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Imagine that you are a crime analyst. You will be creating a geographic crime map that spans three years of crime data.

You will create a CSV file, using an excel document, saved as a CSV file. To build the CSV file you will need to do the following:

Go to the LexisNexis Community Crime Map site: http://communitycrimemap.com/
Choose a crime you would like to search. (e.g., theft, robbery, arson).
Select a geographic area that has 5-10 instances of the specific crime you selected, which occurred in each of the 3 years preceding last year (e.g., if you are taking this course in 2018, then you would search for the specific crime in 2014, 2015, and 2016). NOTE: you want to use a geographic area with a small data set, e.g., a city of a million or more people would likely have a large data set.
Identify 5-10 instances of the crime for each of the 3 years. Click on the icon of the crime incident to ascertain the crime data. For a tutorial on using the crime map, watch the video from the Madison Police department explaining its use: https://www.youtube.com/watch?v=2c09YubbaD8&feature=youtu.be
Using the excel spreadsheet that you will save as a CSV file, create the following columns and input your crime data you have gathered from the crime map:

IR number (“ID” in CSV file)
Name of Crime (“class” in CSV file)
The date and time the crime occurred (“Events time” in CSV file). You must enter the date and time in the following format: yyyy-mm-ddThh:mm:ss
Latitude of crime location (“Point y” in CSV file)
Longitude of crime location (“Point x” in the CSV file)

NOTE: Access the “How to find Latitude and Longitude” document in the Topic 5 Topic Materials as a resource for this assignment.

Conduct analysis on the geographic data using the techniques from the assigned readings. Write a 750-1,000-word summary on the analysis of the data.

Be sure to cite three to five relevant scholarly sources in support of your content.

Utilize the Hunchlab website as a resource for the Topic 5 assignments.

URL:

https://www.hunchlab.com/

Utilize the ArcGIS website as a resource for the Topic 5 assignments.

URL:

http://www.esri.com/arcgis/about-arcgis

Utilize the Maptitude website as a resource for the Topic 5 assignments.

URL:

https://www.caliper.com/maptitude/crime/default.htm

JUS-636 Topic 5 How to Find Latitude and Longitude

In order to locate the Latitude and Longitude of an address:

Put the address into google (maps). Note, if the address contains “x” use “0” as a substitute. For example, if the address is 33xx Camelback road, type in 3300 Camelback road, click “Search”

2. When the address appears (it will be a red blob that looks like a rain-drop), right-click on it. Click on “What’s here?”

3. A small box will appear (see screen shot below)

4. The numbers below the address are the Latitude (33.512802) and Longitude

(-112.127409). Latitude is always listed first, and Longitude second.

March15, 2013

Page 1 of 3

How to Attach Multiple Documents to an
Assignment Submission

1. Access the assignment dropbox. Click the “New Attempt” button in the lower right corner.

2. A “New Attempt” dialogue box will display. Enter the title of the attachment in the “Title”
field. Click “Choose File”

March 15, 2013 Page 2 of 3

3. A file explorer window will display. Find the location of the file to be uploaded. Click on the
file. Click “Open” on the file explorer window.

4. The file will display in the box below the words “Attach Documents.” To attach another file,
click, “Choose File.”

5. A file explorer window will display. Find the location of the file to be uploaded. Click on the
file. Click “Open” on the file explorer window.

March 15, 2013 Page 3 of 3

6. The additional file will display in the box below the words “Attach Documents.” Repeat
steps 3 and 4 to attach additional files. Once all files display in the box below the words

“Attach Documents,” click “Save.”

7. Multiple files will display under the “Attached Documents” area of the assignment dropbox.
To submit the files to TII, click the “Submit” under the Turnitin Report column. Once the TII

report similarity percentage displays, then click the “Submit” button under the “Final

Submission” column.

https://search-credoreference-com.lopes.idm.oclc.org/content/entry/wileycacj/crime_mapping/0?institutionId=5865

(the above is the link if you needed it)

Crime

Mapping

from

The Encyclopedia of

Criminology

and Criminal Justice

Mapping crime has become a routine function of police departments throughout the world. All segments of the criminal justice system use aspects of crime mapping in their daily routine. In addition, crime mapping has been used to advance criminological theory.

crime

· ecology

· environmental criminology

· law enforcement

· spatial analysis

Present day crime mapping is the application of a geographic information system (GIS) in order to understand crime data and the utilization of spatial analysis of crime situations in criminal justice agencies, especially in police work. Even though computer technology has been developed within the last century, the early crime mapping process goes back to the eighteenth century (

Boba

005

). In one of the first instances of crime mapping, crimes against property and persons were plotted on a map in reference to the level of education of the offenders in France. The process of mapping crime was later used in England to study crime situations using area characteristics, such as urbanization and highway proximity (

Paulsen and Robinson 200

The practice of crime mapping was initiated in the United States in the early 1920s by the

Chicago

School. It was done for the purpose of studying the factors behind juvenile delinquency in Chicago. All crime mapping in these early years was done by hand and continued to be done by hand for many decades. It was not until the early 1960s that computers were used to help with the process. In the 1980s the advent of client server technology made GIS more accessible to the police and research communities. For instance, the National Institute of Justice teamed up with researchers and practitioners in five cities (Jersey City, Harfort, San Diego, Pittsburgh, and Kansas City) to use innovative tools in studying drug markets and tracking their movements over time.

Since the 1990s, with the advancement of computer technology as well as police data systems, crime mapping has become a more accessible and practical tool for police practitioners and researchers. GIS software can be installed on desktop computers that have sufficient power to process a large batch of data in seconds. It was during this period that data on crimes, arrests, accidents, and calls for service became available in an electronic format, through either electronic dispatch systems or records management systems. Recent developments in digital mapping (including affordability) and technological advancements in policing have enabled environmental criminologists to explore the special dimensions of criminality in comprehensive ways using crime mapping.

The crime mapping process requires elements of a computer system not much different from other computerized processes. These are computer hardware, software, data, peopleware, and work procedures involving organization policies, safety protocols for the system, and so on. However, there are some requirements specific to computerized crime mapping. For example, certain devices may be needed in the working process (such as a digitizer, a plotter, and a GPS—global positioning system—device). People working in crime mapping should be equipped with backgrounds in GIS technology or have access to a GIS specialist, if needed.

Software products for crime mapping are quite numerous. Some have been created specifically for law enforcement uses, including Crime Stat and RAIDS. Other programs are geographic information software that can be used for the general purpose of mapping. Like other types of software, software for crime mapping can be divided into two categories: open source and proprietary. Open source mapping software is available to any user for little or no fee, and includes such programs as GRASS GIS and SAGA GIS. Proprietary mapping software must be purchased from the provider and includes ArcGIS, Map

D, and Bentley Map, among others.

Creating a crime map incorporates working with data layers. Data involved in the mapping process consist of three parts. The first part is the structure of the layout which is the base map. It contains the basic information (points, lines, and polygons) that make up a map, just like a common map. This base map can be used for mapping in police work, as well as for other purposes of mapping, depending on the user’s needs. The second part, crime data, includes the location and details of the crime incidents that the users intend to incorporate into the crime map. These details may include incident type, date and time of the incident, address or location of the incident, property loss, and so on. This information can be stored in and retrieved from a separate data server or at the same location as the other data used to create the map. The last part is any other data which the users decide to show in the crime map, including points of interest, links to other media (e.g., video clip, audio file, and picture), and other information useful for presenting the map so that it can more easily be understood by viewers.

The process of making a crime map involves putting the data layers together. Recent advances in computer technology, relevant software, and digitized data make it possible to create a crime map digitally. One process crime map creators often need to operate is geocoding, which is the process of transforming the locations of crime data into geographic coordinates, usually in the form of latitude and longitude, ready for crime mapping software to utilize. A geocoder, which could be a piece of software or a web service, can do the transformation for the researcher.

Crime mapping

always involves a large amount of crime and geographic data (more than 10,000 data points are not unusual), making a geocoder a very useful tool. The process of transforming crime locations into coordinates is called address interpolation.

Many theories of crime try to explain individual or group criminal behavior and ignore other contributors to crime, including the environment. Theorists who want to include environmental factors to explain crime have begun to see crime as an event—not just a product of an interaction between people, but an interaction between victims, offenders, and their environments (Paynich and Hill (2009): 97–118). Paynich and Hill mention that after researchers began to examine the contributions of time and space to criminal events, crime mapping became an important tool for both crime fighters and criminologists.

Ecological theories look for explanations of individual actions in general features of the social structure in which an individual is embedded. The social ecology perspective evolved from the early social ecologists in France in the mid 1800s, to the Chicago school in the 1920s, and finally to a recent revival in contemporary studies in the ecology of crime. The social ecology perspective developed into more specifically focused, place-based theories of crime, particularly routine activities theory.

