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Anderson, Casey C.
GIS spatial analysis of multiple scenes in criminal homicides
h [electronic resource] /
by Casey C.Anderson.
[Tampa, Fla] :
b University of South Florida,
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Thesis (M.A.)--University of South Florida, 2009.
Includes bibliographical references.
Text (Electronic thesis) in PDF format.
ABSTRACT: Anthropological studies of community structures and human relationships of today's societies are becoming increasingly important for crime analysis. Law enforcement agencies are often challenged with the task of connecting multiple locations to persons involved in crimes to solve cases. Using the structures of the target communities and the social relationship between the victim and offender, spatial distributions of crimes can be reconstructed. Data used in this analysis were collected from Hillsborough County, Florida (n=420) and Lancaster County, Nebraska (n=48) law enforcement agencies within the years 1997-2007. The hypothesis of this paper is: if the social relationship between the victim and offender affect the spatial distribution of significant locations in a criminal homicide, then by exploiting the relationship of the involved individuals, can one acknowledge the possibility of generalized spatial configurations, depending on the type of community in which it occurred? Geographic distance results are cross-referenced to the relationship of the perpetrator to the victim, and scrutinized with frequencies, chi-square tests, cross-tabulations, correlations, mean comparison, and descriptive statistics. Results show similar frequencies of social relationship categories and the frequencies of victim and offender sex. However, the mechanism of death, victim and offender age differences, victim precipitation, and offender ancestries of domestic homicides, co-habitation cases, and distances between locations differ between the two communities. These variables' frequencies and patterns show some variation between the two regional settings. The goal of this paper is to identify the variables, through assessing community structures and social relationships, which affect the rates of social violence.
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Advisor: Erin H. Kimmerle, Ph.D.
t USF Electronic Theses and Dissertations.
GIS Spatial Analysis of Multiple Scenes in Criminal Homicides by Casey C. Anderson A thesis submitted in partial fulfillment of the requirements for the degree of Master of Arts Department of Anthropology College of Arts and Sciences University of South Florida Major Professor: Erin H. Kimmerle, Ph.D. Lorena Madrigal, Ph.D. E. Christian Wells, Ph.D. Hyun Kim, Ph.D. Date of Approval: April 10, 2009 Keywords: homicide, GIS, Anthropology, social structure, spatial geography Copyright 2009, Casey C. Anderson
Dedication I would like to dedicate this paper to my parents, Joe and Teresa Anderson, for always supporting me throughout my academic endeavors.
Acknowledgements I would like to thank Erin H. Kimmerle, P h.D. for her mentorship and encouragement throughout my coursework and the writing proc ess. Also, I would like to thank my committee members; Lorena Madrigal, Ph.D., E. Christian Wells, Ph.D., and Hyun Kim, Ph.D., for their insightful input s and guidance. Special than ks are also extended to the cooperative law enforcement agencies that allo wed me to use their records: Tampa Police Department, Hillsborough County SheriffÂ’s O ffice, Temple Terrace Police Department, Lancaster County SheriffÂ’s Office, Lincoln Poli ce Department, and the Nebraska Institute of Forensic Science, Inc. Within these ag encies, Dr. Matthias Okoye, Ellen Suri, Sherri Whalen, Detective Eric Hous ton, Corporal Mike Lowell, Detective Jason Mayo, Cory Avery, and Julie Nickel also deserve special recognition for the time spent allowing me to obtain my research, helping me, and re searching missing data. Acknowledgment and thanks is also extended to the anthropology gr aduate students at the University of South Florida who aided in the data collection process: Melissa A. Pope, Rhonda Coolidge, and Samantha M. Seasons.
i Table of Contents List of Tables ................................................................................................................ ...... ii List of Figures ............................................................................................................... .......v Abstract ...................................................................................................................... ....... vii Chapter 1 Introduction ........................................................................................................ .1 Chapter 2 Literature Review ...............................................................................................8 Basis of Study ...........................................................................................................8 Landmark Homicide Study .....................................................................................10 Victim and Offender Demography .........................................................................13 Victim Precipitation ...............................................................................................16 Defining Social Relationships ................................................................................19 Comparing Crime Rates among Societies .............................................................21 Spatial Distributions in Crime ...............................................................................23 Chapter 3 Methods ............................................................................................................ 29 Factors of Homicide ..............................................................................................30 Population Data .....................................................................................................32 Methods of Analysis ...............................................................................................36 Homicide Variables and Definitions ......................................................................37 GIS Methods ...........................................................................................................44 Chapter 4 Results ............................................................................................................ ..48 Demographic Characteristics of Victims and Offenders .......................................48 Case Characteristic Frequencies ...........................................................................58 Cross-tabulations and Chi-square Tests ................................................................66 Comparison of Means ............................................................................................77 Pearson Correlations .............................................................................................78 GIS Results .............................................................................................................80 Chapter 5 Discussion ........................................................................................................9 1 Chapter 6 Conclusion and Recommendations ..................................................................98 Works Cited ................................................................................................................... ..101 Appendices .................................................................................................................... ...108
ii List of Tables Table 3.1 2000 U.S. Census Data for Lancaster and Hillsborough County ..................33 Table 3.2 Data Used for Spatia l Analysis of Criminal Homicides ................................38 Table 3.3 Victim-Offende r Relationship Categories .....................................................42 Table 4.1 Frequenc ies of Offender Age Ranges in Hillsborough and Lancaster Counties .........................................................................................49 Table 4.2 Frequenc ies of Offender Sex in Hillsborough and Lancaster Counties ....................................................................................................... ...50 Table 4.3 Frequencies of Offe nder Ancestries in Hillsborough and Lancaster Counties ..........................................................................................51 Table 4.4 Frequencies of Vic tim Age Ranges in Hillsborough and Lancaster Counties ......................................................................................... .54 Table 4.5 Frequencies of Victim Sex in Hillsborough and Lancaster Counties ................................................................................................... .......55 Table 4.6 Frequencies of Victim Ancestries in Hillsborough and Lancaster Counties ................................................................................................... .......56 Table 4.7 Degree of Ho micide Distributions between Two Populations .......................59 Table 4.8 Presence of Victim Preci pitation Frequencies in Hillsborough and Lancaster Counties ...................................................................................63 Table 4.9 Mechanism of Death Dist ributions in Homi cide Cases in Hillsborough and Lancaster Counties .............................................................63 Table 4.10 Presence of Drugs or Alc ohol for Homicide Cases in Hillsborough and Lancaster Counties ..................................................................................65 Table 4.11 Cross-tabulation of Degr ee of Homicide and Victim-Offender Relationship in Lancaster County ..................................................................67 Table 4.12 Cross-tabulation of Degr ee of Homicide and Victim-Offender Relationship in Hillsborough County ............................................................68 Table 4.13 Victim Ancestry Compar ed to Domestic Homicides in Hillsborough and Lancaster Counties ...........................................................69
iii Table 4.14 Offender Ancestry Compar ed to Domestic Homicides in Hillsborough and Lancaster County ..............................................................71 Table 4.15 Victim-Offender Relationshi p Compared to Recovery Location in Hillsborough County .................................................................................75 Table 4.16 Victim-Offender Relationshi p Compared to Recovery Location in Lancaster County ....................................................................................... 76 Table 4.17 Mean Ages of Offenders by Social Relationship Category to Victims in Hillsborough County..................................................................................79 Table 4.18 Frequency of Geometric Situations in Hillsborough and Lancaster Counties .................................................................................................. .......81 Table 4.19 Average Distance in Miles fr om Specific Locations in Hillsborough and Lancaster County Disposal Cases ............................................................85 Table 4.20 Frequencies of Distances in Miles from Specific Locations in Hillsborough County Disposal Cases .............................................................87 Table 4.21 Frequencies of Distances in Miles from Specific Locations in Lancaster County Disposal Cases ...................................................................87 Table 4.22 Victim-Offender Relationship Frequencies for Disposal Cases in Hillsborough and Lancaster Counties .............................................................87 Table 4.23 Cross-tabulation of Victim-Off ender Relationship with Degree of Murder in Co-habitation Cases from Hillsborough County ...........................88 Table 4.24 Victim-Offender Relationship Fr equencies for Co-habitation Cases in Hillsborough County .......................................................................................9 0
iv List of Figures Figure 1.1 States of Study Areas .........................................................................................4 Figure 3.1 Distribution of Victim sÂ’ Ages in Hillsborough County ..................................39 Figure 3.2 Distribution of OffendersÂ’ Ages in Lancaster County ....................................39 Figure 3.3 Distribution of OffenderÂ’s Ages in Hillsborough County ...............................40 Figure 3.4 Distribution of OffenderÂ’s Age in Lancaster County ......................................40 Figure 3.5 Variations of Spatial Configurations ...............................................................46 Figure 4.1 Distribution of Offende r Age Ranges among Hillsborough and Lancaster Counties .......................................................................................... .49 Figure 4.2 Relevant Frequencies of Of fender Sex in Hillsborough and Lancaster Counties .................................................................................................... .......50 Figure 4.3 Relevant Frequencies of Offender Ancestries in Hillsborough and Lancaster Counties .......................................................................................... .51 Figure 4.4 Distribution of Victim Ag e Ranges among Hillsborough and Lancaster Counties .................................................................................................... .......54 Figure 4.5 Relevant Frequencies of Victim Sex in Hillsborough and Lancaster Counties .................................................................................................... .......55 Figure 4.6 Relevant Frequencies of Vi ctim Ancestries in Hillsborough and Lancaster Counties .......................................................................................... .56 Figure 4.7 Relevant Frequencies of Re lationship Categories in Hillsborough and Lancaster Counties .......................................................................................... .61 Figure 4.8 Degree of Homicide Comparis on to Victim-Offende r Relationship in Lancaster County ............................................................................................ .67 Figure 4.9 Degree of Homicide Compar ison to Victim-Offe nder Relationship in Hillsborough County ......................................................................................6 8 Figure 4.10 Degree of Homicide Compared to Offender Age Range for Lancaster County .................................................................................................... ........71
v Figure 4.11 Degree of Homicide Comp ared to Offender Age Range for Hillsborough County ......................................................................................7 2 Figure 4.12 Victim-Offender Relationship Compared to Body Recovery Location in Hillsborough County.................................................................................75 Figure 4.13 Victim-Offender Relationship Compared to Body Recovery Location in Lancaster County ...................................................................................... 76 Figure 4.14 Hillsborough County Age Correla tion between Victim and Offender .........79 Figure 4.15 Lancaster County Age Correla tion between Victim and Offender ..............81 Figure 4.16 Nebraska Homicide s with Disposal Sites ......................................................82 Figure 4.17 Florida Homicide s with Disposal Sites .........................................................82 Figure 4.18 Hillsborough County Co-habitat ion Homicides with other Murder Sites ..................................................................................................... ...........84 Figure 4.19 Distribution of Victim-Offender Relationship in Disposal Cases from Hillsborough and Lancaster Counties ............................................................88
vi GIS Spatial Comparison of Multiple Scenes in Criminal Homicides Casey C. Anderson Abstract Anthropological studies of community structures a nd human relationships of todayÂ’s societies are becoming increasingl y important for crime analysis. Law enforcement agencies are often challenged with the task of connecting multiple locations to persons involved in crimes to solve cas es. Using the structures of the target communities and the social relationship between the victim and offender, spatial distributions of crimes can be reconstructed. Data used in this analysis were collected from Hillsborough County, Florida (n=420) and Lancaster County, Nebraska (n=4 8) law enforcement agencies within the years 1997-2007. The hypothesis of this paper is : if the social relationship between the victim and offender affect the spatial distribution of signifi cant locations in a criminal homicide, then by exploiting the relations hip of the involved individuals, can one acknowledge the possibility of generalized sp atial configurations, depending on the type of community in which it occu rred? Geographic distance resu lts are cross-referenced to the relationship of the perpetra tor to the victim, and scruti nized with frequencies, chisquare tests, cross-tabulations correlations, mean comparison, and descriptive statistics.
vii Results show similar frequencies of social relationship categories and the frequencies of victim and offender sex. Ho wever, the mechanism of death, victim and offender age differences, victim precipita tion, and offender ancestries of domestic homicides, co-habitation cases, and distances between locations differ between the two communities. These variablesÂ’ frequencies and patterns show some variation between the two regional settings. The goal of this paper is to identify the variables, through assessing community structures and social relationships, which affect the rates of social violence.
1 Chapter 1 Introduction The amount of violence in todayÂ’s society has been a main focus of analysis in many fields, including preventi on and intervention for law enforcement agencies across the country. These agencies are frequently charged with the task of uncovering evidence and the circumstances surrounding criminal acti vities. This duty ranges from revealing associations between multiple locations to the social relationship of the individuals involved (Federal Bureau of Investigation 2004:41). Locations i nvolved in homicides may include a variety of sites, ranging from the victim and the offendersÂ’ residences, to the place where the crime occu rred, as well as a body depositi on site. How these scenes and people connect and interact can play a vital role in solving a case in a timely fashion. Anthropology is well suited for the task of uncovering the under lying social factors, such as community structure and human relationshi p networks, which can affect the movement and interaction of people, including viol ent interaction (Pool and Geissler 2005). Comparative studies allow anthropology to identify the similarities and differences between various communities, such as urban and rural structures, allowing for a more focused perspective (Gagne 1992:387). For ex ample, if an offender kills and buries a family member, the location of the murder site, residences, a nd the bond between the victim and killer can play a vital role in recovering th e body and solving the case.
2 Criminal homicide is defined as, Â“An exch ange between a victim and an offender, within a context, that resulted in the demi se of the victimÂ” (Swa tt and He 2006:279). In criminal homicide cases, the social relations hips between the people involved have the potential to provide importan t information about the connection of scenes (Silverman and Kennedy 1987). Avakame (1998:602) adds further acknowledgement to this theory by stating, Â“The victim-offender relationship is crucial to unlocking the mystery of why people kill each otherÂ”. In the past, scruti nized relationships focused on strangers or Â“intimateÂ”, meaning emotionally close, asso ciations. Numerous studies (e.g., Wolfgang 1958, Wolfgang 1967, Daly and Wilson 1988, Polk 1993, Avakame 1998) have been conducted on the dynamics of relationships i nvolved in criminal homicides, as well as analyses of the distances betw een relevant locations in cer tain crimes (Brantingham and Brantingham 1984, Messner et al. 1999, Canter 2000, Van Pa tten and Delhauer 2007, Grubesic and Mack 2008). However, there is a paucity of research integrating and supporting the two foci using Geographi cal Information System methods. In addition, it has been established (Gastil 1971, Silverman and Kennedy 1987, Sacco et al. 1993) that the differences in homicide rates between urban and rural communities are based on the evidence of different cultural settings. Yet, a geographical spatial analysis, paired with a social relati onship analysis, has not yet been attempted in the context of urban and rural communities. An urban society is characterized as a city with a large, diverse populat ion with a great deal of immigration and residential movement (Frey and Zimmer 2001:25). Three el ements of an urban area are defined by Frey and Zimmer (2001:26-27) as ecological, economic, and de-concentration. These elements refer to the population size and density, non-agriculture economic productivity,
3 and expanded territories due to suburban living (Frey and Zimmer 2001:26-27). Rural societies are mainly comprise d of smaller, less diverse popul ations, little residential migration, and opposite elements as an urban ar ea (Argent 2008). These different groups have the potential for varia tions in demographic and soci al factors that influence homicide because of the population factors surrounding them. In th e current paper, a rural location from Nebraska and an urban lo cation from Florida were chosen as the study sites. Figure 1.1. Social factors involv ed in homicides were explored through a comparison of spatial distributions and the soci al relationships of i nvolved individuals. The researcher then deduced conclusions from the results and suggests social relationships to focus on, within specific sp atial configurations, fo r homicide analyses. A Geographical Information System (GIS) uses spatial information for capturing, storing, analyzing, managing and presenting in formation (Manhein, Listi, and Leitner 2006:171). These data are defi ned as Â“information that ca n be interrelated based on positionÂ” (Brantingham and Brantingham 1984:212). Geographical Information Systems allow for interactive inquiries, spatial analyses, and maps to be conceptualized and developed. Various fields such as; anthr opology, resource management, city planning, criminology, and marketing, use this tool to conduct research. Geographic Information System data repr esent real world ob jects with digital images, which can be achieved through raster or vector data (Wa gner and Fortin 2005). The present geographic study ut ilized vector data in the analysis, which employ geometrical shapes and physical distance to express geographical feat ures (Korte 2001). These shapes triangulate spatial distributi ons and distances to exact measurements, thereby computing highly accurate results to the analysis.
