laboratory ResultsE5 Lab Results for Corridor Segment E5 Samples Table File Storage System Laboratory ResultsF6 Lab Results for Corridor Segment F6 Samples Table File Storage System Laboratory ResultsG7 Lab Results for Corridor Segment G7 Samples Table File Storage System Laboratory ResultsH8 Lab Results for Corridor Segment H8 Samples Table File Storage System Laboratory ResultsI9 Lab Results for Corridor Segment I9 Samples Table File Storage System Laboratory ResultsJ10 Lab Results for Corridor Segment J10 Samples Table File Storage System Laboratory Project 3144060038 Level II Environmental Assessment Level2FieldSample Locations Sample Locations (Checked in and out each day) Point Level 2 Database Internal Level2ProposedSa mpleLoc Proposed Sample Locations Point Level 2 Database Internal RandYardEngineeri ng Rand Maintenance Yard Engineering Drawings CAD File Storage System Earthtech Level2Results Lab Data Table File Storage System Laboratory Project 3144060038 Rand Yard 10 Acre Site Assessment boundary_10acre 10 Acre Site Boundary Polygon 10-Acre Database CSX landuse_clip Existing Land Use Shape, Clipped to 10-Acre Parcel Area Polygon 10-Acre Database SJRWMD riskpotentialsites Potential Risk Site s Point 10-Acre Database Internal soils_clip Existing Soil Composition, Clipped to 10-Acre Parcel Area Polygon 10-Acre Database SJRWMD Sample_Limit Sampling Limits Po lygon 10-Acre Database Internal 10AcrePlume Contamination Plume for 10-Acre Site Polygon 10-Acre Database Internal 10AcreResults Lab Results for 10-Acre Parcel Sampling Table File Storage System Laboratory Project 3144060038 Summary of Activities Report Rand_Sample RandYard Sample Locations Point VSMF Database Internal Taft_Sample Taft Yard Sample Locations Point VSMF Database Internal Yards_Proposed Proposed Sample Locati ons Point VSMF Database Internal Rand_Temp_Statio n Rand Yard Temp Line Line VSMF Database CSX Rand_Measure Rand Yard Line Measure Line VSMF Database CSX Taft_Measure Taft Yard Line Measure Line VSMF Database CSX Project 3144060038 Right-of-Way (ROW) Assessment CSX_ROW_Propos ed Proposed Sample Locations CSX Point ROW Database CSX Corridor_Parcels Parcels (from earthtech) Line Reference Shapes Database Earthtech
96ParcelSampleLocat ions Parcel Points Point ROW Database Earthtech ROW_Samples Actual Sample Locati ons Point ROW Database Internal Project 3144060038 Rand Yard Assessment Rand_Sample Sample Locations Point VSMF Database Internal RandMarker Marker Point VSMF Database Internal Rand_Measure Line Measure Li ne VSMF Database Internal Extent Data Frame frame used for Mapbook Creation Polygon VSMF Database Internal Sample_Line Sampling Line Line VSMF Database Internal RandYard_Earthtec h RandYard CAD CAD File Storage System Earthtech Drainage CAD File Storage System Earthtech Roadway CAD File Storage System Earthtech Survey CAD File Storage System Earthtech Modified CAD File Storage System Earthtech Plan CAD File Storage System Earthtech Project 3144060038 Impact to Construction (ICI) Report RandYard_Earthtec h Rand Yard CAD Data (Contains following features) CAD File Storage System Earthtech Yards_Proposed Rand Yard Proposed Sample Point 10-Acre Database Internal 10_Acre_Proposed 10 Acre Proposed Sample Point 10-Acre Database Internal FieldDataMaster Master Shapefile of all collected Arsenic Sample Locations Point Level I Database Internal FieldDataMaster_E xceed Arsenic Exceedances from Master Shapefile Point Level I Database Internal 10_Acre_Sample GPS Collected Point 10-Acre Database Internal Corridor_Parcels Parcels (from earthtech) Line Reference Shapes Database Earthtech NewTrack Proposed New Track Locations Line Reference Shapes Database Earthtech DblTrack Proposed Double Track Loc ations Line Reference Shapes Database Earthtech StationLines_Updat e Updated Earthtech Station Lines Line Reference Shapes Database Earthtech Project 3144080021 Spur Assessments AlomaSpur Aloma Spur CAD CAD File Storage System Golder Aloma_Features Aloma Spur Li ne Features Line File Storage System Golder Aloma_Proposed_ Sample Aloma Spur Proposed Sample Locations Point File Storage System Internal Project 3144080021 Vehicle Storage & Maintenance Facility Assessment VSMF_Proposed_S ample Vehicle Storage & Maintenance Facility (VSMF) Proposed Sample Locaitons Point VSMF Database Internal VSMF_Final_Samp le VSMF Soil Sample Locations Point VSMF Database Internal VSMF_Groundwate r VSMF Groundwater Samples Point VSMF Database Internal
97AlomaResults VSMF Lab Sample Results Table File Storage System Laboratory Project 3144080021 Double Track Assessment DblTrack_Arsenic Arsenic Ex ceedances Point Double Track Database Internal DblTrack_Sample Sample Locations Point Double Track Database Internal Note: Snapshot of Data taken September 21, 2008
98 Appendix B Federal Geographic Data Committee Content Standard for Geographic Digital Metadata Step Type Domain Short Name 0 Metadata -data about the content, quality, condition, and other characteristics of data. Identification Information 1 Identification Information -basic information about the data set. 1.1 Citation -information to be used to reference the data set. compound citation 1.2 Description -a characterization of the data set, including its intended use and limitations. compound descript 1.2.1 Abstract -a brief narrative summary of the data set. text free text abstract 1.2.2 Purpose -a summary of the intentions with which the data set was developed. text free text purpose 1.2.3 Supplemental Information -other descriptive information about the data set. text free text supplinf 1.3 Time Period of Content -time period(s) for which the data set corresponds to the currentness reference. compound timeperd 1.3.1 Currentness Reference -the basis on which the time period of content information is determined. text "ground condition" "publication date" free text current 1.4 Status -the state of and maintenance information for the data set. compound status 1.4.1 Progress -the state of the data set. text "Complete" "In work" "Planned" progress 1.4.2 Maintenance and Update Frequency -the frequency with which changes and additions are made to the data set after the initial data set is completed. text "Continually" "Daily" "Weekly" "Monthly" "Annually" "Unknown" "As needed" "Irregular" "None planned" free text update 1.5 Spatial Domain the geographic areal domain of the data set. compound Short Name: spdom 1.5.1 Bounding Coordinates the limits of coverage of a data set expressed by latitude and longitude values in the order western-most, eastern-most, northern-most, and southern-most. For data sets that include a complete band of latitude around the earth, the West Bounding Coordinate shall be assigned the value -180.0, and the East Bounding Coordinate shall be assigned the value 180.0 compound bounding 188.8.131.52 West Bounding Coordinate -western-most coordinate of the limit of coverage expressed in longitude. real -180.0 <= West Bounding Coordinate < 180.0 westbc 184.108.40.206 East Bounding Coordinate -eastern-most coordinate of the limit of coverage expressed in longitude. real -180.0 <= East Bounding Coordinate <= 180.0 eastbc 220.127.116.11 North Bounding Coordinate -northern-most coordinate of the limit of coverage expressed in latitude. real -90.0 <= North Bounding Coordinate <= 90.0; North Bounding Coordinate >= South Bounding northbc