Bernard Cohen did a study (1980) on street-level prostitution and identified “hot spots” of prostitution activity. Cohen’s work is one of the first empirical studies to document the spatial and temporal intersection of “motivated” offenders and the crime-facilitating properties of place proposed by the routine activities theory theorized by

Cohen and Felson (1979)

. Modern GIS capabilities, combined with point data on the locations of individual crimes, make studies like those described here feasible by enabling researchers to routinely obtain measures of crime variables at these nontraditional and smaller levels of aggregation. Consequently, several different theoretical approaches have been made possible, including place-based theories derived from routine activities theory (

Cohen and Felson 1979

) and rational choice theory (

Cornish and Clark 1986

), crime pattern theory (Brantingham and Brantingham 1993), and Crime Prevention Through Environmental Design (CPTED).

Mapping crime has become a routine function of police departments throughout the world. Moreover, all segments of the criminal justice system, from courthouses to correctional institutions, from the probation officer’s desktop to the operation commander’s handheld, uses a variety of crime maps. Hot spot to offender behavior, susceptibility analysis to density analysis, and other crime mapping procedures are part of their daily routine (

Can and Leipnik 2010

). Similarly to other general types of data, crime maps can be used by law enforcement to present crime information in many ways. One way to classify the maps used by law enforcement is to look at it from a crime analysis standpoint.

Crime analysis

aligns mapping used by law enforcement into three basic areas: administrative, strategic, and tactical (Paynich and Hill 2009). Under these areas, six categories of mapping types are commonly used: single-symbol mapping, buffers, graduated mapping, chart mapping, density mapping, and interactive crime mapping. Single-symbol mapping, as suggested by its name, utilizes one type of symbol to represent features (e.g., locations of police stations, schools, incidents, and so on). This type of mapping is not suitable for data with multiple incidents occurring at the same specific locations. A buffer map displays a specific area surrounding a feature, making it possible to find other features located in that area. It is suitable for analysis purposes. For instance, an analyst can use a buffer map to show the number of drug-related incidents occurring within a distance of 1,000 feet from schools in a county during a six month period. Graduated mapping utilizes different sizes or colors of features to represent particular values of variables. Differences in size or color reflect variation in value. Chart mapping incorporates charts (pie and bar) into the map. Density mapping conveys the frequency of incidents in specific areas through color shading, with darker colors representing more incidents. Interactive crime mapping is usually offered to users via internet by police departments. The interactive functions enable users, police and citizens, to conduct some basic crime mapping independently. (See

Figures (1

, 2, 3, 4).)

Source: Salih Can and Prapon Sahapattana.

Locations of murder incidents in police precinct 67, Brooklyn. The map indicates that there were 50 murder incidents in the precinct in recent years. Of these incidents, 26 (52%) occurred at avenues.

Source: Salih Can and Prapon Sahapattana.

Weapon distribution of murder incidents in police precinct 67, Brooklyn. Of the 50 incidents, 46 (92%) were committed with a handgun, two with a blunt instrument, one with a knife, and one by fire.

Source: Salih Can and Prapon Sahapattana.

Victim gender distribution of murder incidents in police precinct 67, Brooklyn. Victims were male in 44 (88%) incidents and female in 6 (12%) incidents.

Source: Salih Can and Prapon Sahapattana.

Locations of murder incidents in police precinct 67, Brooklyn. Outside buildings, 29 (58%); inside buildings, 19 (38%); no data, 2 (4%).

Two approaches to analyzing crime situations are using exact locations of incidents to generate hot spots of crime and using tabular data and statistics to show the analysis. “Hot spots” are concentrations or clusters of crimes in space. There are three fundamental ways of identifying crime hot spots; visual inspection, statistical identification, and the theoretical approach. Crime can be mapped in areas or layers (for instance, by socioeconomic status, traffic intersection, or location of places) and then put on top of each other. Viewing these layers together makes it possible to see the natural patterns of movement for the people who use the area, the location of potential offenders, and likewise potential targets. This provides a reasonable initial explanation for a concentration or clustering of crimes. Brantingham and Brantingham (1999) find it helpful to describe these levels or layers of crime potential as the environmental setting, with a more detailed description of normal movement patterns, location of potential crime generators, location of potential crime attractors, characteristics of place, and ecological labels. Hot spots are also directly related to structural setting, which is composed of relatively slowly changing elements (e.g., laws, social norms, class structures, the division of labor, the physical layout of the built environment, the local topography, the local climate, and so forth) and the activity setting, which is composed of routines and adventures. The activity setting is shaped by mobility and awareness factors that are tied to personality, age, social status, income, education, and a variety of other social and psychological characteristics, as well as to positioning in physical space-time.

For purposes of analysis, a statistical classification is beneficial for crime analysts both to understand the crime situation and to present the situation to the audience. That is, the data in focus will be categorized into groups for investigation. A statistical classification is used in crime mapping in order to determine how the data on the map will be displayed in the way that is helpful to the viewers to more clearly understand the data. Generally, four statistical classifications can be found in the crime mapping process: natural breaks, equal interval, quartile, and standard deviation. The type of classification is commonly exhibited in the legend of the map.

Natural breaks classification is the default in many geographic information system programs and also the most popular in crime analysis. Generally, it is exercised to explore the data first and also to obtain a quick analysis of the data. Equal interval classification could be used by a crime analyst for data with the same number of incidents in each interval. The nature of crime incidents, however, does not follow this pattern. Consequently, this type of classification is not often used for the final crime map. A quartile map is used for comparison between data sets in different maps. In each map, the different symbols are displayed in order for viewers to see the crime situation on the map. Finally, a map using the standard deviation classification involves utilizing the means and standard deviations of the data to establish the break points of the groups. This type of map is useful for comparison purposes between different data sets. Finally, an analyst could use a manual method to decide the break points. The method utilized to create a map and how many categories to use, should be determined by the analyst, according to the purpose of and audience for the map.

Crime mapping is unquestionably salient and accepted among law enforcement agencies. Mapping crime data, as well as data related to disorder problems, enables police officers to learn the location of crime or disorder incidents. It also reveals the reasons behind the problems through the process of crime analysis. Research shows that a great number of large police departments have been using computerized crime mapping for almost 15 years (Mamalian & LaVigne 1999). Research also confirms the use of computerized crime mapping by small departments, as well. They use crime maps for staff allocation decisions, evaluation of intervention programs, informing citizens about crime situations in their neighborhoods, and identification of repeat crime locations, as well as for calls for service.

The rapidly decreasing price and user friendly nature of desktop computers and GIS software has made crime mapping much more prevalent. Currently, the integration of crime mapping with GPS has increased the accuracy of the incident locations. People are increasingly familiar with the integration of crime mapping due to web-based applications and multimedia, such as audio, video, and pictures. Adding advanced statistical programs to the picture, more sophisticated analyses are rendered more advantageous to officers and citizens. The trend of crime mapping, as well as crime analysis, is likely to continue to grow in law enforcement agencies, especially as new technologies to enhance the utility of this practice are developed.

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Full text Article

Crime Mapping

World of Criminal Justice, Gale

The term, computerized crime mapping, refers to technology that enables law enforcement agencies to analyze and correlate crime data within a…

lable at ScienceDirect

Applied Geography 79 (2017) 203e211

Contents lists avai

Applied Geography

journal homepage: www.elsevier.com/locate/apgeog

The crime kaleidoscope: A cross-jurisdictional analysis of place
features and crime in three urban environments

Jeremy D. Barnum a, *, Joel M. Caplan a, Leslie W. Kennedy a, Eric L. Piza b

a Rutgers Center on Public Security, Rutgers University School of Criminal Justice, Newark, NJ, USA
b John Jay College of Criminal Justice, City University of New York, USA

a r t i c l e i n f o

Article history:
Received 25 May 2016
Received in revised form
17 October 2016
Accepted 22 December 2016
Available online 17 January 2017

Keywords:
Crime pattern theory
Risk terrain modeling
Spatial influence
Robbery

* Corresponding author. School of Criminal Justice,
123 Washington Street, Newark, NJ, 07102, USA.

E-mail address: jeremy.barnum@rutgers.edu (J.D.

http://dx.doi.org/10.1016/j.apgeog.2016.12.011
0143-6228/

a b s t r a c t

Research identifies various place features (e.g., bars, schools, public transportation stops) that generate or
attract crime. What is less clear is how the spatial influence of these place features compares across
relatively similar environments, even for the same crime. In this study, risk terrain modeling (RTM), a
geospatial crime forecasting and diagnostic tool, is utilized to identify place features that increase the risk
of robbery and their particular spatial influence in Chicago, Illinois; Newark, New Jersey; and Kansas City,
Missouri. The results show that the risk factors for robbery are similar between environments, but not
necessarily identical. Further, some factors were riskier for robbery and affected their surrounding
landscape in different ways that others. Consistent with crime pattern theory, the results suggest that the
broader organization of the environmental backcloth affects how constituent place features relate to and
influence crime. Implications are discussed with regard to research and practice.