5 The purpose of this project is to use sp atial analyses of va rious locations to determine if the relationship of a victim and offender in crim inal homicides, specifically domestic, non-domestic, and stranger homicide s, significantly influe nce the distribution of multiple scenes. It should be noted that the current paper is part of a larger on-going joint research project between the Univers ity of South Florida and Hillsborough County law enforcement agencies, also studying the sp atial distribution of criminal homicides. This paper focuses on case characteris tics, victim-offender demographics and relationships, as well as the sp atial variations of sites found with each t ype of case. The intent of this project is to use the ideas and methods from past studies, and conduct them on an original dataset to sugge st a new investigation strategy for faster body recovery and case closure in criminal homicides. In additi on, the distributions of scenes were analyzed in the cultural context of the region in whic h the murder occurred. By using GIS, the areas of interest and the dist ances between the locations were analyzed to address this issue. Expectations for the results of this study are: The relationship of the offender to the victim will significantly affect the spatial distribution of a murder. This is expected because of the close proximity of social relationships exp ected in domestic homicides, and the reverse for stranger homicides. Domestic and non-domestic homicide lo cations are hypothesized to occur in a smaller spatial area than locations of stranger homici des, particularly
6 in the situation of co-habitation between the offender and victim. This is due to the social relationsh ips occurring within the context of the murders. When a homicide transpir es between strangers, it is hypothesized that the spatial distribution of the different locations will have more distance from each other, particularly the body disposal site from the murder scene. This expectation is based on the assumption that the offender would not want to leave behind evidence of his offense. Homicides that occur in rural areas are expected to have a higher rate of domestic murder, whereas urban area homicides are expected to have a higher prevalence of stranger killings. This rationale is based on the statement by Silverman and Kennedy (1987: 274) that Â“strangers are a real and persistent aspect of urban livingÂ”. Demographic data for both the victim and offender are also expected to reflect the overall demographic composition for each region. The dynamic social structures of each community are the basis for this assumption. Furthermore, murder-suicides are hypot hesized to occur more frequently among domestic homicides. The amount of intimacy between those victims and offenders that fall into the domestic category is expected to weigh more heavily on the offender than a stranger. This study uses the anthropological perspec tive of human social networks and the community structures and factors that influence murder to better understand the phenomena of homicide and the relationshi ps of those involved (Kvamme 2003). The
7 use of anthropology in crime analysis is bene ficial by allowing underlying social factors to emerge, such as relationships, which may contribute to the production of crime. An anthropological perspective also allows recognition of cultural differences between communities, which may lead to differential be havior. Using a holistic perspective, the social configurations of a population can be inferred and analyzed by anthropologists using spatial distributions of si gnificant physical locations and social groups. Interactions between the various groups are also usef ul in determining the underlying cultural structures that can influence social conflicts and connections within the context of the community. By using urban and rural communities in the United States for spatial comparison, a more comprehensive and rounded approach to crime analysis is conducted, while maintaining the integrity of the analysis. Complete understanding of the cultural variables that influence and a ffect homicides, such as soci al relationships and spatial distributions, allow law enforcement agencies to better comprehend the act of homicide.
8 Chapter 2 Literature Review Research conducted on both relationsh ip dynamics (Wolfgang 1967, Silverman and Kennedy 1987, Avakame 1998, Broidy et al. 2006, Barber et al. 2008) and spatial distribution in crimes (Gastil 1971, Brantingham and Brantingham 1984, Sacco et al. 1993, Snook et al. 2005, Santtila et al. 2007, Grubesic and Mack 2008) have been thoroughly conducted in the past, yet rarely has a connection of th e two topics been addressed. By using an original dataset, the ideas and concep ts addressed in this paper, on both social relationships a nd spatial distributions in homicides, are correlated using GIS techniques and statistical analyses. In addition to this appro ach, the study is further enhanced by anthropologically separating the social stru ctures of urban and rural communities, and comparing the results. This section addresses the basis for this study, the selection of variables, the significance of social relationshi ps in crime, the benefits of comparing crime rates among different societies, and the value of using spatial distributions of scenes when ev aluating criminal offenses. Basis of Study Methods of spatial analysis have been used in a wide variety of studies to explore the patterns of criminal beha vior, as well as other relevant fields that reconstruct and analyze social distributions including socio-cultural anthropology and landscape
9 archaeology (Bridges et al. 1987, Messner et al. 1999, Kvamme 2003, Snook et al. 2005, Van Patten and Delhauer 2007). The groundwork for the current project is founded in the methods and concepts of previous research examples. The methods used in the present empirical homicide study are based on an appro ach seen in a resear ch article written on sexual homicides in Los Angeles, California (Van Patten and Delhauer 2007). In the example, geometric spatial an alyses of sexual homicides we re conducted in relation to the body disposal site and the victim and assa ilantÂ’s residences (Van Patten and Delhauer 2007). The Van Patten and Delhauer (2007) st udy used police and medical examinerÂ’s records to obtain data and employed GIS to graphically illustrate the geometric distributions between locations. The geometri c realities of the spatial areas were also used to determine the significance of probability for a case to be closed; simpler geometries had a higher probability of being solved than more complex spatial geometries (Van Patten and Delhauer 2007). Van Patten and Delhauer (2007:1139) concluded a Â“vast majority of crime trips i nvolved neighborhood trips of less than half a mileÂ”. Interpretation of Van Patten and De lhauerÂ’s (2007) conclusions suggest case specifics, such as spatial geometry, victimology, manner of death, and offender motivation, must be considered in determ ining how to best improve investigative approaches (Van Patten and Delhauer 2007:1140). The current study expands on Van Patten and DelhauerÂ’s (2007) methods by adding the component of social relationship between the victim and assailant to better understand location distributions and to compar e domestic to stranger homicides. In addition, Van Patten and DelhauerÂ’s study ( 2007) only focuses on the urban setting of
10 Los Angeles, whereas this research project evaluates both an urban and rural community to identify differences between soci al factors influencing murder. Focusing on the spatial concepts seen in the Los Angeles study by Van Patten and Delhauer (2007), the present paper uses a sim ilar spatial analysis design, for all criminal homicides, to evaluate releva nt scene distributions. The c onceptualization of the other methods and variables are founded upon the growi ng field of spatial analyses to analyze criminal behavior, as seen in additional research (e.g., Messner et al. 1999, Websdale 1999, Snook et al. 2005, Santtila et al. 2007). Landmark Homicide Study Marvin Wolfgang, a principle pioneer of modern crime analysis, was a prominent figure in the research of vict im-offender relationships, partic ularly Â“stranger homicidesÂ”, and the circumstances that surround and define the crime. His book, Studies in Homicide (1967), addressed multiple issues surrounding cr iminal homicides and related research. WolfgangÂ’s (1958) groundbreaking study of homicides in Philadelphia, Pennsylvania (n=588) established generaliza tions of involved indi viduals by analyzing the demographics and victim-offender relati onships of those invol ved in murders around the late 1940s. He analyzed these factor s for both European and African males and females using specific victim-offender rela tionships and patterns among ancestry, sex, and age (Wolfgang 1967). His analysis was a significant step in analyzing demographic groups in the context of criminal homicides. Numerous researchers have come to the conclusion that social structure effects crime behavior, but the relationshi p is not well understood (Bridges et al. 1987:345).
11 Durkheimian theorists believe economic inequa lity creates crime thorough social strain (Merton 1949, Cohen 1995). Weberian theory suggests urban and rural differences in crime rates among ethnicities are seen because the Â“bureaucratic re straints of urban courtsÂ” allow less social discri mination than rural courts, where social status can more directly affect court proceedings (Bridge et al. 1987:347). Bridge a nd co-authors (1987) found ethnic stratification to be a significant factor in im prisonment rates among urban and rural communities. Therefore, these studies show an importance of social stratification, due to community structures, in criminal studies as well as the importance of comparing urban a nd rural populations. WolfgangÂ’s analysis and recognition of vic tim-offender relationships in criminal homicide cases proved to be a significant fa ctor in his researc h. Wolfgang (1958:204) divided social relationship categories into 11 different groups and cross-referenced them using the Philadelphia homicide data. The groups used for classification were: close friend, family, acquaintance, stranger, param our, sex rival, enemy, a lover of offenderÂ’s mate, felon or police officer, innocent bys tander, and homosexual partner (Wolfgang 1958:204). His interpretation of the results re vealed the most common form of victimoffender relationships were Â“primary groupsÂ”, close friends and family, which accounted for 53% of the total cases (Wolfgang 1958:206). Furthermore, Wolfgang focused on the lo cations and methods by which criminal homicides in Philadelphia occurred. He th en analyzed the vari ables by demographic populations to produce general statistics and predictions for victims and offenders based on age, sex, and ethnicity. Results of the st udy revealed that men mo st often killed other men in public places by beating or stabbing, whereas women most often killed in the
12 kitchen or bedroom using a knife to stab the victim (Wolfgang 1967: 21). Relational data between the victims and offenders showed th at most homicides in Philadelphia were committed by the Â“primaryÂ” group, or those with frequent, close, or intimate contact (Wolfgang 1967:23). However, European males were more commonly killed during the commission of a robbery than any other ethn ic group, and therefore were shown to be often killed by strangers (Wolfgang 1967:23). Other unique topics included in the st udy involved victim precipitation, the presence of alcohol during cr iminal homicides and the disc overy of motivations driving the act of murder. Victim-precipitation is defined by Wolfgang (1958: 252) as a situation in which the actions or words of a victim prompt an offender to commit homicide. He uncovered numerous factors significantly a ssociated with the victim-precipitated homicides (n=150) such as, victims and offende rs of African ancestry, domestic slayings, and alcohol (Wolfgang 1967). Alcohol was discov ered as a leading variable in two-thirds of the homicide cases, with significant associ ation to either victims or offenders of African descent (Wolfgang 1967:22). His st udy also revealed five main homicidal motives: domestic disputes, vague alterca tions, money related issues, robbery, and jealousy (Wolfgang 1967:23). He compared these motives to the victim-offender relationship for the purpose of discovering reoccurring themes. His results revealed three of the five main motives were significantl y linked to the Â“primary groupsÂ”, signifying a correlation to motive that warrants fu rther research (Wolfgang 1967). The variables investigated by WolfgangÂ’s study in 1967 are applied in the present research project. The variables were modeled after his example, since they demonstrate a sophisticated analysis for determining associ ations. Similar studies conducted by other
13 researchers provided more in depth explanat ions and analyses for the variables this project uses, and therefore are addressed accordingly. Victim and Offender Demography Demographic statistics of victims and offe nders have been an interest to crime analysts, because the implications inferred from these variables can be invaluable for law enforcement. Various researchers (e. g., Curtis 1974, Daly and Wilson 1988, Harries 1997, Avakame 1998, Lauritsen and Schaum 2004) have explored the various demographic relationships in criminal homic ides and have found a generalized pattern among age, race, and sex variables. Thes e demographic variables are important in understanding the social relationships be tween victims and offenders, as well as understanding the demographic dynamics between them. Age, sex, and ethnicity variables from di fferent populations and time periods have been repeatedly analyzed and are suggested to be strong pred ictors of violence (Wolfgang 1967, Curtis 1974, Harries 1997). General statis tical analyses of demographic crime variables have indicated pa rticular groups of victims and offenders involved in homicides. For example, Harries (1997:17) refers to homicide as, Â“A crime that disproportionately aff ects young adultsÂ”. In addition, Curtis (1974:34) researched the age of individuals involved in homicides and concluded Â“the most dispr oportionate offender [age] range covers young adults in their teens and twen tiesÂ”, with victims appearing in the slightly older age group than the offenders do. In support of these statements, it has been noted across studies (Wolfgang 1967, Daly and Wilson 1988, Harries 1997, Federal Bureau of Investigation
14 1998) that the age of both victims and offende rs seems to concentrate around the 20Â’s age group in criminal homicides. Younger wome n, according to Lauritsen and SchaumÂ’s (2004:340) report, are at a mu ch higher risk of being victimized by Â“strangers, nonstrangers, and intimate partners than older womenÂ”. As su ch, age has consistently and repeatedly been highly correlated with persons involved in criminal homicides (Avakame 1998:609). In addition to age, the sex variable has been used to better understand criminal homicides. In Silverman and KennedyÂ’s st udy (1987:276), females were more likely to be killed by a male, while males were more likely to kill other males. In conjunction with these findings, it appears that males are over-represented as both offenders and victims in homicide studies (Curtis 1974:32, Harries 1997:18, Avakame 1998:609). Nonetheless, Verkko points out that Â“when the general criminal homicide rate is low in a given culture, the percentage of female o ffenders is generally hi gherÂ…thus suggesting a greater stability in the amount of fe male homicideÂ” (Wolfgang 1967:4). Lastly, the ethnicities of victims and offe nders in homicides have been used to uncover intraor inte r-ethnic correlations (Van Patten and Delhauer 2007). Van Patten and Delhauer (2007) found most sexual homicides to be intra-ethnic in the city of Los Angeles, California. Furthermore, Lauritse n and Schaum (2004:325) established female victimization was subject to ethnic group categ orization, which proved to be a significant factor since, Â“Higher levels of intimate partner victimization among blacks versus whites appear to be limited to younger womenÂ”. In WebsdaleÂ’s (1999:79) analysis of 78 malefemale domestic homicides in Florida, resu lts showed an approximately equal proportion of homicides were committed by both Europ ean and African ancestries, followed by
15 Hispanics. Websdale (1999:204) concl uded with a comment regarding the overrepresentation of Afri can individuals involved in dome stic homicides, including the offender. One possibility for this over-r epresentation stems from the Â“subculture of violenceÂ” theory, proposed by Wolfgang and Ferracuti (1967) The Â“subculture of violenceÂ” theory posits that various refe rence groups are fr equently surrounded by violence, which may be seen as an act to be admired or expected (Daly and Wilson 1988:286-287). This theory, however, rema ins a debated topic among anthropologists and sociologists alike for the cultural differences between groups may change over time and do not necessarily predict behavioral ou tcomes of the community (Daly and Wilson 1988). In support of WebsdaleÂ’s (1999) comment regarding over-representation, other studies of homicide demographics have co me to similar conclusions on victim and offender ethnicity. For example, focusing on the African and European ethnicities in the United States during 1967, Curtis (1974:20) esta blished that approximately two-thirds of crime, committed between the two specific et hnicities, was conducted by the African population. Also, Harries (1997: 19) reports a study in 1983 that ascertained homicide to be the leading cause of death for African-Americans, and the f act that they were 6.7 times more likely to be involved in a homicide than European-Americans were. However, Daly and Wilson (1988:170) concluded Â“circumstantial variables are related to the probability in becoming involved in lethal violenceÂ”, even when put in the age-sex perspective. This statement expos es the need to analyze demographics in conjunction with variables that may influence homicides, and is acte d upon in the current paper.