99Coordinate 18.104.22.168 South Bounding Coordinate -southern-most coordinate of the limit of coverage expressed in latitude. real -90.0 <= South Bounding Coordinate <= 90.0; South Bounding Coordinate <= North Bounding Coordinate southbc 1.5.2 Data Set G-Polygon -coordinates defining the outline of an area covered by a data set. compound dsgpoly 22.214.171.124 Data Set G-Polygon Outer G-Ring -the closed nonintersecting boundary of an interior area. compound dsgpolyo 126.96.36.199.1 G-Ring Point -a single geographic location. compound grngpoin 188.8.131.52.1.1 G-Ring Latitude -the latitude of a point of the gring. real -90.0 <= G-Ring Latitude <= 90.0 gringlat 184.108.40.206.1.2 G-Ring Longitude -the longitude of a point of the g-ring. real -180.0 <= G-Ring Longitude < 180.0 gringlon 220.127.116.11.2 G-Ring -a set of ordered pairs of floating-point numbers, separated by commas, in which the first number in each pair is the longitude of a point and the second is the latitude of the point. Longitude and latitude are specified in decimal degrees with north latitudes positive and south negative, east longitude positive and west negative text -90<= Latitude_elements <= 90, =-180 <= Longitude_Elements = 180 gring 18.104.22.168 Data Set G-Polygon Exclusion G-Ring -the closed nonintersecting boundary of a void area (or hole in an interior area). compound dsgpolyx 1.6 Keywords -words or phrases summarizing an aspect of the data set. compound keywords 1.6.1 Theme -subjects covered by the data set (for a list of some commonly-used thesauri, see Part IV: Subject/index term sources in Network Development and MARC Standards Office, 1988, USMARC code list for relators, sources, and description convent ions: Washington, Library of Congress). compound theme 22.214.171.124 Theme Keyword Thesaurus -reference to a formally registered thesaurus or a similar authoritative source of theme keywords. text "None" free text themekt 126.96.36.199 Theme Keyword -common-use word or phrase used to describe the subject of the data set. text free text themekey 1.6.2 Place -geographic locati ons characterized by the data set. compound place 188.8.131.52 Place Keyword Thesaurus -reference to a formally registered thesaurus or a similar authoritative source of place keywords. text "None" "Geographic Names Information System" free text placekt 184.108.40.206 Place Keyword -the geographic name of a location covered by a data set. text free text placekey 1.6.3 Stratum -layered, vertic al locations characterized by the data set. compound stratum 220.127.116.11 Stratum Keyword Thesaurus -reference to a formally registered thesaurus or a similar authoritative source of stratum keywords. text "None" free text stratkt 18.104.22.168 Stratum Keyword -the name of a vertical location used to describe the locations covered by a data set. text free text stratkey 1.6.4 Temporal -time period(s) characterized by the data set. compound temporal 22.214.171.124 Temporal Keyword Thesaurus -reference to a formally registered thesaurus or a similar authoritative source of temporal keywords. text "None" free text tempkt
1001.6.4.2 Temporal Keyword -the name of a time period covered by a data set. text free text tempkey 1.7 Access Constraints -restrictions and legal prerequisites for accessing the data set. These include any access constraints applied to assure the protection of privacy or intellectual property, and any special restrictions or limitations on obtaining the data set. text "None" free text accconst 1.8 Use Constraints -restricti ons and legal prerequisites for using the data set after access is granted. These include any use constraints applied to assure the protection of privacy or intellectual property, and any special restrictions or limitations on using the data set. text "None" free text useconst 1.9 Point of Contact -contact information for an individual or organization that is knowledgeable about the data set. compound ptcontac 1.10 Browse Graphic -a graphi c that provides an illustration of the data set. The graphic should include a legend for interpreting the graphic. compound browse 1.10.1 Browse Graphic File Name -name of a related graphic file that provides an illustration of the data set. text free text browsen 1.10.2 Browse Graphic File Description -a text description of the illustration. text free text browsed 1.10.3 Browse Graphic File Type -graphic file type of a related graphic file. text domain values in the table below; free text browset CGM Computer Graphics Metafile EPS Encapsulated Postscript format EMF Enhanced Metafile GIF Graphic Interchange Format JPEG Joint Photographic Experts Group format PBM Portable Bit Map format PS Postscript format TIFF Tagged Image File Format WMF Windows metafile XWD X-Windows Dump 1.11 Data Set Credit -recognition of those who contributed to the data set. text free text datacred 1.12 Security Information -handling restrictions imposed on the data set because of national security, privacy, or other concerns. compound secinfo 1.12.1 Security Classification System -name of the classification system. text free text secsys 1.12.2 Security Classification -name of the handling restrictions on the data set. text "Top secret" "Secret" "Confidential" "Restricted" "Unclassified" "Sensitive" secclass 1.12.3 Security Handling Description -additional information about the restrictions on handling the data set. text free text sechandl
1011.13 Native Data Set Environment -a description of the data set in the producer's processing environment, including items such as the name of the software (including version), the computer operating system, file name (including host-, path-, and filenames), and the data set size. text free text native 1.14 Cross Reference -information about other, related data sets that are likely to be of interest. compound crossref Data Quality Information 2 Data Quality Information -a general assessment of the quality of the data set. (Recommendations on information to be reported and tests to be performed are found in "Spatial Data Quality," which is chapter 3 of part 1 in Department of Commerce, 1992, Spatial Data Transfer Standard (SDTS) (Federal Information Processing Standard 173): Washington, Department of Commerce, National Institute of Standards and Technology.) compound dataqual 2.1 Attribute Accuracy -an assessment of the accuracy of the identification of entities and assignment of attribute values in the data set. compound attracc 2.1.1 Attribute Accuracy Report -an explanation of the accuracy of the identification of the entities and assignments of values in the data set and a description of the tests used. text free text attraccr 2.1.2 Quantitative Attribute Accuracy Assessment -a value assigned to summarize the accuracy of the identification of the entities and assignments of values in the data set and the identification of the test that yielded the value. compound qattracc 126.96.36.199 Attribute Accuracy Value -an estimate of the accuracy of the identification of the entities and assignments of attribute values in the data set. text "Unknown" free text attraccv 188.8.131.52 Attribute Accuracy Expl anation -the identification of the test that yielded the Attribute Accuracy Value. text free text attracce 2.2 Logical Consistency Report -an explanation of the fidelity of relationships in the data set and tests used. text free text logic 2.3 Completeness Report -information about omissions, selection criteria, generalizati on, definitions used, and other rules used to derive the data set. text free text complete 2.4 Positional Accuracy -an assessment of the accuracy of the positions of spatial objects. compound posacc 2.4.1 Horizontal Positional Accuracy -an estimate of accuracy of the horizontal pos itions of the spatial objects. compound horizpa 184.108.40.206 Horizontal Positional Accuracy Report -an explanation of the accuracy of the horizontal coordinate measurements and a description of the tests used. text free text horizpar 220.127.116.11 Quantitative Horiz ontal Positional Accuracy Assessment -numeric value assigned to summarize the accuracy of the horizontal coordinate measurements and the identification of the test that yielded the value. compound qhorizpa 18.104.22.168.1 Horizontal Positi onal Accuracy Value -an estimate of the accuracy of the horizontal coordinate measurements in the data set expressed in (ground) meters. real free real horizpav 22.214.171.124.2 Horizontal Positional Accuracy Explanation -the identification of the test that yielded the Horizontal Positional Accuracy Value. text free text horizpae 2.4.2 Vertical Positional Accuracy -an estimate of accuracy of the vertical positions in the data set. compound vertacc 126.96.36.199 Vertical Positional Accuracy Report -an explanation of the accuracy of the vertical coordinate measurements and a description of the tests used. text free text vertaccr 188.8.131.52 Quantitative Vertical Positional Accuracy Assessment -numeric value assigned to summarize the accuracy of vertical coordinate measurements and the identification of the test that yielded the value. compound qvertpa
