Introduction The amount of violence in

The amount of violence in today’s society has been a main focus of analysis in

many fields, including prevention 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 activities. This duty ranges from revealing

associations between multiple locations to the social relationship of the individuals

involved (Federal Bureau of Investigation 2004:41). Locations involved in homicides

may include a variety of sites, ranging from the victim and the offenders’ residences, to

the place where the crime occurred, as well as a body deposition 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 underlying social factors, such

as community structure and human relationship networks, which can affect the movement

and interaction of people, including violent 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 example, if an offender kills and buries a

family member, the location of the murder site, residences, and the bond between the

victim and killer can play a vital role in recovering the body and solving the case.

Criminal homicide is defined as, “An exchange between a victim and an offender,

within a context, that resulted in the demise of the victim” (Swatt and He 2006:279). In

criminal homicide cases, the social relationships between the people involved have the

potential to provide important information about the connection of scenes (Silverman and

Kennedy 1987). Avakame (1998:602) adds further acknowledgement to this theory by

stating, “The victim-offender relationship is crucial to unlocking the mystery of why

people kill each other”. In the past, scrutinized relationships focused on strangers or

“intimate”, meaning emotionally close, associations. Numerous studies (e.g., Wolfgang

1958, Wolfgang 1967, Daly and Wilson 1988, Polk 1993, Avakame 1998) have been

conducted on the dynamics of relationships involved in criminal homicides, as well as

analyses of the distances between relevant locations in certain crimes (Brantingham and

Brantingham 1984, Messner et al. 1999, Canter 2000, Van Patten and Delhauer 2007,

Grubesic and Mack 2008). However, there is a paucity of research integrating and

supporting the two foci using Geographical 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 relationship 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 population with a great deal of immigration and residential

movement (Frey and Zimmer 2001:25). Three elements 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,

and expanded territories due to suburban living (Frey and Zimmer 2001:26-27). Rural

societies are mainly comprised of smaller, less diverse populations, little residential

migration, and opposite elements as an urban area (Argent 2008). These different groups

have the potential for variations in demographic and social factors that influence

homicide because of the population factors surrounding them. In the current paper, a

rural location from Nebraska and an urban location from Florida were chosen as the study

sites. Figure 1.1. Social factors involved in homicides were explored through a

comparison of spatial distributions and the social relationships of involved individuals.

The researcher then deduced conclusions from the results and suggests social

relationships to focus on, within specific spatial configurations, for homicide analyses.

A Geographical Information System (GIS) uses spatial information for capturing,

storing, analyzing, managing and presenting information (Manhein, Listi, and Leitner

2006:171). These data are defined as “information that can be interrelated based on

position” (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; anthropology, resource management, city planning,

criminology, and marketing, use this tool to conduct research.

Geographic Information System data represent real world objects with digital

images, which can be achieved through raster or vector data (Wagner and Fortin 2005).

The present geographic study utilized vector data in the analysis, which employ

geometrical shapes and physical distance to express geographical features (Korte 2001).

These shapes triangulate spatial distributions and distances to exact measurements,

thereby computing highly accurate results to the analysis.

Figure

1.1

States

Study

Areas

Nebraska

Legend

Major_Cities

——

Counties

—

Highways

sate

110

220 440

Kilometers

Florida

Legend

—Major_Cities

—

Counties

—

Highways

[__]

sate

105-210

420

Kilometers

By:

Anderson

2009

Projection:

GCS_North_American_1983

The purpose of this project is to use spatial analyses of various locations to

determine if the relationship of a victim and offender in criminal homicides, specifically

domestic, non-domestic, and stranger homicides, significantly influence 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 University of South Florida and Hillsborough County

law enforcement agencies, also studying the spatial distribution of criminal homicides.

This paper focuses on case characteristics, victim-offender demographics and

relationships, as well as the spatial variations of sites found with each type 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 suggest a new investigation strategy for faster body recovery and

case closure in criminal homicides. In addition, the distributions of scenes were analyzed

in the cultural context of the region in which the murder occurred. By using GIS, the

areas of interest and the distances 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 expected in domestic homicides, and the

reverse for stranger homicides.

• Domestic and non-domestic homicide locations are hypothesized to occur

in a smaller spatial area than locations of stranger homicides, particularly

in the situation of co-habitation between the offender and victim. This is

due to the social relationships occurring within the context of the murders.

• When a homicide transpires 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 “strangers are a real

and persistent aspect of urban living”.

• 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 hypothesized 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 perspective of human social networks and the

community structures and factors that influence murder to better understand the

phenomena of homicide and the relationships of those involved (Kvamme 2003). The

use of anthropology in crime analysis is beneficial 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 behavior. Using a holistic perspective, the

social configurations of a population can be inferred and analyzed by anthropologists

using spatial distributions of significant physical locations and social groups. Interactions

between the various groups are also useful 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 affect homicides, such as social relationships and spatial

distributions, allow law enforcement agencies to better comprehend the act of homicide.

Chapter 2

Literature Review

Research conducted on both relationship 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 the two topics been

addressed. By using an original dataset, the ideas and concepts addressed in this paper,

on both social relationships and spatial distributions in homicides, are correlated using

GIS techniques and statistical analyses. In addition to this approach, the study is further

enhanced by anthropologically separating the social structures 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 relationships in crime, the benefits of

comparing crime rates among different societies, and the value of using spatial

distributions of scenes when evaluating criminal offenses.

Basis of Study

Methods of spatial analysis have been used in a wide variety of studies to explore

the patterns of criminal behavior, as well as other relevant fields that reconstruct and

analyze social distributions, including socio-cultural anthropology and landscape

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 approach seen in a research article written on

sexual homicides in Los Angeles, California (Van Patten and Delhauer 2007). In the

example, geometric spatial analyses of sexual homicides were conducted in relation to the

body disposal site and the victim and assailant’s residences (Van Patten and Delhauer

2007).

The Van Patten and Delhauer (2007) study used police and medical examiner’s

records to obtain data and employed GIS to graphically illustrate the geometric

distributions between locations. The geometric 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 “vast majority of crime trips involved neighborhood trips of less than half a

mile”. Interpretation of Van Patten and Delhauer’s (2007) conclusions suggest case

specifics, such as spatial geometry, victimology, manner of death, and offender

motivation, must be considered in determining how to best improve investigative

approaches (Van Patten and Delhauer 2007:1140).

The current study expands on Van Patten and Delhauer’s (2007) methods by

adding the component of social relationship between the victim and assailant to better

understand location distributions and to compare domestic to stranger homicides. In

addition, Van Patten and Delhauer’s study (2007) only focuses on the urban setting of Los

Angeles, whereas this research project evaluates both an urban and rural community to

identify differences between social 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 similar spatial analysis design, for all criminal

homicides, to evaluate relevant scene distributions. The conceptualization of the other

methods and variables are founded upon the growing 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 victim-offender relationships, particularly “stranger homicides”,

and the circumstances that surround and define the crime. His book, Studies in Homicide

(1967), addressed multiple issues surrounding criminal homicides and related research.