16 Studies on demographic representations among criminal homicides (Wolfgang 1967, Curtis 1974, Silverman and Kennedy 1987, Daly and Wilson 1988, Harries 1997, Avakame 1998, Federal Bureau of Inves tigation 1998, Websdale 1999, Lauritsen and Schaum 2004, Van Patten and Delhauer 2007) are continued and expanded within the current study. In the present project, age, sex, and ethnicity of the victims and offenders are used to compare results among each community. These comparisons are then explained using United States census data and used to help understand victimization for each region. Victim Precipitation The act of homicide can be affected or instigated by the phenomena known as victim precipitation. Marvin Wolfgang (1957:2) was the first to construct the concept of Â“victim precipitationÂ” or, the situations in wh ich the victim incites the offender to commit a crime, whether knowingly or unknowingly (Swatt and He 2006:281). Intimate partner homicide has been a main focus for victim precipitated murder studies (Goetting 1987), particularly when a female offender is invol ved. However, victim precipitation is not unknown in stranger homicides (Felson and Me ssner 1998:405), and therefore must be considered as well. The presence of victim precipitation in a homi cide may enhance the understanding of the dynamics between the vic tim and offender, when related to social relationship. According to Goetting (1987), the concep t of victim precipitation is a focal variable when studying domestic homicides involving a female offender, because of the unbalanced ratio of women who kill their abus ers. Wolfgang (1958:260) found that there
17 were more husbands killed by females, compared to males killing wives, in domestic homicides when victim precipitation was invol ved. In addition, Wolf gangÂ’s Philadelphia study (1958) exposed the presence of alcohol as a significant factor in homicide events that involved victim precipitation (Cohe n 1970:462). Campbell (1992), Rosenfeld (1997), and Mann (1998) conducted similar studies on female offenders and the presence of victim precipitation, and their results supported those of Wolfgang (1958) and Goetting (1987). Additionall y, Felson and Messner (1998:406) found that most femalecommitted murders involved victim precipitation, despite the setting of a domestic or non-domestic situation. Nevertheless, it should be noted that vi ctim precipitated homicides do not always indicate a female offender, nor is a female offender indicative of a domestic homicide. Jurik and WinnÂ’s 1990 research on offender pred iction in victim precipitated homicides suggested that demographic va riables were not significant in predicting the offenderÂ’s sex, but situational variables, including victim precipitation, were significant (Swatt and He 2006:283). Furthermore, Felson and Messner (1998:408) suggest th e large amount of victim precipitation seen in female offender domestic homicides may be due to the lack of female victims in the situation. This factor is seen as significant when compared to the non-domestic homicides with female offenders in which females are also victimized (Felson and Messner 1998:408). Situations in which homicide offenses occur are described by Felson and Messner (1998:408) using three models of victim precipitation: Gender-Partner Interaction, Gender Interaction, and Gender Differences. The Gender-Partner Interaction model refers to women who kill their partners while responding to violence towards them,
18 thereby motivating the act of self-defense (Felson and Messner 1998:408). The model labeled Â“Gender-InteractionÂ” refers to Â“a three-way statistical interaction among the gender of the offender, the gender of the vi ctim, and the relationship between the two partiesÂ” (Felson and Messner 1998:408). La stly, the Gender Differences model which explains the differences of actions between men and women. An example of this model is, Â“When women kill, their behavior is more likely to be precipitated by a violent attack than when men kill, because women are usually less violentÂ” (Felson and Messner 1998:409). Nonetheless, homi cides involving children, non-in timate relationships, and strangers may also include the dynamic of vict im precipitation, and therefore, cannot be discounted (Felson and Me ssner 1998:405). Studies have found (Wolfgang 1957, Wolfgang 1967, Goetting 1970, Campbell 1992, Rosenfeld 1997, Mann 1998, and Felson and Me ssner 1998) that females comprise a large percentage of the offenders in domes tic situations because of victim precipitation, whether in self-defense or otherwise. A lthough domestic homicides have been seen as one of the main situations in which this prec ipitation ends in a hom icide, other types of social relationships can result in a vi ctim precipitated homicide as well. Victim precipitation is identified and an alyzed within the present paper for both domestic and other homicide situations. Based on the Gender-Interaction and GenderDifference models defined by Felson and Me ssner (1998) and the ci rcumstances founded by Wolfgang (1957), the presence of victim precipitation will be related to sex, the type of homicide, and the social relationshi p between the victim and offender.
19 Defining Social Relationships The relationship between a victim and hi s/her offender has b een scrutinized by many studies (Wolfgang 1958, Curtis 1974, Silverman and Kennedy 1987, Riedel 1987, Williams and Flewelling 1987, Decker 1993, Avakame 1998, Felson and Messner 1998, Miethe and Regoeczi 2004, Swatt and He 2006). Silverman and Kennedy (1987:273274) state these affiliations are, Â“conside red to be of paramount importanceÂ” when investigating homicides, for th e attachment described through social relationships can be used to explain aspects of crime. Acco rding to Miethe and Regoeczi (2004:227), this stressed importance is the foundation of the a ssumption that victim-o ffender relationships Â“form the basis for fundamentally distinct hom icide situationsÂ”. This belief is supported by Silverman and Kennedy (1987), who maintain the idea that rela tional distances among individuals can have a gr eat influence on predicting elements of homicide. Although studies have used different t ypes of relational systems (Parker and Smith 1979, Messner and Tardiff 1985, Ried el 1993, Decker 1996, Laureitsen and Schaum 2004), the present study follows the ex ample of a variety of others (Riedel 1987, Williams and Flewelling 1987, Decker 1993, Miethe and Regoeczi 2004) who used three encompassing relationship categories: Domestic, which includes intimate partners and family Non-domestic, defined as a friend, ne ighbor, coworker, or acquaintance Stranger The first category of relationships, labe led Â“domesticÂ” is defined as; a group of people romantically involved at one point of time, including ex-husbands and romantically significant othe rs (Silverman and Kennedy 1987: 282). This classification
20 also includes family affiliations such as parents, children, and extended family members. Casual relationships, or the Â“non-domesti cÂ” group, are those th at involve friends, acquaintances, neighbors, and co-workers (Silverman and Kennedy 1987:282). Lastly, strangers are defined as people who have never met before (Silverman and Kennedy 1987, Riedel 1993:1). In numerous academic fields, there has been much debate regarding the existence of differences between relational homici des (Avakame 1998, Felson and Messner 1998, Swatt and He 2006). Avakame (1998:609) addre ssed this dispute by conducting an interand intra-state study using multilevel models of homicide, which suggested age, sex, and weaponry were important factors to consider in homicide correlations. The results of the study implied that across the United States, the incidence rate of stranger and domestic homicides differ significantly. AvakameÂ’ s (1998:624) explanati on is that stranger homicides occur more frequently in urban ar eas due to the social disorganization and population differences. This rationalizati on is theoretically supported by Zimring et al. (1983:910) who states, Â“It is a criminological clichÂ…Â” that an individual will be killed by a known offender rather than a stranger. However, according to Curtis (1974:49), stranger homicides determine less than thirty percent of victim-offe nder relationships in urban populations. Therefore, the present study te sts the victim-offender rela tionship in terms of an urban and rural society. Taking into account the population structures of each community, cases with significant spatial di stributions are cross-referenced with relationship status. Social re lationships are also associat ed to the relevant distance frequencies of the selected spatial sites.
21 The main social relationship focus fo r the current study is the stranger, nondomestic, and domestic categories previous ly described. This paper expands on the relationship paradigm by using empirical evid ence of extant data to demonstrate the usefulness of relationship status in criminal homicides. The importance of relationships is illustrated using by GIS maps and distan ces. This allows the capacity for a more accurate depiction of the physical locations and sites to be spatially correlated in terms of the relationships between the victims and offenders. Comparing Crime Rates among Communities The analysis of crime rates between countries has proven to be a collective success among scholars; however, a comparison of crime at the micro-level, different communities within a country, is lacking rese arch. The current study demonstrates the different variables of criminal homicide between an urban and rural population, and therefore, the framework for so cietal comparison is useful. Supporting evidence for the variation of homicide rates cros s-nationally, caused by cultural differences, can be seen within the literature (Archer and Gartner 1984, Gartner 1990). These cultural variations ar e manifested through exposure to violence, economic inequality, the opportunity for vic timization, and violence as a response to conflict (Gartner 1990:9 6). Gartner (1990:94) notes th e homicide rate in the United States is markedly higher and differently composed than that of other developed countries. Four characteristics of a comm unity that affect homicide rates were interpreted as: economic inequality, social control, population activities and composition, and exposure to violence (Gartner 1990: 94). The applied analysis in the current project
22 tests these ideas on a micro-analysis level with in the context of two differently structured communities. Williams and Flewelling (1988) and P okorny (1965) statistically approach comparisons of homicide rates among American cities. Empirical studi es of this nature include structural and cultural dynamics th at produce homicides within the country (Williams and Flewelling 1990:425). Focusing and separating the victim-offender relationship, demographics, cas e characteristics, and the precipitating event, the authors each find both cultural and structural variab les had a significant effect on homicide rates (Williams and Flewelling 1990:425, Pokorny 1965:480). The method of homicide, location of murder, and relations hip status, among other variab les, all proved to diverge from each other within the context of the specific countie s (Pokorny 1965). The researchers conclude the most effective approach to a comparative homicide analysis is by separating the structures that contribute to the act of homicide itself (Williams and Flewelling 1990, Pokorny 1965). Therefore, th e current paper appl ies the separation method, suggested by Willams and Flewelling (1990) and Pokorny (1965), to achieve an accurate homicide comparison. Numerous research efforts show the im portance of a homicide comparison and the implications the various results have on crime analysis. Using specific variables, a comparison study of an urban and rural comm unity is conducted to identify varied associations between homicides and their relevant locations, w ithin the context of social relationships. The differences in structural and cultural dynamics within the community settings are also addressed, using census in formation of the populati ons and the fluidity of the population structure, fo r the structural aspect.
23 The cultural ideas being tested are: The notion of more Â“strangerÂ” encounters occurring in the urban population, due to the mobile dynamic of the Hillsborough County population. Comparing the similarities and di fferences of the victim-offender relationship of a rural so ciety to an urban society, based on criminal homicide events. Discerning the cultural differences between the two communities, such as case specifics and murder locations. Spatial Distributions in Crime The concept of crime-analysis and mapping has become a growing body of literature in academia. According to Canter (2 000:4), crime analysis is defined as, Â“The collection and analysis of data pertaining to a criminal incident, offender, and targetÂ”. Multiple papers (Amir 1971, Messner et al. 1999, Canter 2000, Lundrigan and Canter 2001, Snook et al. 2005, Santtila et al. 2007) have laid the f oundation for geographically mapping homicide events using GIS, and have gi ven validity to its uses Canter (2000:5) explains the advantages of us ing GIS for tactical crime anal ysis, which is comprised of pattern detection and linkage analysis, a nd how examining aspects of crime in a geographical context can contri bute to a better understandi ng of offender patterns. Messner and coworkers (1999), and Santtila et al. (2007) analyzed homicide patterns through spatial and te mporal analyses surrounding similar communities. These studies focused on the spatial clustering of hom icides on the geographic level, but did not
24 include the demographic and case specific data, as seen in Avakame (1998) and Wolfgang (1958). Both Messner et al. (1999) and Santtila et al. Â’s (2007) studies used victim and offender residences and crime scene locations within their study, as is done in the current paper. Messner et al. (1999) concluded that spatial randomness does not occur among sites and that there is suggestion of a diffusion process within the results. In agreement, Santtila et al. (2007:12-14) discovered the distances between sites differed significantly and it was possible to identify cr ime features that we re correlated with distances. However, both studies are at a disadvantage due to the lack of acknowledgement toward the social relationshi ps between the victim and offender, as well as pertinent case information. The case information and social variables may have explained the non-random movement. Although Pokorny (1965) attempts to address the relationships between the victim and offender, and spatially relate them, his study fails to support his findings with mapping or distances. Anthropologic training can aide a resear cher with integrat ing both the social relationship and the spatial pa tterns of offenders and victims into a powerful tool for crime analysis. The field of anthropology is focused on the recognition of social and cultural structures, which can affect and infl uence crime. The spatial distributions are also applicable to anthropol ogy, because the social stratifi cations and structures of communities are inherently founded on the phys ical layout of the site (Bevan and Conolly 2002, Kvamme 2003). Landscape arch aeology uses the ge ophysical layout of past populations to understand and analyze the Â“patterned geometries of the landscapeÂ” (Kvamme 2003:438). The concept of landscape ar chaeology is utilized in large surveys to study the cultural and structural aspect s of archaeological sites (Kvamme 2003).