1022.4.2.2.1 Vertical Positional Accuracy Value -an estimate of the accuracy of the vertical coordinate measurements in the data set expressed in (ground) meters. real free real vertaccv 184.108.40.206.2 Vertical Positional Accuracy Explanation -the identification of the test that yielded the Vertical Positional Accuracy Value. text free text vertacce 2.5 Lineage -information about the events, parameters, and source data which constructed the data set, and information about the responsible parties. compound lineage 2.5.1 Source Information -list of sources and a short discussion of the information contributed by each. compound srcinfo 220.127.116.11 Source Citation -reference for a source data set. compound srccite 18.104.22.168 Source Scale Denominator -the denominator of the representative fraction on a map (for example, on a 1:24,000-scale map, the Source Scale Denominator is 24000). integer Source Scale Denominator > 1 srcscale 22.214.171.124 Type of Source Media -the medium of the source data set. text "paper" "stable-base material" "microfiche" "microfilm" audiocassette "chart" "filmstrip" "transparency" "videocassette" "videodisc" magnetic tape "online" "CD-ROM" "electronic bulletin board" "electronic mail system" free text typesrc 126.96.36.199 Source Time Period of Content -time period(s) for which the source data set corresponds to the ground. compound 188.8.131.52.1 Source Currentness Reference -the basis on which the source time period of content information of the source data set is determined. text "ground condition" "publication date" free text srccurr 184.108.40.206 Source Citation Abbreviation -short-form alias for the source citation. text free text srccitea 220.127.116.11 Source Contribution -brief statement identifying the information contributed by the source to the data set. text free text srccontr 2.5.2 Process Step -information about a single event. compound procstep 18.104.22.168 Process Description -an explanation of the event and related parameters or tolerances. text free text procdesc 22.214.171.124 Source Used Citation Abbreviation -the Source Citation Abbreviation of a data set used in the processing step. text Source Citation Abbreviations from the Source Information entries for the data set. srcused 126.96.36.199 Process Date -the date when the event was completed. date "Unknown" "Not complete" free date procdate 188.8.131.52 Process Time -the time when the event was completed. time free time proctime 184.108.40.206 Source Produced Citation Abbreviation -the Source Citation Abbreviation of an intermediate data set that (1) is significant in the opinion of the data producer, (2) is generated in the processing step, and (3) is used in later processing steps. text Source Citation Abbreviations from the Source Information entries for the data set. srcprod 220.127.116.11 Process Contact -the party responsible for the processing step information. compound proccont 2.6 Cloud Cover -area of a data set obstructed by clouds, expressed as a percentage of the spatial extent. integer 0 <= Cloud Cover <= 100 "Unknown" cloud
103Spatial Data Organization Information 3 Spatial Data Organization Information -the mechanism used to represent spatial information in the data set. compound spdoinfo 3.1 Indirect Spatial Reference -name of types of geographic features, addressing schemes, or other means through which locations are referenced in the data set. text free text indspref 3.2 Direct Spatial Reference Method -the system of objects used to represent space in the data set. text "Point" "Vector" "Raster" direct 3.3 Point and Vector Object Information -the types and numbers of vector or nongridded point spatial objects in the data set. compound ptvctinf 3.3.1 SDTS Terms Description -point and vector object information using the term inology and concepts from "Spatial Data Concepts," which is Chapter 2 of Part 1 in Department of Commerce, 1992, Spatial Data Transfer Standard (SDTS) (Federal Information Processing Standard 173): Washington, Department of Commerce, National Institute of Standards and Technology. (Note that this reference to the SDTS is used ONLY to provide a set of terminology for the point and vector objects.) compound sdtsterm 18.104.22.168 SDTS Point and Vector Object Type -name of point and vector spatial objects used to locate zero-, one-, and two-dimensional spatial locations in the data set. text (The domain is from "Spatial Data Concepts," which is Chapter 2 of Part 1 in Department of Commerce, 1992, Spatial Data Transfer Standard (SDTS) (Federal Information Processing Standard 173): Washington, Department of Commerce, National Institute of Standards and Technology): Point "Entity point" "Label point" "Area point" "Node, planar graph" Node, network "String" "Link" "Complete chain" "Area chain" Network chain, planar graph "Network chain, nonplanar graph" Circular arc, three point center "Elliptical arc" "Uniform B-spline" Piecewise Bezier "Ring with mixed composition" Ring composed of strings "Ring composed of chains" Ring composed of arcs "G-polygon" "GTpolygon composed of rings" GTpolygon composed of chains Universe sdtstype
104polygon composed of rings Universe polygon composed of chains Void polygon composed of rings "Void polygon composed of chains" 22.214.171.124 Point and Vector Object Count -the total number of the point or vector object type occurring in the data set. integer Point and Vector Object Count > 0 ptvctcnt 3.3.2 VPF Terms Description -point and vector object information using the term inology and concepts from Department of Defense, 1992, Vector Product Format (MILSTD-600006): Philadelphia, Department of Defense, Defense Printing Service Detachment Office. (Note that this reference to the VPF is used ONLY to provide a set of terminology for the point and vector objects.) compound vpfterm 126.96.36.199 VPF Topology Level -the completeness of the topology carried by the data set. The levels of completeness are defined in Department of Defense, 1992, Vector Product Format (MIL-STD-600006): Philadelphia, Department of Defense, Defense Printing Service Detachment Office. integer 0 <= VPF Topology Level <= 3 vpflevel 188.8.131.52 VPF Point and Vector Object Information -information about VPF point and vector objects compound vpfinfo