Wolfgang’s (1958) groundbreaking study of homicides in Philadelphia,

Pennsylvania (n=588) established generalizations of involved individuals by analyzing

the demographics and victim-offender relationships of those involved in murders around

the late 1940s. He analyzed these factors for both European and African males and

females using specific victim-offender relationships 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 relationship is not well understood (Bridges et al. 1987:345).

Durkheimian theorists believe economic inequality 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 “bureaucratic restraints of urban

courts” allow less social discrimination than rural courts, where social status can more

directly affect court proceedings (Bridge et al. 1987:347). Bridge and co-authors (1987)

found ethnic stratification to be a significant factor in imprisonment 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 and rural populations.

Wolfgang’s analysis and recognition of victim-offender relationships in criminal

homicide cases proved to be a significant factor in his research. 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, paramour, sex rival, enemy, a lover of offender’s

mate, felon or police officer, innocent bystander, and homosexual partner (Wolfgang

1958:204). His interpretation of the results revealed the most common form of

victimoffender relationships were “primary groups”, close friends and family, which

accounted for 53% of the total cases (Wolfgang 1958:206).

Furthermore, Wolfgang focused on the locations and methods by which criminal

homicides in Philadelphia occurred. He then analyzed the variables by demographic

populations to produce general statistics and predictions for victims and offenders based

on age, sex, and ethnicity. Results of the study revealed that men most often killed other

men in public places by beating or stabbing, whereas women most often killed in the

kitchen or bedroom using a knife to stab the victim (Wolfgang 1967:21). Relational data

between the victims and offenders showed that most homicides in Philadelphia were

committed by the “primary” 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 ethnic group, and therefore were shown to be

often killed by strangers (Wolfgang 1967:23).

Other unique topics included in the study involved victim precipitation, the

presence of alcohol during criminal homicides and the discovery 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 associated with the victim-precipitated

homicides (n=150) such as, victims and offenders of African ancestry, domestic slayings,

and alcohol (Wolfgang 1967). Alcohol was discovered as a leading variable in two-thirds

of the homicide cases, with significant association to either victims or offenders of

African descent (Wolfgang 1967:22). His study also revealed five main homicidal

motives: domestic disputes, vague altercations, 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 significantly linked to the “primary groups”, signifying a

correlation to motive that warrants further research (Wolfgang 1967).

The variables investigated by Wolfgang’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 associations. Similar studies conducted by other

researchers provided more in depth explanations and analyses for the variables this

project uses, and therefore are addressed accordingly.

Victim and Offender Demography

Demographic statistics of victims and offenders 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 homicides and have found a generalized pattern

among age, race, and sex variables. These demographic variables are important in

understanding the social relationships between victims and offenders, as well as

understanding the demographic dynamics between them.

Age, sex, and ethnicity variables from different populations and time periods have

been repeatedly analyzed and are suggested to be strong predictors of violence (Wolfgang

1967, Curtis 1974, Harries 1997). General statistical analyses of demographic crime

variables have indicated particular groups of victims and offenders involved in

homicides. For example, Harries (1997:17) refers to homicide as, “A crime that

disproportionately affects young adults”.

In addition, Curtis (1974:34) researched the age of individuals involved in

homicides and concluded “the most disproportionate offender [age] range covers young

adults in their teens and twenties”, 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, Harries1997, Federal Bureau of Investigation

1998) that the age of both victims and offenders seems to concentrate around the 20’s age

group in criminal homicides. Younger women, according to Lauritsen and Schaum’s

(2004:340) report, are at a much higher risk of being victimized by “strangers,

nonstrangers, and intimate partners than older women”. As such, 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 Kennedy’s study (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 “when the general criminal homicide rate is low in a given culture,

the percentage of female offenders is generally higher…thus suggesting a greater stability

in the amount of female homicide” (Wolfgang 1967:4).

Lastly, the ethnicities of victims and offenders in homicides have been used to

uncover intra- or inter-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, Lauritsen and Schaum (2004:325) established female

victimization was subject to ethnic group categorization, which proved to be a significant

factor since, “Higher levels of intimate partner victimization among blacks versus whites

appear to be limited to younger women”. In Websdale’s (1999:79) analysis of 78

malefemale domestic homicides in Florida, results showed an approximately equal

proportion of homicides were committed by both European and African ancestries,

followed by Hispanics. Websdale (1999:204) concluded with a comment regarding the

overrepresentation of African individuals involved in domestic homicides, including the

offender. One possibility for this over-representation stems from the “subculture of

violence” theory, proposed by Wolfgang and Ferracuti (1967). The “subculture of

violence” theory posits that various reference groups are frequently surrounded by

violence, which may be seen as an act to be admired or expected (Daly and Wilson

1988:286-287). This theory, however, remains a debated topic among anthropologists

and sociologists alike for the cultural differences between groups may change over time

and do not necessarily predict behavioral outcomes of the community (Daly and Wilson

1988).

In support of Websdale’s (1999) comment regarding over-representation, other

studies of homicide demographics have come 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) established that approximately two-thirds of

crime, committed between the two specific ethnicities, 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 fact 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 “circumstantial variables are

related to the probability in becoming involved in lethal violence”, even when put in the

age-sex perspective. This statement exposes the need to analyze demographics in

conjunction with variables that may influence homicides, and is acted upon in the current

paper.

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 Investigation 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

“victim precipitation” or, the situations in which 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 involved. However, victim precipitation is not

unknown in stranger homicides (Felson and Messner 1998:405), and therefore must be

considered as well. The presence of victim precipitation in a homicide may enhance the

understanding of the dynamics between the victim and offender, when related to social

relationship.

According to Goetting (1987), the concept 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 abusers. Wolfgang (1958:260) found that there

were more husbands killed by females, compared to males killing wives, in domestic

homicides when victim precipitation was involved. In addition, Wolfgang’s Philadelphia

study (1958) exposed the presence of alcohol as a significant factor in homicide events

that involved victim precipitation (Cohen 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). Additionally, 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 victim precipitated homicides do not always

indicate a female offender, nor is a female offender indicative of a domestic homicide.

Jurik and Winn’s 1990 research on offender prediction in victim precipitated homicides

suggested that demographic variables were not significant in predicting the offender’s

sex, but situational variables, including victim precipitation, were significant (Swatt and

He 2006:283). Furthermore, Felson and Messner (1998:408) suggest the 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,

thereby motivating the act of self-defense (Felson and Messner 1998:408). The model

labeled “Gender-Interaction” refers to “a three-way statistical interaction among the

gender of the offender, the gender of the victim, and the relationship between the two

parties” (Felson and Messner 1998:408). Lastly, the Gender Differences model which

explains the differences of actions between men and women. An example of this model

is, “When women kill, their behavior is more likely to be precipitated by a violent attack

than when men kill, because women are usually less violent” (Felson and Messner

1998:409). Nonetheless, homicides involving children, non-intimate relationships, and

strangers may also include the dynamic of victim precipitation, and therefore, cannot be

discounted (Felson and Messner 1998:405).

Studies have found (Wolfgang 1957, Wolfgang 1967, Goetting 1970, Campbell

1992, Rosenfeld 1997, Mann 1998, and Felson and Messner 1998) that females comprise

a large percentage of the offenders in domestic situations because of victim precipitation,

whether in self-defense or otherwise. Although domestic homicides have been seen as

one of the main situations in which this precipitation ends in a homicide, other types of

social relationships can result in a victim precipitated homicide as well.

Victim precipitation is identified and analyzed within the present paper for both

domestic and other homicide situations. Based on the Gender-Interaction and

GenderDifference models defined by Felson and Messner (1998) and the circumstances

founded by Wolfgang (1957), the presence of victim precipitation will be related to sex,

the type of homicide, and the social relationship between the victim and offender.

Defining Social Relationships

The relationship between a victim and his/her offender has been 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, “considered to be of paramount importance” when

investigating homicides, for the attachment described through social relationships can be

used to explain aspects of crime. According to Miethe and Regoeczi (2004:227), this

stressed importance is the foundation of the assumption that victim-offender relationships

“form the basis for fundamentally distinct homicide situations”. This belief is supported

by Silverman and Kennedy (1987), who maintain the idea that relational distances among

individuals can have a great influence on predicting elements of homicide.

Although studies have used different types of relational systems (Parker and

Smith 1979, Messner and Tardiff 1985, Riedel 1993, Decker 1996, Laureitsen and

Schaum 2004), the present study follows the example 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, neighbor, coworker, or acquaintance

• Stranger

The first category of relationships, labeled “domestic” is defined as; a group of

people romantically involved at one point of time, including ex-husbands and

romantically significant others (Silverman and Kennedy 1987:282). This classification

also includes family affiliations such as parents, children, and extended family members.

Casual relationships, or the “non-domestic” group, are those that 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 homicides (Avakame 1998, Felson and Messner 1998,

Swatt and He 2006). Avakame (1998:609) addressed this dispute by conducting an inter-

and 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. Avakame’s (1998:624) explanation is that stranger

homicides occur more frequently in urban areas due to the social disorganization and

population differences. This rationalization is theoretically supported by Zimring et al.

(1983:910) who states, “It is a criminological cliché…” 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-offender relationships in

urban populations.

Therefore, the present study tests the victim-offender relationship in terms of an

urban and rural society. Taking into account the population structures of each

community, cases with significant spatial distributions are cross-referenced with

relationship status. Social relationships are also associated to the relevant distance

frequencies of the selected spatial sites.

The main social relationship focus for the current study is the stranger,

nondomestic, and domestic categories previously described. This paper expands on the

relationship paradigm by using empirical evidence of extant data to demonstrate the

usefulness of relationship status in criminal homicides. The importance of relationships

is illustrated using by GIS maps and distances. 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 research. The current study demonstrates the

different variables of criminal homicide between an urban and rural population, and

therefore, the framework for societal comparison is useful.

Supporting evidence for the variation of homicide rates cross-nationally, caused by

cultural differences, can be seen within the literature (Archer and Gartner 1984, Gartner

1990). These cultural variations are manifested through exposure to violence, economic

inequality, the opportunity for victimization, and violence as a response to conflict

(Gartner 1990:96). Gartner (1990:94) notes the homicide rate in the United States is

markedly higher and differently composed than that of other developed countries. Four

characteristics of a community 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 tests these ideas on a

micro-analysis level within the context of two differently structured communities.

Williams and Flewelling (1988) and Pokorny (1965) statistically approach

comparisons of homicide rates among American cities. Empirical studies of this nature

include structural and cultural dynamics that produce homicides within the country

(Williams and Flewelling 1990:425). Focusing and separating the victim-offender

relationship, demographics, case characteristics, and the precipitating event, the authors

each find both cultural and structural variables had a significant effect on homicide rates

(Williams and Flewelling 1990:425, Pokorny 1965:480). The method of homicide,

location of murder, and relationship status, among other variables, all proved to diverge

from each other within the context of the specific counties (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, the current paper applies the separation

method, suggested by Willams and Flewelling (1990) and Pokorny (1965), to achieve an

accurate homicide comparison.

Numerous research efforts show the importance 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 community is conducted to identify varied

associations between homicides and their relevant locations, within the context of social

relationships. The differences in structural and cultural dynamics within the community

settings are also addressed, using census information of the populations and the fluidity of

the population structure, for the structural aspect.

The cultural ideas being tested are:

• The notion of more “stranger” encounters occurring in the urban

population, due to the mobile dynamic of the Hillsborough County

population.

• Comparing the similarities and differences of the victim-offender

relationship of a rural society 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 (2000:4), crime analysis is defined as, “The

collection and analysis of data pertaining to a criminal incident, offender, and target”.

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 foundation for geographically

mapping homicide events using GIS, and have given validity to its uses. Canter (2000:5)

explains the advantages of using GIS for tactical crime analysis, which is comprised of

pattern detection and linkage analysis, and how examining aspects of crime in a

geographical context can contribute to a better understanding of offender patterns.

Messner and coworkers (1999), and Santtila et al. (2007) analyzed homicide

patterns through spatial and temporal analyses surrounding similar communities. These

studies focused on the spatial clustering of homicides on the geographic level, but did not

include the demographic and case specific data, as seen in Avakame (1998) and Wolfgang

(1958). Both Messner et al. (1999) and Santtila et al.’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 crime features that were correlated with distances.

However, both studies are at a disadvantage due to the lack of acknowledgement toward

the social relationships 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 researcher with integrating both the social

relationship and the spatial patterns 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 influence crime. The spatial distributions are

also applicable to anthropology, because the social stratifications and structures of

communities are inherently founded on the physical layout of the site (Bevan and Conolly

2002, Kvamme 2003). Landscape archaeology uses the geophysical layout of past

populations to understand and analyze the “patterned geometries of the landscape”

(Kvamme 2003:438). The concept of landscape archaeology is utilized in large surveys

to study the cultural and structural aspects of archaeological sites (Kvamme 2003).

Recent advances in the field have allowed for the “wide-area mapping of settlement

spaces to reveal their organization and structure” (Kvamme 2003:436). Bevan and

Conolly (2002) use GIS tools to analyze the spatial organization of an archaeological site

in Greece. Using GIS and surveying techniques, Bevan and Conolly (2002:136) analyzed

“the structure of the modern landscape…, the visibility and definition of archaeological

sites…, [and] the interpretation of site distribution patterns”.

GIS is also useful for illustrating distribution patterns and identifying clusters

from cultural evidence (Lock, Bell, Lloyd 1999; Gillings and Sbonias 1999). Kvamme

(2003:436) states, “Survey of large contiguous areas is … essential for making sense of

patterns in cultural landscapes using geophysical datasets”. Therefore, the utilization of

landscape surveys and GIS interpolation and mapping are useful tools in the geographical

analysis of the current project. The distances 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 cross-national spatial analysis scrutinizing the

distance of residences and disposal 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 included: the disposal sites and the assailant’s domain, as well

as the spatial distributions compared to the assailant’s daily activities (Lundrigan and

Canter 2001, Snook et al. 2005). Also spatially scrutinized were the changes in the size

of the disposal site distances and the “hunting” ranges over time (Lundrigan and 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 Lundrigan and Canter (2001) and Snook et al.

(2005) is based on the degree of comfort the offenders 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 Canter’s (2001:609) and Snook et al.’s (2005:601)

hypotheses and determined that a serial killer’s spatial choices were influenced by

rational choice and routine activity. Although the focus of these studies are directed

specifically toward serial killers, 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 significance between spatial distribution

and area relationships and analyzed four varieties of spatial relationships between the area

of residence and area of crime. The mobility triangle distributions were labeled:

residential, crime, neighborhood, and total (Amir 1971:91). The residential triangle is

described as a situation in which the “offender lives in the area of the offense, but not in

the area of the victim’s residence” (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

elsewhere” (Rand 1986:118). The neighborhood triangle is the relationship in which “the

house of one or more offenders and the place of offense are located in the same

neighborhood” (Amir 1971:91). Lastly, the total mobility triangle is expressed as a

situation where “the offender does not live in the vicinity of the victim or offense” (Rand

1986:118). These combinations of sites, along with the victim and offenders residences,

are included in a “vicinity of crime”, which is defined by Amir as a five city block area

(1971:91).

Although Amir’s study focused on rape, his spatial concepts of crime vicinities are

useful in comparing the factors pertinent to a homicide event. The ideas surrounding his

crime vicinities are utilized in the current project by looking at the proximity of victim

residence to offender residence, the residential distances and their relationship to where

murders are occurring, and the differences in murder and 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 using 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 capabilities of GIS will clarify these variables

through the features of mapping, database capabilities, accuracy, and its ability to

correlate one or more attributes for pattern analysis.

The current study presented here expands on homicide investigations by

incorporating ideas of revolutionary authors in crime analysis 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

movement and vicinities in which relevant locations are positioned. Using a holistic

anthropological perspective that incorporates both cultural and biological variables

influencing and effecting homicide, a culturally-specific model of homicide is

constructed.

Chapter 3

Methods

Population differences of social violence are important for understanding the

circumstances surrounding the crime and preventing increases in crime rates for a specific

location. Homicide data were collected from both a rural and urban location: Lancaster

County, Nebraska (n=48) and Hillsborough 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 cases were excluded from particular analyses, and

are reflected in the sample sizes. The data were used to compare and evaluate the spatial

distributions between the two regional contexts, 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 homicide, which were inferred

both spatially and case specifically. The homicide 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 enforcement organizations consisted of police

reports, case summaries, case supplements Violent 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

Avakame’s statement (1998:608), “Homicide data are among the most accurate criminal

justice data”. 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 spatial distribution of criminal homicides.

Nebraska data were added to the thesis research to add the component of society structure

comparison.

The data representing Hillsborough County, Florida were procured from the

Tampa Police Department, Hillsborough County Sheriff’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 County 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 Sheriff’s Office. The information

collected in both regions are case summaries and details of “closed homicide” cases from

1997-2007.

Factors of Homicide

In the current project, “closed cases” are considered 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 focuses on criminal cases or those which are

“premeditated, felonious, intentional murders” or “slayings in the heat of passion”

(Wolfgang 1967:272). Noncriminal homicides are considered excusable for reasons that

are not seen as relevant for the particular analysis, and therefore are excluded.

The current research project analyzes closed 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 cases in which there were one or more offenders;

however, the focus for this study is only on the person who actually caused a victim’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, such as murder-suicides, are

included in this study because of their potential to portray vital information relevant to the

study. Excluded homicide cases are those 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 applicable to the project because of the lack

of malice and difference in social relationships between those involved. However, it

should be noted that the relationships 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 original attack, and the

spatial proximities to specific locations from the attack site.

Population Data

Census data collected in 2000 and supplementary government documents (U.S.

Department of Commerce) tracking the demographic, migration, tourism, population

growth, and homelessness rates of each population were used to differentiate the

community structures of the study sites. Using total population statistics, ratios of males

to females, median age, and population distribution by race, the differences between

Lancaster County, Nebraska census data and Hillsborough County, Florida census data

are illustrated in Table 3.1.

Data resulting from the 2000 United States Census data, Lancaster County,

Nebraska had a total population of 250,291 people living within the limits, 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 percentage of 90.1% (U.S. Department of Commerce 2000).

Hispanic, African, and Asian ethnicities 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 substantially greater population totaling

998,948 residents and 951 people per square mile (U.S. Department of Commerce 2000).

Male to female ratios are only slightly off-balance, with females 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).

Table 3.1- 2000 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%

American-

Indian

1,599

0.6%

3,879

0.4%

Other

4,374

1.8%

47,266

4.8%

Ethnic profiles show a majority of the residents 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 residents present at percentages of 18.0% and

15.0%, respectfully (U.S. Department of Commerce 2000).

When comparing these demographic statistics, the social composition differences

between the populations are clear. A much larger site is seen within Hillsborough County,

which gives people more opportunity for crimes because of the density of the population,

whereas the smaller Lancaster County population density allows for more space between

individuals. The percentages of ancestry are also different when comparing population to

population. This factor proves to be important when comparing inter- and intra-ethnic

murders and their frequencies.

Major influences on a community’s social structure and composition 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 period of time. For example, Tampa, Florida

houses both an international airport 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 passengers and 185,768 international passengers

flew into and out of the Tampa International Airport during 2007 (Tampa Bay and

Company 2009). The international maritime 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 visiting Hillsborough County significantly affects

the population and community dynamics.

In contrast, Lancaster County has no ship ports or international airports, and

therefore is not a large tourist or visitor location (Nebraska Department of Economic

Development 2008). Approximately one million people visit the Lancaster County area

every year (Lincoln Convention and Visitors Bureau 2008). Therefore, the two

communities have significantly different tourism rates and greatly differ in social

composition, which can affect homicide and crime rates within each area.

Migration is another social factor that can influence population development. The

United States Census Bureau defines population development by using birth rates, death

rates, domestic migration and international 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 Lancaster County had a 2.4% net migration

rate (U.S. Department of Commerce 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 affect 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 poverty status, in each pertinent 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. Department of Commerce 2008).

The total population quantities and the percentage of poverty in each county

shows the more urbanized area seem to have a higher social disequilibrium, when

referring to overall social wealth. In addition to poverty, the percentage of homeless

individuals also affects the social marginality of a community. According to the

Homeless Coalition of Hillsborough County, there were 9,532 homeless men, women,

and children in the Tampa, Florida area during 2005. Lancaster County did not have

specific data related to homelessness; however, the 2002 Census estimates revealed 9.2%

of the Lancaster County population was expected to be below 100% poverty (U.S.

Department of Commerce 2008).

The differences between subject sites suggest 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 understanding the amount and types of crimes

committed within each county.

Methods of Analysis

Data analysis began with the demographic profiles of both victims and offenders,

which consisted of evaluating ancestral backgrounds, 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 protocol is part of a larger research project being conducted

at the University of South Florida, and therefore includes more variables than are

analyzed in the present paper. Additionally, spatial information was analyzed using the

residential addresses of the involved individuals and the addresses of the sites in which

relevant occurrences happened, such as the murder site and the disposal site (Table 3.2).

The demographic characteristics of the entire population associated with

homicides, for each study site, were developed into generalized categories. Ancestries of

the individuals involved the following categories: European, African, Asian, Hispanic,

American-Indian, and Others. Age is represented in years; however, those victims who

were younger than one year of age were represented as 0 for statistical purposes.

Frequencies of ages were conducted by grouping 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 categorized as “stranger” is defined by Silverman and

Kennedy (1987:282) as, “offenders who had no known relationship with the victim”. A

“domestic” relationship categorization was used if any of the following relationship

variables were present between the victim and offender: marriage, divorce, separation,

blood relative living in the same domicile, co-existence, or in a romantic or sexual

relationship (Felson and Messner 1998, Miethe and Regoeczi 2004, Swatt and He 2006).

“Acquaintance” 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).

Table 3.2 – Data Used for Spatial Analysis of Criminal Homicides

Demographics

Age, Sex, and Ancestry

Victim-Offender Relationship

Parent

Child

Romantic

Friend

Acquaintance

Co-Worker

Neighbor

Stranger

Location

Victim’s Residence

Offender’s Residence

Murder Location

Body Deposition Site

Presence of Case Specifics

Degree of Homicide

Victim Precipitation

Drugs and Alcohol

Mechanism of Death

Weapon Used

Body Recovery Location

Murder/Suicide

A “co-worker” relationship is one in which the victim and offender 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 homicides include victim-offender relationships of spouse,

significant other, parent, child, or relative. Non-domestic relationships involve neighbors,

co-workers, friends, or acquaintances. Lastly, the stranger category encompasses only

with offenders who had no previous 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 into degrees which represent the different

types. The degrees of homicide are ordered as first and second degree murder, followed

by voluntary and involuntary manslaughter.

• First-degree murder consists of an intentional killing with both premeditation and

malice aforethought of an act that results in a person’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 are 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 involuntary manslaughter is characterized by “the killing of a

person through gross negligence” (Federal Bureau of Investigation 2004:152).

Table 3.3 – 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

Some states distinguish between vehicular and non-vehicular negligent deaths; therefore,

it should be noted that the current study only focuses on non-vehicular deaths. Other

terms that are of importance to this analysis include: victim precipitation and mechanism

of death. “Victim precipitation” refers to instances in which the victims’ 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 vital functions and the

occurrence of death (Spitz and Fisher 2006:440). The mechanism of death categories

were defined as: single gunshot wound, multiple 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 anthropological and social definition to the spatial

distributions of homicide cases. Statistical analyses of the data, conducted using SPSS

17.0 software, include chi-square tests for independence, cross-tabulations, mean

comparisons, Pearson’s correlations, and frequency 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 persons involved to identify similarities and

differences in homicidal behaviors between communities. Using the locations of scenes

and residences, along with the relationship status of the parties involved, the degree of the

homicide, presence of homicide/suicide, mechanism of death, and circumstances

surrounding the murder occurrence, both the social aspects of the social production of

homicide are revealed.

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 degree of murder instigated and the scene distributions. The

framework of a comparative study is implemented for the research of a rural and urban

region. Involved in the comparative study are 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 locations. Therefore, geocoding was employed

through ArcGIS 9.2 to map the various locations related to a homicide event and use them

as spatial factors in the analysis. Geocoding is defined as, “The process of creating map

features from addresses, place-names, or similar information” (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 analysis. The addresses were each indicated as a point of interest,

and were connected using vectors to achieve a polygonal shape representing the spatial

area involved. Connections of scenes representing the spatial configurations define

distinct situations:

• A solid blue rectangle connected to a solid red triangle represents the

victim and offender’s residences, respectfully.

• An orange dotted circle for a case represents a homicide in which the

victim and offender resided together and the murder occurred in the shared

household, and the victim’s body was not transported 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 connecting line to a solid red triangle

represents a case in which the victim and offender lived in separate

residences and the murder occurred at the victims’ address. In this

situation, no movement of the victim occurs after the homicide.

• A red dotted triangle with a connecting line to a solid blue rectangle

represents a case in which the victim and offender lived in separate

residences and the murder occurred at the offenders’ address. In this

situation, no movement of the victim 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 victim and offender resided at different

locations, and the murder occurred in a location other than their homes.

• Lastly, when any of the previous situations are connected to a solid purple

pentagon, it depicts the disposal site for the murder.

• Figure 3.5

The solid colored shapes represent separate residences and locations, and the

dotted shapes represent locations with multiple importance. The line representation

depicts the geometry of the cases that involve multiple scenes.

Figue3.5

Variations

Spatial

Configurations

One

Scene

Least

Complex

Four

Scenes

Most

Complex

Two

Scenes

Mild

Complexity

Three

Scenes

Moderate

Complexity

Legend

BBB

Victim's

Residence

co-habitation

and

Murder

Location

Offender's

Residence]

victim's

Residence

and

Murder

Location

Murder

Location

offender's

Residence

and

Murder

Location

Disposal

Location

©)

Co-habitation

Location

The number of dots that connects each case depicts 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 regions. Distances, in miles, between the

relevant sites were obtained using 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 established 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 variables pertinent to spatial movement.

Comparing the victim-offender relationship of homicides in an urban and rural

setting and relating them to the spatial distributions of the residences and scenes revealed

the social differences in the production of violence. Statistical analyses and the accuracy

obtained using GIS technology allowed the researcher to confidently draw conclusions on

spatial patterns associated with certain relationship 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 distributions of criminal

homicides.

Chapter 4

Results

In this section, the statistical and geographical results location were calculated

separately and compiled into comparative tables 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 characteristics 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 offenders who were male are very similar at 91.4% and 91.3%,

respectively. The number of female offenders also had a similarly proportionate rate

between the two locations, since Hillsborough 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.

Table 4.1 – 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)

Table 4.2 – 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)

Table 4.3 – 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)

American-

Indian

0.2% (1/832)

4.3% (2/46)

Other

0.0% (0/832)

2.2% (1/46)

The Lancaster County data revealed no offenders between the ages of 50 and 69 during

the ten year period.

The Florida population of offenders is more 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 within 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 than one individual, unlike the Lancaster

County data.

The differences in offender age ranges between the two locales suggest different

types of crimes occurring within each community. The Lancaster County and

Hillsborough County data conform to the standardized model of offenders killing in their

mid- to late-twenties, as seen in the literature. However, elderly groups from the Florida

population indicate more murder-suicides or domestic murders by offenders of these ages.

Ethnic groupings of each county revealed disproportionate offender

demographics as well. Hillsborough County showed surprisingly less ethnic diversity

among offenders than the Lancaster County population. 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 categories were not seen in the offending population

of Florida, whereas the Nebraska 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 European (41.0%) and African (41.2%) groups, with

Hispanics following at 17.5 %. The offenders from Nebraska showed a larger gap

between ethnicities, with the majority associated with the European category (56.5%).

The African (15.2%) and Asian (13.0%) populations followed with a fairly equal

distribution. The non-diverse nature of the offenders in the Tampa area reveals an aspect

that needs to be more closely inspected. The urbanization of the region would usually be

linked with a more diverse ethnic population, yet if certain ethnic groups have lower

offender rates, cultural reasons determining 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 both Hillsborough County (n=419) and Lancaster County

(n=47), with each only lacking one individuals’ 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 period, where as Nebraska had a considerably

lower solved homicide rate of 1.9% during the 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 compared to the Nebraska data.

The percentage of male to female victims between Nebraska and Florida has

similar ratios. The Lancaster County victims 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 noted that the number of females killed in the

urban Florida population were higher by 1.9%.

Table 4.4 – 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)

Table 4.5 – 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)

Table 4.6 – 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)

American-

Indian

0.2% (1/415)

6.4% (3/47)

Other

1.0% (4/415)

0.0% (0/47)

Age range frequencies for the victims from each community divulged interesting

disparities between the populations. 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 year 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. Victims from the Tampa area appear to emerge

from backgrounds that are more diverse; however, the majority of the population is still

centered on the Europeans (48.2%) and Africans (34.7%). Similar to the Hillsborough

County offenders, the next highest ethnic category for victims is Hispanic which accounts

for 15.4% of the remaining population. All census 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 allow the demographic identities of individuals

involved in homicides, at each site, 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.

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 County 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 extremely different from each other, the age

ranges of victims within the Lincoln 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 the amount of influence

variables have on one another within 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 themes or differences seen across the Florida

and Nebraska communities. Statistical analyses that yielded differential results are

reported and used as a basis for more extensive research. These analyses allow further

questions to be addressed with more complex statistics in future studies.

The frequency distributions for the degree 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.

Table 4.7 – 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)

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 Lincoln, Nebraska area showed a slightly 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 Hillsborough County data revealed a more even

distribution of homicides between the first (53.9%) and second degree categories

(33.2%). However, the involuntary manslaughter (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 demographic and type crime being committed in

this area, such as child abuse. In following sections, correlations to victim-offender

relationships will be used to attempt to uncover the reason for the discrepancy. The

distribution of victim-offender relationships were also scrutinized using frequency charts.

The relationships were compressed into three different categories: domestic, non-

domestic, and stranger. Figure 4.7 illustrates the similarities seen in the distributions of

victim-offender relationships that ended in murder.

Both counties show the non-domestic relationship as the foremost category in

criminal homicides, followed by the domestic affiliation. The most interesting aspect of

this frequency is the small role stranger homicides 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.

Yet, the urban setting of Hillsborough County was expected to have a much stronger

representation of stranger homicides due to the population density and amount of

population fluidity within the community. However, 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 Lancaster County population showed a fairly equal

distribution of known homicide cases (n=40) that did or did not have victim precipitation

present. The results demonstrate 52.5% of the victim precipitation cases did not have the

variable present prior to the murder event, while 47.5% of the cases did have victim

precipitation occur. However, the Hillsborough County cases (n=373) showed a larger

amount of distance between the two circumstances 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 precipitation 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 murder, are later performed to unveil the forces

behind the divergences.

The frequency results for the victim’s mechanism of death showed an interesting

social variable that differed between communities. (Table 4.9). By far, gunshot wounds

were the most common mechanism of homicide in the Florida population (52.3%),

followed by sharp force trauma (21.3%) and blunt force trauma (15.7%).

Table 4.8 – Presence of Victim Precipitation 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 – 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 – Gunshot Wound

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 strangulation, with a frequency of 14.9%.

Single gunshot wounds, sharp force trauma, and strangulation are all associated

with intimate killing situations such as murder/suicide, women offenders, and crimes of

passion. These mechanisms are at the forefront of the Nebraska cases, which allow an

assumption to be made that domestic murders 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 presence of abusive substances (62.5%),

during a homicide event. Conversely, Hillsborough County had similar percentages

between the presence (47.7%) and absence (52.3%) of abusive substances, with the

absence being more prevalent. These results indicate the Lancaster County community is

committing homicides under 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 Hillsborough County, Florida and Lancaster County,

Nebraska. Using the variable frequencies uncovered in this segment, further statistical

analyses are conducted to explore and explain the differences in murder circumstances for

each population.

Table 4.10 – 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)

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 the main methods for uncovering associations

between data. Using the categories illustrated by frequency charts, case characteristics

were used to learn more information about homicide patterns within each community.

Results of cross-tabulations for the degree 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 “non-domestic” social

relationship. Also, the cases labeled “domestic” were found to have over half of the

murders classified as first degree, or premeditated, in both communities.

Demographically, both societies showed that 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 victim ethnicity distributions.

As seen in Table 4.13, the Nebraska data reveals the majority of domestic victims

were of European ancestry (80.0%), with no other ancestries involved except Asians

(20.0%). The Florida population had most domestic victims fall within the European

(54.4%) and African (33.6%) ancestries. Hispanics (15.0%) were also more highly

represented as victims in the Florida population.

The domestic homicide offender’s ethnicity was also exposed using

crosstabulations.

Table 4.11 – Cross-tabulation of Degree 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)

Table 4.12 – Cross-tabulation of Degree 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)

Table 4.13 – Victim Ancestry Compared 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% (0/122)

0.0% (0/10)

Other

0.0% (0/122)

0.0% (0/10)

In Table 4.14, the Hillsborough County data, n=413, show a large distribution of

European (50.4%) and African (38.2%) ancestries, 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 having a European ancestral background.

Cross-tabulations of the degree of offense with both Hillsborough and Lancaster

County offender age ranges show different results, comparatively. In Lancaster County a

significant proportion of first degree homicides (55.6%) and second degree homicides

(50.0%) were committed in the 20-29 age range (Figure 4.10 and Figure 4.11).

The Hillsborough County cross-tabulation revealed a similar proportion of the

2029 age range to degree of homicide. However, the 10-19 year range, 30-39 year range,

and 40-49 year age range also showed numerous 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 analysis. A chi-square test of independence

determines if “the observed frequency of observations…is significantly different from

those proposed by a null hypothesis” (Madrigal 1998:196). Implementing this technique,

significant results are found between different variables 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 likely victims in non-domestic murders.

Table 4.14 – Offender Ancestry Compared 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)

Therefore, chi-square analyses for each location exposed a significant difference in victim

sex based on a domestic or non-domestic murder (Florida: χ²=81.095, df =1, p <

0.001, Nebraska: χ²=3.846, df =1, p=0.050).

Also, both locales had similar intra-ethnic murder rates since the majority of cases

in each city had an individual from one ethnicity kill an individual from the same ethnic

category. Chi-square tests of intra-ethnic murders demonstrate these results as

χ²=725.689, df=15, p < 0.001 for the Tampa area and χ²=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-racial homicide exists most

frequently in each population.

Weapon choice between an offender’s sex was significantly different in the

Hillsborough County sample (χ²=21.119, df=7, p=0.004). These chi-square results

characterize Florida female offenders as sharp 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 insufficient sample size of female offenders.

The prevalence of murder/suicides and the relationships under which they occur

were scrutinized using chi-square tests and crosstabluations as well. However, the

Lancaster County data contained only two murder/suicide cases, and therefore, could not

be analyzed. Nonetheless, the Hillsborough County data divulged significant results in

the form of murder/suicides occurring between particular social relationship categories.

Over the 10 year span, 41 murder/suicides occurred in the area in which 85.4% of the

cases were committed by an offender in the “domestic” relationship category. The

chisquare test for these variables was calculated as: χ²=66.518, df=2, p < 0.001.

These results were interpreted to indicate a significant difference between close

relationships committing murder/suicides and those who are not considered “domestic”

and do not commit the act.

Lastly, Tables 4.15 and 4.16 illustrate the environment in which the victim’s body

was recovered, in association to the relationship 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 corpses were recovered in the other

environments, including only two cases at abandoned structures, which were both

committed by “non-domestic” offenders.

The Lancaster County data discloses a different 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 ended in homicide were

found in either a private residence (n=12) or public space (n=12). Interestingly, 66.7% of

stranger homicides had a body recovered 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, χ² = 86.095, df=8, p < 0.001.

However, the Lincoln area did not prove to have significantly different body recovery

sites, possibly due to the smaller sample sizes within each location.

Table 4.15 – 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)

Other

0.0%

(0)

0.0%

(0)

0.0%

(0)

Total

(117)

(210)

(69)

(396)

Table 4.16 – 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)

Railroad Tracks

0.0%

(0)

0.0%

(0)

0.0%

(0)

Other

0.0%

(0)

0.0%

(0)

0.0%

(0)

Total

(10)

(30)

(3)

(43)

Each cross-tabulation and chi-square result reveals a fact pertinent to the homicide

cases for each location. The results are important for expanding research questions by

using sophisticated statistical tests, 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 offender’s age, and

processing them with an analysis of variance (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 offender’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 murder 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 offenders’ age at the Florida site.

The Nebraska site was unable to be tested on account of the small murder/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 the significance through the results: F=41.637, df=1,

161, and p < 0.001. The ethnicity of the offender could not be compared to prevalence of

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 offender to the three collapsed categories of

social relationships. The Nebraska data did not yield a considerable difference of age

between social relationships in criminal homicides; however, the Florida data provided

significant results. Mean ages of the offender 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 relationship the victim had with their killer.

Pearson Correlations

Results for the Tampa area data confirm 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 old, while the median age of offenders was

29.00 years old. This shows that the offenders in the urban area were usually younger

than their victims. However, it must be noted 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 offenders 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.

Table 4.17 – 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

As within the Hillsborough correlation, Figure 4.15, the age of offenders are slightly

younger than males, as well as a few cases represented 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 pattern 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 social relationship correlations to reveal

underlying social structures that indicate a particular spatial distribution of scenes in

criminal homicides per population.

The states of Florida and Nebraska, their 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 relevant frequencies, it was found that the

most significant cases between the areas of study were those that contained 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 than the murder site itself,

were seen in both Hillsborough County (n=20) and Lancaster County (n=6), (Figure 4.17

and 4.18).

Table 4.18 – Frequency of Geometric Situations in Hillsborough and Lancaster

Counties

Symbol

Hillsborough

Lancaster

Victim Alone

Solid Blue Rectangle

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 Pentagon

2.3% (20/853)

6.4% (6/94)

Victim/Murder

Dotted Blue Rectangle

8.9% (76/853)

11.7% (11/94)

Offender/Murder

Dotted Red Triangle

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)

However, the spatial configuration of co-habitations 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 relationship categories to unveil

the occurring victim-offender relationships.

The disposal site cases are subjected to the collapsed relationship categories:

domestic, non-domestic, and stranger. However, the co-habitation cases are all

considered to be in the “domestic” category, and therefore are correlated 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 indicated offender.

After mapping the cases, vector lines are used 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 determined and compared. (Table 4.19).

The range of distances in the Hillsborough 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 Hillsborough 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 County scenes. This discrepancy between

populations may be due to the mobility of each area and the population density.

Table 4.19 – 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)

In Tables 4.20 and 4.21, the frequency distributions of the location 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-habitation cases, for each county, to the victimoffender

relationship are then conducted. Frequencies of the relationships for each type of case

are seen in Tables 4.22, 4.23 and Figure 4.19. The disposal cases are associated with

the three collapsed relationship categories, whereas the co-habitation cases are

compared to the relationships that comprise the “domestic” category.

The cases with disposal sites had similar 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 “stranger” relationship between the victim and offender was seen in

each area of study. The Lancaster County dataset had one case in which the

victimoffender relationship was unknown, and therefore set in a separate category.

The implications of the relationship results 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 location. This is consistent with both Van Patten

and Delhauer’s (2007) and Hakkanen and colleagues’ (2007) research, which also

concluded that disposal sites were sought out more often when the offender knew the

victim. Lancaster County did not reveal any cases with the co-habitation configuration,

although the data from Hillsborough County had three. The victim-offender relationship

of the co-habitation cases suggests spouses and significant others kill their spouses after

intense planning or with the intention of killing themselves and fail.

Table 4.20 – 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)

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 – Freque

ncies of Distance in

County Di

Miles from Specific Lo

sposal Cases

cations in Lancaster

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)

Murder-Victim

100.0% (1/1)

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)

Table 4.22 – Victim-Offender Relationship 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)

Table 4.23 – 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)

When cross-tabulated with degree of murder, the individuals who killed their significant

others were both charged with first degree murder, while the spousal offender was

charged with second degree murder. (Table 4.24).

The use of GIS, distance comparisons, and 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 differences between larger, more

mobile communities, such as Hillsborough County, and the smaller, less fluid populations

like Lancaster County.

Table 4.24 – 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)

Chapter 5

Discussion

The analyses of this study have divulged interesting results in terms of urban and

rural homicide activities and the people who are involved in them. Hillsborough and

Lancaster Counties have similar characteristics in some aspects, and diverge in others.

The implications of these similarities and differences are reviewed and discussed in terms

of urban and rural community structures and social relationships.