25 Recent advances in the field have allowe d for the Â“wide-area mapping of settlement spaces to reveal their organization and structureÂ” (Kvamme 2003:436). Bevan and Conolly (2002) use GIS tools to analyze the spatial organizati on of an archaeological site in Greece. Using GIS and surveying techniqu es, Bevan and Conolly (2002:136) analyzed Â“the structure of the modern landscapeÂ…, the visibility and definiti on of archaeological sitesÂ…, [and] the interpretation of site distributio n patternsÂ”. GIS is also useful for ill ustrating distribution patter ns and identifying clusters from cultural evidence (Lock, Bell, Lloyd 1999; Gillings and Sbonias 1999). Kvamme (2003:436) states, Â“Survey of large contiguous areas is Â… essential for making sense of patterns in cultural landscapes using geophysical datasetsÂ”. Therefore, the utilization of landscape surveys and GIS interpolation and ma pping are useful tools in the geographical analysis of the current project. The distan ces between locations involved in homicides play a large role in understanding the movement and influences of the acts of homicides and the people committing them. The landscape archaeology concept can also be used as a tool for spatial and societal comparison. For example, a crossnational spatial analys is scrutinizing the distance of residences and dis posal sites of serial killers in the United States and United Kingdom was performed by Lundrigan and Canter (2001), while Snook et al. (2005) analyzed spatial distributions of serial homicide offenders in Germany. Spatially tested variables within each study in cluded: the disposal sites a nd the assailantÂ’s domain, as well as the spatial distributions compared to the assailantÂ’s daily activities (Lundrigan and Canter 2001, Snook et al. 2005). Also spatially scrutin ized were the changes in the size of the disposal site distances and the Â“huntingÂ” ranges over time (Lundrigan and
26 Canter 2001, Snook et al. 2005). These analyses are based off the principle of landscape archaeology because they are plotting the human-landscape interaction. The theoretical background of Lund rigan and Canter (2001) and Snook et al. (2005) is based on the degree of comfort the of fenders feel within a certain distance from their residence and where they conduct normal daily activities (Brantingham and Brantingham 1981, Canter and Larkin 1993). Examinations of the geographical distributions supported Lundrigan and CanterÂ’s (2001:609) and Snook et al. Â’s (2005:601) hypotheses and determined that a serial kill erÂ’s spatial choices were influenced by rational choice and routine activity. Although the focus of these st udies are directed specifically toward serial kill ers, the spatial distribution of scenes, in relation to the individuals involved, are comparable methods to those used in the current thesis. Menachem Amir (1971) discussed the si gnificance between spatial distribution and area relationships and analyzed four vari eties of spatial relationships between the area of residence and area of crime. The mobility triangle distributions were labeled: residential, crime, neighborhood, and total (A mir 1971:91). The residential triangle is described as a situation in which the Â“offender lives in the area of th e offense, but not in the area of the victimÂ’s residenceÂ” (Rand 1986: 118). The crime mobility triangle is seen in cases where the offender lives in the vicinity of the victim, but the crime is committed elsewhereÂ” (Rand 1986:118). The neighborhood tria ngle is the relationship in which Â“the house of one or more offenders and the pl ace of offense are located in the same neighborhoodÂ” (Amir 1971:91). Lastly, the to tal mobility triangle is expressed as a situation where Â“the offender doe s not live in the vicinity of the victim or offenseÂ” (Rand 1986:118). These combinations of sites, along with the victim and offenders residences,
27 are included in a Â“vicinity of crimeÂ”, which is defined by Amir as a five city block area (1971:91). Although AmirÂ’s study focused on rape, his sp atial concepts of crime vicinities are useful in comparing the f actors pertinent to a homicide event. The ideas surrounding his crime vicinities are utili zed in the current project by looking at the proximity of victim residence to offender residence, the re sidential distances and their relationship to where murders are occurring, and the differences in murder a nd disposal site in relation to residences for relevant cases. According to Messner et al. (1999), non-random behavior is not a factor in geographic crime analysis, however, non-random behavior in the presented study is expected, and is specifically investigated by us ing case specific, demographic, and geographical data to gain a complete picture of homicide incidences. Criminal homicide analyses, using rigorous methods adapted from the mentioned researchers (Amir 1971, Messner et al. 1999, Canter 2000, Lundrigan and Canter 2001, Snook et al. 2005, Santtila et al. 2007), are used in determining social relationships between individuals and correlating spatial distributions. The capabi lities of GIS will clarify these variables through the features of mapping, database capabilities, accuracy, and its ability to correlate one or more attribut es for pattern analysis. The current study presented here ex pands on homicide investigations by incorporating ideas of revolu tionary authors in crime anal ysis and supporting the results using statistical tests and GIS models to more completely understand criminal homicides. The social relationship between a victim and offender are related to specific case information, demographics of the parties involved, and the spatial patterns of the
28 movement and vicinities in which relevant locations are positi oned. Using a holistic anthropological perspective that incorporat es both cultural and biological variables influencing and effecting homicide, a cu lturally-specific model of homicide is constructed.
29 Chapter 3 Methods Population differences of social viol ence are important fo r understanding the circumstances surrounding the crime and pr eventing increases in crime rates for a specific location. Homicide data were coll ected from both a rural and urban location: Lancaster County, Nebraska (n=48) and H illsborough County, Florida (n=420). All the cases were used in several aspects of the study; however, some of the case information was incomplete. Therefore, the incomplete cas es were excluded from particular analyses, and are reflected in the sample sizes. The da ta were used to compare and evaluate the spatial distributions between the two regional c ontexts, as well as the social relationships between the individuals involved in each case. This analysis allowed implications to be made from differences seen in the communal aspects of hom icide, which were inferred both spatially and case specifically. The hom icide documentation and case information were acquired by accessing law enforcement and autopsy records of several different law enforcement agencies and affiliated institutions from both regions. Records from participating law enforcem ent organizations consisted of police reports, case summaries, case supplements Vi olent Criminal Apprehension Program (ViCAP) records, and autopsy reports. The above resources were considered acceptable for use because of their status as public documents as well as consideration of Edem AvakameÂ’s statement (1998:608), Â“Homicide data are among the most accurate criminal
30 justice dataÂ”. In addition, the protocol used for data collection was made specifically for the purpose of collecting homicide data from primary sources such as police records and other public documents. The current paper is part of a larger on-going joint research project between the University of South Florida and Hillsborough County law enforcement agencies which is studying the sp atial distribution of criminal homicides. Nebraska data were added to th e thesis research to add the component of society structure comparison. The data representing Hillsborough County, Florida were procured from the Tampa Police Department, Hillsborough Count y SheriffÂ’s Office, and the Temple Terrace Police Department. Data were collected by Casey C. Anderson, Erin H. Kimmerle, Ph.D., Rhonda Coolidge, Melissa A. Pope, and Samantha M. Seasons and Corporal Mike Lowell. The Lancaster Count y data, from the Lincoln, Nebraska area, were obtained through autopsy records from the Nebraska Institute of Forensic Science, Lincoln Police Department, and the Lancaster County SheriffÂ’s Office. The information collected in both regions are case summaries and details of Â“closed homicideÂ” cases from 1997-2007. Factors of Homicide In the current project, Â“closed casesÂ” are c onsidered those in which the offender is known and has been arrested, has died, or has been sentenced. Based on the crime analysis foundations laid by Wolfgang (1967), homicide is divided into criminal and noncriminal categories. This study only focu ses on criminal cases or those which are Â“premeditated, felonious, intentional murder sÂ” or Â“slayings in the heat of passionÂ”
31 (Wolfgang 1967:272). Noncriminal homicides ar e considered excusable for reasons that are not seen as relevant fo r the particular an alysis, and therefore are excluded. The current research project analyzes clos ed criminal homicide cases that consist of those in which the primary offender is known and law enforcement is no longer pursuing them. The data involved a few cas es in which there were one or more offenders; however, the focus for this study is only on the person who actually caused a victimÂ’s death. Included in these cases are stranger homicides, domestic homicides, child homicides, murder/suicides, and some cases dismissed by exceptional clearance. Cases closed by exceptional clearance are those in which reasons, outside of police control, occurred to prevent the offender from being arrested, charged, and prosecuted (Federal Bureau of Investigation 2004:150) Certain dismissed cases, su ch as murder-suicides, are included in this study because of their potential to portray vi tal information relevant to the study. Excluded homicide cases are thos e caused by justified police shootings and non-criminal homicides such as self-defense vehicular manslaughter, and those closed by the issue of a warrant. Homicides that were omitted are not applic able to the project because of the lack of malice and difference in so cial relationships between those involved. However, it should be noted that the relati onships of those involved in self-defense homicide cases have the potential to reveal interesting anthropological information, such as the demographics of the victim and offender, motives behind the orig inal attack, and the spatial proximities to specific locations from the attack site.
32 Population Data Census data collected in 2000 and suppl ementary government documents (U.S. Department of Commerce) tr acking the demographic, mi gration, tourism, population growth, and homelessness rate s of each population were us ed to differentiate the community structures of the study sites. Usi ng total population statis tics, ratios of males to females, median age, and population di stribution by race, the differences between Lancaster County, Nebraska census data a nd Hillsborough County, Florida census data are illustrated in Table 3.1. Data resulting from the 2000 United St ates Census data, Lancaster County, Nebraska had a total population of 250,291 peopl e living within the lim its, with a density of 298 people per square mile (U.S. Department of Commerce). The male to female ratio is even at 50 percent each. Median age of this population is 32.0 years (U.S. Department of Commerce 2000). The majority of people in Lancaster County are of a European ethnicity; a population percen tage of 90.1% (U.S. Department of Commerce 2000). Hispanic, African, and Asian et hnicities are the next three represented, however their population percentages only range from 2.8% to 3.4% (U.S. Department of Commerce 2000). Hillsborough County, Florida has a substa ntially greater population totaling 998,948 residents and 951 people per square m ile (U.S. Department of Commerce 2000). Male to female ratios are only slightly off-balance, with fe males having a slightly higher percentage at 51.1% (U.S. Department of Commerce 2000). The median age of the Florida population is 35.1 years (U.S Department of Commerce 2000).
33 Table 3.12000 U.S. Census Data for Lancaster and Hillsborough County (U.S. Department of Commerce 2008 Supplementary Population Report) Lancaster County Hillsborough County Count Percentage Count Percentage Total Population 250,291 100.0% 998,948 100.0% Sex Males 125,029 50.0% 488,772 48.9% Females 125,262 50.0% 510,176 51.1% Ethnicity European 225,426 90.1% 750,903 75.2% African 7052 2.8% 149,423 15.0% Asian 7,162 2.9% 21,947 2.2% Hispanic 8,437 3.4% 179,692 18.0% AmericanIndian 1,599 0.6% 3,879 0.4% Other 4,374 1.8% 47,266 4.8%
34 Ethnic profiles show a majority of the re sidents also have a European background at 75.2 % (U.S. Department of Commerce 2000) However, the Hispanic and African populations show a substantial amount of re sidents present at pe rcentages of 18.0% and 15.0%, respectfully (U.S. Departme nt of Commerce 2000). When comparing these demographic statis tics, the social composition differences between the populations are clear. A much larger site is seen within Hillsborough County, which gives people more opportunity fo r crimes because of the density of the population, whereas the smalle r Lancaster County population density allows for more space between individuals. The percentage s of ancestry are also different when comparing population to population. This factor proves to be important when comparing interand intra-ethnic murder s and their frequencies. Major influences on a communityÂ’s social structure and com position are provided by the opportunities for mobility and fluidity within the population. The tourism industry is one such key influence on a community because it can cause many demographic and population density fluctuations in a short peri od of time. For example, Tampa, Florida houses both an international ai rport and ship dock through which millions of people flow during a single year. The Tampa Bay Convention and Visitors Bureau in Hillsborough County calculated 9.39 million domestic passeng ers and 185,768 international passengers flew into and out of the Tampa Intern ational Airport during 2007 (Tampa Bay and Company 2009). The international maritim e port was responsible for 735,734 people traveling to and leaving from Tampa Bay (Tampa Bay and Company 2009). The vast amount of non-residents continuously visi ting Hillsborough County si gnificantly affects the population and community dynamics.
35 In contrast, Lancaster County has no ship ports or international airports, and therefore is not a large tour ist or visitor location (Nebra ska Department of Economic Development 2008). Approximately one milli on people visit the Lancaster County area every year (Lincoln Convention and Vis itors Bureau 2008). Therefore, the two communities have significantly different t ourism rates and greatly differ in social composition, which can affect homicide and crime rates within each area. Migration is another social factor that can influe nce population development. The United States Census Bureau defines populati on development by using birth rates, death rates, domestic migration and internationa l migration (U.S. Department of Commerce 2008). According to the 2000-2004 U.S. Census estimates, the total net migration for Hillsborough County was 16.9%, whereas Lanc aster County had a 2.4% net migration rate (U.S. Department of Co mmerce 2008). These percentages show a large discrepancy between the migration rates of the populations, for the Florida community had a much higher rate than Nebraska. An additional social variable that can aff ect homicide rates is the poverty level of residents. Referenced in numerous studies (Rosenfeld 1986, Gartner 1990, Eitle et al. 2006, McCall et al. 2008), the social disequilibrium of poverty has the potential to play an important role in the social production of crime. Thus, the percentage of the population who qualified for povert y status, in each pertinen t county, was researched and compared. Again using the 2000 U.S. Census data (U.S. Department of Commerce), Hillsborough County had 12.5% of the population, at all ages, categorized as below the poverty line. Lancaster County had only 9.7% of the population, at all ages, under the poverty line (U.S. Departme nt of Commerce 2008).
36 The total population quantities and the percentage of poverty in each county shows the more urbanized area seem to ha ve a higher social disequilibrium, when referring to overall social w ealth. In addition to povert y, the percentage of homeless individuals also affects the social margin ality of a community. According to the Homeless Coalition of Hillsborough Count y, there were 9,532 homeless men, women, and children in the Tampa, Florida area dur ing 2005. Lancaster County did not have specific data related to homelessness; howev er, the 2002 Census estimates revealed 9.2% of the Lancaster County population was e xpected to be below 100% poverty (U.S. Department of Commerce 2008). The differences between subject sites s uggest a propensity towards higher crime rates in the urban area, due to the higher rate of poverty, migration, and tourism in Hillsborough County. These factors that influence economic disparities between communities play an important role in unde rstanding the amount and types of crimes committed within each county. Methods of Analysis Data analysis began with the demographi c profiles of both victims and offenders, which consisted of evaluating ancestral back grounds, sex, and age from law enforcement documents. The social relationships between the involved individuals were also assessed using case information and documentation. Other specific case data were collected using a standardized protocol for a complete synopsis of the circumstances surrounding the homicide (Appendix A). The prot ocol is part of a larger re search project being conducted at the University of South Florida, and therefore includes more variables than are
37 analyzed in the present paper. Additionall y, spatial information was analyzed using the residential addresses of the i nvolved individuals and the addr esses of the sites in which relevant occurrences happened, such as the murd er site and the disposal site (Table 3.2). The demographic characteristics of th e entire population associated with homicides, for each study site, were developed into generalized categories. Ancestries of the individuals involved the following categ ories: European, African, Asian, Hispanic, American-Indian, and Others. Age is represen ted in years; howeve r, those victims who were younger than one year of age were repr esented as 0 for statistical purposes. Frequencies of ages were conducted by groupi ng ages together in 10 year increments. The age distributions for victims and offenders from each site are illustrated in Figures 3.1 to 3.4. Sex was restricted to only male and female categories. Homicide Variables and Definitions A social relationship cate gorized as Â“strangerÂ” is defined by Silverman and Kennedy (1987:282) as, Â“offenders who had no know n relationship with the victimÂ”. A Â“domesticÂ” relationship categorization was us ed if any of the following relationship variables were present between the victim a nd offender: marriage, divorce, separation, blood relative living in the same domicile, co-existence, or in a romantic or sexual relationship (Felson and Messner 1998, Miet he and Regoeczi 2004, Swatt and He 2006). Â“AcquaintanceÂ” relationships are categorized as those in which the victim and offender have met at least once, or have been friends for any amount of time (Wolfgang 1958:204).
38 Table 3.2 Â– Data Used for Spatial Analysis of Crim inal Homicides Demographics Age, Sex, and Ancestry Victim-Offender Relationship Parent Child Romantic Friend Acquaintance Co-Worker Neighbor Stranger Location VictimÂ’s Residence OffenderÂ’s Residence Murder Location Body Deposition Site Presence of Case Specifics De gree of Homicide Victim Precipitation Drugs and Alcohol Mechanism of Death Weapon Used Body Recovery Location Murder/Suicide
41 A Â“co-workerÂ” relationship is one in which the victim and offe nder have held jobs at the same company during overlapping time periods (Wolfgang 1958:204). During the analysis of these variables, the relationship variables were collapsed together into three smaller categories labeled: domestic, non-domestic, and stranger. (Table 3.3). Domestic ho micides include victim-offender relationships of spouse, significant other, parent, ch ild, or relative. Non-dome stic relationships involve neighbors, co-workers, friends, or acquain tances. Lastly, th e stranger category encompasses only with offenders who had no pr evious connection with the victim. These categories were made to simplify the results while including all possible social relationships that may be seen in criminal homicides. Furthermore, murder is legally divided in to degrees which represent the different types. The degrees of homi cide are ordered as first a nd second degree murder, followed by voluntary and involunta ry manslaughter. First-degree murder consists of an intentional killi ng with both premeditation and malice aforethought of an act that results in a personÂ’s death (Blinn 1950:729). Second-degree murder is a death from an assault with aforethought malice but no premeditation of the act itself (Blinn 1950:730). Voluntary or non-negligent manslaughter ar e considered acts of manslaughter, in which an act is committed in attempt to hurt, but not kill, another human being yet the victim dies in the process (Desch 1963:660). Lastly, negligent or involunt ary manslaughter is characte rized by Â“the killing of a person through gross neglig enceÂ” (Federal Bureau of Investigation 2004:152).