1053.3.2.2.1 VPF Point and Vector Object Type -name of point and vector spatial objects used to locate zero-, one-, and two-dimensional spatial locations in the data set. text (The domain is from Department of Defense, 1992, Vector Product Format (MIL-STD600006): Philadelphia, Department of Defense, Defense Printing Service Detachment Office): Node "Edge" "Face" "Text" vpftype 3.4 Raster Object Information -the types and numbers of raster spatial objects in the data set. compound rastinfo 3.4.1 Raster Object Type -raster spatial objects used to locate zero-, two-, or three-dimensional locations in the data set. text (With the exception of "voxel", the domain is from "Spatial Data Concepts," which is chapter 2 of part 1 in Department of Commerce, 1992, Spatial Data Transfer Standard (SDTS) (Federal Information Processing Standard 173): Washington, Department of Commerce, National Institute of Standards and Technology): Point "Pixel" "Grid Cell" "Voxel" rasttype 3.4.2 Row Count -the maximum number of raster objects along the ordinate (y) axis. For use with rectangular raster objects. Integer Row Count > 0 rowcount 3.4.3 Column Count -the maximum number of raster objects along the abscissa (x) ax is. For use with rectangular raster objects. Integer Column Count > 0 colcount 3.4.4 Vertical Count -the maximum number of raster objects along the vertical (z) axis. For use with rectangular volumetric raster objects (voxels). Integer Depth Count > 0 vrtcount Spatial Reference Information 4 Spatial Reference Information -the description of the reference frame for, and the means to encode, coordinates in the data set. compound spref 4.1 Horizontal Coordinate Syst em Definition -the reference frame or system from which linear or angular quantities are measured and assigned to the position that a point occupies. compound horizsys 4.1.1 Geographic -the quantities of latitude and longitude which define the position of a point on the Earth's surface with respect to a reference spheroid. compound geograph 184.108.40.206 Latitude Resolution -the minimum difference between two adjacent latitude values expressed in Geographic Coordinate Units of measure. real Latitude Resolution > 0.0 latres 220.127.116.11 Longitude Resolution -the minimum difference between two adjacent longitude values expressed in Geographic Coordinate Units of measure. real Longitude Resolution > 0.0 longres
1064.1.1.3 Geographic Coordinate Units -units of measure used for the latitude and longitude values. text "Decimal degrees" "Decimal minutes" "Decimal seconds" "Degrees and decimal minutes" "Degrees, minutes, and decimal seconds" "Radians" Grads geogunit 4.1.2 Planar -the quantities of distances, or distances and angles, which define the positi on of a point on a reference plane to which the surface of the Earth has been projected. compound planar 18.104.22.168 Map Projection -the systematic representation of all or part of the surface of the Earth on a plane or developable surface. compound mapproj 22.214.171.124.1 Map Projection Name -name of the map projection. text "Albers Conical Equal Area" "Azimuthal Equidistant" Equidistant Conic "Equirectangular" "General Vertical Near-sided Projection" "Gnomonic" "Lambert Azimuthal Equal Area" Lambert Conformal Conic "Mercator" "Modified Stereographic for Alaska" "Miller Cylindrical" "Oblique Mercator" "Orthographic" "Polar Stereographic" "Polyconic" "Robinson" "Sinusoidal" "Space Oblique Mercator" "Stereographic" "Transverse Mercator" "van der Grinten" free text mapprojn 126.96.36.199.2 Albers Conical Equal Area -contains parameters for the Albers Conical Equal Area projection. compound albers 188.8.131.52.3 Azimuthal Equidistant -contains parameters for the Azimuthal Equidistant projection. compound Short Name:azimequi 184.108.40.206.4 Equidistant Conic -contains parameters for the Equidistant Conic projection. compound equicon 220.127.116.11.5 Equirectangular -contains parameters for the Equirectangular projection. compound equirect 18.104.22.168.6 General Vertical Near-sided Perspective -contains parameters for the General Vertical Near-sided Perspective projection. compound gvnsp 22.214.171.124.7 Gnomonic -contains parameters for the Gnomonic projection. compound gnomonic 126.96.36.199.8 Lambert Azimuthal Equal Area -contains parameters for the Lambert Azimuthal Equal Area projection. compound lamberta 188.8.131.52.9 Lambert Conformal Conic -contains parameters for the Lambert Conformal Conic projection. compound Short Name:lambertc 184.108.40.206.10 Mercator -contains parameters for the Mercator projection compound mercator
1074.1.2.1.11 Modified Stereographic for Alaska -contains parameters for the Modified Stereographic for Alaska projection. compound modsak 220.127.116.11.12 Miller Cylindrical -contains parameters for the Miller Cylindrical projection. compound miller 18.104.22.168.13 Oblique Mercator -contains parameters for the Oblique Mercator projection. compound obqmerc 22.214.171.124.14 Orthographic -contains parameters for the Orthographic projection. compound Short Name:orthogr 126.96.36.199.15 Polar Stereographic -contains parameters for the Polar Stereographic projection. compound Short Name:polarst 188.8.131.52.16 Polyconic -contains parameters for the Polyconic projection. compound Short Name:polycon 184.108.40.206.17 Robinson -contains parameters for the Robinson projection. compound robinson 220.127.116.11.18 Sinusoidal -contains parameters for the Sinusoidal projection. compound sinusoid 18.104.22.168.19 Space Oblique Mercator (Landsat) -contains parameters for the Space Oblique Mercator (Landsat) projection. compound spaceobq 22.214.171.124.20 Stereographic -contains parameters for the Stereographic projection. compound stereo 126.96.36.199.21 Transverse Mercator -contains parameters for theTransverse mercator projection. compound transmer 188.8.131.52.22 van der Grinten -contains parameters for the van der Grinten projection. compound vdgrin 184.108.40.206.23 Map Projection Parameters -a complete parameter set of the projection that was used for the data set. The information provided shall include the names of the parameters and values used for the data set that describe the mathematical relationship between the Earth and the plane or developable surfac e for the projection. compound 220.127.116.11.23.1 Standard Parallel -line of constant latitude at which the surface of the Earth and the plane or developable surface intersect. real -90.0 <= Standard Parallel <= 90.0 stdparll 18.104.22.168.23.2 Longitude of Central Meridian -the line of longitude at the center of a map projection generally used as the basis for construc ting the projection. real -180.0 <= Longitude of Central Meridian < 180.0 longcm 22.214.171.124.23.3 Latitude of Projecti on Origin -latitude chosen as the origin of rectangular c oordinates for a map projection. real -90.0 <= Latitude of Projection Origin <= 90.0 latprjo 126.96.36.199.23.4 False Easting -the value added to all "x" values in the rectangular coor dinates for a map projection. This value frequently is assigned to eliminate negative numbers. Expressed in the unit of measure identified in Planar Coordinate Units. real free real feast 188.8.131.52.23.5 False Northing -the value added to all "y" values in the rectangular coor dinates for a map projection. This value frequently is assigned to eliminate negative numbers. Expressed in the unit of measure identified in Planar Coordinate Units. real free real fnorth 184.108.40.206.23.6 Scale Factor at Equator -a multiplier for reducing a distance obtained from a map by computation or scaling to the actual distance along the equator. real Scale Factor at Equator > 0.0 sfequat 220.127.116.11.23.7 Height of Perspective Point Above Surface -height of viewpoint above the Earth, expressed in meters. real Height of Perspective Point Above Surface > 0.0 heightpt 18.104.22.168.23.8 Longitude of Projection Center -longitude of the point of projection fo r azimuthal projections. real -180.0 <= Longitude of Projection Center < 180.0 longpc 22.214.171.124.23.9 Latitude of Projection Center -latitude of the point of projection for azimuthal projections. real -90.0 <= Latitude of Projection Center <= 90.0 latprjc
1084.1.2.1.23.10 Scale Factor at Center Line -a multiplier for reducing a distance obtained from a map by computation or scaling to the actual dist ance along the center line. real Scale Factor at Center Line > 0.0 sfctrlin 126.96.36.199.23.11 Oblique Line Azimuth -method used to describe the line along which an oblique mercator map projection is centered using t he map projection origin and an azimuth. compound obqlazim 188.8.131.52.23.11.1 Azimuthal Angle -angle measured clockwise from north, and expressed in degrees. real 0.0 <= Azimuthal Angle < 360.0 azimangl 184.108.40.206.23.11.2 Azimuth Measure Point Longitude -longitude of the map projection origin. real -180.0 <= Azimuth Measure Point Longitude < 180 azimptl 220.127.116.11.23.12 Oblique Line Point -method used to describe the line along which an oblique me rcator map projection is centered using two points near the limits of the mapped region that define the center line. compound obqlpt 18.104.22.168.23.12.1 Oblique Line Latitude -latitude of a point defining the oblique line. real -90.0 <= Oblique Line Latitude <= 90.0 obqllat 22.214.171.124.23.12.2 Oblique Line Longitude -longitude of a point defining the oblique line. real -180.0 <= Oblique Line Longitude < 180.0 obqllong 126.96.36.199.23.13 Straight Vertical Longitude from Pole -longitude to be oriented straight up from the North or South Pole. real -180.0 <= Straight Vertical Longitude from Pole < 180 svlong 188.8.131.52.23.14 Scale Factor at Projection Origin -a multiplier for reducing a distance obtained from a map by computation or scaling to the actual dist ance at the projection origin. real Scale Factor at Projection Origin > 0.0 sfprjorg 184.108.40.206.23.15 Landsat Number -number of the Landsat satellite. (Note: This data element exists solely to provide a parameter needed to define the space oblique mercator projection. It is not used to i dentify data originating from a remote sensing vehicle.) Integer free integer landsat 220.127.116.11.23.16 Path Number -number of the orbit of the Landsat satellite. (Note: This data element exists solely to provide a parameter needed to define the space oblique mercator projection. It is not used to identify data originating from a remote sensing vehicle.) integer 0 < Path Number < 251 for Landsats 1, 2, or 3 0 < Path Number < 233 for Landsats 4 or 5, free integer pathnum 18.104.22.168.23.17 Scale Factor at Central Meridian -a multiplier for reducing a distance obtained from a map by computation or scaling to the actual dist ance along the central meridian. real Scale Factor at Central Meridian > 0.0 sfctrmer 22.214.171.124.23.18 Other Projections Definition -a description of a projection, not defined elsewhere in the standard, that was used for the data set. The information provided shall include the name of the projection, names of parameters and values used for the data set, and the cita tion of the specification for the algorithms that describe the mathematical relationship between Earth and plane or developable surface for the projection. text free text 126.96.36.199 Grid Coordinate System -a plane-rectangular coordinate system usually based on, and mathematically adjusted to, a map projection so that geographic positions can be readily transformed to and from plane coordinates. compound Short Name gridsys
1094.1.2.2.1 Grid Coordinate System Name -name of the grid coordinate system. text "Universal Transverse Mercator" Universal Polar Stereographic "State Plane Coordinate System 1927" State Plane Coordinate System 1983 "ARC Coordinate System" other grid system gridsysn 188.8.131.52.2 Universal Transverse Mercator (UTM) -a grid system based on the transverse mercator projection, applied between latitudes 84 degrees north and 80 degrees south on the Earth's surface. compound utm 184.108.40.206.2.1 UTM Zone Number -identifier for the UTM zone. integer 1 <= UTM Zone Number <= 60 for the northern hemisphere, -60 <= UTM Zone Number <= -1 for the southern hemisphere utmzone 220.127.116.11.3 Universal Polar Stereographic (UPS) -a grid system based on the polar stereographic projection, applied to the Earth's polar regions north of 84 degrees north and south of 80 degrees south. compound ups 18.104.22.168.3.1 UPS Zone Identifier -identifier for the UPS zone. text "A" "B" "Y" "Z" upszone 22.214.171.124.4 State Plane Coordinate System (SPCS) -a planerectangular coordinate system established for each state in the United States by the National Geodetic Survey. compound spcs
1126.96.36.199.4.1 SPCS Zone Identifier -identifier for the SPCS zone. text Four-digit numeric codes for the State Plane Coordinate Systems based on the North American Datum of 1927 are found in Department of Commerce, 1986, Representation of geographic point locations for information interchange (Federal Information Processing Standard 70-1): Washington: Department of Commerce, National Institute of Standards and Technology. Codes for the State Plane Coordinate Systems based on the North American Datum of 1983 are found in Department of Commerce, 1989 (January), State Plane Coordinate System of 1983 (National Oceanic and Atmospheric Administration Manual NOS NGS 5): Silver Spring, Maryland, National Oceanic and Atmospheric Administration, National Ocean Service, Coast and Geodetic Survey. spcszone 188.8.131.52.5 ARC Coordinate System -the Equal Arc-second Coordinate System, a planerectangular coordinate system established in Department of Defense, 1990, Military specification ARC Digitized Raster Graphics (ADRG) (MILA-89007): Philadelphia, Department of Defense, Defense Printing Service Detachment Office. compound arcsys 184.108.40.206.5.1 ARC System Zone Identifier -identifier for the ARC Coordinate System Zone. integer 1 <= ARC System Zone Identifier <= 18 arczone 220.127.116.11.6 Other Grid System's Definition -a complete description of a grid system, not defined elsewhere in this standard, that was used for the data set. The information provided shall include the name of the grid system, the names of the parameters and values used for the data set, and the citation of the specificat ion for the algorithms that describe the mathematical relationship between the Earth and the coordinates of the grid system. text free text othergrd 18.104.22.168 Local Planar -any right-handed planar coordinate system of which the z-axis coincides with a plumb line through the origin that locally is aligned with the surface of the Earth. compound localp 22.214.171.124.1 Local Planar Description -a description of the text free text localpd