1. Introduction

Crime can happen anywhere, but some places are more likely to
experience crime than others. Research has found that a majority of
police demand originates from just a few places (e.g., Braga,
Hureau, & Papachristos, 2011; Braga, Papachristos, & Hureau,
2010; Sherman, Gartin, & Buerger, 1989; Weisburd, Groff, & Yang,
2012). Places are “very small micro units of analysis,” including
specific addresses, groups of addresses, block faces, or street seg-
ments (Weisburd, 2008, p. 2). Crime concentrates at certain places
because of their unique social and physical qualities, which creates
context that invites and sustains legally problematic behavior (Eck
& Weisburd, 1995; Kennedy, Caplan, & Piza, 2011).

Environmental criminological theories (Wortley & Mazerolle,
2008) frame crime events within the context of the environ-
mental backcloth (Brantingham & Brantingham, 1993). Distributed
through this backcloth are place features, such as bars, schools, or
public transportation stops that generate and attract crime (e.g.,
Brantingham & Brantingham, 1995). However, environments are
highly complex, and though many of the same features exist within
different environments, their overall form and function is distinct

Rutgers University e Newark,

Barnum).

(Lynch, 1960). Kennedy (1983) refers to the kaleidoscopic organi-
zation of place features about the urban landscape resulting from
variety of forces (i.e., historical, cultural political, and economic)
that influences its past and ongoing development. Poon (2015)
posits that environments have their own “spatial DNA.” Given the
relative organization of each jurisdiction’s environmental back-
cloth, the spatial influence of constituent place features on crime
may not necessarily generalize across environments, even for the
similar types of crime.

This study compares the criminogenic spatial influence of place
features in different urban environments. It is hypothesized that
place features commonly assumed to correlate with crime may not
have a static influence, even across similar types environments for
similar types of crime. Risk terrain modeling (RTM), a geospatial
crime forecasting and diagnostic tool (Caplan, Kennedy, & Miller,
2011), is utilized to identify place features that increase the risk
of robbery and their particular spatial influence in Chicago, Illinois;
Newark, New Jersey; and Kansas City, Missouri. The results show
that the significant risk factors for robbery were similar across
environments, but not necessarily identical. In other words, just
because a given place feature aggravated robbery in one jurisdic-
tion does not necessarily mean it did so in another. Further, some
factors were riskier for robbery and affected their surrounding
landscape in different ways than others. Consistent with theories of
environmental criminology, the results suggest that the broader

mailto:jeremy.barnum@rutgers.edu

http://crossmark.crossref.org/dialog/?doi=10.1016/j.apgeog.2016.12.011&domain=pdf

www.sciencedirect.com/science/journal/01436228

http://www.elsevier.com/locate/apgeog

http://dx.doi.org/10.1016/j.apgeog.2016.12.011

J.D. Barnum et al. / Applied Geography 79 (2017) 203e211204

organization of the environmental backcloth affects how constit-
uent place features relate to and influence crime, which has im-
plications for theories of crime and place and policy implications
pertaining to the ways in which police should respond to prob-
lematic places throughout their jurisdiction to achieve crime
prevention.

2. Conceptual framework

In the mid-20th century Shaw and McKay (1942) observed that
juvenile delinquency was unevenly distributed throughout the
environmental landscape of Chicago. Specifically, they demon-
strated that delinquency was highly concentrated in areas sur-
rounding the center of the city and gradually declined in areas
moving radially outward towards the edges of the city in a fashion
consistent with the concentric zone model develop by Burgess
(1925). Further, they found that delinquency remained highly
concentrated in these particular areas over time, regardless of the
people who lived there.5They attributed their findings to social
disorganization caused by broader structural forces of communities
such as poverty, residential mobility, and demographic
heterogeneity.

Subsequent research had difficulty generalizing these particular
spatial patterns of crime to other cities (Bursik, 1988). Following
World War II there was a large-scale population movement; “Dirty
industries left [the inner city] to be in the suburbs, or even other
developing countries. Downtown living became a luxury, and
former working class neighborhoods [were] invaded by pro-
fessionals in the process of gentrification” (Andresen, 2014, p. 21).
Ecological change was the norm and the concentric zone model
used by Shaw and McKay to illustrate the distribution of crime in
Chicago did not necessarily “fit” other cities. Indeed, alternative
ecological models such as the sector model (Hoyt, 1939) and the
multiple nuclei model (Harris & Ullman, 1945) were developed to
describe the ecological structure of other cities. Another limitation
of this research was that “environments” from this perspective
largely referred to community structural characteristics of an area,
rather than the physical qualities of places (Kennedy, 1983). Yet, the
built environment plays an important role in organizing human
behavior and thereby providing ample opportunity for crime.

Crime pattern theory (Brantingham & Brantingham, 2008) in-
tegrates notions of rational choice (Clarke & Cornish, 1985) and
routine activities (Cohen & Felson, 1979) to describe this relation-
ship. Essentially, crime is the product of decisions about offending
and the distribution of offenders, targets, and guardians, each of
which are shaped by the physical environmental landscape. Spe-
cifically, willing offenders are cued as they encounter viable op-
portunities for crime. Decision templates provide offenders with a
mechanism for recognizing and discerning good from bad targets.
Decisions that lead to successfully carrying out a criminal act
reinforce the template; if unsuccessful, the template is revised to
avoid such decisions in the future.

Crime opportunities arise within the context of the environ-
mental backcloth (Brantingham & Brantingham, 1993), which in-
cludes individuals’ routine activities and the underlying networks
of roads, buildings, and other infrastructure. Offenders and victims
traverse the environment, engaging in their normal routines and
traveling among their regular activity spaces. Crime occurs when
offenders’ encounter a target that fits their decision template. Such
encounters are more likely to occur at places that facilitate the

5 “Areas” is utilized intentionally. The bulk of urban ecological research has
focused on large areal units, such as census tracts or blocks. In contrast, more recent
research has focused specifically on “places” as described by Weisburd (2008).

“overlapping lifestyles or spatio-temporal movement patterns” of
offenders and targets (Brantingham & Brantingham, 2008, p.87).
Certain places do so more than others because they contain features
that generate or attract crime (Brantingham & Brantingham, 1995).
Crime generators concentrate a large number of people, both po-
tential offenders and victims, in specific locations at the same time.
Crime is more likely at crime generators because of the large
number of interactions that take place. Conversely, crime attractors
specifically draw motivated offenders given well-known criminal
opportunities.

Place features that generate and attract crime are distributed
throughout the landscape along various paths, or the routes people
take (e.g., roads, sidewalks, etc.) and edges, or distinct changes in
the landscape (e.g., railways, changes in land use, neighborhood
boundaries, etc.), which create nodes, or areas of intense activity
(Brantingham & Brantingham, 1984). The distribution of these
features throughout each jurisdiction’s environmental backcloth is
unique as the result of various processes involving local policies and
regulations with regard to zoning, infrastructure, and urban plan-
ning. Physical landscapes are constructed around natural terrains
and molded around particular social, cultural, historical, and eco-
nomic systems all of which influence their unique form and func-
tion and ongoing change and development. The combination of
these forces ensures distinctiveness in the image of cities and the
ways in which behavior within them unfolds (Lynch, 1960).
Kennedy (1983, p.11) conceptualizes this through the analogy of a
kaleidoscope (see Fig. 1). The kaleidoscope represents an environ-
ment (e.g., City A) and the shards of glass embody place features
(e.g., bars, restaurants, public transportation stops) within that
environment. The arrangement of place features encompasses an
environment’s form. Moving from one environment to the next
(e.g., from City A to City B), or turning the kaleidoscope, alters the
form of that environment. Central to the analogy is that the
patterning of features varies between environments, but the parts
and processes that create these patterns are the same. Thus, it is the
particular combinations of features at places in different environ-
ments that must be identified to understand the distribution of
behaviors and crime.

In sum, early ecological research demonstrated that crime is
more likely in some areas of a city compared to others and sug-
gested that there is value in considering what it is about those
areas, beyond the individuals that exist there, that foster illegal
behavior. However, this perspective primarily focused on commu-
nity structural characteristics and largely neglected the influence of
the physical features of environments on crime. Modern advances
in data and technology have allowed researchers to demonstrate
that crime is highly concentrated at very specific places throughout
the geographic landscape. In this regard, several perspectives have
emerged, falling under the broader realm of environmental crimi-
nology to provide a theoretical basis to this phenomenon. These
perspectives discuss how physical place features throughout the
environmental backcloth can generate or attract crime by struc-
turing the everyday routines of individuals and creating good op-
portunities for offending. However, each jurisdiction has a unique
backcloth and the particular ways in which certain features come
together to create conditions for illegal behavior may not gener-
alize, even for the same crime. Therefore, it is important to identify
these patterns within the environmental backcloth of each juris-
diction to better understand the more localized spatial dynamics of
crime.