42 Table 3.3 Â– Victim-Offender Relationship Categories Domestic Marriage Divorce Separation Significant Other Parent Child Co-existence Blood Relative Non-Domestic Friend Acquaintance Co-Worker Neighbor Stranger No relationship exists
43 Some states distinguish betw een vehicular and non-vehicular negligent deaths; therefore, it should be noted that the current st udy only focuses on non-vehicular deaths. Other terms that are of importance to this analysis include: victim precipitation and mechanism of death. Â“Victim precipitationÂ” refers to instances in which the victimsÂ’ actions or words resulted in their demise by eliciting a deadly response from the killer (Felson and Messner 1998, Wolfgang 1967:24) Mechanism of death is defined by Adams et al. as the moment or event that causes the cessation of vita l functions and the occurrence of death (Spitz and Fisher 2006: 440). The mechanism of death categories were defined as: single gunshot wound, mu ltiple gunshot wounds, blunt force trauma, sharp force trauma, strangulation, and other. The variables listed in Table 3.2 are used in statistical analyses to provide anthropol ogical and social definition to the spatial distributions of homicide cas es. Statistical analyses of the data, conducted using SPSS 17.0 software, include chi-square tests fo r independence, cros s-tabulations, mean comparisons, PearsonÂ’s correlations, and freque ncy distributions to identify significant factors and relationships among sites. The research design is original in that it associates spatial analyses of individual homicides and the relationships between pers ons involved to identify similarities and differences in homicidal behaviors between communities. Using the locations of scenes and residences, along with the relationship stat us of the parties involved, the degree of the homicide, presence of homicide/suicide, mechanism of death, and circumstances surrounding the murder occurrence, both the so cial aspects of the social production of homicide are revealed.
44 The foundation of this research is based on the different social factors that are found in communities with varied population structures and the implications that social relationships play in the degr ee of murder instigated and the scene distributions. The framework of a comparative study is implemen ted for the research of a rural and urban region. Involved in the comparative study ar e the relationships of individuals and the distributions of sites in both geographical regions by using GIS and statistical analyses. GIS Methods The information used for the GIS analysis was identified as residential addresses and physical street addresses of relevant lo cations. Therefore, geocoding was employed through ArcGIS 9.2 to map the various locatio ns related to a homicide event and use them as spatial factors in the analysis. Ge ocoding is defined as, Â“The process of creating map features from addresses, place-na mes, or similar informationÂ” (Ormsby et al. 2004:429). Using the StreetMapUSA 2005 data and a commercial address locator supplied by ESRI 2006, the addresses were input and digitally represented on the state plane projections of Nebraska and Florida. The cases that involved locations outside of the two states were excluded in the spatial an alysis. The addresses were each indicated as a point of interest, and were connected using vectors to achieve a polygonal shape representing the spatial area i nvolved. Connections of scen es representing the spatial configurations define distinct situations: A solid blue rectangle connected to a solid red triangle represents the victim and offenderÂ’s resi dences, respectfully.
45 An orange dotted circle for a case represents a homicide in which the victim and offender reside d together and the murder occurred in the shared household, and the victimÂ’s body was not tr ansported to another location. A dotted yellow diamond represents a homicide event where the victim and offender resided together, but the murder, represented by a solid green star, occurred elsewhere. A blue dotted rectangle with a conne cting line to a solid red triangle represents a case in which the vic tim and offender lived in separate residences and the murder occurred at the victimsÂ’ address. In this situation, no movement of the vi ctim occurs after the homicide. A red dotted triangle with a connecti ng line to a solid blue rectangle represents a case in which the vic tim and offender lived in separate residences and the murder occurred at the offendersÂ’ address. In this situation, no movement of the vi ctim occurs after the homicide. A solid blue rectangle connected to a solid red triangle and a solid green star represents a case in which the vi ctim and offender resided at different locations, and the murder occurred in a location other than their homes. Lastly, when any of the previous situa tions are connected to a solid purple pentagon, it depicts the dispos al site for the murder. Figure 3.5 The solid colored shapes represent sepa rate residences and locations, and the dotted shapes represent locations with mu ltiple importance. The line representation depicts the geometry of the cases that involve multiple scenes.
47 The number of dots that connects each case depi cts the complexity of the geometry. The analyzed cases were chosen by using frequency data to determine the situations that were most unlike each other between the two regi ons. Distances, in miles, between the relevant sites were obtained us ing statistical geometry within the ArcGIS 9.2 software. Associations between the physical distances and the social relationships are then conducted to find spatial trends among each region. The points of interest and their establishe d distances were subjected to geographic distance analyses to uncover commonalities and disparities of scene locations. These spatial distributions were then cross-referenced to the relationship status for the purpose of identifying universal social variab les pertinent to spatial movement. Comparing the victim-offender relationshi p of homicides in an urban and rural setting and relating them to the spatial distribu tions of the residences and scenes revealed the social differences in the production of viol ence. Statistical analyses and the accuracy obtained using GIS technology allowed the resear cher to confidently draw conclusions on spatial patterns associated with certain rela tionship categories within both an urban and rural community. These conclusions can be used as an investigative tool by law enforcement agencies across the United States, for it uses specific case information, spatial patterns, and social relationships to determine spatial distri butions of criminal homicides.
48 Chapter 4 Results In this section, the statistical and ge ographical results location were calculated separately and compiled into comparative ta bles and figures. The results are then discussed and compared within the sections for each method of calculation. Comparison of the results ensures similarities and differences between the two regions are properly discovered and addressed. Demographic Characteristics of Victims and Offenders The specific demographic characteristic s of the offenders in Hillsborough County and Lancaster County are seen in Tables 4.1 through 4.3 and Figures 4.1 through 4.3. Within the total number of cases in Hillsborough County (n=420) and Lancaster County (n=48), the percent of offe nders who were male are very similar at 91.4% and 91.3%, respectively. The number of female offenders also had a similarl y proportionate rate between the two locations, since Hillsbor ough County (n=36) had a rate of 8.6% and Lancaster County (n=4) had 8.7%, only one-tenth of a percent higher. The age ranges of offenders between the two populations are different in distribution. In the Nebraska area, over one half of the offender ages are in the 20-29 year old category (52.2%), followed by the 30-39 age category, which comprises 17.4% of the offenders.
49 Table 4.1 Â– Frequencies of Offender Age Ranges in Hillsborough and Lancaster Counties Hillsborough County Lancaster County 0-9 0.2% (1/411) 0.0% (0/46) 10-19 12.9% (53/411) 13.0% (6/46) 20-29 37.7% (155/411) 52.2% (24/46) 30-39 23.8% (98/411) 17.1% (8/46) 40-49 14.4% (59/411) 15.2% (7/46) 50-59 6.3% (26/411) 0.0% (0/46) 60-69 2.9% (12/411) 0.0% (0/46) 70-79 0.7% (3/411) 2.2% (1/46) 80-89 1.0% (4/411) 0.0% (0/46) 90-99 0.0% (0/411) 0.0% (0/46)
50 Table 4.2 Â– Frequencies of Offender Sex in Hillsborough and Lancaster Counties Hillsborough County Lancaster County Males 91.2% (383/419) 91.3% (42/46) Females 8.6% (36/419) 8.7% (4/46)
51 Table 4.3 Â– Frequencies of Offender Ancestries in Hillsborough and Lancaster Counties Hillsborough County Lancaster County European 41.0% (171/417) 56.5% (26/46) African 41.2% (172/832) 15.2% (7/46) Asian 0.0% (0/832) 8.7% (4/46) Hispanic 17.5% (73/832) 13.0% (6/46) AmericanIndian 0.2% (1/832) 4.3% (2/46) Other 0.0% (0/832) 2.2% (1/46)
52 The Lancaster County data revealed no offe nders between the ages of 50 and 69 during the ten year period. The Florida population of offenders is mo re evenly distributed throughout all age ranges, but is still heavily focused on the 20-29 and 30-39 age categories. The highest frequency of offenders was found to be w ithin the 20-29 age range (37.7%), followed by the 30-39 range at 23.8%. Hillsborough County offender age ranges of 60-69 (n=12), 7079 (n=3), and 80-89 (n=4) contained more th an one individual, unlike the Lancaster County data. The differences in offender age ranges be tween the two locales suggest different types of crimes occurring within each community. The Lancaster County and Hillsborough County data conform to the standard ized model of offenders killing in their midto late-twenties, as seen in the literat ure. However, elderly groups from the Florida population indicate more murder-suicides or domestic murders by offenders of these ages. Ethnic groupings of each county re vealed disproportionate offender demographics as well. Hillsborough County showed surprisingly less ethnic diversity among offenders than the Lancaster County popul ation. This result was unexpected due to the population density and fluid nature of the Tampa area. Asians and people not identified as one of the major census categor ies were not seen in the offending population of Florida, whereas the Nebras ka offenders had at least one individual within each census group. The Hillsborough County data showed a greater and almost equal ethnic distribution of offenders between the Eu ropean (41.0%) and African (41.2%) groups,
53 with Hispanics following at 17.5 %. The offe nders from Nebraska showed a larger gap between ethnicities, with the majority asso ciated with the Europ ean category (56.5%). The African (15.2%) and Asian (13.0%) populations followed with a fairly equal distribution. The non-diverse natu re of the offenders in the Tampa area reveals an aspect that needs to be more closely inspected. Th e urbanization of the region would usually be linked with a more diverse et hnic population, yet if certa in ethnic groups have lower offender rates, cultural reasons determin ing this disparity should be addressed. Demographic data from the victim of homicide in each county were also collected. (Tables 4.4 4.6 and Figures 4.4 4.6 ). Closed homicide cases in which the victim was identified prevailed within bot h Hillsborough County (n =419) and Lancaster County (n=47), with each only lacking one i ndividualsÂ’ identity. Considering the number of closed homicides to the population of each society, Florida had a calculated solved homicide rate of 4.2% over the 10 year peri od, where as Nebraska had a considerably lower solved homicide rate of 1.9% during th e same period. Therefore, proportionate to population growths over the 10 year study period, the Florida data was calculated to have a 2.3% rate increase of homicides co mpared to the Nebraska data. The percentage of male to female vi ctims between Nebraska and Florida has similar ratios. The Lancaster County vi ctims were 70.2% male, whereas Hillsborough County had 68.3% of their victims identified as male. Female victims had a comparable rate of frequency between the locations at 29.8% in the Nebraska location and 31.7% in the Florida region. However, it should be not ed that the number of females killed in the urban Florida population were higher by 1.9%.
54 Table 4.4 Â– Frequencies of Victim Age Ranges in Hillsborough and Lancaster Counties Hillsborough County Lancaster County 0-9 7.7% (32/417) 8.5% (4/47) 10-19 10.3% (43/417) 21.3% (10/47) 20-29 25.4% (106/417) 21.3% (10/47) 30-39 25.7% (107/417) 23.4% (11/47) 40-49 15.1% (63/417) 14.9% (7/47) 50-59 9.6% (40/417) 4.3% (2/47) 60-69 3.1% (13/417) 2.1% (1/47) 70-79 2.2% (9/417) 4.3% (2/47) 80-89 0.7% (3/417) 0.0% (0/47) 90-99 0.2% (1/417) 0.0% (0/47)
55 Table 4.5 Â– Frequencies of Victim Sex in Hillsborough and Lancaster Counties Hillsborough County Lancaster County Males 68.3% (286/419) 70.2% (33/47) Females 31.7% (133/419) 29.8% (14/47)
56 Table 4.6 Â– Frequencies of Victim Ancestries in Hillsborough and Lancaster Counties Hillsborough County Lancaster County European 48.2% (200/415) 61.7% (29/47) African 34.7% (144/415) 12.8% (6/47) Asian 0.5% (2/415) 8.5% (4/47) Hispanic 15.4% (64/415) 10.6% (5/47) AmericanIndian 0.2% (1/415) 6.4% (3/47) Other 1.0% (4/415) 0.0% (0/47)
57 Age range frequencies for the victims fr om each community divulged interesting disparities between the popul ations. The Tampa area had the majority of victims concentrated around the 20-29 year old (25.4% ) and 30-39 year old (25.7%) age ranges, whereas the Lincoln area had the majority of victims within the 10-19 year old (21.3%), the 20-29 year old (21.3%). and 30-39 y ear old (23.4%) age categories. Lastly, the ethnicities of the victims also showed a difference among the populations. The Lancaster County data exposes the majority of the individuals were recognized as descending from a European line at 61.7%, followed by Africans at 12.8% and Asians at 10.6 %. The only ethnic group without a victim from the Nebraska population is the miscellaneous category. Vic tims from the Tampa area appear to emerge from backgrounds that are more diverse; howev er, the majority of the population is still centered on the Europeans (48.2%) and African s (34.7%). Similar to the Hillsborough County offenders, the next highest ethnic cate gory for victims is Hispanic which accounts for 15.4% of the remaining population. All cen sus groups contained at least one victim from the Florida data. The differences in the ethnicities of each location strongly suggest the composition of the demographic populations of the areas. Victims and offenders with similar ethnic patterns and frequencies allo w the demographic identities of individuals involved in homicides, at each s ite, to be revealed. In Lancaster County, the majority of victims are most likely to be males of European descent, ranging in age from 10 to 39 years of age. Offenders from the region are expected to also be European males within 20 to 49 years of age.
58 Hillsborough County offenders are expected to most likely be either European or African males, with a slightly higher rate of African ancestry, and have an age within 10 to 49 years old. Victims within Hillsborough C ounty are also expected to be a male in the 20 to 39 year age range and be of European descent. Therefore, the male offender precedent was met, as well as the age at which victims and offenders of homicides become involved. Although these expectations are not extrem ely different from each other, the age ranges of victims within the Li ncoln area and the ethnicity of offenders within the Tampa area are distinctive of each community. Further statistical analyses are utilized to address questions raised by the frequency results, as well as to analyze th e amount of influence variables have on one another with in the context of a homicide. Case Characteristic Frequencies The characteristic variables that surround and identify each case were subjected to frequency testing to uncover any underlying th emes or differences seen across the Florida and Nebraska communities. Statistical anal yses that yielded di fferential results are reported and used as a basis for more extens ive research. These analyses allow further questions to be addressed with more co mplex statistics in future studies. The frequency distributions for the de gree of murder committed, between the two regions, are different. (Table 4.7). According to police and state attorney conviction and charge records, the Tampa area had 53.9% rate of first degree homicides and a 33.2% rate of second degree homicides.