Both populations showed a number of similar results, including 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 between victims and offenders. First degree

murders were seen as the most common type of homicide and intra-ethnic murders were

most frequent in both locations. Van Patten 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 contradictory to the expected high frequency. The

population density and composition of the urban community were the reasons supporting

the expectation.

Also, females were killed more often than males in domestic homicides, while

males were more frequently killed in non-domestic or stranger homicides. Therefore, the

victim’s sex was a noteworthy factor in victim-offender relationships. The sex results

coincide with the literature of previous researchers and were not expected to be different

between populations. In addition, both communities showed the highest frequency of

victim-offender relationships as the non-domestic category, followed by domestic then

stranger. This finding is surprising and contradictory to the original 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 density data and the social mobility seen

within each. However, these conclusions are synonymous to Curtis’ (1974) results since

his study suggested “stranger” incidents 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, since the offender was primarily younger than

the victim. This was unexpected due to the prediction of more “stranger” homicides in

the urban area, including gang activity and random disputes. However, the previous

research has yielded information pertaining to this age association, and therefore is not

questioned or discarded.

Although many variables did not have any differences between the study sites

according to location or community structure, other variables showed interesting

disparities. The demography of the victims and offenders did not prove to be different

between the communities, however there were discrepancies. The age of the offender in

the rural population was concentrated mainly on the 20-29 year range, whereas the urban

population’s frequency distribution was more evenly distributed for age. Hillsborough

County was expected to have a more diverse offender age range due to the size of the

population and number and type of criminal homicides being committed.

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 distribution across the European, African, and

Hispanic ethnicities, whereas victims from Nebraska had a higher concentration of

Europeans within the dataset. The wide variety of ethnic populations in a large,

urbanized area accounts for the Hillsborough County frequency. Rural areas are not seen

to be as diverse, and therefore it is expected the ethnicity of victims would be more

concentrated.

Victim precipitation proved to be an extremely interesting variable, since the

Hillsborough data had little victim precipitation within its homicides as well as less

stranger homicide. However, the Nebraska data had a 50% split of cases that did and did

not have victim precipitation. The lack of victim precipitation may be the factor that

dampens the amount of stranger homicide happening in the urban population.

Wolfgang (1967) relied heavily 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 community. The constantly changing social

dynamics of urban communities and the rise of mobility in rural communities could

explain this discrepancy; therefore, victim 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 found frequently in the Nebraska population.

Conversely, gunshot wounds account for the majority of death mechanisms in the Florida

population. These factors may indicate differences 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. Also, the vast social mobility of the urban population could

account for the less intimate killings.

When comparing domestic cases to offender ancestry, Lancaster County offenders

were associated mostly with the European ethnicity. On the other hand, Hillsborough

County offenders had a fairly equal distribution between European and African, again

supporting the notion of urban diversification.

The GIS results of disposal and co-habitation cases from each population also

exposed similarities and differences in the spatial distribution of scenes. For disposal

cases, the range of distances was much greater in the urban area most likely due to the

greater mobility and transient nature 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 developed than urbanized Florida. Therefore, the

factors hindering a farther disposal site in the rural area should be studied further.

Co-habitation cases in which a murder took 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 opportunities 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 (n=3)

and therefore is also subject 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 mobility of these areas and the transient or non-

transient nature of their residents most likely account for this movement.

When related with specific location distances, condensed to five mile increments,

the frequencies of distance between the two sites were discovered. The urban population

revealed a fairly equal amount of cases happening 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 residences in Lancaster County were always seen

within 10 miles from each other. The offender’s residence to the murder location and

victim’s residence to the murder scene showed regional distance variation as well. The

Lancaster County cases all confirmed a distance of less than five miles from the

offender’s house to the murder site, as did the distance from the victim’s house to the

murder location. This proximity is interesting because the close range of victim to

offender corresponds to the more intimate relationship category in homicides. Domestic

and non-domestic murders are expected to occur between persons that are within a close

distance to each other, due to the intimate social nature of their relationship. These more

intimate relationships are seen in high frequency 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 addition, the corresponding distances to social

relationships make it possible to identify specific crime features.

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

offender’s house to the murder site. Also, the Florida data disclosed the victim’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 communities themselves. The highly mobile

circumstances surrounding the populated Tampa area allows people to more easily

connect with each other, despite a lengthy distance. Again, this supports Messner and

coresearchers’ work (1999) for spatial randomness 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 are extremely

small and should be examined more closely and with 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 counties; Hillsborough (45.4% each) and Lancaster

(33.3% each). Similarly, in each area, only one documented case of body disposal

occurred from a “stranger” relationship with the victim. In addition, the co-habitation

cases found in Hillsborough County were analyzed with the components that were

included in the “domestic” category which revealed two relationships in which the

particular situation occurred. Only the spousal and significant other group had cases that

demonstrated this particular spatial configuration, with the spousal category having one

more case. However, the sample sizes of the particular configurations must again be

noted, because the frequency of these cases is not enough to begin making generalizations

about spatial movement within urban and rural communities.

Chapter 6

Conclusion and Recommendations

This project focuses on the importance of social relationships, and the spatial

distributions affected by them, within specific community contexts to approach crime

analysis in a holistic manner. Criminal homicides 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 provided, 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 indicate specific populations’ structures that actively

influence criminal homicides. Age, sex, community mobility and fluidity, and the social

relationships between a victim and offender have all proved to have impact on homicide

rates. Age is a large component in the production of homicides, 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 provide stronger evidence for the effects of age on the

production of violence.

The role that and individual’s sex plays in the production of violence is also a

strong component. A community’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 more passive population. However, if the sex

ratio is heavily weighted toward males, the population is at risk of a higher rate of

homicide. The passive or aggressive nature of the sexes has the ability to greatly affect

the amount of violence within a community.

Furthermore, the roles of migration and tourism also affect 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). The amount of population density in an urban

area can cause arguments and violence to occur naturally; however, when tourism,

migration, and homelessness are added, the structure of the community is disrupted. This

disruption can cause more conflict and violence to arise, thereby increasing the rates of

homicide.

Suggestions for similar studies in the future involve the inclusion of more social

variables of the individuals and communities. For example, the actual socio-economic

status of both the victim and offender could shed light on the social factors that influence

homicide, such as social segregations and economic impacts on crime rates. The previous

criminal records of persons, whether victim or offender, involved in homicide cases could

also validate and explain the situations and social affiliations in which they are involved.

A history of violence may help raise awareness of some criminals’ actions, as well as help

to understand the type of social network with whom they associate. Incorporating other

urban and rural areas could also improve the dataset, because it would give a broader

picture of the population structures and the spatial distributions seen among each form of

community.

Limitations of the current study include 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 spatial distributions of these crimes to other

societies.

The current paper reflects the emerging importance of applied anthropology in

public service fields such as criminal investigation. The use of human relationships and

community structures help to unveil the cultural issues surrounding 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 and rural community studied, it does reveal

factors that could prove interesting with further investigation. Furthermore, the Tampa

and Lincoln areas are thoroughly neglected in research, and have 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 involved in criminal homicides.

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