3. The study

The purpose of this study is to examine the physical landscapes
of different environments and their relative influence on crime.

Fig. 1. (A) The kaleidoscope (i.e., environment) is composed of shards of glass (i.e.,
place features). The image (i.e., environmental form) that emerges is the result of the
particular arrangement of glass shards. Turning the kaleidoscope (i.e., viewing another
environment) alters this image. Crime concentrates at certain places within the
environment, given the confluence of certain features that come together and create
conditions that are conducive to offending.

6 Funded by the National Institute of Justice (Award #2012-IJ-CX-0038).
7 http://www.infogroup.com/.

J.D. Barnum et al. / Applied Geography 79 (2017) 203e211 205

More specifically, we utilize RTM to diagnose and compare crimi-
nogenic place features and their spatial influence on robbery in
Chicago, Newark, and Kansas City. Consistent with environmental
theories of crime it is assumed that certain place features will in-
crease the risk for robbery. However, it is hypothesized that these
features and their influence will vary across environments because
they are uniquely patterned throughout each jurisdiction’s envi-
ronmental backcloth. Fig. 1 illustrates how place features are
distributed throughout the environmental backcloth in a way that
is conceptually similar to the shards of glass in a kaleidoscope.
Crime concentrates at places within any given environment given
the confluence of features that create conditions that are conducive
to offending. However, observing another environment, or turning
the kaleidoscope, alters these patterns and the resulting image. As
environments change, so too do the distributions of people, of-
fenders, targets, and guardians, and their routines, and the ideal
opportunities for crime. Thus, it is important to examine the unique
ways in which the environmental backcloth of different jurisdic-
tions allows crime to emerge and persist.

3.1. Risk terrain modeling

The premise of RTM is that certain places have particular fea-
tures that, when combined in prescribed ways, create context in
which crime becomes more likely (Caplan et al., 2011, p. 365). The
purpose of RTM is to diagnose place-based risk factors for crime
and then identify where they are collocated to increase vulnera-
bility to crime (Kennedy, Caplan, Piza, & Buccine-Schraeder, 2015).
The process of RTM involves a general series of steps beginning
with the selection of an outcome event, a study setting, and time
period. Then, all potentially risky place features are identified. The
spatial influence of each feature is operationalized to a continuous
surface of raster GRID cells in a geographic information system

(GIS). This produces a set of separate layers representing the spatial
influence of each feature at every micro-level place (i.e., cell). Each
risk layer is then empirically tested, weighted, and then combined
with other statistically significant layers to create a composite risk
terrain layer with each cell containing a value indicating the spatial
influence of all risky place features throughout the entire
geography.

3.2. Data and study settings

All data for this study were obtained as part of a larger study
carried out in six cities across the United States (see Kennedy,
Caplan, & Piza, 2015).6 This presented a unique opportunity to
investigate how place features influence crime across multiple
environments at the same time in a consistent and standardized
way. All data were sourced from the administrative records of po-
lice departments, purchased from Infogroup7, or collected online
from publicly available databases as shapefiles or XY coordinates.

The study settings included Chicago, Illinois, Kansas City, Mis-
souri, and Newark, New Jersey. Chicago has the largest population
with nearly 2.7 million people over a total land area of approxi-
mately 227 square miles. Kansas City has the second largest pop-
ulation, with about 460,000 people, and the largest land area of
about 315 square miles. Finally, Newark has both the smallest
population and land area with approximately 277,000 residents
living within just 24 square miles. However, the population density
in Newark (11,458 persons per square mile) is similar to Chicago
(11,841 persons per square mile), where Kansas City is much less
densely populated (1459 persons per square mile).

3.3. Outcome event

Outcome events were calendar year 2012 robbery incidents (see
Table 1). Robberies are often a substantial source of fear among
urban residents (Wright & Decker, 1997). Because over half involve
a weapon, robberies have a high potential for serious harm (Federal
Bureau of Investigation, 2012). The robbery rate in each study
setting was three to six times higher than the national average
(Federal Bureau of Investigation, 2012).

3.4. Potential risk factors

Various place features may aggravate or otherwise increase the
risk for robbery. In total, 14 types of place features were selected in
accordance with theory or existing empirical research to be tested
for association with robbery in each study setting (see Table 1).
Crime pattern theory describes how place features can act as crime
generators by bringing together a large number of individuals for
otherwise legitimate activity thereby increasing the potential for
offenders to encounter ideal targets absent sufficient guardianship.
Such features may include restaurants, retail shopping outlets,
entertainment venues, commercial businesses, or public trans-
portation stops (Brantingham & Brantingham, 1995). Conversely,
place features may function as crime attractors, specifically draw-
ing in individuals seeking to exploit well-known criminal oppor-
tunities. Crime attractors may include bars, illicit markets, or
insecure parking lots (Brantingham & Brantingham, 1995). Gener-
ally speaking, classification of particular place features as crime
generators or attractors is based on theoretical insights pertaining
to the particular mechanisms that tie together those features and
the specific crime problem at hand. Research in Chicago, for

6 reasons why a clean data foundation is crucial for AI marketing

Table 1
Place feature counts and analysis parameters of risk terrain models for calendar year
2012 robbery incidents in Chicago, Kansas city, and Newark.

Variable Chicago Kansas City Newark

Outcome Event
Robbery Incidents 13,480 1638 2001
Place Features
Drug Arrests 3334 2521 4778
Parks 10,581 24,037 1350
Pawn Shops 68 14 36
Bars 1316 106 192
Foreclosures 15,305 311 779
Gas Stations 140 52 41
Grocery Stores 933 75 223
Health Centers & Gyms 176 34 11
Laundromats 173 28 37
Liquor Stores 926 258 87
Parking Stations 218 23 28
Schools 1021 171 124
Variety Stores 124 34 7
Bus Stops 10,711 3790 922

Model Parameters;
Cell Size/Block Length (in feet): Chicago (213/426); Kansas City (231/462); Newark
(226/452).
Spatial Operationalization: Proximity and Density (Parks as Proximity; Drug Mar-
kets as Density).
Spatial Influence Extent/Analysis Increments: 3 Blocks/Half Blocks.

8 RTMDx can also create “protective” models, which search for negative spatial
correlations between place features and crime. These features mitigate or otherwise
decrease the risk of crime.

9 Arbitrary nodes were removed to ensure breaks in street segments only

J.D. Barnum et al. / Applied Geography 79 (2017) 203e211206

example, examined the spatial dynamics of drug dealing and found
that incidents of marijuana, heroin, crack, and cocaine dealing each
exhibited unique set of place-based risk factors with variable
spatial influence (Barnum et al., 2016).

Existing research provides guidance on specific place features
that facilitate robbery. For example, an important factor in a rob-
ber’s decision to offend is the presence or availability of cash or
other goods that can be quickly converted into cash (St. Jean, 2007;
Wright & Decker, 1997). Bernasco and Block (2011) examined
numerous place features in Chicago that operate “small cash
economies” where several individuals are likely to have cash on
hand and found that several features, such as bars, liquor stores,
grocery stores, gas stations, laundromats, liquor stores, pawnshops,
and drug markets, among others, were related to robbery. Wright
and Decker (1997) and St. Jean (2007), based on interviews with
actual robbers, explain how these types of place features may
attract robbers for additional reasons. For example, bars or liquor
stores contain intoxicated patrons who are particularly vulnerable
because they are not in a position to offer resistance (see also Groff,
2014; Roncek & Bell,1981; Roncek & Maier,1991). Pawnshops allow
robbers to easily and quickly convert stolen goods into cash.
Moreover, they could provide robbers with easy access to weapons
to commit their offense. Drug markets may facilitate robbery
because drug buyers and sellers, owing to their extralegal activities,
are not likely to report their victimization.

Other important elements that inform the decision calculus of
robbers relate to the accessibility and familiarity of places. In other
words, robbers prefer places that are easy to get to and offer quick
escape once they have committed their robbery (St. Jean, 2007;
Wright & Decker, 1997). This is likely to include places with fea-
tures such as grocery stores, gas stations, laundromats, or other
types of retail and commercial stores (Bernasco & Block, 2011;
Smith, Frazee, & Davison, 2000). These types of businesses are
likely to be located in popular areas that are frequented by a large
number of people. They are convenient to travel to, owing to their
connectedness to the rest of the city to enhance people traffic to
increase business. They are likely to be located near parking loca-
tions or public transportation stops, other features that have been
associated with crime, and specifically robbery (Bernasco & Block,

2011; Groff & Lockwood, 2014; Hart & Miethe, 2014; Smith et al.,
2000). These features also reduce the risk for detection because
they have an ongoing rhythm of activity in which to easily blend in
(St. Jean, 2007; Wright & Decker, 1997).