59 Table 4.7 Â– Degree of Homicide Distributions between Two Populations Hillsborough County Lancaster County First Degree 53.9% (226/419) 60.4% (29/48) Second Degree 33.2% (139/419) 20.8% (10/48) Voluntary Manslaughter 8.6% (26/419) 2.1% (1/48) Involuntary Manslaughter 4.3% (18/419) 16.7% (8/48)
60 The degrees of homicide were composed of both convictions and charges due to the awaiting trials of numerous defendants, thereby preventing convictions to be the sole factor for analysis. The Linc oln, Nebraska area showed a sl ightly different distribution then the Tampa, Florida area, because first degree homicides appeared 60.4% of the time, while second degree and involuntary manslaughter homicides had a similar distribution at 20.8% and 16.7%, respectfully. The Hillsbor ough County data revealed a more even distribution of homicides between the first (53.9%) and second degree categories (33.2%). However, the involuntary mans laughter (4.3%) and voluntary manslaughter (8.6%) categorizations were drastically lower. These distributions show the majority of crimes within each community are premeditated murders. However, the Lincoln area has a higher percentage of involuntary murders, which may indicate a certain dem ographic and type crime being committed in this area, such as child abuse. In foll owing sections, correlati ons to victim-offender relationships will be used to attempt to uncover the reason for the discrepancy. The distribution of victim-offender rela tionships were also scrutinized using frequency charts. The relati onships were compressed into three different categories: domestic, non-domestic, and stranger. Figure 4.7 illustrates the similarities seen in the distributions of victim-offender re lationships that ended in murder. Both counties show the non-domestic rela tionship as the foremost category in criminal homicides, followed by the domestic affiliation. The most interesting aspect of this frequency is the small role stranger homic ides seem to play within each society. The lack of stranger homicides was not much of a surprise for the rural area, due to the smaller, more intimate setting of the community.
62 Yet, the urban setting of Hillsborough Count y was expected to have a much stronger representation of stranger homicides due to the populat ion density and amount of population fluidity within the community. Ho wever, the Tampa data revealed less than one hundred cases (n=74) for the area (15.16%). Victim precipitation is also a variable that showed different patterns during frequency analysis. In Table 4.8, the Lancas ter County population showed a fairly equal distribution of known homicide cases (n=40) that did or di d not have victim precipitation present. The results demonstr ate 52.5% of the victim precip itation cases did not have the variable present prior to th e murder event, while 47.5% of the cases did have victim precipitation occur. However, the Hillsbor ough County cases (n=373) showed a larger amount of distance between the two circumst ances frequencies, because 60.9% of the cases had no victim precipitation and only 39.1% of the cases were categorized as the having the variable present. The amount of cases, from each area, with victim precipitation present may indicate the relationship status of the involved individuals. The smaller amount of cases with victim precipitatio n in Hillsborough County may indicate non-domestic relationships that began as an altercation. Cross-tabulations of the variable and the relationship, as well as the degree of murd er, are later performe d to unveil the forces behind the divergences. The frequency results for the victimÂ’s mechanism of death showed an interesting social variable that differed between commun ities. (Table 4.9). By far, gunshot wounds were the most common mechanism of homi cide in the Florida population (52.3%), followed by sharp force trauma (21.3%) and blunt force trauma (15.7%).
63 Table 4.8 Â– Presence of Victim Precip itation Frequencies in Hillsborough and Lancaster Counties Hillsborough County Lancaster County Present 39.1% (146/373) 47.5% (19/40) Not Present 60.9% (227/373) 52.5% (21/40) Table 4.9 Â– Mechanism of Death Distributions in Homicide Cases from Hillsborough and Lancaster Counties Hillsborough County Lancaster County Single GSW 31.7% (131/413) 29.8% (14/47) Multiple GSW 20.6% (85/413) 12.8% (6/47) Blunt Force Trauma 15.7% (65/413) 12.8% (6/47) Sharp Force Trauma 21.3% (88/413) 29.8% (14/47) Strangulation 6.3% (26/413) 14.8% (7/47) Other 4.4% (18/413) 0.0% (0/47) *GSW Â– Gunshot Wound
64 However, the Nebraska population revealed an equal reliance (29.8% each) on single gunshot wounds and sharp force trauma as means to commit murder. These mechanisms are then followed by strangulati on, with a frequency of 14.9%. Single gunshot wounds, sharp force trauma, and strangulation are all associated with intimate killing situations such as mu rder/suicide, women offenders, and crimes of passion. These mechanisms are at the forefr ont of the Nebraska cases, which allow an assumption to be made that domestic murder s are a more common occurrence within the community. The Florida cases do not support this assumption for the community because of the overrepresented reliance on only single and multiple gunshot wounds. The presence of drugs or alcohol during a homicide incident suggested differences in the role of abusive substances within each community. Table 4.10. Lancaster County homicides reveal a higher percentage for the pr esence of abusive substances (62.5%), during a homicide event. Conversely, Hillsborough County had similar percentages between the presen ce (47.7%) and absence (52.3%) of abusive substances, with the absence being more preval ent. These results indicate the Lancaster County community is committing homicides unde r the influence of a substance, which may result in a crime of passion rather than a planned murder. The frequency outputs in this section show interesting factors between the criminal homicide cases in Hillsboro ugh County, Florida and Lancaster County, Nebraska. Using the variable frequencies uncov ered in this segment, further statistical analyses are conducted to explore and explai n the differences in murder circumstances for each population.
65 Table 4.10 Â– Presence of Drugs or Alcohol for Homicide Cases in Hillsborough and Lancaster Counties Hillsborough County Lancaster County Present 47.7% (115/241) 62.5% (20/32) Not Present 52.3% (126/241) 37.5% (12/32)
66 Cross-tabulations and Chi-Square Tests Due to the categorical nature of the majority of variables, cross-tabulations and chi-square tests of independence were th e main methods for uncovering associations between data. Using the categories illustrate d by frequency charts, case characteristics were used to learn more information about homicide patterns within each community. Results of cross-tabulations for the de gree of homicide in relation to victimoffender association revealed the offender was known more frequently with all degree levels within both populations. Table 4.11, Table 4.12, Figure 4.8 and Figure 4.9. All four degree categories, in each population, were lead by the Â“non-domesticÂ” social relationship. Also, the cases labeled Â“dom esticÂ” were found to have over half of the murders classified as first degree, or premeditated, in both communities. Demographically, both societies showed th at males killed males in the majority of homicide cases, as well as the fact that males murder females more often than females kill each other or males. In addition, when related to domestic encounters, the two communities showed different vict im ethnicity distributions. As seen in Table 4.13, the Nebraska data re veals the majority of domestic victims were of European ancestry (80.0%), with no other ancestries involved except Asians (20.0%). The Florida populat ion had most domestic victims fall within the European (54.4%) and African (33.6%) ancestries. Hi spanics (15.0%) were also more highly represented as victims in the Florida population. The domestic homicide offenderÂ’s ethnicity was also exposed using crosstabulations.
67 Table 4.11 Â– Cross-tabulation of Deg ree of Homicide and Victim-Offender Relationship in Lancaster County Domestic Non-domestic Stranger Total First Degree 70.0% (7) 55.9% (19) 75.0% (3) (29) Second Degree 10.0% (1) 23.5% (8) 25.0% (1) (10) Voluntary Manslaughter 0.0% (0) 2.9% (1) 0.0% (0) (1) Involuntary Manslaughter 20.0% (2) 17.6% (6) 0.0% (0) (8) Total (10) (34) (4) (48)
68 Table 4.12 Â– Cross-tabulation of Deg ree of Homicide and Victim-Offender Relationship in Hillsborough County Domestic Non-domestic Stranger Total First Degree 60.0% (75) 51.8% (114) 50.0% (37) (226) Second Degree 25.6% (32) 36.4% (80) 36.5% (27) (139) Voluntary Manslaughter 9.6% (12) 8.2% (18) 8.1% (6) (36) Involuntary Manslaughter 4.8% (6) 3.6% (8) 5.4% (4) (18) Total (125) (220) (74) (419)
69 Table 4.13 Â– Victim Ancestry Compa red to Domestic Homicides in Hillsborough and Lancaster Counties Hillsborough County Lancaster County European 54.4% (68/122) 80.0% (8/10) African 33.6% (42/122) 0.0% (0/10) Asian 0.0% (0/122) 20.0% (2/10) Hispanic 15.0% (12/122) 0.0% (0/10) American-Indian 0.0% (0/1 22) 0.0% (0/10) Other 0.0% (0/122) 0.0% (0/10)
70 In Table 4.14, the Hillsborough County data n=413, show a large distribution of European (50.4%) and African (38.2%) ancestri es, in terms of domestic offenders. However, the Lancaster County data (n=43) exhibits more than three-fourths of the domestic offender population (80.0%) as ha ving a European ancestral background. Cross-tabulations of the degree of offe nse with both Hillsborough and Lancaster County offender age ranges show different resu lts, comparatively. In Lancaster County a significant proportion of firs t degree homicides (55.6%) and second degree homicides (50.0%) were committed in the 20-29 ag e range (Figure 4.10 and Figure 4.11). The Hillsborough County cross-tabulation re vealed a similar proportion of the 2029 age range to degree of homicide. Howe ver, the 10-19 year range, 30-39 year range, and 40-49 year age range also showed nu merous amounts of first and second degree murders being committed. These differences reflect the demographic disparities between the two communities and the impacts they have on the rates of victims and offenders in domestic homicides. Chi-square tests for independence, when paired with cross-tabulations, are effective statistical methods in crime anal ysis. A chi-square test of independence determines if Â“the observed frequency of observationsÂ…is significan tly different from those proposed by a null hypothesisÂ” (Madriga l 1998:196). Implementing this technique, significant results are found between different va riables within each social context. For example, in each community, females were found to be the more likely victim in a domestic murder, but males were more like ly victims in non-domestic murders.
71 Table 4.14 Â– Offender Ancestry Compa red to Domestic Homicides in Hillsborough and Lancaster Counties Hillsborough County Lancaster County European 50.4% (62/123) 80.0% (8/10) African 38.2% (47/123) 10.0% (1/10) Asian 0.0% (0/123) 10.0% (1/10) Hispanic 11.4% (14/123) 0.0% (0/10) American-Indian 0.0% (0/123) 0.0% (0/10) Other 0.0% (0/123) 0.0% (0/10)
73 Therefore, chi-square analyses for each lo cation exposed a significant difference in victim sex based on a domestic or non-domestic murder (Florida: =81.095, df =1, p < 0.001, Nebraska: =3.846, df =1, p =0.050). Also, both locales had similar intra-ethnic mu rder rates since the majority of cases in each city had an individual from one ethni city kill an individual from the same ethnic category. Chi-square tests of intra-ethni c murders demonstrate these results as =725.689, df =15, p < 0.001 for the Tampa area and =64.002, df =20, p < 0.001 as results for the Lincoln area. Each output indicates a significant difference between the victim and offender ethnicities, suggesting that intra-raci al homicide exists most frequently in each population. Weapon choice between an offenderÂ’s sex was significantly different in the Hillsborough County sample ( =21.119, df =7, p =0.004). These chi-square results characterize Florida female offenders as sh arp object wielders for weapon, while male Floridian offenders most often used guns. The Nebraska sample was unable to be analyzed for this relationship due to the in sufficient sample size of female offenders. The prevalence of murder/suicides and th e relationships under which they occur were scrutinized using chi-square tests and crosstabluations as well. However, the Lancaster County data containe d only two murder/suicide cas es, and therefore, could not be analyzed. Nonetheless, the Hillsborough C ounty data divulged significant results in the form of murder/suicides occurring between particular social rela tionship categories. Over the 10 year span, 41 murder/suicides oc curred in the area in which 85.4% of the cases were committed by an offender in the Â“domesticÂ” relationship category. The chisquare test for these variables was calculated as: =66.518, df =2, p < 0.001.
74 These results were interpreted to indicat e a significant difference between close relationships committing murder/suicides and those who are not considered Â“domesticÂ” and do not commit the act. Lastly, Tables 4.15 and 4.16 illustrate the environment in which the victimÂ’s body was recovered, in association to the relations hip category to which the offender belonged. In Figures 4.12 and 4.13, the Hillsborough County cases show the majority of bodies found in public places were homicides committed by known individuals (49.3%), while bodies found in private residences were mostly committed by the domestic (42.4%) or non-domestic (51.6%) categories. Few co rpses were recovered in the other environments, including only two cases at abandoned structures, which were both committed by Â“non-domesticÂ” offenders. The Lancaster County data discloses a di fferent distribution of locations, since only the public area and private residences had significant amounts of recoveries. Homicides committed by a person in a domestic relationship had a single body recovery site, a private residence (n=10). Non-domestic relationships that ende d in homicide were found in either a private reside nce (n=12) or public space (n =12). Interestingly, 66.7% of stranger homicides had a body reco vered within a residence. This suggests that strangers who commit murder in the Nebraska community target or attack victims within their own homes, instead of in public locations. Chi-square tests used to analyze these data established a significant difference between body recovery site within the Tampa area, = 86.095, df =8, p < 0.001. However, the Lincoln area did not prove to have significantly diffe rent body recovery sites, possibly due to the smaller sample sizes within each location.
75 Table 4.15 Â– Victim-Offender Relationship Compared to Recovery Location in Hillsborough County Domestic Non-domestic Stranger Total Public Space 11.5% (15) 49.3% (64) 39.2% (51) (130) Private Residence 42.4% (92) 51.6% (112) 6.0% (13) (217) Along Roadside 16.7% (4) 66.6% (16) 16.7% (4) (24) Wooded Area/ Field 26.1% (6) 69.6% (16) 4.3% (1) (23) Abandoned Structure 0.0% (5) 100.0% (2) 0.0% (0) (2) Railroad Tracks 0.0% (0) 0.0% (0) 0.0% (0) (0) Other 0.0% (0) 0.0% (0) 0.0% (0) (0) Total (117) (210) (69) (396)
76 Table 4.16 Â– Victim-Offender Relationship Compared to Recovery Location in Lancaster County Domestic Non-domestic Stranger Total Public Space 0.0% (0) 92.3% (12) 7.7% (1) (13) Private Residence 40.0% (10) 52.0% (13) 8.0% (2) (25) Along Roadside 0.0% (0) 100.0% (3) 0.0% (0) (3) Wooded Area/ Field 0.0% (0) 100% (2) 0.0% (0) (2) Abandoned Structure 0.0% (0) 0.0% (0) 0.0% (0) (0) Railroad Tracks 0.0% (0) 0.0% (0) 0.0% (0) (0) Other 0.0% (0) 0.0% (0) 0.0% (0) (0) Total (10) (30) (3) (43)
77 Each cross-tabulation and chi-square re sult reveals a fact pertinent to the homicide cases for each location. The resu lts are important for expanding research questions by using sophisticated statistical test s, as well as providing predictive factors. Interestingly, victim precipitation was not seen as an important variable in the analyses for these communities, unlike many previous studies. Comparison of Means Comparing the means of particular case characteristics to the offenderÂ’s age, and processing them with an analysis of varian ce (ANOVA), allows the researcher to draw conclusions about the factors involved with the production of criminal homicides. The tests in this section pertain to the offenderÂ’s age, which was compared to the degree of murder, the occurrence of murder/suicide, and the category of relationship for each location. The results for the degree of murd er comparison proved that age did not significantly influence the type of murder committed in either community. The ANOVA calculated a p -value of 0.456 for the Nebraska data and p =0.545 for the Florida data. The murder/suicide factor, however, is associated with the offendersÂ’ age at the Florida site. The Nebraska site was unable to be tested on account of the small mu rder/suicide sample size (n=2). The Hillsborough County sample (n=46) of murder/suicide occurrences established a mean age of 45.38 years old for offenders who commit this particular crime (n=39). The ANOVA test supports th e significance through the results: F =41.637, df =1, 161, and p < 0.001. The ethnicity of the offender coul d not be compared to prevalence of
78 murder/suicides in Hillsborough County because of the small sample sizes for some of the ethnicity categories. The last test compared the age of the o ffender to the three collapsed categories of social relationships. The Nebraska data di d not yield a considerab le difference of age between social relationships in criminal hom icides; however, the Florida data provided significant results. Mean ages of the offende r within each category are seen in Table 4.17. The ANOVA produced for this dataset revealed statistics of: F =15.964, df =6, 234, and p < 0.001. Therefore, the age of an offender is an important variable to consider when categorizing or predicting the type of rela tionship the victim had with their killer. Pearson Correlations Results for the Tampa area data confir m a correlation between the offender and victim ages with a p < 0.001 and a Pearson correlation value of r =0.393. The median age of victims in this dataset was 33.00 years ol d, while the median age of offenders was 29.00 years old. This shows th at the offenders in the ur ban area were usually younger than their victims. However, it must be not ed that a small group of offenders that were older are connected with a small sample of extremely young victims. This grouping represents the adults who committed child homicides within the 10 year period. The Lancaster County data for victim and offender ages also proved to have a significant correlation. A median age of 29.00 years old was calculated for victim age, whereas 25.50 was the median age found for offende rs from this region. The results of a computed Pearson correlation was r =0.370, with a p =0.011. Figure 4.14 illustrates the correlation.