111local planar system. 126.96.36.199.2 Local Planar Georeference Information -a description of the information provided to register the local planar system to the Earth (e.g. control points, satellite ephemeral data, inerti al navigation data). text free text localpgi 188.8.131.52 Planar Coordinate Information -information about the coordinate system developed on the planar surface. compound planci 184.108.40.206.1 Planar Coordinate Encoding Method -the means used to represent horizontal positions. text "coordinate pair" "distance and bearing" "row and column" plance 220.127.116.11.2 Coordinate Representation -the method of encoding the position of a point by measuring its distance from perpendicular reference axes (the "coordinate pair" and "row and column" methods). compound coordrep 18.104.22.168.2.1 Abscissa Resolution -the (nominal) minimum distance between the "x" or column values of two adjacent points, expressed in Planar Distance Units of measure. real Abscissa Resolution > 0.0 absres 22.214.171.124.2.2 Ordinate Resolution -the (nominal) minimum distance between the "y" or row values of two adjacent points, expressed in Planar Distance Units of measure. real Ordinate Resolution > 0.0 ordres 126.96.36.199.3 Distance and Bearing Representation -a method of encoding the position of a poi nt by measuring its distance and direction (azimuth angle) from another point. compound distbrep 188.8.131.52.3.1 Distance Resolution -the minimum distance measurable between two points, expressed Planar Distance Units of measure. real Distance Resolution > 0.0 distres 184.108.40.206.3.2 Bearing Resolution -the minimum angle measurable between two points, expressed in Bearing Units of measure. real Bearing Resolution > 0.0 bearres 220.127.116.11.3.3 Bearing Units -units of measure used for angles. text "Decimal degrees" "Decimal minutes" "Decimal seconds" "Degrees and decimal minutes" "Degrees, minutes, and decimal seconds" "Radians" Grads decimal seconds" "Radians" "Grads" bearunit 18.104.22.168.3.4 Bearing Reference Direction -direction from which the bearing is measured. text "North" "South" bearrefd 22.214.171.124.3.5 Bearing Reference Meridian -axis from which the bearing is measured. text "Assumed" "Grid" "Magnetic" "Astronomic" "Geodetic" bearrefm 126.96.36.199.4 Planar Distance Units -units of measure used for distances. text "meters" "international feet" "survey feet" free text plandu 4.1.3 Local -a description of any coordinate system that is not aligned with the surface of the Earth. compound local 188.8.131.52 Local Description -a description of the coordinate system and its orientation to the surface of the Earth. text free text localdes 184.108.40.206 Local Georeference Information -a description of the information provided to register the local system to the Earth (e.g. control points, satellite ephemeral data, inertial navigation data). text free text localgeo 4.1.4 Geodetic Model -parameters for the shape of the earth. compound geodetic
1220.127.116.11 Horizontal Datum Name -the identification given to the reference system used for defining the coordinates of points. text "North American Datum of 1927" "North American Datum of 1983" free text horizdn 18.104.22.168 Ellipsoid Name -ident ification given to established representations of the Earth's shape. text "Clarke 1866" "Geodetic Reference System 80" free text ellips 22.214.171.124 Semi-major Axis -radius of the equatorial axis of the ellipsoid. real Semi-major Axis > 0.0 semiaxis 126.96.36.199 Denominator of Flattening Ratio -the denominator of the ratio of the difference between the equatorial and polar radii of the ellipsoid when the numerator is set to 1. real Denominator of Flattening > 0.0 denflat 4.2 Vertical Coordinate System Definition -the reference frame or system from which vertical distances (altitudes or depths) are measured. compound vertdef 4.2.1 Altitude System Definition -the reference frame or system from which altitudes (elevations) are measured. The term "altitude"' is used instead of the common term "elevation" to conform to the terminology in Federal Information Processing Standards 70-1 and 173. compound altsys 188.8.131.52 Altitude Datum Name -the identification given to the surface taken as the surfac e of reference from which altitudes are measured. text "National Geodetic Vertical Datum of 1929" "North American Vertical Datum of 1988" free text altdatum 184.108.40.206 Altitude Resolution -the minimum distance possible between two adjacent altitude va lues, expressed in Altitude Distance Units of measure. real Altitude Resolution > 0.0 altres 220.127.116.11 Altitude Distance Units -units in which altitudes are recorded. text "meters" "feet" free text altunits 18.104.22.168 Altitude Encoding Method -the means used to encode the altitudes. text "Explicit elevation coordinate included with horizontal coordinates" Implicit coordinate "Attribute values" altenc 4.2.2 Depth System Definition -the reference frame or system from which depths are measured. compound depthsys
122.214.171.124 Depth Datum Name -the identification given to surface of reference from which depths are measured. text "Local surface" "Chart datum; datum for sounding reduction" Lowest astronomical tide "Highest astronomical tide" "Mean low water" Mean high water "Mean sea level" "Land survey datum" Mean low water springs "Mean high water springs" "Mean low water neap" Mean high water neap "Mean lower low water" "Mean lower low water springs" "Mean higher high water" "Mean higher low water" Mean lower high water "Spring tide" "Tropic lower low water" "Neap tide" High water "Higher high water" "Low water" "Low-water datum" Lowest low water "Lower low water" "Lowest normal low water" "Mean tide level" "Indian spring low water" "Highwater full and charge" Low-water full and charge "Columbia River datum" "Gulf Coast low water datum" "Equatorial springs low water" "Approximate lowest astronomical tide" No correction free text depthdn 126.96.36.199 Depth Resolution -the minimum distance possible between two adjacent depth values, expressed in Depth Distance Units of measure. real Depth Resolution > 0.0 depthres 188.8.131.52 Depth Distance Units -units in which depths are recorded. text "meters" "feet" free text depthdu 184.108.40.206 Depth Encoding Method -the means used to encode depths. text "Explicit depth coordinate included with horizontal coordinates" Implicit coordinate "Attribute values" depthem Entity and Attribute Information 5 Entity and Attribute Information -details about the information content of the data set, including the entity types, their attributes, and the domains from which attribute compound eainfo
114values may be assigned. 5.1 Detailed Description -description of the entities, attributes, attribute values and related characteristics encoded in the data set. compound detailed 5.1.1 Entity Type -the definition and description of a set into which similar entity instances are classified. compound enttype 220.127.116.11 Entity Type Label -the name of the entity type. text free text enttypl 18.104.22.168 Entity Type Definition -the description of the entity type. text free text enttypd 22.214.171.124 Entity Type Definition Source -the authority of the definition. text free text enttypds 5.1.2 Attribute -a defined characteri stic of an entity. compound attr 126.96.36.199 Attribute Label -the name of the attribute. text free text attrlabl 188.8.131.52 Attribute Definition -the description of the attribute. text free text attrdef 184.108.40.206 Attribute Definition Source -the authority of the definition. text free text attrdefs 220.127.116.11 Attribute Domain Values -the valid values that can be assigned for an attribute. compound attrdomv 18.104.22.168.1 Enumerated Domain -the members of an established set of valid values. compound edom 22.214.171.124.1.1 Enumerated Domain Value -the name or label of a member of the set. text free text edomv 126.96.36.199.1.2 Enumerated Domain Value Definition -the description of the value. text free text edomvd 188.8.131.52.1.3 Enumerated Domain Value Definition Source -the authority of the definition. text free text edomvds 184.108.40.206.2 Range Domain -the minimum and maximum values of a continuum of valid values. compound rdom 220.127.116.11.2.1 Range Domain Minimum -the least value that the attribute can be assigned. text free text rdommin 18.104.22.168.2.2 Range Domain Maximum -the greatest value that the attribute can be assigned. text free text rdommax 22.214.171.124.2.3 Attribute Units of Measure -the standard of measurement for an attribute value. text free text attrunit 126.96.36.199.2.4 Attribute Measurement Resolution -the smallest unit increment to which an attribute value is measured. real Attribute Measurement Resolution > 0.0 attrmres 188.8.131.52.3 Codeset Domain -reference to a standard or list which contains the members of an established set of valid values. compound codesetd 184.108.40.206.3.1 Codeset Name -the title of the codeset. text free text codesetn 220.127.116.11.3.2 Codeset Source -the authority for the codeset. text free text codesets 18.104.22.168.4 Unrepresentable Domain -description of the values and reasons why they cannot be represented. text free text udom 22.214.171.124 Beginning Date of Attribute Values -earliest or only date for which the attribute values are current. In cases when a range of dates are provided, this is the earliest date for which the information is valid. 126.96.36.199 Ending Date of Attribute Values -latest date for which the information is current. Used in cases when a range of dates are provided. date free date enddatea 188.8.131.52 Attribute Value Accuracy Information -an assessment of the accuracy of the assignment of attribute values. compound attrvai 184.108.40.206.1 Attribute Value Accuracy -an estimate of the accuracy of the assignment of attribute values. real free real attrva 220.127.116.11.2 Attribute Value Accuracy Explanation -the definition of the Attribute Value Accuracy measure and units, and a description of how the estimate was derived. text free text attrvae
118.104.22.168 Attribute Measurement Frequency -the frequency with which attribute values are added. real "Unknown" "As needed" "Irregular" "None planned" free text attrmfrq 5.2 Overview Description -summary of, and citation to detailed description of, the information content of the data set. compound overview 5.2.1 Entity and Attribute Overview -detailed summary of the information contained in a data set. text free text eaover 5.2.2 Entity and Attribute Detail Citation -reference to the complete description of the entity types, attributes, and attribute values for the data set. text free text eadetcit Distribution Information 6 Distribution Information -information about the distributor of and options for obtaining the data set. compound distinfo 6.1 Distributor -the party from whom the data set may be obtained. compound distrib 6.2 Resource Description -the identifier by which the distributor knows the data set. text free text resdesc 6.3 Distribution Liability -statement of the liability assumed by the distributor. text free text distliab 6.4 Standard Order Process -the common ways in which the data set may be obtained or received, and related instructions and fee information. compound stdorder 6.4.1 Non-digital Form -the description of options for obtaining the data set on non-computercompatible media. text free text nondig 6.4.2 Digital Form -the description of options for obtaining the data set on computer-compatible media. compound digform 22.214.171.124 Digital Transfer Information description of the form of the data to be distributed. compound digtinfo
1126.96.36.199.1 Format Name -the name of the data transfer format. text ARCE ARC/INFO Export format; ARCG ARC/INFO Generate format; ASCII ASCII file, formatted for text attributes, declared format; BIL Imagery, band interleaved by line; BIP Imagery, band interleaved by pixel; BSQ Imagery, band interleaved sequential; CDF Common Data Format; CFF Cartographic Feature File (U.S. Forest Service); COORD Usercreated coordinate file, declared format; DEM Digital Elevation Model format (U.S. Geological Survey); DFAD Digital Feature Analysis Data (National Imagery and Mapping Agency); DGN Microstation format (Intergraph Corporation); DIGEST Digital Geographic Information Exchange Standard; formname DLG Digital Line Graph (U.S. Geological Survey) DTED Digital Terrain Elevation Data (MIL-D-89020) DWG AutoCAD Drawing format DX90 Data Exchange '90 DXF AutoCAD Drawing Exchange Format ERDAS ERDAS image files (ERDAS Corporation) GRASS Geographic Resources Analysis Support System HDF Hierarchical Data Format
117 IGDS Interactive Graphic Design System format (Intergraph Corporation) IGES Initial Graphics Exchange Standard MOSS Multiple Overlay Statistical System export file netCDF network Common Data Format NITF National Imagery Transfer Format RPF Raster Product Format (National Imagery and Mapping Agency) RVC Raster Vector Converted format (MicroImages) RVF Raster Vector Format (MicroImages) SDTS Spatial Data Transfer Standard (Federal Information Processing Standard 173) SIF Standard Interchange Format (DOD Project 2851) SLF Standard Linear Format (National Imagery and Mapping Agency) TIFF Tagged Image File Format TGRLN Topologically Integrated Geographic Encoding and Referencing (TIGER) Line format (Bureau of the Census) VPF Vector Product Format (National Imagery and Mapping Agency) 188.8.131.52.2 Format Version Number -version number of the format. text free text formvern
1184.108.40.206.3 Format Version Date -date of the version of the format. date free date formverd 220.127.116.11.4 Format Specification -name of a subset, profile, or product specification of the format. text free text formspec 18.104.22.168.5 Format Information Content -description of the content of the data encoded in a format. text free text formcont 22.214.171.124.6 File Decompression Technique -recommendations of algorithms or processes (including means of obtaining these algorithms or processes) that can be applied to read or expand data sets to which data compression techniques have been applied. text "No compression applied", free text filedec 126.96.36.199.7 Transfer Size -the size, or estimated size, of the transferred data set in megabytes. real Transfer Size > 0.0 transize 188.8.131.52 Digital Transfer Opti on -the means and media by which a data set is obtained from the distributor. compound digtopt 184.108.40.206.1 Online Option -information required to directly obtain the data set electronically. compound onlinopt 220.127.116.11.1.1 Computer Contact Information -instructions for establishing communications with the distribution computer. compound computer 18.104.22.168.1.1.1 Network Address -the electronic address from which the data set can be obtained from the distribution computer. compound networka 22.214.171.124.126.96.36.199 Network Resource Name -the name of the file or service from which the data set can be obtained. text free text networkr 188.8.131.52.1.1.2 Dialup Instructions -information required to access the distribution computer remotely through telephone lines. compound dialinst 184.108.40.206.220.127.116.11 Lowest BPS -lowest or only speed for the connection's communication, expr essed in bits per second. integer Lowest BPS >= 110 lowbps 18.104.22.168.22.214.171.124 Highest BPS -highest speed for the connection's communication, expr essed in bits per second. Used in cases when a range of rates are provided. integer Highest BPS > Lowest BPS highbps 126.96.36.199.188.8.131.52 Number DataBits -number of data bits in each character exchanged in the communication. integer 7 <= Number DataBits <= 8 numdata 184.108.40.206.220.127.116.11 Number StopBits -number of stop bits in each character exchanged in the communication. integer 1 <= Number StopBits <= 2 numstop 18.104.22.168.22.214.171.124 Parity -parity error checking used in each character exchanged in the communication. text "None" "Odd" "Even" "Mark" "Space" parity 126.96.36.199.188.8.131.52 Compression Support -data compression available through the modem se rvice to speed data transfer. text "V.32" "V.32bis" "V.42" "V.42bis" free text compress 184.108.40.206.220.127.116.11 Dialup Telephone -the telephone number of the distribution computer. text free text dialtel 18.104.22.168.22.214.171.124 Dialup File Name -the name of a file containing the data set on the distribution computer. text free text dialfile 126.96.36.199.1.2 Access Instructions -instructions on the steps required to access the data set. text free text accinstr 188.8.131.52.1.3 Online Computer and Operating System -the brand of distribution computer and its operating system. text free text oncomp 184.108.40.206.2 Offline Option -in formation about media-specific options for receiving the data set. compound offoptn 220.127.116.11.2.1 Offline Media -name of the media on which the data set can be received. text "CD-ROM" "3-1/2 inch floppy disk" "51/4 inch floppy disk" "9-track tape" "4 mm cartridge tape" "8 mm cartridge tape" 1/4-inch cartridge tape free text offmedia
118.104.22.168.2.2 Recording Capacity -the density of information to which data are written. Used in cases where different recording capacities are possible. compound reccap 22.214.171.124.2.2.1 Recording Density -the density in which the data set can be recorded. real Recording Density > 0.0 recden 126.96.36.199.2.2.2 Recording Density Units -the units of measure for the recording density. text free text recdenu 188.8.131.52.2.3 Recording Format -the options available or method used to write the data set to the medium. text "cpio" "tar" "High Sierra" "ISO 9660" ISO 9660 with Rock Ridge extensions "ISO 9660 with Apple HFS extensions" free text recfmt 184.108.40.206.2.4 Compatibility Information --description of other limitations or requirements for using the medium. text free text compat 6.4.3 Fees -the fees and terms for retrieving the data set. text free text fees 6.4.4 Ordering Instructions -general instructions and advice about, and special terms and services provided for, the data set by the distributor. text free text ordering 6.4.5 Turnaround -typical turnaround time for the filling of an order. text free text turnarnd 6.5 Custom Order Process -description of custom distribution services availabl e, and the terms and conditions for obtaining these services. text free text custom 6.6 Technical Prerequisites -description of any technical capabilities that the consumer must have to use the data set in the form(s) provided by the distributor. text free text techpreq 6.7 Available Time Period -the time period when the data set will be available from the distributor. compound availabl Metadata Reference Information 7 Metadata Reference Information -information on the currentness of the metadata information, and the responsible party. compound metainfo 7.1 Metadata Date -the date that the metadata were created or last updated. date free date metd 7.2 Metadata Review Date -the date of the latest review of the metadata entry. date free date; Metadata Review Date later than Metadata Date metrd 7.3 Metadata Future Review Date -the date by which the metadata entry should be reviewed. date free date; Metadata Future Review Date later than Metadata Review Date metfrd 7.4 Metadata Contact -the party responsible for the metadata information. compound metc 7.5 Metadata Standard Name -the name of the metadata standard used to document the data set. text "FGDC Content Standard for Digital Geospatial Metadata" free text metstdn 7.6 Metadata Standard Version -identification of the version of the metadata standard used to document the data set. text free text metstdv 7.7 Metadata Time Convention -form used to convey time of day information in the metadata entry. Used if time of day information is included in the metadata for a data set. text "local time" "local time with time differential factor" "universal time" mettc 7.8 Metadata Access Constraints -restrictions and legal prerequisites for accessing the metadata. These include any access constraints applied to assure the protection of privacy or intellectual property, and any special restrictions text free text metac
120or limitations on obtaining the metadata. 7.9 Metadata Use Constraints -restrictions and legal prerequisites for using the metadata after access is granted. These include any metadata use constraints applied to assure the protection of privacy or intellectual property, and any special restrictions or limitations on using the metadata. text free text metuc 7.10 Metadata Security Information -handling restrictions imposed on the metadata because of national security, privacy, or other concerns. compound metsi 7.10.1 Metadata Security Classification System -name of the classification system for the metadata. text free text metscs 7.10.2 Metadata Security Classification -name of the handling restrictions on the metadata. text "Top secret" "Secret" "Confidential" "Restricted" "Unclassified" "Sensitive" free text metsc 7.10.3 Metadata Security Handling Description -additional information about the restrictions on handling the metadata. text free text metshd 7.11 Metadata Extensions a reference to extended elements to the standard which may be defined by a metadata producer or a user community. Extended elements are elements outside the Standard, but needed by the metadata producer. If extended elements are created, they must follow the guidelines in Appendix D, Guidelines for Creating Extended Elements to the Content Standard for Digital Geospatial Metadata. compound metextns 7.11.1 Online Linkage -the name of an online computer resource that contains the metadata extension information for the data set. Entries should follow the Uniform Resource Locator convention of the Internet. text free text onlink 7.11.2 Profile Name -the name given to a document that describes the application of t he Standard to a specific user community. text free text metprof
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Managing geographic data as an asset :
b a case study in large scale data management
h [electronic resource] /
by Clay Smithers.
[Tampa, Fla] :
University of South Florida,
Title from PDF of title page.
Document formatted into pages; contains 120 pages.
Thesis (M.A.)--University of South Florida, 2008.
Includes bibliographical references.
Text (Electronic thesis) in PDF format.
ABSTRACT: Geographic data is a hallowed element within the Geographic Information Systems (GIS) discipline. As geographic data faces increased usage in distributed and mobile environments, the ability to access and maintain that data can become challenging. Traditional methods of data management through the use of file storage, databases, and data catalog software are valuable in their ability to organize data, but provide little information about how the data was collected, how often the data is updated, and what value the data holds for an organization. By defining geographic data as an asset it becomes a valuable resource that requires acquisition, maintenance and sometimes retirement during its lifetime. To further understand why geographic data is different than other types of data, we must look at the many components of geographic data and specifically how that data is gathered and organized. To best align geographic data to the asset management discipline, this thesis will focus on six key dimensions, established through the work of Vanier (2000, 2001), which seek to evaluate asset management systems. Using a conceptual narrative linked to an environmental analysis case study, this research seeks to inform as to the strategies for efficiently managing geospatial data resources. These resources gain value through the context applied by the inclusion of a standard structure and methodologies from the asset management field. The result of this thesis is the determination of the extent to which geographic data can be considered an asset, what asset management strategies are applicable to geographic data, and what are the requirements for geographic data asset management systems.
Mode of access: World Wide Web.
System requirements: World Wide Web browser and PDF reader.
Advisor: Steven Reader, Ph.D.
Return on investment
Spatial data infrastructure
t USF Electronic Theses and Dissertations.