Other features may create suitable conditions for robbery
because they lack sufficient guardianship and thereby enhance the
probability that a robber will successfully offend and remain
anonymous. For example, Groff and McCord (2012) found that
parks increased crimes in the surrounding areas, particularly when
they had characteristics that reduced guardianship. Because they
are unattended, foreclosures may also make robbery more likely.
Spelman (1993) found that crime was higher near abandoned
properties; a number of additional studies have documented a link
specifically between foreclosures and violent crime (Baumer, Wolff,
& Arnio, 2012; Bess, 2008; Ellen, Lacoe, & Sharygin, 2013). Several
studies have found schools to be associated with higher levels of
violent crime (Bernasco & Block, 2009; Roncek & Faggiani, 1985;
Roncek & Lobosco, 1983). Bernasco and Block (2009) suggest that
schools provide good targets, but schools themselves may have
high surveillance. However, this might not be the case in the sur-
rounding areas, or after hours, where individuals may congregate
unsupervised. Finally, health centers and gyms have received little
attention in prior research, but may provide ideal targets for rob-
bers. For example, these features may be attractive to robbers
because they are often open around the clock and unstaffed (and
consequently unguarded). People visit the gyms late at night to
avoid crowds, often carrying only their wallet or a small electronic
device such as an iPhone or iPod that can be easily grabbed as
someone is coming or going to the gym and then converted into
cash (e.g., see Caplan & Kennedy, 2016, p. 99). Also, gyms are often
located in highly trafficked areas owing to the need to be easily
accessible for people on the way to and from work, school, or other
daily activities.

3.5. Model parameters and operationalization

The Risk Terrain Modeling Diagnostics Utility (RTMDx; Caplan &
Kennedy, 2013) was used to create RTMs for calendar year 2012
robbery incidents and examine risky place features and their
spatial influence in Chicago, Newark, and Kansas City. Besides the
study setting boundaries, all other parameters were standardized
across models to generate results that reflected variation in each
jurisdiction’s environmental backcloth rather than variations in
model parameters or methods of testing.

First, “aggravating” models were specified, which identify pos-
itive spatial associations between potential risk factors and the
outcome event, to determine risky place features8. Next, “cell sizes”
and “block lengths,” which served as the units of analysis for each
model, were specified as one-half the average block length and the
average block length in each study setting, respectively (Caplan,
Kennedy, & Piza, 2013). This is consistent with research suggest-
ing the importance of examining the dynamics of crime at the
micro unit of analysis (Weisburd, Bernasco, & Bruinsma, 2008). Cell
sizes and block lengths were determined via street centerline files9

(see Table 1).
The next set of parameters included “spatial operationalization,”

“maximum spatial influence,” and “analysis increments” for each
place feature tested. Spatial operationalization describes the

occurred at true intersections.

Table 2
Risk terrain model results for calendar year 2012 robbery incidents in Chicago,
Kansas city, and Newark.

Risk factor Chicago Kansas City Newark

O/SI RRV O/SI RRV O/SI RRV

Foreclosures P/852 4.51 P/1386 1.68 P/1356 9.61
Gas Stations P/213 4.60 D/462 2.11 P/226 2.65
Grocery Stores P/1065 1.57 P/1386 1.73 D/1356 1.47
Health Centers & Gyms e e e e e e
Laundromats P/213 2.27 e e P/226 2.89
Parking Stations P/213 1.96 e e P/904 1.53
Variety Stores P/1278 1.25 D/1386 1.64 e e
Bus Stops D/426 2.55 D/1155 5.38 P/226 3.68
Bars P/213 1.83 e e P/678 1.46
Drug Markets D/1065 2.36 D/231 8.69 D/226 2.39
Schools P/1278 1.39 e e P/1356 1.57
Parks e e P/1386 1.57 e e
Liquor Stores P/213 2.97 P/1386 2.19 P/1356 1.50
Pawn Shops P/1278 1.29 e e e e

Note: O: Operationalization (P¼Proximity or D ¼ Density); SI: Spatial Influence (in
feet) RRV: Relative Risk Value.

J.D. Barnum et al. / Applied Geography 79 (2017) 203e211 207

particular influence of each feature; that is, crime risk can be higher
at places as a function of proximity to features or at places with a
dense concentration or clustering of features. Theoretically, either,
both, or neither operationalization of features could be true, given
the particular environment (for a detailed discussion of oper-
ationalizing spatial influence, see Caplan, 2011). To determine
when features generate the most risk, the spatial operationalization
of most features was tested as both proximity and density10. There
were two exceptions. First, parks were represented as polygon
shapefiles, but RTMDx only accepts point features. Therefore, park
polygons were converted to a representative grid of points and
tested as proximity only. Second, drug arrest incidents11 represent a
“fleeting” phenomenon that may reflect a standing quality of the
environment when concentrated at places (Caplan et al., 2013) and
tested as density only.

Finally, research suggests that the spatial influence of place
features is geographically limited and decays with distance (e.g.,
Groff & Lockwood, 2014). Therefore, the spatial influence of each
feature was tested to a maximum extent of three blocks at half-
block analysis increments. In sum, the spatial influence of each
place feature was tested at one-half block; one block; one-and-one-
half a block; two blocks; two-and-one-half blocks, and three blocks,
as a function of proximity and density, respectively.

3.6. Statistical analysis

The statistics of RTMDx are only briefly described here (for a
detailed discussion, see Heffner, 2013, p. 35). RTMDx begins by
building an elastic net penalized regression model assuming a
Poisson distribution of events. Cross-validation is used to reduce
the initial set of variables.12RTMDx continues by building additional
models, one assuming a Poisson distribution and the other a
negative binomial distribution. The Bayesian Information Criteria
(BIC) is measured for a null model and then again as each new
variable is added. RTMDx continues in this iterative fashion until
the addition or removal of a new variable does not allow the model
to surpass the BIC score of the previous candidate model. RTMDx
finishes by choosing the model with the lowest BIC score between
the two distributions. Coefficients for the remaining variables (i.e.,
risk factors) are rescaled between the minimum and maximum risk
values to produce relative risk values (RRV),13 weighting each factor
relative to one another. Relative risk scores (RRS) are computed for
each cell within the study setting.

4. Results

Table 2 presents the risk factors for robbery in each study
setting. For each risk factor, the models identified the optimal
spatial operationalization, the maximum spatial influence, and a
RRV. Of the 14 types of place features tested,12 were risk factors for
robbery in Chicago; 8 in Kansas City; and 10 in Newark. RRVs
ranged from 1.25 to 4.60 in Chicago, from 1.57 to 8.69 in Kansas City,
and from 1.46 to 9.61 in Newark. Gas stations were the riskiest

10 RTMDx measures proximity using Euclidean (i.e., straight-line) distance. Kernel
density estimation (KDE) is used to measure density.
11 Following previous studies, drug arrest incidents are utilized to represent drug
markets (e.g., see Eck, 1995; Rengert, Ratcliffe, & Chakravorty, 2005; Weisburd &
Green, 1995).
12 Spatial autocorrelation can be problematic to the extent that when present,
significance values may be affected and Type I errors are more likely. However,
RTMDx employ cross-validation, which deemphasizes significance testing for var-
iable selection (see Heffner, 2013: 38).
13 Relative risk values are calculated by exponentiating risk factor coefficients
provided by the risk terrain model.

factor in Chicago. Places located within 213 feet (i.e., one-half a
block) of a gas station were at 4.60 more risky for robbery compared
to places absent the spatial influence of any risk factors. In Kansas
City, the riskiest factor was drug markets and places where drug
arrest incidents clustered within a 231-foot (i.e., one-half block)
bandwidth were 8.69 times more risky for robbery compared to
places absent any risk factors’ influence. Foreclosures were the
riskiest feature in Newark and places within 1356 feet (i.e., three
blocks) of them were 9.61 as risky for robbery compared to places
where no risk factors’ influence was present.

Six features, including foreclosures, gas stations, grocery stores,
bus stops, drug markets, and liquor stores were risk factors for
robbery in all three study settings. This is largely consistent with
findings reported by other studies (e.g., Bernasco & Block, 2011;
Hart & Miethe, 2014; Smith et al., 2000; Spelman, 1993; St. Jean,
2007; Wright & Decker, 1997). Some commonly identified corre-
lates of robbery (as reported in aforementioned studies), however,
were not risk factors in one or more study settings despite theo-
retical expectations or extant research suggesting they would have
been. For example, parks were not risk factors for robbery in Chi-
cago; laundromats, parking stations, bars, schools, and pawn shops
were not risk factors in Kansas City; and variety stores, parks, and
pawn shops were not risk factors in Newark. Health centers and
gyms were not risk factors for robbery in any study setting.

Variations were observed in risk factors’ spatial influences,
specifically, their operationalizations, extents of influence, and
weights of influence. With regard to operationalization, risk could
be higher for robbery near features or at places where features
clustered. Although foreclosures, gas stations, grocery stores, bus
stops, and liquor stores were risk factors for robbery in all three
study settings, only foreclosures and liquor stores shared the same
operationalization. In all three study settings, risk was higher near
foreclosures and liquor stores. Operationalizations varied for gas
stations, grocery stores, and bus stops. For example, risk was also
higher near gas stations in Chicago and Newark, and near grocery
stores in Chicago and Kansas City. On the hand, risk was higher at
places where gas stations clustered in Kansas City and at places
where grocery stores clustered in Newark. Similarly, risk was
higher at places where bus stops clustered in Chicago and Kansas
City, but near bus stops in Newark. Drug markets was tested only as
a function of density and was therefore operationalized as such in
all three study settings.

The spatial influence of each feature was tested to a maximum
extent of 3 blocks in half-block increments and the results show

J.D. Barnum et al. / Applied Geography 79 (2017) 203e211208

that the risk factors for robbery affected a different range of space
across the three study settings. For example, although the spatial
influence of liquor stores extended to just half a block in Chicago, it
reached a full three blocks in Kansas City and Newark. Likewise, the
spatial influence of drug markets in Chicago extended 2.5 blocks,
but was limited to just half a block in Kansas City and Newark. Such
variations could be observed for other risk factors between study
settings, such as parking stations, bus stops, and bars. However,
other risk factors, such as foreclosures, gas stations, grocery stores,
Laundromats, variety stores, and schools, had similar extents of
spatial influence.

Finally, some risk factors aggravated robbery more in certain
study settings compared to others. This was measured as a function
of each risk factors’ RRV, essentially weights of influence. This was
most salient for foreclosures, which were much riskier for robbery
in Newark (RRV ¼ 9.61) than Chicago (RRV ¼ 4.51) or Kansas City
(RRV ¼ 1.68). Moreover, gas stations generated over twice as much
risk for robbery in Kansas City (RRV ¼ 2.11) and Newark
(RRV ¼ 2.65), but nearly five times as much risk in Chicago
(RRV ¼ 4.60). Drug markets were nearly nine times as risky for
robbery in Kansas City (RRV ¼ 8.69), but just over twice as risky in
Chicago (RRV ¼ 2.36) and Newark (RRV ¼ 2.39). Finally, bus stops
were over twice as risky for robbery in Chicago (RRV ¼ 2.55), over
three times as risky in Newark (RRV ¼ 3.68), and over five times as
risky in Kansas City (RRV ¼ 5.38). Although the same risk factors
were oftentimes more problematic in one setting compared to
another, some consistencies emerged. For example, though signif-
icant aggravators for robbery, grocery stores, parking stations, bars,
and schools were generally less problematic than other place
features.

5. Discussion

RTM, a geospatial crime forecasting and diagnostic method, was
utilized to identify risk factors for robbery and their spatial influ-
ence for comparison in Chicago, Newark, and Kansas City. The goal
was to examine the physical landscape of different urban envi-
ronments and their relative influence on crime. As environmental
theories of crime would suggest, it was assumed that certain place
features would increase the risk for robbery. However, it was hy-
pothesized that these features and their particular influence on
robbery would vary from one environment to the next given their
unique patterning throughout each jurisdiction’s environmental
backcloth. Upon peering through each environment’s crime risk
kaleidoscope, a number of interesting findings emerged. First, each
study setting had a unique set of risk factors for robbery. A second
important finding was that the risk factors were oftentimes more
problematic in some study settings as compared to others. Third,
the spatial influence of risk factors oftentimes varied across study
settings. In other words, risk was higher near features in some
settings and at places where features clustered in other settings.
Moreover, while the spatial influence of certain features extended
several blocks in some settings it was relatively limited in others.
Collectively, these findings suggest a unique set of underlying
spatial dynamics that influence the emergence and persistence of
robbery at places within each jurisdiction. Crime pattern theory
(Brantingham & Brantingham, 2008) suggests that these divergent
spatial dynamics are the product of nuances in the physical aspects
of each jurisdiction’s environmental backcloth. As human creations
and artifacts of more localized social, cultural, legal and economic
processes, built environments are unique in their forms and func-
tions. The ways in which physical landscapes shape opportunities
for crime is not necessarily the same from one environment to the
next.

This is reflected by differences in risk factors for robbery and

their spatial influence across jurisdictions. Although it is not
possible to speculate about all the variations observed in the
findings, more broad theoretical considerations are warranted. For
example, risk was higher near features in some jurisdictions but
higher at places where the same types of features clustered in other
jurisdictions. One possible explanation is that risk as a result of
proximity to features may be reflective of the feature’s crime
attracting properties (Brantingham & Brantingham, 1995). In other
words, proximity to gas stations in Chicago and Newark could
suggest that in these jurisdictions, gas stations have specific qual-
ities that make robbery more likely nearby, such as being open late,
providing a place for people to hang out (if there is a convenience
store that sells cheap food and drink), and drawing a regular supply
of customers who have cash on hand that make good targets.
Conversely, risk at places due to the clustering of features may be
more reflective of the feature’s crime generating properties
(Brantingham & Brantingham, 1995). Perhaps in a large, relatively
dispersed jurisdiction such as Kansas City, many gas stations are
otherwise isolated in low traffic areas so proximity to just any gas
station is not very risky. However, places where gas stations are
clustered may be high activity nodes whereby both offenders and
targets are concentrated and interact more frequently to increase
the chances that good opportunities for crime will arise.

The distribution of place features along certain paths and rela-
tive to various types of edges may help to explain differences in
extents of spatial influence too (Brantingham & Brantingham,
1993). For example, paths consist of the underlying network of
roads, sidewalks, or other routes of travel. Place features commonly
located along more complex path networks may allow for a greater
extent of influence as these networks have a greater capacity to
support the everyday travel of offenders and victims in nearby
areas. Conversely, robbery is more likely to happen at or very close
to features that are located on a single, isolated road (e.g., see
Beavon, Brantingham, & Brantingham, 1994; Davies & Johnson,
2015). Edges, or physical and perceptual barriers in the environ-
ment, could also play an important role in shaping the extent of
influence of risk factors. For example, features near strong and clear
edges are likely to host crimes within a very small area compared to
features near “fuzzy” edges, which disperse crime over a large area
(Brantingham, Brantingham, Vajihollahi, & Wuschke, 2009). Addi-
tional research is required to directly incorporate path networks
and the various edges throughout the environment backcloth to
better understand how these elements work in conjunction to
shape the influence of the physical landscape on crime.

Another important consideration is that the spatial arrangement
of place features relative to one another throughout the environ-
ment may affect how they collocate and interact or work together
to generate robbery risk. When certain features converge at places
it creates a dynamic that is conducive to illegal behavior. Certain
features, such as drug markets, may increase the risk for robbery
because they operate primarily in cash and lack a formal third party
to resolve disputes among competing dealers and their customers.
Thus, robbery is primarily the result of participants in the drug
market targeting one another. However, when collocated with or
nearby other features, such as gas stations or public transportation
stops, the criminogenic mechanisms may be enhanced because the
latter features draw a larger number of people into the area,
enabling a new set of suitable victims. For example, drug users may
begin to target people who do not participate in the drug markets
for their cash or valuables to purchase drugs. Thus, place features
may increase robbery risk in their own right, but may become
exponentially riskier when located near other features with crim-
inogenic qualities. Indeed, features themselves may only become
risky as a result of such co-effects. This study did not explicitly test
the interaction effects between the risk factors to examine how the

J.D. Barnum et al. / Applied Geography 79 (2017) 203e211 209

influence of certain features shapes that of other nearby features,
but this is an important avenue for future inquiry.

At the same time, the distribution of place features throughout
an environment may explain why certain features are not risky at
all. Some place features typically associated with robbery may be
located near “protective” features that undermine crime potential
through enhanced guardianship (Clarke & Eck, 2005). For example,
studies have found that vacant lot cleaning and greening reduces
crime and fear of crime (Branas, Cheney, MacDonald, Tam, Jackson,
& Ten Have, 2011; Garvin, Cannuscio, & Branas, 2012). As a result,
clean and green vacant lots might function as a crime mitigator by
increasing the levels of residents’ activities in the community.
Another study found that levels of violence were lower in areas
with more churches per capita (Lee, 2006), suggesting that
churches may be another place feature with high levels of guard-
ianship that may negate the aggravating mechanisms of nearby
features (see also Eck & Weisburd, 1995). Additional research is
necessary to examine whether certain features do indeed provide a
protective effect against crime, and moreover, how protective fea-
tures interact with nearby risk factors.

These findings should be interpreted cautiously in light of a
number of important limitations. First, and as with any study, these
findings are directly tied to the quality of the data utilized in the
analyses. With regard to the outcome event, this study utilized
police recorded incidents of robbery. Although prior studies have
utilized robbery incident data (e.g., Braga et al., 2011), they could be
limited due to individuals’ failing to report crimes or police de-
cisions not to record crimes. Another issue was that it was not
possible to make potentially relevant distinctions in the outcome
event. For example, it is possible that the different findings would
have emerged by distinguishing street and commercial robbery.
Data quality is an equally important issue for the independent
variables, or in this case potential risk factors for robbery, because
improper classification or missing data can lead to model mis-
specification. Most place feature data utilized in this study were
purchased from Infogroup. However, some place feature data were
obtained from police departments or from open data portals. This
could have introduced inconsistencies across jurisdictions.
Although the place feature data utilized in the study were inspected
for accuracy and completeness, it was not possible to visit the
location of every place feature in each jurisdiction for true
verification.

A second limitation was that the analyses here were exclusively
concerned with the effects of place features on crime. Although
place features do influence crime, it is likely that other variables are
relevant, such as the broader community structural forces of
poverty, population heterogeneity, neighborhood instability, and
collective efficacy (Sampson & Groves,1989; Sampson, Morenoff, &
Gannon-Rowley, 2002). This is an important limitation, but it is
worthwhile to note that a number of studies have examined the
effects of place features on crime while controlling for community
structure. For example, Groff and Lockwood (2014) controlled for
population size and heterogeneity, disadvantage, and neighbor-
hood stability and found that certain features, such as bars and
public transportation stops, remain important predictors of violent
crime. In another study Drawve, Thomas, and Walker (2016) uti-
lized RTM to identify risk factors for violent crime and then created
an aggregate neighborhood risk of crime measure (ANROC). They
found that the ANROC measure was a significant and important
predictor of violent crime, even when controlling for concentrated
disadvantage and neighborhood stability. These studies demon-
strate that place features can play an important role in crime, above
and beyond community structure. However, it is likely that both
environmental and social variables work together in complex ways
to produce illegal behavior. For example, a recent study by Piza,

Feng, Kennedy, and Caplan (2016) found that the spatial influence
of risk factors for motor vehicle theft and motor vehicle recovery
varied across different neighborhood contexts. More specifically,
they determined that certain place features increased the risk of
motor vehicle theft and recovery, but that the criminogenic influ-
ence of these factors was either heightened or mitigated by certain
neighborhood dynamics. They concluded that theories of envi-
ronmental criminology and social disorganization are comple-
mentary perspectives. Although these studies add to the validity of
the current study, future research that examines differences in risk
factors for crime and their spatial influence should seek to incor-
porate community structure, as well as other possible sources of
variation (e.g., policing intensity).

That this study did not include a temporal component was a
third limitation because research has shown that risk factors’
spatial influences can vary greatly across time periods (Irvin-
Erickson, 2014). Finally, risk factors for robbery were compared
across urban environments. It is less clear if similar findings would
hold in smaller, more homogenous, suburban and rural settings.
Future work that overcomes these limitations would greatly
enhance the validity of the current findings and provide a better
understanding of the kaleidoscope of risky places for crime.

Keeping in mind these limitations, this study has important
policy implications. For example high crime places, also referred to
as “hot spots” given their relatively high density of crime relative to
other places throughout the broader jurisdiction (Sherman, 1995),
provide an important avenue for crime prevention. It is well-
documented in the criminal justice literature that police operate
more efficiently and effectively when focusing their resources and
efforts on high crime places (Braga, Papachristos, & Hureau, 2012).
However, whereas hot spots inform police about places that are
exposed to crime, they do less to describe the particular qualities of
the places that make them vulnerable to crime (Kennedy et al.,
2015). The methods utilized in this study can be incorporated
into regular police practice to provide guidance about what exactly
police should focus on at high crime places (see also Kennedy et al.,
2011). This is particularly important given the current finding that
the characteristics of high crime places depend upon the jurisdic-
tion under consideration.

Police agencies can view specific crime problems through their
own crime risk kaleidoscope to better understand and diagnose
local crime vulnerabilities with appropriately customized in-
terventions. This may include deploying common police tactics that
have been found to be effective in addressing high crime places,
such as directed patrol (Koper, Taylor, & Woods, 2013; Rosenfeld,
Deckard, & Blackburn, 2014; Sherman & Weisburd, 1995) or foot
patrol (Novak, Fox, Carr, & Spade, 2016; Piza & O’Hara, 2014;
Ratcliffe, Taniguchi, & Wood, 2011), but with an even more
refined focus on the risk factors that are present. For example, while
on patrol, officers may direct their attention towards specific risk
factors, whether that includes reporting unsecured vacant prop-
erties to other municipal agencies, approaching people at public
transportation stops to educate them of recent crime problems and
good ways to stay safe, or increasing their visibility and presence
around bars. What is important is police can identify and prioritize
actual crime vulnerabilities.

Tactics like directed patrol and foot patrol are important crime
prevention measures, but more holistic long-term strategies, such
as problem-oriented policing (Goldstein, 1990) or situational crime
prevention (Clarke, 1980), are likely to be the most effective to this
end (Skogan & Frydl, 2004). The ultimate goal of any place-based
police strategy is to fundamentally change the characters of pla-
ces that are problematic (Braga & Clarke, 2014). RTM can provide an
empirical basis to problem assessments and inform the develop-
ment of tailored interventions to address the localized conditions

J.D. Barnum et al. / Applied Geography 79 (2017) 203e211210

that affect crime (see Caplan & Kennedy, 2016). For example,
Kennedy, Caplan, and Piza (2015) examined the crime prevention
value of various police activities that were specifically designed to
mitigate or eliminate the criminogenic spatial influence of crime
risk factors in multiple jurisdictions across the United States. They
found that by employing risk reduction initiatives, measurable re-
ductions in crime could be achieved. In one study setting, robbery
was reduced by 42% in target areas relative to control areas. By
addressing the environments that foster opportunities for crime, it
is more likely that any crime prevention gains can be sustained over
time.

Funding

This research was supported in part by a grant from the National
Institute of Justice (2012-IJ-CX-0038). The views presented are
those of the authors and do not necessarily represent the position
of the National Institute of Justice.

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The crime kaleidoscope: A cross-jurisdictional analysis of place features and crime in three urban environments

1. Introduction
2. Conceptual framework
3. The study
3.1. Risk terrain modeling
3.2. Data and study settings
3.3. Outcome event
3.4. Potential risk factors
3.5. Model parameters and operationalization
3.6. Statistical analysis
4. Results
5. Discussion
Funding
References

RESEARCH ARTICLE

Predictive crime mapping

J. Fitterera, T.A. Nelsona* and F. Nathoob

aSpatial Pattern Analysis & Research (SPAR) Laboratory, Department of Geography, University
of Victoria, Victoria, BC, Canada; bDepartment of Mathematics & Statistics, University of
Victoria, Victoria, BC, Canada

Geographic Information Systems (GIS) have emerged as a key tool in intelligence-led
policing and spatial predictions of crime are being used by many police services to
reduce crime. Break and entries (BNEs) are one of the most patterned and predictable
crime types, and may be particularly amendable to predictive crime mapping. A pilot
project was conducted to spatially predict BNEs and property crime in Vancouver,
Canada. Using detailed data collected by the Vancouver Police Department on where
and when observed crimes occur, the statistical model was able to predict future BNEs
for residential and commercial locations. Ideally implemented within a mobile GIS,
the automated model provides continually updated predictive maps and may assist
patrol units in self-deployment decisions. Future research is required to overcome
computational and statistical limitations, and to preform model validation.

Keywords: break and entries (BNEs); predictive mapping; Geographic Information
Systems (GIS); intelligence led policing; Vancouver; statistical modeling

Introduction

Intelligence-led policing is a growing discipline where data, analysis, and criminal the-
ory are used to guide police allocation and decision-making (Ratcliffe, 2012). Given that
crime occurs within a geographical context that includes both space and time, informa-
tion to support intelligence-led policing is increasingly map-based and can benefit from
platforms that allow integration with Geographic Information Systems (GIS) (Chainey
& Ratcliffe, 2005). Developments in mobile GIS technology (Tsou & Kim, 2010) are
providing new opportunities for spatially explicit approaches to intelligence-led policing.
For example, mapping of mobile phone calls and cell towers has enabled mobile GIS to
be used to track perpetrators (Saravanan, Thayyil, & Narayanan, 2013). Another exam-
ple of mobile GIS systems used for policing is mobile mapping systems installed in
police vehicles that are providing patrol units with near real-time information on crime
patterns (Wang, 2012).

In some regions, spatial predictions of crime are already being used by police to
reduce crime. For example, the Los Angeles Police Department has used spatial predic-
tions of crime to preemptively allocate patrol units and have estimated that geographical
criminal intelligence have decreased violent crimes by 5.4% and homicides by 22.6%
(Uchida et al., 2012). Similarly, identifying and policing crime hot spots has signifi-
cantly reduced calls for service throughout the regions of Minneapolis (Sherman &
Weisburd, 1995), Jersey City (Braga et al., 1999; Weisburd & Green, 1995), and Kansas

*Corresponding author. Email: trisalyn@uvic.ca

Police Practice and Research, 2015
Vol. 16, No. 2, 121–135, http://dx.doi.org/10.1080/15614263.2014.972618

mailto:trisalyn@uvic.ca

http://dx.doi.org/10.1080/15614263.2014.972618

City (Sherman et al., 1995) for a variety of offences including drug, violent, and
property crimes.

Break and entries (BNEs) are one of the most patterned and predictable crime types
(Short, Brantingham, Bertozzi, & Tita, 2010; Short, D’Orsogna, Brantingham, & Tita,
2009). For example, it has been observed that the probability of a repeat offence
increases for the subsequent weeks and for the homes near the original BNE site (Short
et al., 2009, 2010). Knowledge acquired during the original BNE regarding possessions,
entry, and escape, make nearby homes ‘easy’ targets (Short et al., 2009; Wright &
Decker, 1994) that often lead to localized hot spots of property crimes (Farrell, 1995;
Johnson et al., 2007; Short & Hestbeck, 1995).

The goal of this paper is to present results from a pilot project to spatially predict
property crime in Vancouver, Canada. The model was used to predict crime six times
per day, at 4-h intervals, and outputs were designed to integrate with mobile mapping
systems that provide maps and statistics of observed crime trends to patrol units. Models
were generated using the Vancouver Police Districts (VPD) data on crime as well as
additional data on the urban and human environment. To inform the model, we began
with exploratory analysis that examined the space–time patterns of BNEs across
Vancouver between 2001 and 2012. Based on the results of the exploratory analysis,
separate models were developed for commercial and residential properties.

Study area and data

Vancouver is a metropolitan city housing more than 2.3 million residents and 22
neighborhoods (City of Vancouver, 2011) (see Figure 1).

Figure 1. Study area. Vancouver, British Columbia, Canada divided by neighborhood. Neighbour-
hood delineations obtained from http://www.vancouver.ca/your-government/open-data-catalogue.aspx.

122 J. Fitterer et al.

http://www.vancouver.ca/your-government/open-data-catalogue.aspx

The majority of the population resides in the West End and Downtown proper
surrounded by English Bay and Burrard Inlet. Lifestyles span from suburban single-family
dwellings to high-rise living in the Downtown, West End, and West Point Grey areas;
however all regions, with the exclusion of city parks, are highly urbanized offering many
opportunities for break and entry offences (City of Vancouver, 2011). There were over 1.4
million property crimes recorded in the Vancouver metropolitan region between 2002 and
2011 (BC Ministry of Justice, 2012).

Crime data

To predict the spatial and temporal patterns of property crimes, we used point occur-
rence residential and commercial BNEs data from 2001 to 2012. These data were pro-
vided by the Vancouver Police Department’s PRIME database. Attributes included x, y
coordinate locations, reported crime category, and estimated time and date of occur-
rence. Crime data were converted to binary grids. To enable map-based prediction of
crime probabilities six times per day (1:00–4:00, 5:00–8:00, 9:00–12:00, 13:00–16:00,
17:00–20:00, 21:00–23:00), crime data were formatted as presence/absence grids for six
4-h time intervals using a 200 m by 200 m grid of 3014 cells (see Figure 2).

The transformation resulted in 6,618,744 possible space–time locations for BNE
occurrences. A value of one in the grid cell indicated break and entry occurrence, while
zero indicated crime absence.

Urban and human environment data

Beyond the pattern of past crime, space–time patterns of future crime were related to
the distributions of people, properties, and property types. Several data-sets on the urban

Figure 2. The study area divided into a crime prediction grid with each cell measuring 200 m by
200 m.

Police Practice and Research: An International Journal 123

environment and socio-demographics were integrated into the models. To control for
variations in crime associated with variance in population density (Hipp & Roussell,
2013), we used 2011 census dissemination block population estimates (see Figure 3(a))
and LandScan ambient population data (see Figure 3(b), which represent human activity
patterns. The urban environment was represented using the national road network of pri-
mary and secondary roadways available from the GeoBase data repository (http://www.
geobase.ca) (see Figure 3(c)) and street light density (Figure 3(d)), which is a measure
of how urban an area is.

The density of a road network can indicate land use type in addition to transporta-
tion access for escape and potential sightings of perpetrators (Andresen, 2005).
Criminologists have established that property types (Lens, 2013) and value (Pope &
Pope, 2012) are key indicators of break and entry risk along with perceived ‘lawless-
ness’ of an area (Wilson & Kelling, 1982). To represent social instability, we used point
graffiti data collected by municipal workers of the city of Vancouver available on from
Vancouver’s open data license (http://vancouver.ca/your-government/open-data-cata
logue.aspx) (see Figure 4).

Research-restricted municipal tax information also supplied information on the aver-
age property values, number of residential and commercial properties, and the dominant
housing types across Vancouver (see Figures 5(a)–(d) and 6). All data were formatted
into the same 200 m by 200 m grid format as the crime data.

Figure 3. Vancouver’s demographic and amenity information. Map (a) displays the disaggregated
census population count, map (b) the ambient population, and maps (c) and (d) display the road
density and streetlight count per 200 m grid.

124 J. Fitterer et al.

http://www.geobase.ca

http://vancouver.ca/your-government/open-data-catalogue.aspx

Figure 4. Vancouver’s graffiti rate by 200 m grid cell.

Figure 5. Vancouver’s property information derived from geocoded municipal tax information.
Maps (a) and (b) display the number and land values of the residential properties across
Vancouver, while maps (c) and (d) display the commercial property counts and values.

Police Practice and Research: An International Journal 125

Methods

Summarizing patterns in observed BNEs

Prior to modeling, we analyzed the space–time dynamics of BNEs. First we conducted
frequency graphing of residential and commercial break-ins by hour, day, month, and
year from 2001 to 2012 to illuminate trends in criminal behavior. Subsequent to analysis
through time, we used density mapping (see O’Sullivan & Unwin, 2010 for method
detail) of BNEs from 2001 to 2012 to examine spatial patterns and locate BNE hot
spots.

In order to build associations through space and time into the model, we needed to
characterize space–time clustering of BNE events. We employed Ratcliffe’s (2009) near-
repeat calculator to measure the spatial and temporal distance between each residential
and commercial crime event that occurred. We assessed residential and commercial
BNEs that were within 500, 850, and 1000 m from the originating event and from one
day up to 30 days since the event occurred. Observed patterns were compared to ran-
dom patterns to determine if the pattern of repeat or near repeat offences was signifi-
cantly different from random.

Predicting future BNEs

Separate predictive models were developed for residential and commercial crimes, as
crimes separated by property type had different observed patterns. We also built two
models for each property type. Model 1 (Residential and Commercial) was based on
integrating crime data and with ancillary data on the urban and human environment (see
Table 1), in a regression model (generalized linear logistic regression models with

Figure 6. Vancouver’s dominant property types by 200 m grid cell.

126 J. Fitterer et al.

parameters fit using a logit link function and maximum likelihood estimation) (Burns &
Burns, 2008).

Model 2 used only observed crime data in the regression model. Prior to running
models, we assessed variable importance (using a Knox ratio statistic) and assessed vari-
able correlation to ensure models were statistically robust. Crime data were summarized
both for general trends through all years, with more detailed patterns quantified from
the past 240 days of data, which maximized detail while keeping computations manage-
able. Predictions were mapped and visually compared between observed and predicted
spatial patterns to assess model accuracy.

Results

Summarizing patterns in observed BNEs

Exploratory analysis of observed data revealed distinct patterns in the characteristics of
break and entry offences across Vancouver. Observing annual trends, we found a

Table 1. The human and urban environmental covariates used in Model 1.

Data Source Description

Road
Density

GeoBase National Road Network
Version 2 (http://www.geobase.ca)

A collection of primary and secondary
roadways at 1:100,000 scale resolution

Dominant
property
type