79 Table 4.17 Â– Mean Ages of Offenders by Social Relationship Category to Victims in Hillsborough County Mean Age Standard Deviation Domestic 37.38 (n=122) 14.360 Non-Domestic 31.91 (n=218) 13.097 Stranger 26.56 (n=71) 10.553 Total 32.61 (n=411) 13.565
80 As within the Hillsborough correlation, Figure 4.15, the age of offenders are slightly younger than males, as well as a few cases repres ented in the graph that indicate an older offender killed a child. However, it should be noted that there is a number of possible outliers in the upper left quadrant of the correlation. Both Figures 4.14 and 4.15 use linear lines to illustrate the patter n of ages within each community. GIS Results In addition to the statistical analysis, ArcGIS 3.2 software was employed to plot the significant murder cases in each area and compare the spatial geometry and distances. The mapped cases are then subjected to so cial relationship corre lations to reveal underlying social structur es that indicate a particular sp atial distribution of scenes in criminal homicides per population. The states of Florida and Nebraska, th eir major roadways, and major cities are seen in Figure 4.16. To plot the locations for each case, geocoding is employed and a symbol is placed on the maps according to its physical address. During the analysis of the homicide scenes, two interesting spatial patterns emerged from the configuration frequencies in both counties. Table 4.18. After reviewing and comparing the releva nt frequencies, it was found that the most significant cases between the areas of study were those that cont ained disposal sites, and those in which the victim and offender resided together, but the murder occurred outside the home. Murders with body disposal sites, other th an the murder site itself, were seen in both Hillsbor ough County (n=20) and Lancaste r County (n=6), (Figure 4.17 and 4.18).
81 Table 4.18 Â– Frequency of Geometric Situ ations in Hillsborough and Lancaster Counties Symbol Hillsborough Lancaster Victim Alone Solid Blue Recta ngle 23.2% (198/853) 28.7% (27/94) Offender Alone Solid Red Triangle 25.7% (219/853) 25.5% (24/94) Murder Alone Solid Green Star 20.8% (177/853) 19.1% (18/94) Disposal Alone Solid Purple Pent agon 2.3% (20/853) 6.4% (6/94) Victim/Murder Dotted Blue Recta ngle 8.9% (76/853) 11.7% (11/94) Offender/Murder Dotted Red Triangl e 5.0% (43/853) 7.4% (7/94) Victim/Offender Dotted Yellow Hexagon 0.5% (4/853) 0% (0/94) All Dotted Orange Circle 12.9% (110/853) 7.4% (7/94)
83 However, the spatial configuration of co-habit ations with an outside murder location was only seen, although rarely, in Hillsborough County (n=3). Figure 4.18. Both of these unusual spatial patterns are then associated with social relati onship categories to unveil the occurring victim-offe nder relationships. The disposal site cases are subjected to the collapsed relationship categories: domestic, non-domestic, and stranger. Ho wever, the co-habitation cases are all considered to be in the Â“domesticÂ” category, and therefore are correl ated with the more specific components of the collapsed category. The intent of the association is to determine the type of offenders committing these specific crimes. This relationship analysis can help law enforcement personnel focus on the homicide suspects related to the victims, in accordance with the specific situations and the i ndicated offender. After mapping the cases, vector lines are us ed to connect the scenes in each case to determine the spatial geometry. Distances in miles, between each scene are computed using the geometric calculation function in ArcGIS 3.2. The mean distance to each scene, within the study areas, is then de termined and compared. (Table 4.19). The range of distances in the Hillsbor ough County disposal cases were 0.05 miles to 103.17 miles. Lancaster County disposal site distances ranged from 0.4 miles to 37.89 miles. The co-habitation cases in Hillsbo rough County had a distance range of 0.95 miles to 12.69 miles. The average distances for each county expose that only the murder-disposal site distance are similar. All other distances show the Hillsborough County scenes are much farther in distance than the Lancaster C ounty scenes. This discrepancy between populations may be due to the mobility of each area and the population density.
85 Table 4.19 Â– Average Distance in Miles from Specific Locations in Hillsborough and Lancaster County Disposal Cases Hillsborough Lancaster Victim-Offender 19.51 miles (n=11) 5.16 miles (n=4) Offender-Murder 18.21 miles (n=4) 1.58 miles (n=1) Murder-Victim 11.81 miles (n=6) 0.9 miles (n=1) Murder-Disposal 12.49 miles (n=14) 13.37 miles (n=3) Offender-Disposal 10.62 miles (n=7) 6.07 miles (n=3) Victim-Disposal 12.22 miles (n=15) 3.96 miles (n=5)
86 In Tables 4.20 and 4.21, the frequency dist ributions of the lo cation distances are grouped by five mile increments. These increments are discussed to simplify the distributions of the disposal and co -habitation cases in each area of study. Associations of the disposal and co-habita tion cases, for each county, to the victimoffender relationship are then conducted. Freq uencies of the relationships for each type of case are seen in Tables 4.22, 4.23 and Figure 4.19. The disposal cas es are associated with the three collapsed relationship categor ies, whereas the co-habitation cases are compared to the relationships that comprise the Â“domesticÂ” category. The cases with disposal sites had sim ilar distributions between counties. Hillsborough County and Lancaster County had an equal amount of domestic and nondomestic relationships that fielded disposal sites away from the murder. Only one disposal case with a Â“strange rÂ” relationship between the vi ctim and offender was seen in each area of study. The Lancaster County data set had one case in which the victimoffender relationship was unknown, and theref ore set in a separate category. The implications of the relationship resu lts suggest that offenders who know the victim, whether intimately or casually, are more prone to move the body after the murder event, regardless of an urban or rural locati on. This is consistent with both Van Patten and DelhauerÂ’s (2007) and Hakkanen and co lleaguesÂ’ (2007) research, which also concluded that disposal site s were sought out more often when the offender knew the victim. Lancaster County did not reveal any cases with the co-habit ation configuration, although the data from Hillsborough County had three. The victim-offender relationship of the co-habitation cases sugge sts spouses and significant othe rs kill their spouses after intense planning or with the intention of killing themselves and fail.
87 Table 4.20 Â– Frequencies of Distances in Miles from Specific Locations in Hillsborough County Disposal Cases Within 5 Miles 5-10 Miles More than 10 Miles Victim-Offender 36.3% (4/11) 36.3% (4/11) 27.4% (3/11) Offender-Murder 50.0% (2/4) 0.0% (0/4) 50.0% (2/4) Murder-Victim 33.3% (2/6) 16.7% (1/6) 50.0% (3/6) Murder-Disposal 64.3% (9/14) 0.0% (0/14) 35.7% (5/14) Offender-Disposal 57.1% (4/7) 14.3% (1/7) 28.6% (2/7) Victim-Disposal 26.7% (4/15) 33.3% (5/15) 40.0% (6/15) Table 4.21 Â– Frequencies of Distance in Mile s from Specific Locations in Lancaster County Disposal Cases Within 5 Miles 5-10 Miles More than 10 Miles Victim-Offender 40.0% (2/5) 60.0% (3/5) 0.0% (0/5) Offender-Murder 100.0% (1/1) 0.0% (0/1) 0.0% (0/1) Murder-Victim 100.0% (1/1) 0.0% (0/1) 0.0% (0/1) Murder-Disposal 66.6% (2/3) 0.0% (0/3) 33.3% (1/3) Offender-Disposal 33.3% (1/3) 66.6% (2/3) 0.0% (0/3) Victim-Disposal 60.0% (3/5) 20.0% (1/5) 20.0% (1/5) Table 4.22 Â– Victim-Offender Re lationship Frequencies for Disposal Cases in Hillsborough and Lancaster Counties Hillsborough Lancaster Domestic 45.4% (5/11) 33.3% (2/6) Non-domestic 45.4% (5/11) 33.3% (2/6) Stranger 9.1% (1/11) 16.7% (1/6) Unknown 0.0% (0/11) 16.7% (1/6)
88 Table 4.23 Â– Cross-tabulation of Victim-Offender Relationship with Degree of Murder in Co-habitation Cases from Hillsborough County Spouse Boyfriend/Girlfriend First Degree 0.0% (0/1) 100.0% (2/2) Second Degree 100.0% (1/1) 0.0% (0/2) Voluntary Manslaughter 0.0% (0/1) 0.0% (0/2) Involuntary Manslaughter 0.0% (0/1) 0.0% (0/2)
89 When cross-tabulated with degree of murder the individuals who killed their significant others were both charged with first degr ee murder, while the spousal offender was charged with second degree murder. (Table 4.24). The use of GIS, distance comparisons, a nd social relationship associations in criminal analyses allow law enforcement personnel to gain a better understanding of the crimes and the movement seen within them. Comparisons between urban and rural communities are also an important aspect for law enforcement agencies when analyzing the crimes in their locations Anthropology is well suited for this because it employs a holistic perspective that can be used to recognize the diffe rences between larger, more mobile communities, such as Hillsborough Count y, and the smaller, less fluid populations like Lancaster County.
90 Table 4.24 Â– Victim-Offender Relationship Frequencies for Co-habitation Cases in Hillsborough County Spouse 66.7% (2/3) Parent 0.0% (0/3) Child 0.0% (0/3) Boyfriend/Girlfriend 33.3% (1/3)
91 Chapter 5 Discussion The analyses of this study have divulged interesting results in terms of urban and rural homicide activities and the people w ho are involved in them. Hillsborough and Lancaster Counties have similar characteristics in some aspects, and diverge in others. The implications of these similarities and di fferences are reviewed and discussed in terms of urban and rural community stru ctures and social relationships. Both populations showed a number of si milar results, includ ing the relevant frequencies of victim-offender relationships, the frequencies of the types of charges and convictions, the sex of the victim in reference to relationship of the offender, as well as age differences and ethnic similarities be tween victims and offenders. First degree murders were seen as the most common type of homicide and intr a-ethnic murders were most frequent in both locations. Van Pa tten and Delhauer (2007) and Lauritsen and Schaum (2004) both found connections to inter-ethnic murder and therefore these findings were expected based on the literature. However, the lack of inter-ethnic murders in the urban area was found to be contradict ory to the expected high frequency. The population density and composition of the ur ban community were the reasons supporting the expectation. Also, females were killed more often th an males in domestic homicides, while males were more frequently killed in non-domes tic or stranger homicides. Therefore, the victimÂ’s sex was a noteworthy factor in vic tim-offender relationshi ps. The sex results
92 coincide with the literature of previous rese archers and were not expe cted to be different between populations. In addition, both comm unities showed the highest frequency of victim-offender relationships as the non-domestic categor y, followed by domestic then stranger. This finding is surprising and cont radictory to the origin al hypothesis for the urban area was expected to have a more significant amount of stranger homicides, whereas the rural area was expected to have a higher frequency of domestic homicides. The hypothesis was based on the population dens ity data and the social mobility seen within each. However, these conclusions are synonymous to CurtisÂ’ (1974) results since his study suggested Â“strangerÂ” in cidents accounted for less 30% of urban homicides. Lastly, the age differences between the victim and offender proved to be an important factor within each population, si nce the offender was primarily younger than the victim. This was unexpected due to the prediction of more Â“strangerÂ” homicides in the urban area, including gang activity and random disputes However, the previous research has yielded informati on pertaining to this age asso ciation, and therefore is not questioned or discarded. Although many variables did not have a ny differences between the study sites according to location or community structur e, other variables showed interesting disparities. The demography of the victims and offenders did not pr ove to be different between the communities, however there were di screpancies. The age of the offender in the rural population was concentrated mainly on the 20-29 year range, whereas the urban populationÂ’s frequency distribution was more evenly distributed for age. Hillsborough County was expected to have a more divers e offender age range due to the size of the population and number and type of criminal homicides being committed.
93 Offender ethnicity between communities also revealed interesting and differing frequencies, because Lancaster County showed all ethnicities participating in murder. Hillsborough County offenders, however, were more concentrated on the European and African ancestries. In addition, the victim ethnicity was dissimilar across populations, for the Florida community had a more even di stribution across the Eu ropean, African, and Hispanic ethnicities, whereas victims from Nebraska had a higher concentration of Europeans within the dataset. The wide variety of ethnic popul ations in a large, urbanized area accounts for th e Hillsborough County frequency. Rural areas are not seen to be as diverse, and therefore it is expect ed the ethnicity of victims would be more concentrated. Victim precipitation proved to be an extremely interesting variable, since the Hillsborough data had little victim precipita tion within its homicides as well as less stranger homicide. However, the Nebraska data had a 50% split of ca ses that did and did not have victim precipitation. The lack of victim precipitation may be the factor that dampens the amount of stranger homicid e happening in the urban population. Wolfgang (1967) relied heav ily on the factor of victim precipitation in his study. However, the data analyzed in the current project was not found to extremely affect the production of homicide within each commun ity. The constantly changing social dynamics of urban communities and the rise of mobility in rural communities could explain this discrepancy; therefore, vict im precipitation should not be excluded from expanding studies. Mechanisms of death, such as strangulation and sharp force trauma, are seen as intimate categories of homicide, and are f ound frequently in the Nebraska population.
94 Conversely, gunshot wounds account for the major ity of death mechanisms in the Florida population. These factors may indicate differenc es in community structures, for the rural population has the potential to know each other more than the urban area due to the size of the total population. Als o, the vast social mobility of the urban population could account for the less intimate killings. When comparing domestic cases to offe nder ancestry, Lancaster County offenders were associated mostly with the European ethnicity. On the other hand, Hillsborough County offenders had a fairly equal distri bution between European and African, again supporting the notion of ur ban diversification. The GIS results of disposal and co-h abitation cases from each population also exposed similarities and differences in the sp atial distribution of s cenes. For disposal cases, the range of distances was much greate r in the urban area mo st likely due to the greater mobility and transient na ture of the population. In contrast, the Nebraska disposal cases had a very limited mobility range. This is an interesting fact, for the landscape of Nebraska is much more open and less develope d than urbanized Florid a. Therefore, the factors hindering a farther dis posal site in the rural area s hould be studied further. Co-habitation cases in which a murder t ook place outside of a shared residence were only found in the urban population. This situation may be localized to urban communities due to the amount of increased opp ortunities an offender has to remove him or herself and their victim from a shared residence in order to commit the crime. However, the social implications of this phenomenon are unclear, and should be additionally researched. The range of distances for these cases was very limited, only approximately 11 miles at the most. However, the sample size was extremely small
95 (n=3) and therefore is also s ubject to further investigation. Also, the mean distances in miles from scene to scene showed a much larger spatial distribution in Hillsborough County than Lancaster County. Again, the mob ility of these areas and the transient or non-transient nature of their residents mo st likely account for this movement. When related with specific location distan ces, condensed to five mile increments, the frequencies of distance be tween the two sites were disc overed. The urban population revealed a fairly equal amount of cases ha ppening at the 5, 10, and greater than 10 mile increments when looking at the vicinity of the victim to offender residences. However, the expanses of victim to offender residen ces in Lancaster County were always seen within 10 miles from each other. The offenderÂ’s residence to the murder location and victimÂ’s residence to the murder scene show ed regional distance variation as well. The Lancaster County cases all confirmed a dist ance of less than five miles from the offenderÂ’s house to the murder site, as did the distance from the victimÂ’s house to the murder location. This proximity is interes ting because the close range of victim to offender corresponds to the more intimate rela tionship category in homicides. Domestic and non-domestic murders are e xpected to occur be tween persons that are within a close distance to each other, due to the intimate soci al nature of their relationship. These more intimate relationships are seen in high fr equency within the Lancaster County data, thereby supporting the statistical outcome. The results from the Lancaster County data support the conclusions made by Messner et al. (1999) and Santtila et al. (2007), since spatial randomness was not seen to exist in the rural community. In addi tion, the corresponding di stances to social relationships make it possible to identify specific crime features.
96 In contrast, Hillsborough County was evenly split between the less than five mile increment and the over 10 mile increment when calculating the distance from the offenderÂ’s house to the murder site. Also, the Florida data disc losed the victimÂ’s residence as over 10 miles away from the murder site in 50% of the cases. The other half were mostly within a five mile radius. These differences can be explained with the population structures and size of the comm unities themselves. The highly mobile circumstances surrounding the populated Ta mpa area allows people to more easily connect with each other, despite a lengthy dist ance. Again, this supports Messner and coresearchersÂ’ work (1999) for spatial randomne ss in homicides was not seen in the Tampa area. Yet the factors were too widespread to relate to Santtila et al. (2007) and their identification of specific features of crime. However, it should be noted that the sample sizes of the disposal and co-habitation cases in the areas of study ar e extremely small and should be examined more closely and w ith greater sample sizes to expose any generalized spatial patterns. Lastly, a few cases showed interesting social relationship patterns after being associated to other variables. The disposal cases were evenly split between the domestic and non-domestic categories in both coun ties; Hillsborough (45.4% each) and Lancaster (33.3% each). Similarly, in each area, only one documented case of body disposal occurred from a Â“strangerÂ” relationship with the victim. In addition, the co-habitation cases found in Hillsborough County were analyzed with the components that were included in the Â“domesticÂ” category which re vealed two relationships in which the particular situation occurred. Only the spous al and significant other group had cases that demonstrated this particular spatial configur ation, with the spousal category having one
97 more case. However, the sample sizes of th e particular configurations must again be noted, because the frequency of thes e cases is not enough to begin making generalizations about spatial movement within urban and rural communities.
98 Chapter 6 Conclusion and Recommendations This project focuses on the importance of social relationshi ps, and the spatial distributions affected by them, within speci fic community contexts to approach crime analysis in a holistic manner. Criminal hom icides are a significant part of organized societies, and although many efforts are made to prevent them, success is not always achieved. By using the information pr ovided, along with further research, law enforcement agencies have the opportunity to incorporate the social structure of the population, the victim-offender relationship, and the means by which scenes related to the homicide event are generally distributed as a new paradigm in crime analysis. The results of the current project indi cate specific populations Â’ structures that actively influence criminal homicides. Age, sex, community mobility and fluidity, and the social relationships between a victim a nd offender have all proved to have impact on homicide rates. Age is a large component in the production of homici des, for the rate of the crime drastically drops after an age of 30. In urban areas, the roles of gang violence and social unrest increase these rates, and pr ovide stronger evidence for the effects of age on the production of violence. The role that and individualÂ’s sex plays in the production of violence is also a strong component. A communityÂ’s sex ratio can depend on the rate of violence seen in that area. The extremely low frequencies of female offenders indicate that a community with a higher ratio of women would be a mo re passive population. However, if the sex
99 ratio is heavily weighted toward males, th e population is at risk of a higher rate of homicide. The passive or aggres sive nature of the sexes has the ability to greatly affect the amount of violence within a community. Furthermore, the roles of mi gration and tourism also a ffect the social production of violence. The unstable population density and demographics of a fluid community can create situation more conducive to violence. The Hillsborough County data in the current study had a much larger sample size (n=420) of homicide cases within the 10 year span than the Lancaster County data (n=48). Th e amount of population density in an urban area can cause arguments and violence to o ccur naturally; howeve r, when tourism, migration, and homelessness are added, the struct ure of the community is disrupted. This disruption can cause more conflict and violence to arise, thereby in creasing the rates of homicide. Suggestions for similar studies in the futu re involve the inclus ion of more social variables of the individuals and communities. For example, the actual socio-economic status of both the victim and offender could sh ed light on the social factors that influence homicide, such as social se gregations and economic impacts on crime rates. The previous criminal records of persons, whethe r victim or offender, involved in homicide cases could also validate and explain the situat ions and social affiliations in which they are involved. A history of vi olence may help raise awareness of some criminalsÂ’ actions, as well as help to understand the type of so cial network with whom they associate. Incorporating other urban and rural areas co uld also improve the dataset, because it would give a broader picture of the population structures an d the spatial distributions seen among each form of community.
100 Limitations of the current study includ e the lack of statistical tests for the Nebraska population due to a small sample size. Although the study spanned a period of 10 years, the deficit of homicide related crimes did not allow a thorough analysis. Additionally, the small sample sizes of both the significant communities are problematic when attempting to relate the nature and spat ial distributions of these crimes to other societies. The current paper reflects the emerging importance of applied anthropology in public service fields such as criminal inves tigation. The use of human relationships and community structures help to unveil the cu ltural issues surroundi ng homicide events. Although further research must be conducted to prepare a definitive method of tracking and predicting homicide factors within both an urban and rural community, this research allows for a solid base on which to build an applied anthropology tool for methods of crime analysis. Although the data and results presented in the current paper do not reflect staggering differences between the urban a nd rural community studied, it does reveal factors that could prove intere sting with further investigat ion. Furthermore, the Tampa and Lincoln areas are thoroughly neglected in research, and ha ve much to contribute to the academic and practical world. Overall, this research project is considered a success in uncovering the social aspects of an urban and rural environment that affect the spatial distributions and the populations invo lved in criminal homicides.
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109 Appendix A Spatial Analysis of Crime Scene Locations in Cases of Criminal Homicide, 1997-2007 Data Collection Protocol Last Mod. April 2008 E.H. Kimmerle, Ph.D. Research Collaborators: Casey C. Anderson, MA Student, Dept. Anthropology Hillsborough Co. Sheriff Dept.; Tampa Police De partment; Temple Terrace Police Department; Nebraska Institute of Forensic Sciences, Inc. Introduction: Data is collected for criminal homicides and manslaughter and therefore excludes justifiable or lawful homicide or vehicular homicide. Data should be collected for each victim or each assailant when mo re than two people are involved. Definitions: First-degree murder consists of bot h premeditation and malice aforethought. Second-degree murder there is malice aforethought without premeditation. In other words, the offender intends to kill the victim but does not plan the lethal act. An act of voluntary or non-negligent manslaughter is committed when a person attempts to hurt, but not kill, another human beingÂ—but the victim dies in the process. Negligent or involuntary manslaughter is characterized by accidental death. Some states distinguish between vehicular and non-vehicular accidental death and others do not. "Victim precipitated" homicide refers to those in stances in which the victims' actions resulted in their demise. In this form of murder, the deceas ed may have made a menacing gesture, was first to pull a weapon, or merely used words to elicit a deadly response from the killer.
110 Appendix A Continued "Hate" homicide is a form of killing involving taking the life of "victims who are targeted because they differ from the perpetra tor with respect to such characteristics as race, religion, ethnic origin, sexual orientation, gender, or disability status" (Fox and Levin 2001, p. 128).
111 Appendix A Continued DATE AND RECORDER: POLICE AGENCY: POLICE REPORT NO.: AUTOPSY NO.: DATE OF INCIDENT: DATE/YEAR OF POLICE REPORT: OBS. 01: TYPE OF HOMICIDE: 1=criminal homicide, first degree 2=criminal homicide, second degree 3=manslaughter, negligent/involuntary 4=manslaughter, non-negligent/voluntary CONTEXT: OBS. 02 Was this is a "victim precipitated" homicid e? 1=Yes 2=No 3=Unknown OBS. 03 Was this is a Â“hate murderÂ”? 1=Yes 2=No 3=Unknown OBS. 04 Was this a domestic dispute or familial killing? 1=Yes 2=No 3=Unknown OBS. 05 Was there an associated robbery? 1=Yes 2=No 3=Unknown OBS. 06 Was there an associated rape? 1=Yes 2=No 3=Unknown OBS. 07 Was this a sexual homicide? 1=Yes 2=No 3=Unknown OBS. 08 Was there extortion? 1=Yes 2=No 3=Unknown OBS. 09 Was there an associated kidnapping? 1=Yes 2=No 3=Unknown OBS. 010 Was the assailant known by the victim? 1=Yes 2=No 3=Unknown OBS. 11: What was the nature of the assaila ntÂ’s relationship to the victim? 1=stranger 2=spouse (married, separated, divorced) 3=parent 4=child 5=boyfriend/girlfriend 6=coworker 7=neighbor 8=other: ( list) 9=unknown OBS. 12 : Was the victim a prostitute? 1=Yes 2=No 3=Unknown OBS. 13 : If yes, was assailant any of the following? 1=prostitute 2=pimp 3=client Demographic Information of Decedent (repeat if multiple victims): OBS. 14: Sex 1=Male 2=Female OBS. 15: Age (years) OBS. 16: Ancestry: 1=Caucasian 4=Hispanic 6=Other (list) 2=African-American 5=Am erican-Indian 7=Unknown 3=Asian Demographic Information of Offender (repeat if multiple assailants): OBS. 17: Sex 1=Male 2=Female OBS. 18: Age (years) OBS. 19: Ancestry: 1=Caucasian 4=Hispanic 6=Other (list) 2=African-American 5=Am erican-Indian 7=Unknown 3=Asian
112 Appendix A Continued OBS. 20: Nature of Injury Â– Mechanism of Death: 1=Single GSW 4=SFT 6=Other (list and describe) 2=Multiple GSW 5=Strangulation 7=Unknown 3=BFT OBS. 21: Nature of other injury associated with attack: 1=Single GSW 4=SFT 7=Unknown 2=Multiple GSW 5=Strangulation 3=BFT 6=Other (list and describe) OBS. 22: Weapon: 1= Handgun 5=Blunt (list specific) 9=Unknown 2= Shotgun 6=Ligature (list specific) 3= Rifle 7=Manual strangulation 4= Sharp (list specific) 8=Other (list) OBS. 23: Location of Fatal Injuries (on body): 1= Head 4=Abdomen 7=Back 2=Neck 5=Upper Extremity 8=Combination (list) 3=Thorax 6=Lower Extremity 9=Other OBS. 24: Location of other non-fatal injuries (list all that apply): 1=Head 4=Abdomen 7=Back 2=Neck 5=Upper Extremity 8=Combination (list) 3=Thorax 6=Lower Extremity 9=Other Skeletal Fracture Patterns (refer to survival time protocol) LOCATION OF CRIME SCENES: OBS. 25: Was the body moved following the murder? 1=Yes 2=No 3=Unknown OBS. 26: Provide complete street addresses for the following: VictimÂ’s Residence, Assailants Residence, 1st Encounter, 1st attack, 2nd attack, Murder Location, Body deposition Â– primar y location, Secondary body deposition BURIAL FACTORS OBS. 27: Context of burial location: 1= Surface deposition 2= Sub-surface Burial 3= Dismemberment 4= Water (list type of body of water, i.e. river, bay) 5= Burning/fire or cremation OBS. 28: Environment where body was recovered: 1= Public space 2= Private residence 3= Along roadside 4= Wooded area/field 5= Abandoned structure 6 =Railroad tracks 7= Other (please list):
113 Appendix A Continued OBS. 29: Container: 1= blanket 2= shower curtain 3= carpet 4= trash bin/dumpster 5= other (list) OBS. 30: Was there post-mortem modification to the body? 1=Yes 2=No 3=Unknown If so, describe OBS. 31: Was there an attempt to alter the scene? 1=Yes 2=No 3=Unknown If so, describe OBS. 32: Was there evidence to st age the crime scene? 1=Yes 2=No 3=Unknown If so, describe OBS. 33: What was the position of the body? OBS. 34: What was the direction the body was facing? OBS. 35: What was found with the victim? 1= Clothing 2= Jewelry 3= Weapon 4= Identification papers 5=Other (list) CIRCUMSTANCES OF DISCOVERY OBS. 36: Who found the body? 1= spouse 2= neighbor 3= poli ce 4=stranger 5=Other (list) Time since death? **List both time of discovery and time of death. Indicate if this time is known or estimated** OBS. 37: Date/time of death: OBS. 38: Date/time of discovery: OBS. 39: State of preservation/decomposition? 1= Fresh 8= mutilated/dismembered 2= Early Decomposition 9= body fragment/part recovered only 3= Advanced Decomposition 10= Other (list) 4= Mummified 5 =Skeletonized 6= Burned 7 =Decomposing but in water
114 Appendix A Continued OBS. 40: Was trace evidence found? 1=Yes 2=No 3=Unknown If yes, what was found and at which of the locations? OBS. 41: Was DNA evidence found? 1=Yes 2=No 3=Unknown If yes, described was found/whose DNA? OBS. 42: Was this case closed by arrest? 1=Yes 2=No 3=Unknown If not, list other means of closing case (List):
115 Appendix A Continued Spatial Analysis of Crime Scene Locati ons in Cases of Cr iminal Homicide, 1997-2007 Data Form Â– Page 1 of 2 DATE AND RECORDER: POLICE AGENCY: POLICE REPORT NO.: AUTOPSY NO.: DATE OF INCIDENT: DATE/YEAR OF POLICE REPORT: OBS. 1: OBS. 2: OBS. 3: OBS. 4: OBS. 5: OBS. 6: OBS. 7: OBS. 8: OBS. 9: OBS. 10: OBS. 11: OBS. 12: OBS. 13: OBS. 14: OBS. 15: OBS. 16: OBS. 17: OBS. 18: OBS. 19: OBS. 20: OBS. 21: OBS. 22: OBS. 23: OBS. 24:
116 Appendix A Continued OBS. 25: OBS. 26: SEE ATTACHED OBS. 27: OBS. 28: OBS. 29: OBS. 30: OBS. 31: OBS. 32: OBS. 33: OBS. 34: OBS. 35: OBS. 36: OBS. 37: OBS. 38: OBS. 39: OBS. 40: OBS. 41: OBS. 42: COMMENTS:
117 Appendix A Continued Spatial Analysis of Crime Scene Locati ons in Cases of Criminal Homicide, 1997-2007 Data Form Page 2 of 2 DATE AND RECORDER: POLICE AGENCY: POLICE REPORT NO.: AUTOPSY NO.: DATE OF INCIDENT: DATE/YEAR OF POLICE REPORT: OBS. 26: Provide complete street addresses for the following: DecedentÂ’s Residence: AssailantÂ’s Residence: 1st Encounter: 1st attack: 2nd attack: Murder Location: Body deposition Â– primary location: Secondary body deposition: Comments: