- The command staff has approached you regarding how best to distribute resources in order to curtail crime in various beats.
- Go to the Chicago crime map (http://gis.chicagopolice.org/CLEARMap/startPage.htm#) and select four “beats” that are in different parts of the city. In the “Optional” settings, click on “Index”- More Serious Offenses. Select four types of crime that you will be comparing for each of the four “beats”.
- From that data for each “beat”, compare the beats, and determine the highest frequency of each of the four types crimes for each beat, including the highest and lowest frequency by month. Create a chart to compile this data.
- Create a Memo with the chart embedded, to the command staff that summarizes the analysis of your findings regarding the comparison of the various types of crimes in the four beats. In the memo, explain to the command staff everything that he might need to know to make the best decision. Support your analysis with Research.
Be sure to cite three to five relevant scholarly sources in support of your content.
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Optimized Community Policing
through Locational Analytics
By Roger Chin and Jake Campbell
system can help law enforcement analytics alleviate
contention between officers and the communities
they serve, deter crime and improve accountability
and transparency.
Combined with quantitative and qualitative data,
locational analytics allows modern policing to
help the public report problems and officers to
respond. Law enforcement agencies that actively
collect crime data must take similar approaches
toward community satisfaction surveys. GIS
can map the locational origins and distribution
of quantitative crime data and incorporate
qualitative citizen feedback on police services. The
resulting assessment can inform decisionmaking
to determine high crime and low favorability
neighborhoods where public satisfaction with police
services needs improvement. The assessment can
influence resource allocation, allow officers to be a
proactive presence in high crime areas and help to
deter crime.
The continuous process improvement model can
map community policing efforts, and analysis of new
data can create an ongoing cycle of crime reduction
and increased cordial relations with the public by
redirecting efforts and retargeting communities.
Locational analytics can determine the optimal
locations for outreach events—churches, schools,
community centers or other sites—that coincide
with high crime areas where officers already seek a
proactive presence. Ideally, their presence may help
deter crime by demonstrating that a particular area
is not without enforcement, while simultaneously
improving relations, engaging the public and
demonstrating that specific neighborhoods are not
neglected.
In an increasingly data-centric society,
analytics integration has become
common in public organizations. As
agencies learn to integrate data into
their operations, they must have a clear
strategy to maximize their efforts; data
collection alone does not adhere to
analytical best practices. Data must be
acquired and integrated into a system
of continuous process improvement
in which the data analysis shapes
decisionmaking and provides a basis
for reevaluating existing policy. Yet
organizations often face pitfalls when
collecting data without analysis, or
when the analysis does not influence
and guide organizational strategies and policies.
A coherent analytical operation can maximize
effectiveness and improve the way organizations
achieve target outcomes. Geographic information
systems (GIS) and the value associated with their
integration provide a path forward.
General Shifts in Policing
The concept of “traditional policing” has evolved
in recent decades. Previously, officers conducted
foot patrols and fielded service calls from street-
side call boxes. Now, they use cars equipped with
300 horsepower and advanced computers. They
are perceived as much more than enforcers of the
law, but as social workers, therapists, mediators,
community leaders, role models and public relations
specialists. These increasing obligations amid
declining budgets constantly challenge officials
to find innovative solutions. Indeed, some crime
prevention programs, though designed to enhance
safety, have drawn adverse public reaction. One way
to deter crimes is through proactive tactics in high
risk areas, rather than training officers to merely
react to illegal activities.
Locational Analytics for Modern Policing
The use of spatial and temporal data in crime
analysis has set a new standard for law enforcement
agencies. GIS hardware and software support the
collection and analysis of quantitative, qualitative
and spatial data for location integrated analytics.
When used in a continuous process improvement
system, GIS can help agencies reach targeted
outcomes and maximize effectiveness. To better
understand this comprehensive, iterative approach,
we apply the model to community policing. This continued on page 28
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28 WINTER 2017
23.2 23.5 23.3
29.3
29.8
30.7
32.2
20
22
24
26
28
30
32
34
FY
2011 FY
2012 FY
2013 FY
2014 FY
2015 FY
2016 FY
2017
Se
rv
ic
es
C
on
ta
c
t
s
Pe
r
R
es
id
en
t
Increase
of
Support
Service
Productivity
After
QA
Implementation
QA
Program
Implemented
2013
The QA project simultaneously brought more hard-
to-reach residents into services and increased the
frequency that all residents used the program.
The Bottom-Up Experience
Three years later, SAHA took a bottom-up approach
to QA for family services. SAHA’s data management
culture and skill had transformed since the 2013
implementation and management decided to
broaden involvement to generate buy-in and
improve the program. A working group of 10 direct
service staff agreed on family program goals and
used them to build a QA program design. Staff tested
the guidelines quarterly and reconvened over their
performance reports for feedback and revision. No
formal performance evaluation ensued; periodic
report cards were for staff information only.
The bottom-up approach was well suited to family
services as staff need more discretion and flexibility
for providing families a wider range of programs
and referrals compared to senior communities.
Some heads of households need income and
employment support; others only want referrals to
youth programs, savings tools, recreation, health
services or scholarship opportunities. Many families
prefer targeted, less frequent contact because they
work multiple jobs and provide care to multiple
generations.
The bottom-up program is too recent to evaluate
turnover impacts and success, but SAHA already
detects positive staff morale. Without a high-pressure
data accountability regime, service coordinators
report new understanding and enjoyment of data
and quicker resolution of errors and inconsistencies
through peer support. Staff believe more strongly
in their purpose and outreach strategies, increasing
their efficacy in gaining client participation. Staff
now drive innovation around youth programming,
expanding their activity offerings and recruiting
greater volunteer support at their buildings.
Employee stewardship happens when employees
take ownership of their work product and make
excellence their goal, rather than perform to top-
down management objectives. SAHA’s bottom-
up approach recruited direct service staff in
organizational transformation, while transforming
the staff into stewards of data-driven change.
Chris Hess, an MPA candidate at Presidio Graduate
School in San Francisco, is director of resident services
at Satellite Affordable Housing Associates in Berkeley.
He can be reached at chris.hess@presidio.edu.
OPTIMIZED COMMUNITY POLICING
continued from page 26
The community policing example demonstrates
an ideal application of locational analytics and its
functions within a system of continuous process
improvement. Rather than view their adoption
as a hindrance or costly endeavor, administrators
should consider the long-term benefits of improved
strategic planning and resource allocation.
In addition to improving accountability and
transparency, locational analytics can support
optimized strategies to increase officer productivity,
raise morale, enhance safety, refine patrol patterns
and improve community satisfaction.
Challenges to Overcome
Like all professions, modern policing must adapt
to changing times and learn from experiences.
Locational analytics must be executed so data
analysis and policy review function as part of an
ongoing cycle of strategic planning and assessment.
The continuous process improvement procedure
is not without challenges and is not a panacea for
every problem that every law enforcement and
public sector agency faces. GIS tools only work
if the organization adopting them acknowledges
the benefits to be gained. If a department focuses
too much on rewarding officers for the number
of arrests made, officers may be less inclined to
use GIS insights to support community outreach
efforts. Additionally, long-term implementation
of GIS may help to curb costs, but agencies may
face funding constraints, making it difficult to
implement technology and hire analysts who can
use and interpret the outputs. Yet in overcoming
these challenges, locational analytics can help bring
community policing and organizational operations
into the 21st century.
Roger Chin is a Ph.D. candidate in political science
and information systems at Claremont Graduate
University and a faculty associate at Arizona State
University. He can be reached at roger.chin@cgu.edu.
Jake Campbell is a Ph.D. candidate in political
science at Claremont Graduate University and an
adjunct professor at California State University,
Long Beach. He can be reached at
jake.campbell@csulb.edu.
Copyright of PA Times is the property of American Society for Public Administration and its
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articles for individual use.
O R I G I N A L P A P E R
Elise Wisnieski • Stephanie Bologeorges •
Tina Johnson • David B. Henry
Published online: 19 September 2013
� Society for Community Research and Action 2013
Abstract Research has shown variable conceptualiza-
tions of neighborhood, often inconsistent with administra-
tive boundaries. The present investigation seeks to quantify
the geographic area encompassed by citizens’ reporting of
crime. Two Chicago violence prevention organizations
gathered near real-time citizen reports of crime and other
precursors of violence in a south side community. Over the
course of 6 months, 48 community residents participated in
a weekly telephone survey about incidents occurring in
their community, including crime, incivilities, and disor-
der. For each incident reported in the study community,
respondents were asked to specify its location, whether it
was witnessed or heard about, and if it occurred within one
block of their residence. Incident locations were geocoded
and used to compute distance from residence. Incident
reporting radii were calculated for all types of incidents.
Calculated distances of events reported within a block
revealed discrepancies between resident perceptions and
geographic apportionments. On average, incident reports
spanned just over a half-mile geographic radius from
respondents’ residences. Reporting radii were greater for
more violent incidents and shorter for incidents witnessed
directly. There was no effect of age, gender, length of
residence, or length of participation in the study on
reporting radii. Descriptions of reporting radii and impli-
cations for crime prevention efforts and research are
discussed.
Keywords Crime reporting � Crime prevention �
Neighborhood
Introduction
Time and time again, neighborhood-level research has put
forth competing definitions and conceptualizations of
neighborhood and community (Coulton et al. 2001).
Scholars have debated the utility and efficacy of pre-
defined administrative boundaries compared to more fluid
boundaries framed by resident perceptions, the
census tract
versus the block group, and various concepts of what
constitutes a block (Taylor 1997; Taylor et al. 1984;
Coulton et al. 2001; Cromartie and Swanson 1996;
O’Campo 2003; Sharkey and Horel 2008; Chilensky 2011).
Others have suggested using radius criteria, such as the
Bayesian model of critical acceptable pedestrian walking
distances or the area surrounding one’s residence (Lee and
Moundon 2008; Seneviratne 1985).
A body of research has been dedicated to deciphering how
best to measure and evaluate neighborhood and other vari-
ables likely to influence and be influenced by neighborhood.
In a study of perceived neighborhood advantage, Cantillon
(2006) concluded that administratively defined boundaries
do not best serve the field of neighborhood effects; instead,
researchers should consider smaller neighborhood concep-
tualizations, such as the street block, to direct
community
development and organization efforts. Other studies have
suggested a .25 mile radius as a reliable definition for
E. Wisnieski (&) � T. Johnson
CeaseFire/Cure Violence, University of Illinois at Chicago,
1603 W. Taylor St., Chicago, IL, USA
e-mail: elisedw@uic.edu
S. Bologeorges
School of Public Health, University of Illinois at Chicago,
1603 W. Taylor St., Chicago, IL, USA
D. B. Henry
Institute for Health Research and Policy, University of Illinois at
Chicago, 1747 W. Roosevelt Rd., Chicago, IL, USA
123
Am J Community Psychol (2013) 52:324–332
DOI 10.1007/s10464-013-9597-z
neighborhood based on measures of social contact with
neighbors, neighborhood social perceptions, fear of neigh-
borhood crime, effects of built environment, and satisfaction
with neighborhood quality of life (Kruger 2008).
Additional research advocates the use of multiple defini-
tions and sources of data in the same analysis to accommo-
date the study of various neighborhood processes (O’Campo
2003). For example, Weiss et al. (2007) propose the use of
both direct observation and respondent input; however, such
techniques are often deemed too subjective, inconsistent, and
labor- and time-intensive. In a pilot study comparison of
researcher and resident-defined neighborhoods, Coulton
et al. (2001) overlaid resident-drawn neighborhood maps
with census maps—ultimately concluding that there are
discrepancies between conceptualizations. Although resi-
dent-reported neighborhoods were similar in square mileage
(a mean area of .32 square miles) to corresponding census
tracts, the area did not map directly onto the census tract,
implying that while each conceptualization may be close in
size, boundaries do not align. The authors further emphasize
that such varying geographic apportionments likely result in
different values of neighborhood constructs.
Past neighborhood-level analyses have examined
neighborhood geography and its effects on a range of
variables, including but not limited to: social cohesion,
place attachment, collective efficacy, and perceived crime.
An examination of Baltimore neighborhoods concluded
that, ‘‘… people perceive common boundaries for their
neighborhoods (that is, people define their environment
using a common set of blocks, a larger area or a city) and
have common perceptions of the quality of life and safety
of the environment in these neighborhoods’’ (Wilson et al.
2009). Although researchers have experimented with sur-
veying residents to gauge perceptions of crime and safety
in neighborhoods, knowledge of the geography encom-
passed by resident reports of actual crime in their neigh-
borhoods is sparse (Sampson et al. 1997; Hipp 2007). The
present study zeroes in on the geography of near real-time
crime reporting across one Chicago community.
Crime has traditionally been measured using victim-
ization surveys and officially collected statistics. Similar to
more novel attempts to survey residents on perceived
incivilities and disorder, the present study utilizes data
collected from a survey designed to gather citizen reports
of incivilities, disorder, and crime. However, unlike pre-
vious studies, where reports tend to be gathered retro-
spectively, this study is distinctive in that citizen crime
observations are collected in near real-time and are used to
understand resident perceptions of neighborhood bound-
aries (Sampson and Groves 1989; Slocum et al. 2010).
Specifically, the present investigation seeks to quantify the
geography encompassed by citizens’ surveillance of crime
and examine how findings may impact future community-
based crime prevention and research. We attempt to answer
the following questions:
1. What is the average radius of
citizen crime reports?
2. To what extent do the distances of citizen crime
reporting vary with the incident-level predictors of
crime type, time, and whether the respondent wit-
nessed or heard about the incident?
3. To what extent do observer characteristics (gender,
age, and length of residence) influence the distances of
citizen crime reports?
4. To what extent do resident perceptions agree with
administrative definitions of community boundaries
and blocks?
This paper first provides the relevant study background
followed by a rationale for the selection of the pilot com-
munity. Then, the process behind reporting radii calcula-
tions and results are discussed, with special emphasis on
variations by crime type, event salience, and how the
respondent found out about the incident. Next, a compar-
ison is drawn between resident perceptions of community
geography against official geographic apportionments.
Finally, implications for neighborhood violence prevention
and research are considered.
Methods
Study Background
This study stems from an ongoing partnership between
CeaseFire, a violence prevention program, and the Chicago
Center for Youth Violence Prevention (CCYVP).
In 2005, the CCYVP and CeaseFire formed a working
research group to enrich the field of violence prevention by
improving the recruitment, training, and deployment of
violence prevention practitioners. The data for this study
were gathered as part of a project that attempted to assess
informative signatures for identifying communities likely
to experience increased youth violence (Henry et al. 2013).
Selecting a Pilot Community
The pilot community selected was the south side Chicago
community area of Englewood. According to the 2010
census, Englewood has a population of 37,403, spanning 3.1
square miles and 11 census tracts. A number of decisions
went into choosing the pilot community. First, existing ties to
CeaseFire were required to ease recruiting community
respondents, explaining the study purpose, and garnering
and sustaining community involvement. Second, high
numbers of crime incidents assured sufficient data to sustain
resident involvement. In 2011 alone, 526 robberies, 704
Am J Community Psychol (2013) 52:324–332 325
123
assaults, and 80 shootings were reported in Englewood.
Englewood is situated in the police district with the highest
shooting and homicide rate in all of Chicago (City of Chicago
2011). Table 1 provides a brief snapshot of the target com-
munity’s demographics. The population is primarily African
American with low educational attainment, low median
household income, and a low rate of home ownership.
Participants
Participant Recruitment
The aim was to achieve geographic representativeness
across the 11 census tracts to maximize geographically
dispersed reporting. Business owners, residents, employees,
and stakeholders were recruited across the 11 Englewood
census tracts. Researchers took two thorough inventories of
community businesses prior to recruitment and then part-
nered with CeaseFire staff to identify recruitment territories.
In keeping with previous research on recruiting ethnic
minorities, potential respondents were approached by
workers familiar with Englewood to overcome respondent
fear and distrust (Arean and Gallager-Thompson 1996;
Thompson et al. 1996). Study flyers were distributed in
mailboxes, and several area non-profit organizations
advertised the study. In the 8 weeks of recruitment,
respondents were invited to participate in a brief weekly
telephone survey about select incidents they may have
observed in their neighborhood over a 6-month period (June
2011–November 2011). Respondents were informed they
would receive a $100 gift-card for their participation. After
informing respondents about confidentiality, privacy, and
their right to withdraw at any time without penalty, partic-
ipants consented to participation. Researchers then recorded
a telephone number, interview availability times, and resi-
dent home address for each respondent.
Study Sample
Participant recruitment yielded 59 potential respondents,
48 of whom actually participated in the study. Table 2
depicts demographic characteristics of the sample. Partic-
ipants were predominantly African–American (97.9 %), a
proportion consistent with the neighborhood population.
The median age was 37 years, slightly older than the
population median age (30.7 years). Figure 1 is a dot
density map depicting the geographic distribution of
respondents. As shown, respondents were successfully
recruited in 9 of the 11 census tracts and were clustered
near the center of Englewood. Of the 48 respondents, 81 %
Table 1 Englewood community profile (US Census Bureau 2010)
Community characteristic Englewood
Population 37,403
Area square mileage 3.07
% African American 97.30 %
% Bachelor’s degree 4.50 %
Median household income $24,308
% Owner occupied housing unit 36.70 %
Median sale price (single family detached) in
2009
$10,000
% Household income under $25 K 54.00 %
2011 Homicide rate 28 (74 per 100,000)
2011 Shooting rate 80 (213 per
100,000)
Table 2 Sample demographic characteristics
Sample characteristics Active sample (n = 48)
Male 45.8 % (n = 22)
Female 54.2 % (n = 26)
Age M = 38.76, SD = 11.142
Length of time living in target
community
M = 15.56 years,
SD = 11.92
Fig. 1 Geographic distribution of active respondents. This figure is a
dot density map depicting the geographic distribution of active
respondents. To protect confidentiality, dots do not represent actual
respondent addresses, but a random distribution of respondents per
census tract
326 Am J Community Psychol (2013) 52:324–332
123
reported home addresses within Englewood boundaries and
19 % in the surrounding areas.
Measures
A five-item questionnaire was administered to participants
on a weekly basis via telephone interview. Interviewers
asked a series of closed-ended questions (yes/no) about five
categories of incidents they might have witnessed or heard
about in the preceding week in their community, Engle-
wood. The five incident categories were:
1. Threats/bullying/intimidation
2. Fights/beatings
3. Shootings/stabbings/
other use of weapons
4. Other incidents, including robberies, sexual assaults,
and vandalism
5. New graffiti.
If a participant responded ‘‘yes’’ to any of the five
incident categories, they were then asked to specify: (1) if
the incident happened within a block of their reported
home address, and (2) if they witnessed the incident
themselves or heard about it from another source.
Qualitative, open-ended items regarding incident
descriptions and locations were then asked to ascertain
exact location and incident type. Interviewers used probing
techniques to aid respondents in pinpointing cross streets,
landmarks, and businesses proximal to the incident loca-
tion. Any additional information given about a particular
incident was recorded (e.g. age of the victim, injuries
sustained by parties involved, approximate date and/or time
of occurrence). This question pattern was repeated for each
incident reported, and participants were able to report more
than one incident per category.
Procedure
Over the course of 24 weeks, beginning in June 2011,
community respondents gave weekly reports of minor
incidents perpetrated in the community via telephone
interview. Each reported incident was geocoded using
ArcGIS software.
Computing Incident Reporting Radii
All participant home addresses and incidents reported were
mapped using ArcGIS software and geocoded to obtain X
and Y coordinate location data. Coordinate data were then
entered into SPSS version 20.0 for analyses. To obtain the
average radius of citizen incident reports, the distance
between each respondent’s home address and the location
of the incident reported was calculated by first converting
X and Y coordinates from degrees to radians. The Haver-
sine formula was used to obtain the distance
(in miles)
from location of residence (coordinate pair 1) to location of
each incident (coordinate pair 2):
a ¼ sin dlat=2ð Þð Þ2þcos lat 1ð Þ � cos lat 2ð Þ
� sin dlon=2ð Þð Þ2
c ¼ 2 � arctan 2
p
a;
p
1 � að Þð Þ
d ¼ 3961 � c
where dlon is the difference in longitudes between the
individual’s residence and incident locations, dlat is the
difference in latitudes, lat_1 is the residence latitude and
lat_2 is the incident latitude, all in radians.
The individual reporting radius for each participant was
computed by taking the mean of the distances between the
residence and the incidents. Each incident was coded
according to whether it was witnessed or heard about.
Incidents were also coded by whether they were perceived
as having occurred ‘‘within a block’’ of the residence.
Finally, incidents were coded according to the type of
incident reported (threats/bullying/intimidation, fights/
beatings, stabbings/shootings/other use of weapons, other
incidents, new graffiti).
Examining Predictors of Reporting Radii
To determine if the distances of citizen crime reports
varied by the incident-level predictors of crime type, time,
whether the respondent witnessed or heard about the inci-
dent, and/or respondent-level predictors of gender, age, and
length of residence in the community, a generalized linear
mixed model was employed using a person-period data set.
The unit of analysis was the incident, and the dependent
variable was the distance from the respondent’s home
address to the incident location. Other predictors were
incident type, week of report, and whether the incident was
witnessed or heard about, gender, age, and length of resi-
dence in Englewood. The model included random inter-
cepts for the individual.
Calculating Block-Level Perceptions
Resident perception of a block was calculated using the
mean of all distances from residences to incidents for
incidents reported to have occurred within one block of
residence. The distances of incidents reported as having
occurred within one block were compared to the adminis-
trative definition of one Chicago city block, .125 miles (8
city blocks = 1 mile). The number of incidents reported as
within one block that were actually within .125 miles was
divided by the total number of incidents reported to be
Am J Community Psychol (2013) 52:324–332 327
123
within one block to obtain the block reporting accuracy rate
by incident. As an additional exploration of block-level
accuracy, a second accuracy rate was calculated by
respondent. In this calculation, the percentage of incidents
correctly reported to actually be within 1 block (within
.125 miles of residence) was calculated as the average
accuracy rate by survey participant.
To examine if block reporting accuracy rates were more
likely to coincide with the administrative definition of a
Chicago city block if the event was witnessed by the
respondent, separate accuracy percentages were calculated
for incidents reported to have been witnessed within one
block of residence. The number of incidents reported to
have been witnessed within one block of residence that
were actually within a .125 mile radius of residence were
divided by the total number of incidents reported as wit-
nessed within one block. Block reporting accuracy rates by
incident were calculated separately for each of the five
categories of incidents.
To determine if distances of citizen crime reports that
were reported as having occurred within one block of the
respondent’s place of residence (block-level perceptions)
varied by the incident-level predictors of crime type, time,
whether it was witnessed or heard about, and/or respondent-
level predictors of gender, age and length of residence in the
community, a person-period dataset was created in SAS.
Each incident reported was considered its own case in this
dataset. Crime type was coded categorically (1–5), time was
reported in intervals by week of data collection (1–24), and
how the respondent found out about the incident was coded
in binary (witnessed = 0, heard about = 1).
A generalized linear mixed model was employed for the
binary outcome of whether or not the incident was reported
as being within one block of the respondent’s address. The
model used a binomial distribution with a logit link func-
tion. The predictor variables were identical to the model for
the distances.
Results
The study yielded a total of 644 completed surveys and 459
incident reports by 48 active respondents. Of the 48 active
respondents, 47 of the 48 reported home residences that
could be geocoded (97.9 %). Of the 459 incident reports,
415 could be geocoded (90.4 %). Given that the home
address of one respondent could not be geocoded (and that
respondent reported 3 incidents), 412 incident pairings
could be mapped using ArcGIS for both incident and res-
idence. These 412 incidents were selected for calculations.
Distances from home residence to reported incident loca-
tion ranged from .0004 miles to 7.6650 miles. The number of
incidents reported by participant that could be geocoded for
both residence and incident location ranged from 0 to 36
incidents. The individual reporting radii of individual
respondents ranged from .008 to 1.941 miles, with a mean
individual reporting radius of .55733 miles. The incident
reporting radius for all 412 incidents (weighted mean) was
.54640 miles, indicating citizen crime reports span just over a
half-mile geographic radius from a residence location.
For seven incidents, the respondent did not provide
further information regarding how they had learned of the
incident (witnessed, heard about, both) or whether the
incident had occurred within 1 block of their residence.
Only 405 incidents were used in further calculations. The
reporting radii for witnessed incidents (n = 245) was
0.43885 miles and .69617 miles for incidents heard about
(n = 149).
A total of 252 incidents were reported to have occurred
within one block of a respondent’s residence. For these
incidents, the overall reporting radius was 0.29972 miles,
which indicates the geographic distance residents perceive
to encompass one block. The reporting radius for witnessed
incidents that occurred ‘‘within 1 block’’ (N = 174) was
0.28763 miles, for incidents heard about (N = 70) was
0.31059 miles, and for incidents both witnessed and heard
about (N = 8) was 0.30750 miles.
Reporting radii by survey question for each of the five
incident types were calculated similarly, first for the
aggregate total by question and then for whether incidents
were witnessed or heard about or both, and whether or not
incidents occurred within one block of respondent resi-
dence. A total of 68 reporting radii were calculated. A
summary of all reporting radii are presented in Table 3.
As expected, reporting radii differed significantly by
whether the respondent witnessed the incident or heard
about it, with shorter distances for witnessed incidents
(B = .23, t(395) = 2.84, p \ .01), and longer distances for
shootings and stabbings (B = .22, t(380) = 1.96, p = .05).
There were no significant effects by type of incident, age,
gender, length of residence, or the week of the study. Per-
ceptions that an incident had occurred on the respondent’s
block varied significantly by the distance from the respon-
dent (B = -1.53, t(394) = 6.20, p \ .01, OR = 0.21) and
by whether the respondent witnessed or heard about the
incident (B = -1.10, t(394) = 6.20, p \ .01, OR = 0.33).
Although the omnibus test for incident type was not sig-
nificant, the parameter for fights/beatings was (B = 1.06,
SE = 0.45, t(394) = 2.34, p \ .05), suggesting that
respondents were more likely to perceive an incident as
having occurred within a block of their homes if the incident
was a fight or beating. Perceptions of incidents occurring
within a block of the respondent’s home did not vary by any
other predictor.
Approximately half of all incidents reported to have
occurred in Englewood (n = 233) fell within the
328 Am J Community Psychol (2013) 52:324–332
123
Table 3 Reporting radii calculations for all reported and geocoded incidents
N Distance (in miles)
All incident reporting radii 412 0.54640
Witnessed 245 0.43885
Heard about 149 0.69617
Both 11 0.35164
* missing data for 7 incidents
Incidents reported ‘‘Within 1 Block’’
Within 1 block reporting radii 252 0.29972
Witnessed 174 0.28763
Heard about 70 0.31059
Both 8 0.30750
Total (N) Witnessed (N) Heard about (N) Both (N)
Question 1: threats/bullying/intimidation
All incident radii 0.27467 (60) 0.16031 (32) 0.40833 (27) 0.32500 (1)
Reported within 1 block radii 0.14372 (43) 0.10965 (26) 0.19582 (17) N/A
Not within 1 block radii 0.60588 (17) 0.37983 (6) 0.76960 (10) 0.32500 (1)
Question 2: fights/beatings
All incident radii 0.49700 (93) 0.46774 (61) 0.59603 (29) 0.13100 (3)
Reported within 1 block radii 0.34100 (68) 0.35453 (47) 0.33450 (18) 0.13100 (3)
Not within 1 block radii 0.92184 (25) 0.84779 (14) 1.01609 (11) N/A
Question 3: stabbings/shootings
All incident radii 0.72589 (149) 0.56470 (67) 0.88600 (76) 0.49783 (6)
Reported within 1 block radii 0.42111 (79) 0.38620 (51) 0.48617 (24) 0.47600 (4)
Not within 1 block radii 1.06986 (70) 1.13369 (16) 1.07054 (52) 0.54150 (2)
Question 4: other incidents
All incident radii 0.37135 (48) 0.31930 (30) 0.47547 (17) 0.16300 (1)
Reported within 1 block radii 0.06420 (30) 0.06261 (18) 0.05782 (11) 0.16300 (1)
Not within 1 block radii 0.88378 (18) 0.70433 (12) 1.24117 (6) N/A
Question 5: new graffiti
All incident radii 0.48078 (55) 0.48078 (55) N/A N/A
Reported within 1 block radii 0.30347 (32) 0.30347 (32) N/A N/A
Not within 1 block radii 0.59323 (22) 0.59323 (22) N/A N/A
* 1 incident not indicated if within 1 block
Table 4 Within one block accuracy rates
Percentage of incidents
reported ‘‘within a block’’
actually within
.125 miles (%)
Average distance of
incidents reported
‘‘within one block’’
(in miles)
Percentage of incidents reported
as both witnessed and ‘‘within
a block’’ actually within
.125 miles (%)
Q1: threats/bullying 60.47 0.14372 61.54
Q2: fights/beatings 54.41 0.34100 54.00
Q3: stabbings/shootings/
other use of weapons
41.77 0.42111 50.91
Q4: other incidents 83.33 0.06420 78.95
Q5: new graffiti 78.13 0.30347 78.13
All incidents 58.40 0.29972 61.00
Am J Community Psychol (2013) 52:324–332 329
123
administrative boundaries of Englewood. Another 27.5 %
mapped onto the community immediately west, West
Englewood. The remaining 22 % were spread across
neighboring communities.
Within-block accuracy rates are presented in aggregate
form as well as by survey question in Table 4. Of the 252
incidents reported to be within one block of residence, only
146 (57.94 %) had calculated distances from residences that
were B0.125 miles, the definition of a Chicago city block.
The average distance of incidents reported as within one
block from residence was 0.29972, suggesting resident
perceptions of a block span this geographic distance. Of all
incidents reported in the study, 149 actually fell within .125
miles of respondent residences. When divided by the num-
ber of study participants (n = 48), this shows an average of
3.10 violent incidents occurred within one administrative
block of participant’s homes over the 24 weeks of the study.
Further, dividing these 149 incidents by the total number of
incidents that could be calculated for distance from resi-
dence (n = 412) reveals that 36.17 % of all incidents
reported occurred within .125 miles of respondents’ homes.
Discussion
This study sheds light on the geography of citizen crime
reporting in a number of ways. First, this report adds to this
line of research by quantifying the area community
respondents perceive themselves as both belonging to and
being in their sphere of awareness. Moreover, the range in
reporting radii exemplifies the inherent difficulties in
assessing resident perceptions of neighborhood geography.
The geography of citizen crime reporting is variable,
influenced both by event severity and whether the
respondent witnessed or heard about an incident. Notably,
individual-level respondent characteristics (including age,
gender, and length of residence) and time do not have an
effect on reporting radii. Results indicate that there are
discrepancies between administrative and resident defini-
tions of neighborhood.
The radius of citizen crime reporting suggests that, when
asked to place crime in physical space, it appears residents
perceive the half-mile radius surrounding their residence as
their neighborhood. Other research has substantiated a .25
mile radius as an acceptable definition for examining both
the effects of built environment and pedestrian walking
distance (Kruger 2008; Seneviratne 1985). It seems that in
the case of reporting crime, radii are nearly doubled—
perhaps reflecting the salience of crime events.
That citizen crime reporting radii vary greatly depend-
ing on the type of incident reported suggests event severity
is a contributing factor to this phenomenon. In the report-
ing distance linear mixed model, there was an overall effect
of the type of incident. Residents were more likely to report
stabbings and shootings that occurred at greater distances
than more minor violent crimes, suggesting an interaction
between event severity and physical proximity. More
severe events may truly ‘‘hit close to home.’’ Respondents
were more likely to recount severe events at a greater
distance from home than less serious events such as bul-
lying or new graffiti, which are more likely to go unnoticed
unless in close proximity to the respondent’s home.
The models of distance and within-a-block perceptions
showed expected effects of whether an incident was wit-
nessed or heard about from others. Not surprisingly, radii
were greater for incidents that were heard about. Common
in child and adolescent developmental research, the con-
cept of ‘home range,’ or the distance individuals travel
away from their residence in the course of their daily
routines and pursuits, may prove useful in understanding
the implications of these results. ‘Home range’ spans pri-
vate and public spaces and has been quantified by using
both an area and a perimeter (Spilsbury 2005). Previous
studies have found an inverse effect of neighborhood vio-
lence on size of ‘home range’ (Matthews 1992). With
respect to this study, the radius encompassing respondent-
witnessed incidents may constitute their ‘home range.’ On
the other hand, if community observers heard about an
event from another source, the reporting radii increased by
roughly 60 %. It seems this extended radius may be due, in
part, to the breadth of social network ties and information
exchange related to violent crime and minor offenses.
There were no effects of age, gender, or length of
respondent’s residence in the community for either the
distance model or the model of within-block perceptions.
These results may have encouraging implications for
efforts to recruit community members for monitoring
activities, either for research or in connection with com-
munity crime prevention efforts. Recruitment may be
rendered less cumbersome because individual-level pre-
dictors showed no effects on either distance or within-
block perceptions, and thus may not need to be taken into
consideration when recruiting community members for
similar efforts. However, given that this was a small, non-
random sample not intended to be representative of any
group other than the neighborhood, future research should
examine how individual-level predictors matter with dif-
ferent populations.
Despite having enrolled respondents from only 9 of the
11 tracts, incident reports were spread across a total of 61
census tracts in Englewood and contiguous areas. As
expected, respondents did not confine incident reports to
administratively defined areas. The fact that roughly half of
the incidents reported actually fell into the administratively
defined boundaries of Englewood provides further evi-
dence of the high level of geographic discordance between
330 Am J Community Psychol (2013) 52:324–332
123
resident perceptions and administrative demarcations of
neighborhood (Cantillon 2006; Coulton et al. 1996).
Yet another indicator of discrepant resident and
administrative definitions involves city block-level esti-
mations. Interestingly enough, resident perceptions of a
block span a far greater distance than administratively
defined city blocks (0.29972 miles vs 0.125 miles). Overall,
only 58 % of resident-defined blocks aligned with the
administrative block designation of .125 miles. Although
some research has lauded the block as a more objective and
identifiable neighborhood boundary, the crime reporting
radii results suggest that the block may not be as objective
as previously thought (Brown et al. 2004). In fact, asking
respondents to report on an administratively defined
neighborhood block may lead to ‘‘spatial mismatch,’’
whereby a respondent recalls an area encased by bound-
aries different from the area where he or she truly lives
(Sampson and Raudenbush 2004). In turn, such mismatch
can pose a threat to the validity of neighborhood-level
analyses and lead to serious information biases (Lebel et al.
2007). The findings of the present investigation lend cre-
dence to these concerns.
Past research has employed measures of neighborhood
social organization based on constructs such as friendship
networks, neighboring, social ties, sense of community,
and civic participation (Leventhal and Brooks-Gunn 2000;
Sampson et al. 2002; Shinn and Toohey 2003). Studies
have also documented the association between neighbor-
hood attachment and heightened vigilance and protective-
ness over fellow neighbors and their residences (Felson
1987). Wilson et al. (2009) contend that the connection
between geography and crime needs take a forefront when
designing community-based crime prevention initiatives,
such as neighborhood watches. If violence prevention
practitioners are able to gauge the area encompassed by
citizen crime reporting, results can then be used in forming
and supporting block clubs and neighborhood watch
groups. Although the present investigation is limited in its
ability to draw conclusions about the relation between the
geography of crime reporting and neighborhood awareness,
social cohesion, and perceptions of safety, this line of
inquiry is recommended for future research.
Limitations
These findings should be reviewed with some caution.
First, the small sample size of this study is not represen-
tative of other populations or neighborhoods, so general-
izations should not be made until the study can be
replicated with a larger sample. Since the population was
entirely African-American and in a high-violence neigh-
borhood, it is unknown how well the results would
generalize to lower-crime neighborhoods with different
racial/ethnic compositions. Also, along the same lines, very
few respondent demographics were collected. This too
makes generalizations difficult to establish. In addition, the
questionnaire items were left open to interpretation of the
respondents (e.g., other violent incidents). Future research
should zero in on specific incident types to see if different
types influence resident perceptions of geography. One
additional limitation is that respondents did not have a
visual reference source to determine when and where
incidents occurred. This could be improved in future
studies by using recall aids (e.g., calendars and community
maps) to obtain more accurate data.
Conclusion
Not only do reporting radii vary considerably based on
crime severity and whether respondents witness or hear
about incidents, resident-based definitions of community
differ from administrative boundaries. Importantly, indi-
vidual-level characteristics of respondents do not have an
effect on reporting radii. Results are useful in launching
new discussion on the geography of crime, neighborhood
definitions, and community-based crime prevention.
Acknowledgments The authors gratefully acknowledge the contri-
bution of Shango Johnson for his insights and help with study
implementation. This study was funded by the National Center for
Injury Prevention and Control, Centers for Disease Control and Pre-
vention U81/CCU517816 (University of Chicago, Illinois). The
findings and conclusions in this report are those of the authors and do
not necessarily represent the official position of the Centers for Dis-
ease Control and Prevention.
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- The Geography of Citizen Crime Reporting
Abstract
Introduction
Methods
Study Background
Selecting a Pilot Community
Participants
Participant Recruitment
Study Sample
Measures
Procedure
Computing Incident Reporting Radii
Examining Predictors of Reporting Radii
Calculating Block-Level Perceptions
Results
Discussion
Limitations
Conclusion
Acknowledgments
References
at SciVerse ScienceDirect
Applied Geography 41 (2013) 139e146
Contents lists available
Applied Geograph
y
journal homepage: www.elsevier.com/locate/apgeog
The space/time behaviour of dwelling burglars: Finding near repeat
patterns in serial offender data
Derek Johnson*
Northumbria University, Ellison Building, Ellison Place, Newcastle upon Tyne NE1 8ST, United Kingdom
Keywords:
Burglary
Clustering
Serial offending
Crime prevention
Repeat
* Corresponding author. Tel.:þ44 191 243 7812
.
E-mail address: Derek.Johnson@Northumbria.ac.uk
0143-6228/$ e see front matter � 2013 Elsevier Ltd.
http://dx.doi.org/10.1016/j.apgeog.2013.04.001
a b s t r a c t
Whilst analysis of crime for tactical and strategic reasons within the criminal justice arena has now
become an established need, predictive analysis of crime remains, and probably always will be, a goal to
be desired. Opening a window on this over the last 2 decades, prominent research from academia has
focused on the phenomenon of repeat victimisation and more recently ‘near repeat’ victimisation, both
firmly grounded in the geography of crime. Somewhat limited to the establishment of near repeat
behavioural patterns in whole area data, these can be utilised for crime prevention responses on a local
scale. Research reported here however, explores the phenomenon through the examination of serial
offending by individual offenders to establish if such spatio-temporal patterns are apparent in the spatial
behavioural patterns of the individual burglar, and if so how they may be defined and therefore utilised
on a micro rather than macro scale. It is hypothesised that offenders’ responsible for more than one
series of offences will display consistency across their crime series within time and distance parameters
for their closest offences in space. Results improve upon current knowledge concerning near repeat
offending being the actions of common offenders. Testing of the extracted data indicates that offenders
maintain personal boundaries of ‘closeness’ in time and space even when actions are separated by sig-
nificant time spans, creating stylised behavioural signatures appertaining to their use of and movement
through space when offending.
� 2013 Elsevier Ltd.
All rights reserved.
Introduction
As spatial analysis and the availability of G.I.S. has blossomed so
the relevance of its use, and indeed the concept of an empirical
geography of crime, has become embedded within the Criminal
Justice System of England & Wales. Chainey & Ratcliffe (2006)
devote a number of pages succinctly explaining its use within a
variety of functions of such agencies, whilst their book title and
potential audience indicates the recognised importance of the
subject to practitioners in the crime arena. Introducing an issue of
the Professional Geographer devoted to spatial methodologies for
studying crime Le Beau and Leitner (2011) set out a time line of
developments in the geography of crime together with three
claims. Whilst the first two refer to past developments his third
considers the future:
“.the academic niche for the geography of crime will very
likely be shared with the new fields of environmental criminology,
.
All rights reserved.
spatial criminology, and crime science.” He concludes by asserting
an upward trajectory for the geography of crime and in particular
for the geographical and spatial analysis of crime due to its
importance to society. In the same issue Andresen (2011) reports on
empirical research studying crime rates using an alternative mea-
sure of the population at risk. Given results showing a marked
difference when using these alternative measures he comments on
the importance of policy makers remaining current with
geographical data sets and geographical analysis in relation to
crime to avoid bias in their work. Alternatively Breetzke (2012)
considers an aspect of physical geography and how the surround-
ing terrain may affect risk of victimisation of burglary in South
Africa. Rather less contemporary but remaining pertinent, Herbert
in 1989 was of the view that the geographers interest in space and
place had much to offer criminological research.
Whilst maintaining the theme of geographic analysis of the
spatial patterns of crime and criminals this paper reports on the
spatial analysis of burglary offences committed by individual of-
fenders. By moving forward with recent research reported within
the criminology and crime science literature as suggested by Le Beu,
this research indicates a predictability to an individual’s offending
Delta:1_
Delta:1_given name
mailto:Derek.Johnson@Northumbria.ac.uk
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1
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D. Johnson / Applied Geography 41 (2013) 139e146140
behaviour that changes little over time, suggesting that a geography
of individual serial offenders can be defined on a micro scale.
Repeat victimisation
Predictive patterns of crime in the form of repeat victimisation
was a phenomenon perhaps first identified by Johnson, Kerper,
Hayes, and Killenger in a 1973 study ‘The Recidivist Victim: A
Descriptive Study’. This was furthered by Sparks in 1981 through
identifying several key themes that can be linked to what he
termed ‘multiple victimisations’; describing views that can be
adopted to explain why an individual may become a victim, but
more importantly, a victim multiple times (Sparks, 1981, pp. 772e
777). In 1993 Farrell and Pease (1993, pp. 6e7) evidenced repeat
victimisation accounting for a significant amount of crime in En-
gland and Wales. They provided analysis of British Crime Surveys
reporting that between seventy-one and eighty-one percent of
victims surveyed had suffered two or more victimisations within
the twelve months prior to the survey. As a result of these, and
other academic explorations U.K. Police Forces began to develop
crime reduction processes to counter repeat victimisation; first
steps towards prediction of crime events and a suitable preventa-
tive reaction. By 1995 the U.K. Home Office were issuing Crime
Detection & Prevention Series papers on the topic that were citing
10 or more previous linked papers and Police Forces had annual
targets to reduce or at least maintain below target, the number of
repeat victimisations in their force areas
Near repeat victimisation
Following hot on the trail of successful action to reduce repeat
victimisation, studies began to emerge identifying patterns of
crime clustering not only in space but also in time. Morgan (2001, p.
87) highlights research conducted in the early nineties by Polvi,
Looman, Humphries, and Pease (1990) which showed that the
risk of repeat victimisation was heightened over a short time
period, but that for the first month, risk of repeat burglary vic-
timisation was twelve times greater than expected. Subsequent
research supported this suggesting that between the first and
second month risk is temporarily heightened following a residen-
tial burglary, but that there are also limits on the spatial risk.
Johnson & Bowers term this the spatio-temporal buffer (Bowers &
Johnson, 2005; Johnson & Bowers, 2004).
Working in Australia Morgan (2001) conducted research into
the repeat burglary phenomenon in Perth discovering the presence
of what he termed ‘near repeats’, repeat victimisations closely
occurring in both time and space to an initial victimisation but not
actually at the same (‘repeat’) location. Shaw & Pease reported in
2000 on research of repeat offending in Scotland finding distinct
spatial features. On 68% of occasions, if the first dwelling burglary
was at a house with an even number, the next property to be
burgled was also an even number. This pattern held for odd
numbers. Thirty percent of dwelling burglaries on the same street
occurred within 6 numbers either side of the first property
attacked, the authors referring to this as the penumbra of risk.
In 2000, Townsley, Homel, and Chaseling, again in Australia,
considered this further by analysing residential burglary crime for
clusters of offences ‘close’ in space and time, near repeat offences in
terms of being near to a previous crime event in both dimensions
rather than true repeat victimisation of the same location.
This research suggested that, much like disease spreads be-
tween people who are classed as potential hosts (those who have
the right characteristics to contract a disease) the process trans-
lated into dwelling burglary, finding that areas of largely
homogenous housing, were far more susceptible to near repeat
victimisation than areas of heterogeneous housing.
Johnson, Bowers and Pease invoke Optimal Foraging Theory
derived from behavioural ecology as a potential explanation for the
behavioural pattern of near repeat offences. Searching for food an-
imals endeavour to maximise resources acquired, simultaneously
minimising chances of capture and effort expended. The analogy
between animals and offenders is clear. In their search for food
animals are likely to learn much about the environment they move
through such as high yield locations, escape routes, hiding areas and
safe places. If offenders act as optimal foragers it was anticipated
that the same would be true; offenders would learn about likely
yields, security measures, potential escape routes from their pre-
vious actions, using this information for future offending. Extending
this they suggest that repeat location offences can then be consid-
ered a form of optimal foraging (Johnson, Bowers, & Pease, 2005).
Policing response
Academic research activity in this search for predictive analyt-
ical power has received significant impetus through work such as
that described. Promulgating that the risk of burglary victimisation
can be likened to that of a contagious disease, those premises
nearest to the initial burglary event being at heightened risk of
future attack and such risk decaying both over distance and time, is
a useful analogy. Most important from a predictive sense is that
parameters from both dimensions can be articulated (Bowers,
Johnson, & Pease, 2004; Townsley et al., 2000 and others).
In 2005 Police in Bournemouth, a popular U.K. south coast town,
undertook a burglary reduction initiative based on similar near-
repeat analysis. Patterns of space/time clusters were evident in
the towns recorded burglary data with two dimensional parame-
ters of 200 m and 48 hours for highest risk. Rapid delivery of
reduction advice to residents within 200 m of an initial burglary
and 48 h of its report resulted in increased crime reduction, but
perhaps more significant was an apparent change in offender
spatial behaviour in the areas of intervention (Johnson, 2008). Such
predictive analysis has now been adopted with significant fanfare
by others, particularly Greater Manchester Police. However the
proactive Policing response has taken a global approach of estab-
lishing near repeat patterns within area based data to intelligently
lead the deployment of prevention and patrol resources, refining
the original work of Johnson (2008) in Bournemouth.
Research objectives
Research reported here explores the phenomenon through the
examination of individual offender data to establish if time and
space patterns are apparent in the spatial behavioural patterns of
the individual burglar. If so it is asked how such patterns may be
defined and therefore utilised on a micro rather than meso or
macro scale. Such work has the advantage of approaching data from
a known situation, namely that a series of crimes were the actions
of one individual and therefore display personalised behavioural
patterning.
It is hypothesised that offenders’ responsible for more than one
series of offences will display consistency across their crime series
within time and distance parameters for their closest offences in
space. It is suggested that each offender will have personal defini-
tions of ‘closeness’ in space and ‘closeness’ in time in a similar way
that we each have our own activity spaces (Brantingham &
Brantingham, 1990), although closeness in time may be driven by
an individual’s needs and are likely to be more fluid. In addition it
was considered that if serial offenders were to display consistent
near repeat offending this may create opportunities to develop
D. Johnson / Applied Geography 41 (2013) 139e146 141
predictive analytics utilising these as stylised behavioural patterns
akin to crime ‘signatures’.
Literature on repeat victimisation strongly suggests common of-
fenders for repeat offences (Ashton, Brown, Senior, & Pease, 1998;
Hearnden & Magill, 2004; Kleemans, 2001; Pease, 1998; Polvi, 1991;
Wright & Decker, 1994) but such studies have tended to rely on
victim/crime scene data orinterviewaccounts with offenders.To date
little published work on near-repeat burglaries has been undertaken
using offender data. Examination of modus operandi facets of bur-
glary has been undertaken on Liverpool data (Bowers & Johnson,
2004) suggesting common offenders are responsible for near
repeat offences and the original burglary event, but was based on
datawith no reference to identified offenders.Bernasco(2008) points
out that the theoretical claim that the original and subsequent near
repeat offence (in terms of burglary particularly) are the work of the
same perpetrator relies on limited evidence. He states that until his
work of 2008 no such research had utilised offender data. Bernasco
examines Police recorded detected offence data from the Hague and
surrounding area over an 8 year period, providing empirical evidence
that offences related in time and space are highly likely to indicate
same offender activity. However he does not investigate the spatial
point patterns of identified individual offenders.
Data for this research was drawn from the English south coast
conurbation of Bournemouth and Poole, the first stage involving
analysing police recorded burglary data to ascertain whether such
near repeat patterns were apparent. Townsley et al. describe using
a Knox test to build a non-cumulative table of the number of
burglaries actually committed (observed) over various distances
and time periods. Such a table allows comparison of the number
of burglaries committed with those that may be expected
by chance (Townsley et al., 2000). This method was used to
establish the presence of near repeat patterns in the Bournemouth
& Poole data.
It had been anticipated that it would be similarly effective for
identifying such patterns in serial offender data, however, due to
comparatively low volumes of offences within serial offending it
was found incompatible and a new method was developed.
Material & methods
Police recorded residential burglary crime for the calendar years
2002e2006 inclusive for the coterminous Police divisions were
obtained for analysis. Knowledge of the burglary reduction inter-
vention in Bournemouth (Johnson, 2008) prompted the Bourne-
mouth data to be split into two time periods, before and after the
intervention start date. No such intervention had taken place in the
Poole policing area.
Linked to recorded crime was data enabling the identification of
all identified offenders for residential burglary within the extracted
r ¼ x � e=
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
eð1 � row proportion of xÞð1 � column proportion of xÞ
q
(2)
data set. Further filtering enabled extraction of those responsible
for ten or more residential burglaries. Forty-four offenders formed
this category but only 14 of those had committed two distinct series
of crimes, each having been imprisoned following their first series
of crimes for varying terms. Post release a further series had been
committed. One offender was responsible for three separate pe-
riods of serial offending.
As with most recorded acquisitive crime exact dates and times
burglaries occur are rarely known. Time and date fields within the
data consisted of ‘from’ and ‘to’ dates and times; when the premises
were last known to be in order (‘from’) and when the burglary was
discovered (‘To’), so giving a time window when the offence could
have occurred. Distributions indicated a prevalence of offences
committed within a one day time window indicating that use of the
‘from date’ field in all three data sets as a time reference was valid.
Three further data sets consisting of only those offences committed
during such a 24 h window were then created so ensuring time
accuracy.
The methodology of Townsley et al. (2000) was used in order to
establish the presence or otherwise of near repeat offences. This
utilises the Knox method whereby a non cumulative table can be
built of the volume of burglary offences within certain time (t) and
distance (d) bands. Each cell in the table reports the number of
burglaries within t and d parameters such as in Table 1.
By utilising column and row totals the expected values (e) for
each cell are also calculated as at formula f1.
e ¼
�
yi � yj
�.
yk (1)
Limitations to this methodology are well known, being that time
and distance parameters are set by the researcher and should
therefore utilise some form of empirical measure. By considering
relevant results from previous empirical research integrity can be
built in to the Knox analysis through empirically informed cate-
gorisation. Time is dealt within previous research concerning
repeat burglaries, suggesting a tendency for them to occur soon
after previous events and generally within 2 months (Anderson,
Chenery, & Pease, 1995; Bowers & Johnson, 2004; Pease, 1998;
Polvi, 1991). Concerning Bournemouth the 2005 Dorset Police
reduction intervention established high risk at much shorter in-
tervals, certainly being apparent at 7 days from an initial event.
Time bands of 7 days were therefore utilised extending over a
period of 6 months. Anderson et al. found addresses two doors
away from a burgled premise to be at slightly higher risk than those
further away (Anderson et al., 1995). A 2002 study also found that
houses on the same side of a street were at heightened risk
(Everson, 2002). Actual distances are unknown but a few hundred
metres can certainly be inferred. For this research a distance vari-
able of 200 m was chosen extending to 2000 m overall.
CrimeStatIII software (Levine, 2004) provides Knox test func-
tionality and reports x values as described in Table 1. To establish
which cells in the 3 tables established experienced a greater fre-
quency of events than could be expected by chance adjusted re-
siduals (r) were then calculated for each cell as shown at formula f2.
As described by Townsley et al. (2000) “The residual scores
measure how many standard deviations the observed frequency is
from the expected”. Values �2 reflect a 5% chance of a type I error.
To limit this, minimum values of 3 could be utilised thus only 1% of
cells will potentially display chance values. The built tables for the
0
2
4
6
8
10
12
14
1 2 3 4 5
6 7 8 9 10 11
Fr
eq
ue
nc
y
Distance bins (Km)
Fig. 1. Histogram of Inter-event distances.
Table 1
Time.
Distance 0 to d d to 2d 2d to 3d Totals
0 to t x yj � row total
t to 2t xi
2t to 3t xj
Totals yi � column total yk � row and column total
d represents a distance parameter set by the user e.g. 200 m
t represents a time parameter set by the user e.g. 7 days.
x represents the number of burglaries between 0 to t time and 0 to t distance.
xi represents the number of burglaries between t and 2t time and d and 2d distance.
xj represents the number of burglaries between 2t and 3t time and 2d and 3d
distance.
D. Johnson / Applied Geography 41 (2013) 139e146142
Bournemouth and Poole data contained 280 cells each and there-
fore residual values of 3 and above were deemed significant,
limiting the number of cells displaying Type I error values to a
maximum of 3.
Ultimately when used with offender data the Knox method
becomes unstable with low values, high residual scores showing
significance against observed values of only one burglary. Given
such instability with low values of serial offending a second
methodology was developed to identify near repeat patterns in
serial offending and a number of requirements for the analysis were
formulated, namely to identify within a series of burglary crimes:
� Those offences close in space,
� The time distribution relevant to spatially close offences,
� To quantify the ‘closeness’ of space relevant to the individual
offender and,
� To quantify the ‘closeness’ of time relevant to the individual
offenders spatially close offences.
Variables for time (t) and distance (d) now translated into
defining what could be considered as ‘close’ given an individuals’
serial behaviour. Literature appertaining to near repeats over-
whelmingly suggests small distances of a few hundred m, (Bowers
et al., 2004; Johnson et al., 2005, 2007) particularly for Bourne-
mouth (Johnson, 2008). Consequently an aim of establishing the
minimum distances within an offenders’ spatial distribution of
crimes was selected.
Straight line distances between crime events were utilised to
populate a table of distances between all burglary events and those
future to them in the series. For each row of data the minimum
distance was extracted. Unlike nearest neighbour analysis which
considers events past and future row minimum distances refer to
each events future nearest neighbour. Future nearest neighbour
distances therefore determine that for each event except the last in
the series there is at least one other event that is ‘close’ to it. An
assumption is made that events are ordered chronologically.
Table 2 examples the inter event distances for offender D series
2 in the sample. Fig. 1 displays the frequency histogram of inter
Table 2
Inter event distances offender D series 2.
Min. Distance Event: 1 2 3 4 5
0.752 1 1.54 3.72 0.75 2
0.390 2 4.62 2.03
3
1.878 3 2.97 1
1.067 4 1
0.446 5
1.101 6
0.187 7
6.352 8
6.628 9
2.649 10
event distances and Fig. 2 the corresponding histogram of the
distribution of future nearest neighbour distances (column ‘Min.
Distance’ from Table 2). Table 3 reports descriptive statistics cor-
responding to the future nearest neighbour distribution. All dis-
tances are in kilometres.
For this offender we can conclude that offences cluster at small
distances � median and this can be visualised in Fig. 3, a simple plot
of the grid references pertinent to this example.
Skew values for the distribution of future nearest neighbour
distances were obtained by using the Pearson coefficient of
skewness:
skew ¼ 3 � ðmean � medianÞ=standard deviation
Skewness is a dimensionless measure descriptive of the relevant
distribution. Its descriptive nature is succinctly put by Tabachnick &
Fidell (2001, p. 73e77) “If there is positive skewness, there is a
pileup of cases to the left and the right tail is too long: with negative
skewness, there is a pileup of cases to the right, and the left tail is
too long.” Using the Pearson coefficient of skewness secures a guide
of significance as values greater than þ1 can be considered notably
positively skewed whilst values less than �1 indicate notable
negative skewness (Pearson, 1895; Rees, 2001, p. 43).
Skew values for the future nearest neighbour distance distri-
bution describe that distributions tendency or otherwise to cluster
towards small or larger distances. Within a skewed distribution
median values are representative of the nature of the data sets
distribution and central tendency, therefore if an offenders serial
offending displayed a positively skewed distribution of future
nearest neighbour distances (as with offender D series 2 in Table 2)
6 7 8 9 10 11
.43 2.38 1.52 10.39 8.10 1.51 1.81
.78 3.83 0.39 9.66 8.40 0.57 3.01
.88 2.31 4.30 9.03 4.82 4.13 1.97
.76 1.80 1.87 10.06 7.43 1.78 1.07
0.45 3.58 10.46 6.69 3.46 0.84
3.67 10.88 7.12 3.57 1.10
9.34 8.01 0.19 2.78
6.35 9.22 9.90
7.82 6.63
2.65
0
1
2
3
4
0.5 1.0 1.5 2.0 2.5 3.0 3.5
Fr
eq
ue
nc
y
Distance Bins (Km)
Fig. 2. Histogram of future nearest neighbour distances.
0.088
0.090
0.092
0.094
0.096
0.098
0.100
0.102
0.399 0.400 0.401 0.402 0.403 0.404 0.405 0.406 0.407 0.408
Y
(
m
e
t
r
e
s
)
x1
M
ill
io
n
X (metres)
x1 Million
Fig. 3. x/y plot of offences.
D. Johnson / Applied Geography 41 (2013) 139e146 143
it shows a tendency to commit offences close in space. These Me-
dian values provide a cut-off measure at which events with inter
event distances � to this distance can be selected. Such events
represent those that have taken place at close distances with
respect to the overall spatial distribution, thus identifying offences
within a burglary series that are ‘close’ in space. Time spans for
these spatially clustered events were then calculated and by uti-
lising the same methodology of selecting those with a time
span � median time span a simple matrix was compiled of offences
close in both time and space.
If an offenders’ behaviour is such that, in chronological
sequence, his/her closest future nearest neighbour offences always
follow the immediately previous offence such a matrix would show
populated cells across the diagonal. Such time/space patterning can
be summarised by the simple proportion of populated cells in this
diagonal where the total number of possible nearest time/space
neighbours ¼ n (No of offences in series) � 1. This proportion can be
seen as an index score for time/space nearest neighbours.
Such a sequence can be imagined as a series of clustered
events on a straight line, perhaps a single street, where chrono-
logically events move along the street from left to right or vice-
versa. Other configurations can be imagined but in every case
events move along a time line and are further away from the
event prior to the immediately preceding event. In this case the
index score obtained would equal 1 and these closest nearest
neighbours could be referred to as first order time/space neigh-
bours. Second order time/space neighbours would relate to the
next offence but one i.e. offence 3 to offence 1, offence 4 to
offence 2 and so on. Again an index can be calculated. High index
scores for k order space/time neighbours will indicate a repeating
pattern of behaviour. The relevance of the k order neighbour is
however dependent on the number of crimes in the series. Crime
opportunities available ¼ n � k therefore the 8th order neigh-
bours in a series of 11 events only represents three possible crime
opportunities.
Within an offenders’ data the future nearest neighbour distance
data sets from their series of crimes were compared using a Fisher
exact test on the median values. The data sets of the two series of
Table 3
Future nearest neighbour distribution.
Q1 Std deviation Mean Median Skew Range
Min. Distance 0.523 2.406 2.145 1.084 1.323 6.441
each offender were amalgamated and a combined median value
calculated. A 2 � 2 table was constructed (Fig. 4).
Fisher’s exact test calculates the exact probability that a table
could be obtained that differs from the expected values as much as
or more than the actual table of values by effectively generating all
possible tables given the margins of the observed values. Unlike a
chi-squared test the Fisher exact test can utilise small values (<5)
hence its preferred use in this case. The null hypotheses (Ho)
state that the medians of future nearest neighbour distances for
each series of crimes with a common offender are the same. This
same method was applied to the tables of time spans between
events where the inter event distance � median future nearest
neighbour distance. In all cases a double sided p-value was sought
as direction was unknown. In each instance the alternative hy-
potheses states that the medians would be different as an indi-
vidual offender maintains no personal concept of ‘closeness’ in
terms of distance or time between offending locations.
Results
Area results
Knox analysis showed significant timeespace clustering in the
data from both towns. A marked difference between the two
Bournemouth data sets was observed, the post intervention anal-
ysis showing a considerable decline in such clustering.
Poole data returned significant residual scores at 14 days up to
400 m. All residual values greater than three were sourced from
observed values of actual burglaries that were at least 20 offences
greater than their respective expected values. Bournemouth data
provided a considerable contrast against that for Poole. Given that
they reflect different time periods and that such crime had been
noticeably falling comparisons are however jeopardous. High
scores (>8) were reported at 200 m up to 21 days, similar to pa-
rameters set by the analysis undertaken for the Police reduction
initiative (Johnson, 2008). Observed values for the first 14 days at
200 m were at least 100 offences greater than expected. For the
period post April 2005 in Bournemouth risk remained high at
Series 1 Series 2
No of values > combined median
No of values< combined median
Fig. 4. Fisher exact test: 2 � 2 table.
D. Johnson / Applied Geography 41 (2013) 139e146144
200 m but for only 7 days. As this data reflects the reduction
intervention it is interesting to note the considerable change.
Offender data analysis
Offenders with multiple series were required in order to facili-
tate comparison between series. All selected offenders were lone
offenders; data did not reflect others being proceeded against for
the same offences. Only one had committed more than two series of
crimes (offender F, 3 series) leaving potential comparisons limited.
Table 4 reports time and distance parameters for each offender
and respective series of crimes. These values were concluded by
reference to the skew value obtained, the median or mean value as
appropriate and frequency distributions. Unless the skew value
indicated a distribution close to symmetrical the median value was
concluded as the better descriptor.
‘Close offences’ provides the parameter for those offences
determined as close in space and close in time for the individual
series of offences being examined, whilst the ‘All offending’ value
reports a similar statistic for the distribution of all crimes within
the series. This table also reports the results from the Fisher’s exact
test carried out on each pair of crime series. This test sought to
establish if the ‘Close offences’ parameter in relation to one series
was statistically the same or significantly different from the ‘Close
offences’ parameter in a second series of offences.
Discussion
There are a number of caveats to consider when using Police
recorded crime data, notably that not all crime is either reported or
recorded. For the whole area research under reporting/recording of
crime was not considered problematic due to the volume of data
obtained, however exploring individual offending and relying on
recorded data may create bias. There are two issues, the potential
for the offender to have committed offences that went unreported
(or reported but unrecorded) and/or the potential for the offender
to have committed more offences than are known to have been his
responsibility. In many ways these are limitations that are forced
Table 4
Spatial offending patterns and series comparisons.
Offender Series 1 Series 2
Close offences All offending
n t nc r km Days km Days n t nc r
A 39 155 38 0 0e0.4 0e28 2e4 0e5 12 14 14 0
B 22 90 17 0 0e0.4 0e10 0e2 0e2 6 10 2 0
C 14 114 8 0 0e0.6 0e28 0e1 0e3 15 46 11 0
D 13 39 11 0 0e0.65 0e15 5e10 0e4 11 34 8 0
E 9 79 7 0 0e0.4 0e30 0e1 0e10 10 53 7 0
F 8 125 4 0 0e0.8 0e40 0e3 0e11 15 85 18 0
Offender ‘F’ Series 3
F 11 51 11 1 0e0.6 0e23 0e2 0e6 e e e
F e e e e e e e e e e e e
G 18 124 15 8 0e1 0e1 0e5 0e1 11 34 6 0
H 6 34 3 0 0e0.5 0e8 0.2e0.8 0e6 9 27 6 1
J 15 313 25 0 0e0.7 0e25 0e2.5 1e3 17 40 15 0
K 20 142 14 0 0e0.5 0e10 0e1.5 0e2 17 33 12 1
L 11 44 7 2 0e0.2 0e1 0e1 0e1 10 95 5 1
M 28 92 28 0 0e0.9 0e12 0e5 0e2 17 47 5 0
N 7 98 4 1 0e0.8 0e10 0e2 0e10 9 21 5 1
P 39 56 41 0 0e0.3 0 0e3 0 38 30 40 0
Significance level * ¼ p < 0.05, ** ¼ p < 0.01. n ¼ volume of offences. t ¼ time span of series in days. nc ¼ number of future nearest neighbours with inter event distance � the median futur r ¼ number of repeat offences in series.
upon researchers, there probably is no better available data to
work with.
Regarding offences simply not reported to the police domestic
burglary is one that routinely shows a high reporting/recording
rate. The 2010/11 British Crime Survey (Chaplin, Flatley, & Smith,
2011) reported that “over eight in ten burglaries where some-
thing was stolen (82%) and over three-quarters of burglary with
entry were reported (79%)”.
In this case detected offences were considered those offences
where an offender had been brought to justice as opposed to
being arrested for it without further action being taken. In all
cases offenders were prosecuted for a sample of the offences in
their series of offending and asked the court to then take the
remaining offences into consideration (TIC). Whilst not foolproof
personal knowledge of relevant investigative procedures and
methods indicate to the author that the technique of detecting
offences by way of confession and ‘TIC’ is reasonably robust. In the
majority of cases offenders accept that they have little to lose once
formally charged with a sample of substantive burglary offences
and that it can help their case by showing a willingness to co-
operate. During the period when this data was collected it was
common practice for an offender to be driven around an area and
be asked to point out premises attacked. If an indicated address
had no associated recorded crime an enquiry would be made with
the householder, recording a detected burglary offence rather
than an undetected one obviously being more favourable.
For this research the results obtained indicate that data sets
utilised were probably consistently accurate with regard to these
non recording/non detecting issues. Consistent results such as
those obtained would not be anticipated had there been non
recording issues apparent in individual offender’s data sets.
Another important issue concerning offender data is that it only
represents those offenders brought to justice. Whilst the offender
data examined appears representative of the most prolific of-
fenders it only concerns a proportion of total offending. In this case
21.29% of all burglary offences in the data set were marked as
detected, whilst the offences committed by the selected prolific
offenders amounted to 5.87% of all offences.
Fisher’s exact test
between series
Comments
Close offences All offending
km Days km Days p distance p time
0e0.6 0e12 1e3 1 0.320 0.000023**
0e0.6 3e4 0e4 0e6 0.280 0.485
0e0.8 0e5 0e3 0e5 0.449 0.369
0e1 0e25 0e3 3e6 0.669 1.000
0e1.1 5e10 1e2 0e5 0.153 0.286
0e0.6 0e32 0e1.6 0e4 0.659 1.000 Test on series 1/2
e e e e 0.637 1.000 Test on series 1/3
e e e e 1.000 0.064 Test on series 2/3
0e0.6 0e5 0e5 0e1 0.310 0.361
0e0.4 0e10 0e0.8 1e4 0.592 1.000
0e0.4 0e15 0e1 0e1 1.000 0.000774**
0e0.4 0e11 0e2 0e2 1.000 1.000
0e0.2 0e15 0e1 0e10 1.000 0.00126**
0e0.4 0 0e1 0e2 0.004** 0.048*
0e1 0e2 0e2 0e5 1.000 1.000
0e0.3 0 0e3 0 1.000 0.822
e nearest neighbour distance.
D. Johnson / Applied Geography 41 (2013) 139e146 145
All offenders selected, bar one, had committed the majority of
their offending in either Bournemouth, Poole or both towns, but
there were instances of some offenders travelling considerable
distances (tens of kilometres) to commit one or two offences. It is
suggested that the most likely scenario concerns visits by offenders’
to associates, committing burglaries whilst ‘en route’. Such activity
would be representative of crime pattern theory (Brantingham &
Brantingham, 1990) and entirely expected. In the context of this
research such distant offending has the effect of literally skewing
the results in that offenders with such patterns will potentially
generate high skew values in relation to distance over their entire
offending. Should the distant offence be a lone event in time, such
as one offence preceded and followed by offences in Bournemouth,
it will be recorded as one of the future nearest neighbour distances
and could therefore significantly skew the future nearest neighbour
distance distribution. Such activity was present in the data for 3 of
the series of crimes examined (D1, J1, E1).
Within offender analysis there is a degree of dependency in the
data. This concerns distances for future nearest neighbour events
and those that occur at a distance � the median measure of that
distribution, as one data set is a derivation of the other. However
derived data sets are not formally compared nor tested against their
origins but used only as tools to gather further descriptive infor-
mation, namely time spans between close events in space. Similarly
Fisher’s exact test is conducted on data sets derived from different
series of offender’s crimes. The offender is a common factor but the
crime series from which the two sets of data originate are tempo-
rally independent.
Offender data analysis sought to establish if near repeat patterns
could be observed within a series of burglary crimes and therefore
within individual offending behaviour, and a methodology was
devised to achieve this. A second question asked was whether such
‘near repeat’ spatial and temporal patterns could be considered a
‘signature’ of the individual offender. Patterns in offender data
generally reflected the parameters established by the area analysis.
Of 29 series of offences 18 displayed spatially close offences taking
place within 14 day time spans and a further three within 15 days.
Distances did not reflect the area results quite so well with only 11
series reporting small distances �400 m. This may be because the
offender data only accounts for a relatively small proportion of the
data used for the area analysis. It is plausible, given low detection
rates, that offender data for burglary does not accurately represent
all offenders.
Stylising offender spaceetime behaviour could advantageously
provide investigative opportunities for undetected series’ of of-
fences. Results in this research suggest many maintain a spatial and
temporal approach to offending, even when such acts are separated
by significant time periods. Results show little differentiation in the
‘closeness’ of time or distance between offenders with regard to
their minimum distances and time lags. Testing between inter
event distances across different offenders’ serial burglaries may be
more informative. This would tend towards a fuller description of
their spatial offending behaviour. As it is results suggest small scale
spatial and temporal offending features are aspects that could add
to undetected serial crime analysis of modus operandi features.
Index scores for spaceetime k order nearest neighbours also
indicate a tendency for the majority to commit burglary offences at
their individualised shortest distances in space and time. With
almost all offenders committing, at some point, future nearest
neighbour offences that were actually consecutive in time (1st order
space/time nearest neighbours) the importance of the time element
is highlighted within offender decision making, perhaps giving an
indication of the needs of the offender at that particular time.
Establishing the offenders’ yield at an initial offence may be infor-
mative of offences which become a ‘seed’ offence to a near repeat.
Conclusion
Area analysis confirms near repeat residential burglaries in the
two towns, suggesting such patterns should also be discernible in
offender data. Fisher’s exact tests between offender’s series only led
to the rejection of Ho in five cases, 4 rejections being in respect to
time only. For only one series was Ho rejected for both time and
distance, thus suggesting offenders particularly maintain distance
‘mental maps’ over significant periods with respect to ‘close’
offending.
This research adds to existing literature expressing the view that
crime reduction work should follow quickly in the footsteps of of-
fenders and take on a targeted ‘small area approach’ as well as
focussing on an attacked premise (Farrell & Pease, 1993; Polvi et al.,
1990, 1991; Townsley et al., 2000). Whilst future risk at burgled
premises is significant there is now substantial evidence to suggest
that current repeat victimisation policies focussing solely on an
attacked home would benefit from an expanded approach.
Offender analysis upholds this view and provides further evidence
that serial offenders commit spatially and temporally clustered
crime.
Merry considers the ability to link both past and present of-
fences with common offender(s) to be the “essence of operational
crime analysis” (Merry, 2000) and such work is a core activity of the
operational crime analyst. Considerable literature exists concerning
methods to link offences to common offenders, although it is
perhaps most prevalent concerning sexual and serious violent
offending rather than volume property crime. By far the most
common approach used by Police analysts (barring evidence such
as DNA or fingerprints) is examination of behavioural modus
operandi features from crime scenes. However research indicates
the preferred approach would utilise spatial information concern-
ing crime locations as well (Ewart, Oatley, & Burn, 2005; Goodwill &
Alison, 2006). This research provides further evidence of the
importance of spatial consideration when searching for linked
crime events but emphasises the need to consider space/time re-
lationships in doing so.
Acknowledgments
The assistance of Dorset Police in the provision of data which
made this work possible is acknowledged, together with the always
constructive encouragement and proof reading of Dr M. Barke of
Northumbria University Geography. The insightful and constructive
comments of two anonymous reviewers are greatly appreciated.
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- The space/time behaviour of dwelling burglars: Finding near repeat patterns in serial offender data
Introduction
Repeat victimisation
Near repeat victimisation
Policing response
Research objectives
Material & methods
Results
Area results
Offender data analysis
Discussion
Conclusion
Acknowledgments
References
RESEARCH ARTICLE
GIS supporting intelligence-led policing
Tegan Herchenradera* and Steven Myhill-Jonesb
aLatitude Geographics, Kitchener, Canada; bLatitude Geographics, Victoria, Canada
Tightening budgets and increased demand for public accountability has placed
additional stress on already limited police department resources. Web-based crime
mapping provides significant improvement over previous methods of information
dissemination, allowing police departments to continue to work quickly and effi-
ciently within these limitations. This modern technology has enabled a more proac-
tive approach to policing, including intelligence led-policing and public facing crime
maps. As such, officers are now able to better consider spatial patterns related to
historic crime, and determine more informedly where crimes may occur in the future,
and allocate their limited resources accordingly.
Keywords: intelligence-led policing; transparency; GIS; web-based mapping;
ArcGIS®; Geocortex®
Introduction
In an information-driven society, police departments are under increasing pressure to
run an intelligence-led police model. This model asserts that police can spend less time
reactively responding to crime if supported by a system that provides data analysis and
crime intelligence, allowing officers to reduce, disrupt, and prevent crime (Ratcliffe,
2008, n.d.). Alongside this drive for information is the ongoing demand for departments
to provide increased transparency to the media and citizens. The Waterloo Region Police
Service (WRPS) and the
(VPD) are two Canadian organi-
zations which have taken the use and sharing of information to the next level through
the implementation of an intelligence-led policing model. As this paper will explore,
this has been supported, in part, by providing web-based mapping and basic geographic
data analysis capabilities to an expanded audience of stakeholders. In addition to
empowering police officers with the information they need to do their jobs better, this
work has been naturally extended to serve transparency goals by simultaneously deliver-
ing a subset of these data and application capabilities to the general public.
In both the WRPS and the VPD, Geographic Information System (GIS) technology
is viewed as a means by which the organization can work more proactively to analyze
and prevent crime. A GIS solution ‘integrates hardware, software, and data for captur-
ing, managing, analyzing, and displaying all forms of geographically referenced infor-
mation’ (‘What is GIS?’). This allows users ‘to view, understand, question, interpret,
and visualize data in many ways that reveal relationships, patterns, and trends in the
form of maps … reports, and charts’ (‘What is GIS?’). Both WRPS and VPD have had
long-standing enterprise GIS deployments based on ESRI® ArcGIS® technology. Given
the movement towards an-intelligence led policing model, they sought to extend the
*Corresponding author. Email: therchenrader@latitudegeo.com
© 2014 Taylor & Francis
Police Practice and Research, 2015
Vol. 16, No. 2, 136–147, http://dx.doi.org/10.1080/15614263.2014.972622
mailto:therchenrader@latitudegeo.com
http://dx.doi.org/10.1080/15614263.2014.972622
capabilities of existing desktop technology through the development of web-mapping
applications with assistance from Latitude Geographics and their Geocortex® software
technology for ArcGIS® Server.
With mature GIS implementations already in place, web-based mapping enables
organizations to reach a wider audience and more fully leverage their investment in GIS
technology by using GIS-publishing platforms like ESRI®’s ArcGIS® Server and
ArcGIS® Online. These technologies allow organizations to publish their spatial data
and related information to the web in the form of services and applications. The services
include base maps which show a basic representation of the geography, as well as layers
which are a visual representation of discrete types of features, such as property bound-
aries, building footprints, or census data. Geocortex® helps organizations build applica-
tions which consume the published services and introduce various visualization and
analytical tools which can be used by end users.
Key advantages of using a highly configurable commercial off-the-shelf (COTS)
solution like Geocortex® come from the significant amount of pre-built and easily con-
figurable functionality that adapts over time as technologies progress, the regular addi-
tion of new capabilities and options, and the amortization of development costs across
numerous licensee organizations. Alternatively, much of the functionality offered by
Geocortex® would need to be developed by in-house developers or through third-party
professional services. For example, the mapping viewer (which allows a user to view
the maps and layers published through ArcGIS® Server and/or ArcGIS® Online) and
associated capabilities might typically be developed as custom code or built using free
templates as a starting point. Properly engineered COTS solutions can help public safety
organizations deliver applications more quickly and focus on domain-specific business
problems instead of financing the one-off development of software applications and
infrastructure that invariably require subsequent ongoing investment to keep pace with a
rapidly changing technology space.
Following the intelligence-led policing model, WRPS and VPD emphasized making
high-quality current data available to officers in their patrol cars to help them be more
proactive and informed in their patrol tactics. The opportunity to be more forward-looking
in their actions is due to the capacity of empirical data to complement an officer’s
experience, hunches, and instincts related to geographic attention and pattern recognition.
The applications currently show officers information on crime occurrences across
their district for specified time periods. As the applications evolve over time, the plan is to
add other types of information to the maps, such as lists of known sex offenders or
individuals on parole (Herchenrader, personal communication, 13 August 2013; 6
September 2013).
Fulfilling the initial objectives for increased public transparency has been met
through development of public websites that display generalized occurrence information
suitable for public consumption and the protection of privacy. Citizens are able to visu-
alize crimes across a general area as well as in defined locations (e.g. their neighbor-
hood or child’s school).
The goal of this study is to examine the usefulness of web-based GIS and mapping
applications in a police setting using two real-world Geocortex®-based implementations
as case studies. To do so, we will outline how each of the respective police services dis-
seminated information to their officers and to the public prior to the implementation of
the Geocortex® solution, what issues both VPD and WRPS experienced with these
methods, what the Geocortex® solution entailed, what the challenges were with
Police Practice and Research: An International Journal 137
implementing the solution, how the VPD and WRPS plan on developing the application
in the future, and what the feedback has been from both officers and the public.
Waterloo regional police service
The problem
Prior to their Geocortex® implementation, the WRPS informed their officers going out
for patrol through two methods: paper briefings and internal message boards. To inform
the public about crimes in their neighborhood or in the region in general, the Service
posted maps rendered in static PDF format of the jurisdiction on their website. These
methods of supplying information to officers and the public had enduring drawbacks
that warranted attention.
The internal electronic message board available to officers allowed them to post
information regarding an incident that occurred during their patrol. A limitation to this
method was the time required for the officer to sit down and write a post. Given various
time constraints, their availability to do so was at worst minimal and at best variable.
Posting to the board was not mandatory and it was up to officers to make time to write
about incidents. As such, the method could not be relied upon to be kept up to date on
a consistent basis. Though any entry was helpful, by its nature, it was an incomplete
data source that offered limited potential for consistent use or meaningful pattern recog-
nition. Another limitation of this method was that there was no way to search the board
for particular items. Officers gathered information by scrolling through posts. As such,
it was easy for officers to miss information or be unaware of it altogether. Paper brief-
ings, created by the Service’s crime analyst, occurred at the beginning of each shift.
Briefings could be missed for a variety of reasons, such as illness or rushing out due to
a call (Herchenrader, personal communication, 13 August 2013).
Given that the information provided in the briefing was not available afterwards, the
Service was experiencing an inefficient use of already time-constrained resources. First,
the Service’s crime analysts were regularly being asked routine questions, thus taking
their time away from other important tasks. Second, during an officer’s downtime on
patrol, they were more likely to place themselves in a location that was ‘convenient and
safe’ (Herchenrader, personal communication, 13 August 2013), meaning they would go
somewhere which their previous experience informed them would be a likely place for
problems to occur. Readily available and up-to-date information could more accurately
and precisely inform an officer so they could locate themselves at a particular block or
building, or at a new and previously unknown location where crime would be more pos-
sible to occur.
To inform the public about incidents in the region, static maps of the region were
made available on the Service’s website. While these maps provided a wealth of infor-
mation at a defined map scale, this became a drawback in coming to any useful conclu-
sions. There were many different symbols on the map indicating different types of
crime and due to the inability to zoom into the map, it was difficult for the user to get a
proper understanding of what was going on in any particular area.
The solution
In the move towards an intelligence-led policing model, as well as to provide insight
and transparency to the public, the WRPS decided that a third-party GIS solution, which
138 T. Herchenrader and S. Myhill-Jones
offered a dynamic, user-adjustable map populated with current information, was the
answer. They sought to deliver this through an offering of several interactive mapping
applications, with appropriate data, visualization, and analysis tools for each intended
audience. Spatially visualizing and highlighting specific crime data types makes it easier
for officers to observe and draw correlations between occurrences. Over time, this also
helps officers better identify and track crime as it increases or decreases and shifts or
maintains its location (Gotway & Schabenberger, 2009). Analysis can also be extended
beyond the proximity of the crimes. Geospatial data can also allow officers to take into
account variables such as neighborhood type, street accessibility, type of property
(Malleson, 2011) as well as various other factors that relate to the ‘multidimensional,
multifaceted crime problem’ (Rich, 1995). Being enabled to account for a variety of
factors can help officers draw more informed conclusions concerning where crime might
occur in the future (Malleson, 2011). The interactive functionality of these mapping
systems can also provide information that enables officers to better use available
resources to work towards the prevention of crime.
To expand upon their pre-existing ESRI® GIS implementation, the Service opted to
go with a third-party solution. Reasons included a lack of specialized resources at the
Service that could deliver a product that not only met the needs of the Service but that
could also be delivered in the allotted time with the given funds (Herchenrader, personal
communication, 23 July 2013; Rich, 1995). After reviewing proposals, web-mapping
software firm Latitude Geographics (developers of Geocortex®) was selected. With a
mature, established product in the ESRI® COTS space, Geocortex® provided the most
required capabilities out of the box. With minimal configuration required to deploy, the
IT team was able to get the core capabilities deployed quickly, and turn their attention
instead to configuration of domain-specific capabilities and data integration
(Herchenrader, personal communication, 23 July 2013).
The three primary features of the internal Geocortex® application include a targeted
query tool (see Figure 1), charting of query results (see Figure 2) as well as a time
slider (see Figure 3) to view crime occurrences over time.
WRPS also added a warrants layer so that officers could query for and view images
of individuals with an outstanding arrest warrant. The targeted query tool allows officers
to query precisely across dates (start and end date, days of the week, hours of the day),
dispatch codes, and areas of the jurisdiction. Depending on the extent, results from a
query appear in clusters, with the number of results indicated at the center of the cluster,
making it easier to identify hotspots when the user is zoomed out (see Figure 3). As the
user zooms into the map, these clusters disperse, indicating the individual occurrences.
When an officer clicks on a cluster of occurrences, the details of the various occurrences
appear in the results sidebar. An officer can select one of these occurrences to open up
a window which provides further details. Officers can perform multiple queries and have
these results appear as different layers on a map, providing a powerful analysis tool to
use while on patrol.
The time slider and charting tools are both used in conjunction with the query tool.
The charting allows officers to graph occurrence results either by line, bar, or pie chart,
providing an alternative analytical representation. Given the power and flexibility of the
tool, officers may require some additional training and experience to employ it to its full
potential (Herchenrader, personal communication, 23 July 2013). Officers who do not
have the skill set or inclination to create these charts may continue to push many data
analysis tasks back upon the crime analysts. Nonetheless, it is there for those who, of
their own accord, see value in coupling their investigative skills with data exploration.
Police Practice and Research: An International Journal 139
The time slider, on the other hand, allows officers to animate the data and show how
they appear over time, rather than simultaneously for the entire specified date range.
Officers can define a time range and animate the data over that range or view specific
instances showing a particular moment in time within a given subset of data.
The public map allows citizens to query recent occurrences as well as historical
occurrences. When querying occurrences, the precise locations of crimes are generalized
to approximate locations to avoid data misinterpretation and to protect privacy. When
querying for recent occurrences, users can select one or multiple occurrence types as
well as the zone or division of interest. Individual occurrences are then mapped, with
result details appearing in the results sidebar. Each result in the sidebar shows a date
range in which the occurrence happened and provides a link for users to contact the Ser-
vice with more information about the incident. When querying historical occurrences,
users can also select up to six occurrence types, provide a start and end date of the
range they are interested in, as well as select the days of the week that they wish to
include. The map then color codes the zones of the region, given the number of occur-
rences that happened there. Clicking on a zone lists the details of the query as well as
the total number of occurrences per crime type. The public map can be accessed at:
http://maps.policereporting.ca/SilverlightMap/Viewer.html?Viewer=OccurrenceMaps. In
addition to keeping the public informed and the police accountable, public crime maps
have the added benefit of mitigating the public’s fear of victimization, not only by
showing where crime occurs, but also by sanitizing or otherwise limiting the level of
detail of information that they can access about specific crimes (Ratcliffe, 2002).
As with any project, there were challenges. The largest of these was the short time-
line allocated for the project. The Service had been given a grant with a specified end
Figure 1. WRPS-targeted query tool.
140 T. Herchenrader and S. Myhill-Jones
http://maps.policereporting.ca/SilverlightMap/Viewer.html?Viewer=OccurrenceMaps
date and the project needed to be completed by this date, regardless of obstacles experi-
enced. The project timeline was further shortened by the amount of time that it took for
Latitude Geographics and related contractors to get clearance to work with the sensitive
police data (Herchenrader, personal communication, 23 July 2013).
Figure 2. WRPS charting tool.
Figure 3. WRPS time slider and result clustering.
Police Practice and Research: An International Journal 141
The second challenge was assembling source data to efficiently feed the web-
mapping applications. The first issue was that the existing data came from various
sources with varying levels of quality assurance and consistency. This meant that much
of the data needed to be massaged to make it consistent and readable by the application
(Herchenrader, personal communication, 23 July 2013). The other issue with organizing
the data was the amount of information related to an event. Arrests, for example,
include person, location, crime type, etc. and each of these can be related to other
elements or events. As such, instead of pulling directly from the data, the team needed
to build summary tables that would be able to make those connections and present them
in a usable way (Herchenrader, personal communication, 12 July 2013).
A third challenge was maintaining the scope of the project. WRPS deals with a wide
variety of events and the team was continually pressured to offer, from the outset, other
data layers, in addition to occurrence information and warrants. While such additions
provide value to many, the intent of the initial implementation was to quickly deploy
technology provided by Latitude Geographics to establish a foundation from which the
Service could continue to build on, over time (Herchenrader, personal communication,
23 July 2013).
Following the launch, WRPS received significant positive feedback, both from offi-
cers and the public. Officers report that they find the application easy to use and have
found it useful. It is believed this is partially due to the application infrastructure, but
also a result of the considerable data improvement that occurred during the project. For
the public website, traditional news media have been the Service’s biggest source of
feedback, stating that the application is easy to use and provides the necessary informa-
tion they require (Herchenrader, personal communication, 13 August 2013). The public
site endeavors to provide maximum possible information, but given the sensitive nature
of the information, this amounts to fairly limited detail and locational precision about
occurrences. Nonetheless, the Service has been able to reduce staff time consumed by
directing inquiries to the public map. The Inspector for media and public affairs noted
that where before it may have taken him five to thirty minutes to look up an inquiry, it
now takes him seconds (Herchenrader, personal communication, 13 August 2013). Hav-
ing occurrence information readily available to officials and the public helps to ‘diffuse
potential conflicts between community and police’ (Friedman, Gordon, & Maltz, 2000)
as management is able to address inquiries with ‘more timely, focused information’
(Friedman et al., 2000) that allows them to ‘correct inaccurate accounts’ (Friedman
et al., 2000), while the general public and media are also able to approach interactions
better informed themselves.
The Service has planned for the continued development and enhancement of the
internal application. A significant amount of effort will go towards adding more layers
to the map, including those for compliance checks and high-risk offenders. As before, a
challenge will be defining and maintaining the scope of the project, as well as ensuring
that resources continue to be allocated towards its development (Herchenrader, personal
communication, 23 July 2013).
Vancouver Police Department
The problem
Unlike WRPS, prior to implementing their Geocortex®-based solution, the VPD had
previously hired a consultant to implement a customized ArcGIS® Server-based public
safety application, referred to internally as GeoDash. Like the Geocortex® solution for
142 T. Herchenrader and S. Myhill-Jones
WRPS, the GeoDash application allowed VPD staff to use basic analytical tools to iden-
tify and explore crime patterns and trends on their own, rather than requiring the ser-
vices of a crime analyst. In turn, this allowed the Department’s crime analysts to spend
their time doing higher value analysis. The GeoDash application allowed officers to
view crime distribution, as well as the ability to add a distance buffer to visualize and
measure where and how close crimes are in relation to specific locations, such as
schools, transit centers, and so on. A built-in reporting feature also allowed users to
view crime by concentration, type, and transition at daily, weekly, or monthly intervals.
Although it was generally regarded as an improvement over previous approaches,
which involved data redundancy, duplication of effort, and the manual transfer of data
between systems, the first-generation GeoDash application had some important limita-
tions due to the original development technology on which it was built. The most sig-
nificant limitation was that the application was accessible only while in the office via
PCs connected to the web. Officers often had insufficient time to gather the information
they needed before going out on patrol. It was therefore difficult for them to collate
enough relevant information that would adequately inform their patrol, making the
application ineffective for officers out on the beat (Manning, 2001). This rush to get
enough information before departure was further complicated by the application’s some-
what unwieldy-to-navigate menu structure, to which underlying alterations were chal-
lenging, given the generation of the original development technology. The application
as it was customized also did not include the level of charting and analysis capabili-
ties required by the CompStat accountability standard that the VPD subscribes to
(Herchenrader, personal communication, 13 August 2013). For example, the first-
generation GeoDash application was not developed to compare time of day and/or day
of week crimes by location or buffer, nor did the application ‘establish threshold
analysis of specific crime types by CompStat period’ (Herchenrader, personal
communication, 13 August 2013).
The solution
Since the primary issue was limited access to required data and analysis capabilities to
support patrol activities, the VPD considered developing an additional web-mapping
application to provide up-to-date information and crime analysis tools to patrol officers
in their vehicles. It was hypothesized that by providing basic crime analysis capabilities
to the patrol officer, they could combine bigger picture data with their own instincts and
experience to be more proactive in their patrols. Crime analysts would be free to pursue
more complex crime analysis, rather than responding to routine inquiries. After
weighing the merits of different approaches, the Department decided to again partner
with a third-party vendor as they did not have the in-house expertise with available
cycles to undertake or maintain a custom software development project of this
envisioned magnitude. Latitude Geographics was selected as the vendor, and its
Geocortex® software was selected as the COTS solution based on its successful track
record being used by other organizations deploying ESRI®’s ArcGIS® Server custom-
ers and its demonstrated ability to deliver a solution meeting the specific requirements
of the Department (Herchenrader, personal communication, 13 August 2013).
The three primary goals for the Geocortex® solution were: deployment to the touch-
screen interface of the Panasonic Toughbook Mobile Display Terminals (MDT) in patrol
cars, simplification and enrichment of the user experience, and minimization of data
Police Practice and Research: An International Journal 143
transferred to the mobile application (to reduce cellular data costs) (Herchenrader,
personal communication, 23 July 2013).
To address requirements around deploying to the MDTs and a revised user interface
required for an in-car, touchscreen environment, the user interface of the mapping viewer
was modified. Though Geocortex® provides an out-of-the-box view intended for mobile
browsing, it required further refinement to be deployed on the MDT PCs that are oper-
ated largely by keyboard and touch-based input (instead of a mouse or related pointer).
Given the constraint that pressing on the top of the screen could cause the screen to tilt
backwards, the primary navigation elements were moved to the bottom of the window to
minimize this likelihood. The dashboard-mounted notebook PC configuration also meant
that traditional left-aligned navigation would also be problematic for right-hand use
because the user’s arm would obscure visibility of much of the screen during operation.
Once again, navigation controls were relocated to minimize this potential issue. Finally,
the fact that officers would at times be using the interface with a gloved hand meant that
the interface, originally designed for mouse clicks and typical fingertips, was modified to
accommodate the oversized digit (see Figure 4) (Herchenrader, personal communication,
23 July 2013).
The remaining primary goal of the project also proved to be its greatest challenge:
the need to minimize the amount of data transferred to the MDT. With most
Geocortex® implementations, the viewer is configured to dynamically request all data
from a web-connected map server. A major component of data transfer is any tiled map
services (typically, this refers to the base maps or related aerial imagery that
web-mapping applications provide to orient the user). This normally consists of large
Figure 4. VPD interface.
144 T. Herchenrader and S. Myhill-Jones
data files that contain static map data. To avoid transmitting this large amount of data
over the cellular network whenever the viewer was refreshed, the Geocortex®
application was modified to load the base map tile package directly to the laptop while
still at the office. While conceptually simple, this change required modification to the
pre-built viewer via programming and other non-routine configuration. The challenge
associated with this undertaking was compounded by the requirement that all testing be
completed in the secure VPD environment, rather than at the development laboratory
(Herchenrader, personal communication, 23 July 2013).
When the second-generation GeoDash application is implemented in the police vehi-
cles, VPD officers will have more ubiquitous access to data and crime analysis capabili-
ties. Once a user-specified crime analysis query is completed, the results are presented
alongside a standardized set of charts (see Figure 5).
A benefit of this simple, pre-defined interface and analysis capability ensures that
users require little to no training in order to submit relevant crime analysis queries,
create charts, and view pertinent crime data. It was recognized that the MDT experience
needed to be appealing and intuitive in its design to attract and retain users
(Herchenrader, personal communication, 23 July 2013). Removing barriers to real-world
use and making it as easy as possible for officers to access relevant data and crime
analysis serves the overarching goal of helping VPD become more proactive in their
policing workflows.
Like WRPS, the VPD is also committed to providing the general public with more
crime data and basic analysis tools by making available a public-facing crime data
application in early 2014. To avoid any requirement for the public to have a browser
Figure 5. VPD charting options and result clustering.
Police Practice and Research: An International Journal 145
plug-in installed on desktops or a native app installed on tablets and smartphones, and
to generally maximize access across traditional PCs, tablets, and smartphones, the VPD
decided to implement their public-facing crime map using a still-maturing HTML5
viewer approach.
In the future, with an effort that joins community awareness with intelligence-led
policing, the VPD plans to introduce a reporting feature that will allow citizens to report
suspicious activity in their neighborhood via the public-facing crime-mapping applica-
tion. This differs from the WRPS application which allows citizens to provide further
information about occurrences that have already been reported (Herchenrader, personal
communication, 13 August 2013). The ability to access community-provided informa-
tion on suspicious activities would provide VPD officers with another data source to
inform their policing decisions and opportunities to operate preventatively.
Continuing to look towards the future, the VPD is working with Latitude
Geographics and the Spatial Pattern Analysis and Research Laboratory at the University
of Victoria to develop a specialized property crime-prediction algorithm to further
enhance the internal crime-mapping application. This algorithm is being developed for
use against property crimes such as car theft and burglary, which tend to be more predict-
able than crimes against people (Herchenrader, personal communication, 13 August
2013). In order to test the validity of the prediction algorithm, the VPD plans to imple-
ment a pilot program, which will place officers in locations where a crime is predicted to
occur to validate if there is a net reduction in crime at those locations (Herchenrader, per-
sonal communication, 13 August 2013). Previous studies have indicated that police pres-
ence in crime hotspots results in a decrease in crime (Bayley & Garofalo, n.d.; Koper,
1995; Sherman & Weisburd, n.d.). This trial is planned to test the predictive capability of
the algorithm as well as to determine whether a more targeted police presence will pro-
duce similar crime reduction results. A challenge will likely be allocating sufficient
resources towards putting the officers in these locations, as this method remains unvali-
dated in Vancouver (Herchenrader, personal communication, 13 August 2013).
Conclusion
Responding to crime will undoubtedly continue to be a core focus for police. However,
especially when faced with tighter budgets and increasing demands for public account-
ability, police departments are working harder than ever to do more with less.
Web-based crime-mapping and analysis capabilities provide considerable improvements
over previous paper-based methods of information dissemination, with the benefits of a
COTS software application, allowing agencies to quickly and efficiently focus their
resources on custom requirements. Using modern technology to implement web-based
crime-mapping and analysis capabilities at relatively low cost and risk can empower
officers to target crime more proactively while on patrol, offering real potential to mark-
edly increase their overall effectiveness. Additionally, providing anonymized crime data
to the public satisfies increased expectations for transparency while introducing exciting
potential for collecting tips and information from the community to further enhance
intelligence-led policing. It is in this context that proactive approaches to policing, such
as intelligence-led policing, as well as public-facing crime maps, have gained promi-
nence in recent years. With technologies such as GIS able to reach more stakeholders,
law enforcement now has the capability to not only better understand spatial patterns
related to historic crime, but also consider, in a more informed manner, where they
might occur in the future – and allocate the service’s limited resources more efficiently.
146 T. Herchenrader and S. Myhill-Jones
Notes on contributors
Tegan Herchenrader is a technical writer and training coordinator at Latitude Geographics. She
graduated with BA honours in Arts and Business at the University of Waterloo in 2010 and later
received her MA in Globalization from McMaster University in 2012. She started working for
Latitude Geographics shortly after her graduation, assisting in the establishment of their Ontario
office and development of their online training program. An active outdoorswoman, she can often
be found hiking or horseback riding the region with her dog, or immersed in tending to her vege-
table garden.
Steven Myhill-Jones (BSc. Geography) is the founder and CEO of Latitude Geographics, an
ESRI® Platinum Partner headquartered in Victoria, Canada. He established and spearheaded the
growth of Geocortex®, which is a software technology that helps organizations around the world
accomplish even more with ESRI® web-mapping technology. Since 1999, Latitude has assisted
with developing and implementing hundreds of web-mapping applications for clients across
sectors, including public safety.
Bayley, D., & Garofalo, J. (n.d.). The management of violence by police patrol officers. Criminol-
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Friedman, W., Gordon, A., & Maltz, M. (2000). Mapping crime in its community setting: Event
geography analysis. New York, NY: Springer-Verlag. Retrieved from http://books.google.ca/
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ots=rmlipqCwzd&sig=TizFPe7OZGrbnnpY2ciQa2XOOSo
Gotway, C. A., & Schabenberger, O. (2009). Statistical methods for spatial data analysis. Taylor
& Francis.
Koper, C. (1995). Just enough police presence: Reducing crime and disorderly behaviour by
optimizing patrol time in crime hot spots. Justice Quarterly, 12(4), 649–672.
Malleson, N. (2011). Crime analysis – Exploiting geospatial datasets. GIS Professional, 43, 25–26.
Manning, P. K. (2001). Technology’s ways: Information technology, crime analysis and the ratio-
nalizing of policing. Criminology and Criminal Justice, 1, 83–103.
Ratcliffe, J. (2002). Damned if you don’t, damned if you do: Crime mapping and its implications
in the real world. Policing and Society, 12, 211–225. Retrieved from http://jratcliffe.net/
papers/Ratcliffe(2002) Damned if you don’t damned if you do
Ratcliffe, J. (2008). Intelligence led policing. Cullompton: Willan.
Ratcliffe, J. (n.d.). What is intelligence led policing? Retrieved from http://jratcliffe.net/research/
ilp.htm
Rich, T. F. (1995, July). The use of computerized mapping in crime control and prevention
programs. Retrieved from https://www.ncjrs.gov/txtfiles/riamap.txt
Sherman, L. W., & Weisburd, D. (n.d.). General deterrent of police patrol in ‘crime hotspots’: A
randomized controlled trial. Justice Quarterly, 12(4). Retrieved from http://www.houseof
mouse.ca/sites/default/files/Sherman-General deterrent effects of police patrol in crime-1995.
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http://books.google.ca/books?hl=en&lr=&id=XRSjd1RxeAYC&oi=fnd&pg=PR9&dq=benefitsofmappingcrime&ots=rmlipqCwzd&sig=TizFPe7OZGrbnnpY2ciQa2XOOSo
http://books.google.ca/books?hl=en&lr=&id=XRSjd1RxeAYC&oi=fnd&pg=PR9&dq=benefitsofmappingcrime&ots=rmlipqCwzd&sig=TizFPe7OZGrbnnpY2ciQa2XOOSo
http://jratcliffe.net/papers/Ratcliffe(2002) Damned if you don’t damned if you do
http://jratcliffe.net/papers/Ratcliffe(2002) Damned if you don’t damned if you do
http://jratcliffe.net/research/ilp.htm
http://jratcliffe.net/research/ilp.htm
http://https://www.ncjrs.gov/txtfiles/riamap.txt
http://www.houseofmouse.ca/sites/default/files/Sherman-General deterrent effects of police patrol in crime-1995
http://www.houseofmouse.ca/sites/default/files/Sherman-General deterrent effects of police patrol in crime-1995
http://www.houseofmouse.ca/sites/default/files/Sherman-General deterrent effects of police patrol in crime-1995
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individual use.
- Abstract
- Introduction
- Waterloo regional police service
- Conclusion
- Notes on con„trib„u„tors
The problem
The solution
Vancouver Police Department
The problem
The solution
References
lable at ScienceDirect
Applied Geography 69 (2016) 65e74
Contents lists avai
Applied Geography
journal homepage: www.elsevier.com/locate/apgeog
Street profile analysis: A new method for mapping crime on major
roadways
Valerie Spicer*, Justin Song, Patricia Brantingham, Andrew Park, Martin A. Andresen
Institute of Canadian Research Studies, Simon Fraser University, Burnaby, BC, Canada
a r t i c l e i n f o
Article history:
Received 10 November 2015
Received in revised form
16 February 2016
Accepted 21 February 2016
Available online 4 March 2016
Keywords:
Crime mapping
Environmental criminology
Human movement
Street profile analysis
* Corresponding author.
E-mail addresses: vspicer@sfu.ca (V. Spicer), jdson
sfu.ca (P. Brantingham), apark@tru.ca (A. Park), andre
http://dx.doi.org/10.1016/j.apgeog.2016.02.008
0143-6228/
© 2016 Elsevier Ltd. All rights reserved.
a b s t r a c t
Street profile analysis is a new method for analyzing temporal and spatial crime patterns along major
roadways in metropolitan areas. This crime mapping technique allows for the identification of crime
patterns along these street segments. These are linear spaces where aggregate crime patterns merge with
crime attractors/generators and human movement to demonstrate how directionality is embedded in city
infrastructures. Visually presenting the interplay between these criminological concepts and land use
can improve police crime management strategies. This research presents how this crime mapping
technique can be applied to a major roadway in Burnaby, Canada. This technique is contrasted with other
crime mapping methods to demonstrate the utility of this approach when analyzing the rate and velocity
of crime patterns overtime and in space.
© 2016 Elsevier Ltd. All rights reserved.
1. Introduction
Modern cities are transforming at a fast pace and adapting to the
changing demands of urban living. Developing multi-use buildings
and meeting transportation needs while maintaining livability and
public safety is a primary planning strategy for many urban centers
(Loukaitous-Sideris, 2014; Newton, 2004; Skogan, 2015; Smith,
Phillips and King, 2010). These competing infrastructures can
sometimes create very specific crime dynamics that if left unat-
tended over time alter, or in some cases contradict, the original
planning concept for an area (Knapp, 2013; Spicer, 2012). The new
crime analysis technique presented in this paper can be used to
identify areas where crime surges along major roadways and to
compare these patterns to transecting roadways. This mapping
technique can clearly visualize temporal variances, crime type
comparisons and historical crime trends.
Street profile analysis is ideal for small and linear places where
conventional analytical approaches are not fully suitable for visu-
alizing of crime in these spaces. Most often, practitioners use maps
to visualize crime patterns such as kernel density maps and
aggregate address count maps (Chainey & Ratcliffe, 2005; Chainey,
Tompson, and Uhlig, 2008; Eck and Weisburd, 2005). These
g@sfu.ca (J. Song), pbrantin@
sen@sfu.ca (M.A. Andresen).
techniques are useful in presenting crime patterns throughout an
area in order to expose crime hot spots and high crime locations.
However, in order to demonstrate crime velocity or variance along
a linear space, it may be preferable to engage in a graph approach,
called street profile analysis, where the roadway is the x axis and
crime count the y axis.
To the knowledge of the authors, this is a new crime mapping
technique that can be utilized to study small urban areas along
major roadways and to better understand the dynamics in these
places. The research presented in this paper examines a major
roadway in Burnaby, British Columbia. Burnaby in a jurisdiction in
Metro Vancouver and the area under study contains several ele-
ments including a large regional shopping centre, a mass trans-
portation station, a major roadway, a bike path, businesses and
multi-dwelling residences. Several street profile views of this
place are presented to demonstrate the variety of crime dynamics
and the utility of this new mapping technique. A transect meth-
odology is used in conjunction to compare and contrast roadways
that bisect this major roadway.
From a practitioner perspective, street profile analysis is “user
friendly” and can be produced using most analytical packages. The
advantage of this approach is that it can clearly define where crime
specifically peeks, both in space and in time, thus optimizing pre-
ventative strategies. Compared to techniques such as kernel density
that diffuses the visual image of crime, this street profile technique
sharpens the situation and can clearly demonstrate the problem.
The street profile analysis is compared and contrasted to three
Delta:1_given name
Delta:1_surname
Delta:1_given name
Delta:1_surname
Delta:1_given name
mailto:vspicer@sfu.ca
mailto:jdsong@sfu.ca
mailto:pbrantin@sfu.ca
mailto:pbrantin@sfu.ca
mailto:apark@tru.ca
mailto:andresen@sfu.ca
http://crossmark.crossref.org/dialog/?doi=10.1016/j.apgeog.2016.02.008&domain=pdf
www.sciencedirect.com/science/journal/01436228
http://www.elsevier.com/locate/apgeog
http://dx.doi.org/10.1016/j.apgeog.2016.02.008
http://dx.doi.org/10.1016/j.apgeog.2016.02.008
http://dx.doi.org/10.1016/j.apgeog.2016.02.008
V. Spicer et al. / Applied Geography 69 (2016) 65e7466
other techniques. The strength and weaknesses of each technique is
discussed.
2. Mapping framework
Environmental Criminology provides a theoretical framework
for mapping crime in urban areas. Urban infrastructure and its
impact on human movement and directionality influences crime
occurrences by concentrating them into small, definable places.
Crime analysis and mapping techniques can imbed these theoret-
ical concepts into specific approaches that help to further define
and understand these crime dynamics. The street profile mapping
technique is based on these concepts of the urban infrastructure
and is designed to demonstrate how crime occurs in small defin-
able places and can surge due to specific dynamics in the
environment.
2.1. City infrastructure
The urban infrastructure contains nodes, paths and edges where
crime is concentrated (Brantingham & Brantingham, 1984). These
are geographic spaces that also transition through temporal vari-
ances creating definable crime patterns (Brantingham &
Brantingham, 1984, 1993a, b). Nodes are places where human ac-
tivity is concentrated such as the crossing of two paths or an
attractive place such as a mall. The crime patterns at nodes should
be viewed as temporal because the activity at these places is not
generally consistent. As a simple example, malls are not usually
open 24 h per day therefore and the potential for shoplifting is
completely eliminated by the closure of the mall while this same
closure creates the potential for burglary.
Paths are channels designed for human movement (vehicle e
pedestrian e mass transportation e bicycle or foot paths). Edges are
boundaries between places that transition from one type of place to
another such as a single-family dwelling area to a commercial zone.
Like nodes, paths and edges transition through various temporal
states that impact crime patterns. Within this framework, the street
network is of interest because it links and defines the interaction
between these elements (Brantingham & Brantingham, 2015;
Davies & Johnson, 2015; Johnson & Summers, 2015; Vandeviver,
Van Daele, & Vander Beken, 2015).
In certain places in the urban environment these three elements
are consolidated and in some ways compressed along certain street
segments. This can create crime surges and the street profile
analysis can locate these places, then assist in analyzing the tem-
poral and crime dynamics. In particular, major roadways that
contain activity nodes, high volume pathways and edges are sus-
ceptible to these crime dynamics. Within this context, the street
profile analysis can display the variance in crime density in a
manner that clearly defines the impact of these three elements on
crime patterns.
2.2. Effectively mapping small places
Crime place theory focuses on crime events in small places such
as specific addresses, business types and block faces (Eck and
Weisburd, 1995). These small places can be categorized by
feature, cluster or facility (Eck and Weisburd, 1995). Features
include aspects such as physical or social structure, while clusters
can be understood as hot or cool spots, and facilities, or addresses,
are places such as bars, problem premises, or parks (Eck and
Weisburd, 1995).
Major roadways contain successive small places that create
variability and sudden increases in criminal events along their
trajectory. In a spatial analysis of street segments in Seattle, WA,
Groff, Weisburd and Yang (2010) found that contiguous street
segments could have very different (sometimes opposite) trajec-
tories. These increases or decreases in crime can be better under-
stood using the elements defined in crime place theory (features e
clustering e facilities). For instance, the presence of a facility like a
mall on a major roadway produces criminogenic features such as
reduced guardianship and increased target opportunity, and also
creates a clustering of criminal events that may lead to small places
next to one another having very different crime patterns. Another
example is a strip of licensed establishments also generating a
crime surge.
The street profile analysis can describe the linearity of a major
roadway while at the same time exposing the multiple variances
that can occur in such a place. In particular, this graph technique
simplifies crime patterns and can produce comparisons on a single
graph which allows for detailed analysis of crime, place and time.
2.3. Vizualizing the effect of crime attractors and crime generators
Crime attractors and crime generators are both small places
with specific characteristics that make them higher crime areas
(Brantingham & Brantingham, 1995). Crime generators are places
that attract a large number of people such as a shopping or enter-
tainment district, or a sporting venue. They produce crime because
there are many people in attendance and also many potential tar-
gets, thus the opportunity for crime is present, en masse. Crime
attractors are also small places, however these are well-known for
their criminal opportunities and, therefore, attract criminals.
Strongly motivated offenders, usually not from that area, attend
these places for criminal purposes. Some examples of crime
attractors are drug or prostitution markets, or shopping malls near
a major transit hub.
Crime patterns along major roadways may vary because of the
number and size of crime attractors and generators they contain.
Major roadways are linear spaces in the urban infrastructure that
often bisect multiple neighborhoods. Crime peaks along these
roadways, and their variance through time and crime type, can be
better explained using the concepts of attractors and generators. As
well, when considered longitudinally, the variation in crime peaks
or the emergence of a crime surge may be the result of a generator
turning into an attractor. The street profile analysis technique ex-
poses crime attractors and generators by clearly defining crime
density along the roadway.
2.4. Conceptualizing urban directionality
The relationship between urban directionality and crime has a
long history founded on the concept of spatial criminology (Frank,
Andresen, Cheng, & Brantingham, 2011; Rengert & Wasilchick,
1985). Research has demonstrated the influence of crime on
macro urban directionality through the criminal attractiveness of
town centers, the impact of mass transportation and the formation
of criminogenic streets and neighborhoods (Herrman, 2013; Song,
Spicer, Brantingham and Frank, 2013). The micro and individual
aspect of directionality is explained by the geometry of crime
(Brantingham & Brantingham, 1981). This perspective helps ex-
plains and further clarify factors such as temporal constraint
(Ratcliffe, 2006), directional bias by crime type (Van Daele &
Bernasco, 2012), and more recently the directional bias of repeat
property offender within a large-scale sample (Frank, Andresen, &
Brantingham, 2012; Frank et al., 2011).
The analysis of major roadways is a meso analysis of urban
directionality. Within large metropolitan cities there are smaller
sub-sets of areas and pathways where human activity is concen-
trated for various reasons. These may include attractive pedestrian
V. Spicer et al. / Applied Geography 69 (2016) 65e74 67
areas, shopping strips, an area known for pubs and restaurants,
business districts, or a college campus. The street profile mapping
technique allows researchers and practitioners to further under-
stand the impact of these factors on crime patterns along major
roadways. This technique also lends itself to comparative analysis
between crime density and other factors such as vehicle or
pedestrian traffic.
3. Research study
3.1. Study area
Fig. 1 is the study area and major roadway called Kingsway runs
through this area from west to east. This arterial street traverses
diagonally three major municipalities in the Metro Vancouver re-
gion (Vancouver e Burnaby e New Westminster). In some portions
of this roadway, a Skytrain route runs parallel to Kingsway. The
Skytrain is a light-rail mass transit metro route that is mostly
elevated above ground and services the Metro Vancouver region.
The study area also includes a bike path that runs parallel to
Kingsway. At the center of the study area is a regional shopping
centre. This shopping centre is the largest mall in British Columbia.
There are business towers attached as well as high-density dwell-
ing residences surrounding this mall. The transecting roadways in
this study area are mostly collector streets except for Royal Oak that
is a minor arterial street servicing Burnaby. Two transecting
Fig. 1. Stud
roadways e Willingdon Ave and Royal Oak Ave e are highlighted in
Fig. 1
3.2. Data
This study utilizes data from the Police Information Retrieval
System (PIRS) and GIS Innovation data.
3.2.1. PIRS
The Crime Data-Warehouse (CDW) is a collection of datasets
that contains officially reported crime events for Royal Canadian
Mounted Police (RCMP) jurisdictions in British Columbia. RCMP
jurisdictions vary in size of police membership and also area
covered. This dataset contains approximately 4.4 million crime
events. The study area is located within the jurisdiction of Burnaby
RCMP. There are 38,855 crime events from the middle of 2001 to
the middle of 2006 in the stud
y area.
The crime events are reported
offences to the Burnaby RCMP. These events are varied including,
but not limited to, property crime, violent crime, drug and traffic
offences. These data contain attributes about the crime event such
as date, time, location, offender information, and specific crime
type.
3.2.2. GIS innovations data
The 2006 road network data from a company named GIS In-
novations were used to geocode crime event locations. The data
y area.
V. Spicer et al. / Applied Geography 69 (2016) 65e7468
were interpolated to a 98.8% geocoding success rate. This road
network data were also used to visualize the output results.
3.3. Mapping methodology
Five mapping techniques are compared to demonstrate the
utility of the new technique proposed in this study. The first three
are often used for crime analysis: kernel density, aggregate count to
address and aggregate count to street segment (Chainey & Ratcliffe,
2005; Weisburd, Groff, & Yang, 2012). These techniques visualize
crime using a map. The proposed street profile methodology pre-
sents spatial data in an abstract format on a graph. This technique is
beneficial when studying major roadways because it lends itself
well to temporal and crime comparison analysis. As well, when
merged with the transect mapping methodology, crime distribu-
tion on adjacent and transecting roadways further amplifies the
crime patterns on the major roadway.
3.3.1. Kernel density
The kernel density function is used in a first instance to visualize
the data in this study. The search radius was set for three different
distances: 50, 100, and 250 m. In all three instances, the maps were
produced using 50 m rasters. A 50-m raster size was selected
because this distance covers on average a half block. Therefore, this
raster size shows variation at the block level.
3.3.2. Aggregate count to address
This technique aggregates crime to specific addresses. Then
further classes of aggregation are formed to show high and low
crime locations. Those crime locations that contain one to three
crime incidents were treated with a slight random perturbation to
ensure de-identification for privacy purposes and does not affect
the visualization of the results.
3.3.3. Aggregate count to street segment
This technique is a more recent development in crime analysis.
Both Weisburd et al. (2012) and Curman, Andresen, and
Brantingham (2015) demonstrate the utility of this analysis spe-
cifically when looking at historical crime patterns. In this tech-
nique, crime count is aggregated to the street segment and then
further classes of aggregation can be formed to show high crime
street segments.
3.3.4. Street profile
Unlike the three previous methods, the street profile method is
presented on a graph and used to study areas in a different manner
to provide another description of the crime problem. The street
profile is created using successive circular buffers that have a 50-
m radius, overlapped at the center point, and aligned with the
roadway. Fig. 2 illustrates the location of the buffers along the
roadways and how these are overlapped in order to consolidate the
crime that is shown in the street profile.
Once these data are collated, the output is converted to a line
graph and can be exported to Excel and made into a chart. Trans-
ecting streets can be labeled on the vertical axis to help orient the
viewer.
3.3.5. Line-transect methodology
Line-transect methodology is most often used in ecological
sampling for animals or plants (Manly & Navarro Alberto, 2015).
Lines are placed through the study area in order to establish sys-
tematic sampling methodology. In this study, we adapt this
approach to the street network in order to analyze patterns of crime
on the streets that transect the major roadway. When working with
the street profile method, the line-transect methodology reveals
the condensed and directional nature of crime patterns and how
transecting streets have alternative dynamics. We further add cir-
cular 50 m buffers to demonstrate crime directionality through a
static visualization. The direction of the buffers is angled in order to
encompass both sides of each street.
4. Results
The crime events in this study are analyzed and visualized using
the four methods: kernel density, aggregate to address, aggregate
to street segment and street profile. These visualizations are dis-
cussed in terms of their utility and limitations.
4.1. Kernel density
This first method utilizes the kernel density function. This is a
common technique used in crime analysis and typically produces
hotspot maps. In these examples, the study area is quite small
therefore the pixelization is very pronounced. More often, the
hotspot maps produced with this technique are of larger areas and
the pixelization is more smoothed. Such representations can be
problematic. When producing a value for each kernel, the kernel
density method uses a bandwidth to capture the number of events
within a specified area and then applies a spatial average (Bailey &
Gatrell, 1995). Though it may be true that most users of kernel
density functions are aware of this limitation, not all of those who
interpret the resulting maps will be. Three different search radii
were utilized to create the maps in Fig. 3 and are displayed using
50 m rasters.
The map that utilizes 50-m search radius for single crime events
in Fig. 3 produces a confusing result in that there appears to be
great variation within the study area. This variation may also lead to
false conclusions about the actual location of crime hotspots (Song,
Frank, Brantingham, & LeBeau, 2012). The inherent smoothing ef-
fect of the kernel density function can actually create a hotspot
between two crime locations rather than showing the reality of the
situation because of the bandwidth and spatial averaging of the
function as mentioned above (Song et al., 2012). As the search
radius is increases to 100 m and 250 m in Fig. 3, the hotspot be-
comes more generalized. Overall, the kernel density function is best
used to provide a broad idea of crime and to locate high crime areas.
However, in order to understand the specific location and dynamics
of crimes, other techniques are necessary.
4.2. Aggregate count to address
This second method is also commonly used in crime analysis. In
Fig. 4 crimes are displayed using dots with each one indicating a
crime. Multiple instances can then be aggregated to display clus-
ters. Different classes can be created to show high crime locations.
This technique is useful in identifying high crime locations.
Specifically, the aggregation of crime events is particularly suitable
when trying to identify high crime locations. Because this tech-
nique is location specific, conducting temporal or crime compari-
sons is not visually suitable on a single map. Rather, two maps need
to be placed side by side in order to compare things such as crime
events by time of day, crime type or over time. Additionally, as the
density of events at a particular location increases, these dot maps
become difficult to interpret. If one dot represents each event a high
volume location becomes saturated with dots quickly. This issue
can be resolved to some extent with the use of graduated dots
(larger dot for a greater number of points). Finally, another signif-
icant concern with this technique, especially when used for public
distribution, is individual privacy (Kounadi, Bowers and Leitner,
2015). Privacy concerns arise in areas where there are fewer
Fig. 2. Street profile technique.
Fig. 3. Kernel density comparative visualization 250 m-100 m-50 m Rasters.
V. Spicer et al. / Applied Geography 69 (2016) 65e74 69
crimes and the marked crime location can potentially identify the
victim.
4.3. Street segment crime density
This third analysis technique is not as commonly used in crime
analysis, but has become common within the crime and place
literature – see Weisburd (2015) for a recent review and discussion
of this literature. In Fig. 5, the crime events are aggregated to the
street segments and, like the aggregate count to address, crime
events on street segments can be further aggregated and placed
into defined classes. Research that investigated the trajectories of
street segments over time has labeled them in the various
permutations of low, medium, and high-crime as well as stable,
increasing, and decreasing (Curman et al., 2015; Weisburd et al.,
2012).
This visualization technique is very useful in order to identify
high crime street segments (Curman et al., 2015; Weisburd et al.,
2012). These high crime places could be further analyzed in order
to determine the environmental dynamics in these locations.
However, like the previous technique, this one also has comparative
limitations. In order to visually compare street segments for such
things as night and day crime, longitudinal analysis or crime type
comparison, two or more maps would need to be compared.
Fig. 4. Aggregate count to address.
V. Spicer et al. / Applied Geography 69 (2016) 65e7470
4.4. Street profile
Unlike the three previous techniques, the street profile tech-
nique is not presented on a map, but rather on a graph. This sim-
plifies the visualization and therefore allows for comparative
analysis on a single chart. In Fig. 6, crime events are displayed using
a line graph and this single line shows how crime fluctuates along a
roadway. In this first example, three separate years are compared
(2003e2004 e 2005). This longitudinal analysis shows how crime
is increasing at regional shopping centres and becomes an obvious
crime attractor. Table 1 accompanies the map to show the actual
percentage increase as well as the raw numbers.
There are several benefits to this technique. First, the graph is
simple to read even for non-subject matter experts. Second, the
graph describes the crime dynamic well e is crime going up or
down? Third, the graph shows the variation of crime on roadways
and helps clearly define high crime places.
Other comparisons can also be completed. In Fig. 7, crime by day
and night are compared. Clearly, more crime occurs during the day,
which is congruent with this particular high crime location e a
regional shopping centre.
In Fig. 8, crimes are compared by type. Again, this further clar-
ifies the problem with property crime prevailing, also explained by
the type of location.
4.5. Transect analysis
The transect analysis is a means to describe in a static manner
the dynamics of crime directionality that is exposed using the
street profile analysis. The street profile graph reveals a significant
crime surge at the mall with two other lower surges at the nearby
intersecting streets (Willingdon Ave and Royal Oak Ave). A further
analysis of these intersecting streets using the transect methodol-
ogy shows the prominent directionality of crime along Kingsway. In
Fig. 9, 50 m buffers are used to demonstrate directionality with the
line transect at intersections. There exists an eastward pull on
Kingsway between the intersections of Willingdon Ave and Royal
Oak Ave. The directional aspect of crime dissipates quite rapidly
towards the north and south of Kingsway. As well, the crime den-
sity at these intersections is highly varied with a higher density on
Kingsway. This contrast is of particular note at Kingsway and
Willingdon where crime density is both at the highest crime den-
sity category in the Kingsway buffers and second lowest density in
the Willingdon buffer.
5. Conclusion
In this study, we explore a new technique for understanding
crime in small places within the urban domain. This mapping
technique utilizes a graph approach that can be applied to major
roadways in urban areas. While this technique is applied to
Fig. 5. Street segment crime density.
Fig. 6. Street profile crime density.
V. Spicer et al. / Applied Geography 69 (2016) 65e74 71
reported crime on a roadway, this method would also be useful in
other types of analysis pertaining to roadways such as traffic
analysis.
In this study, Kingsway is a major pathway for vehicles, a light-
rail mass transit system (Skytrain) line that runs parallel, a bike
path that also runs parallel, and pedestrians who attend the area for
business, shopping and entertainment. The study area contains
very prominent activity nodes such as the Skytrain station and the
Table 1
Percentage crime by year.
2003 2004 2005
Crime counts 6524 8465 9526
Increase rate (%) e 29.8% 13.7%
Fig. 7. Street profile: night and day comparison.
Fig. 8. Street profile: crime type comparison.
V. Spicer et al. / Applied Geography 69 (2016) 65e7472
largest shopping mall in British Columbia. There are interesting
temporal variations that are revealed using this technique that
allow practitioners and policy makers to better understand the
crime dynamics of major roadways.
This graph approach utilized to display major roadways allows
for numerous comparisons that can help further understand the
dynamics of these places. In particular, this visualization is easy to
interpret, making it a good tool for describing crime problems to
policy makers and civic personnel. The most common spatial vi-
sualizations are displayed on maps such as kernel density and
aggregate address counts and these are not as visually simple as the
street profile. Comparative analyses using maps requires multiple
maps, whereas the street profile technique allows for comparisons
on a single graph. Moreover, because of their calculations, these
other methods are prone to false inferences regarding the location
they represent, particularly kernel density. However, the street
profile method handles temporal and longitudinal analysis very
well and can help expose the growing nature of a crime generator.
Analyzing major roadways is a means to better understand
crime distribution and, thus, allocate resources. In certain in-
stances, major roadways can be densely distributed crime areas
where crime does not bleed significantly past these areas. This ef-
fect is shown when looking at the transecting streets. In this study,
the streets that cross Kingsway do not experience the same crime
surge as there is along Kingsway. Enforcement would likely be
more effective if it mimicked this crime pattern with concentrated
enforcement along the roadway and targeted crime prevention
Fig. 9. Line-transect: density buffer analysis.
V. Spicer et al. / Applied Geography 69 (2016) 65e74 73
with the businesses and multi-dwelling residences in that area.
Future research into this visualization technique will utilize data
from other major cities in order to further define the dynamics that
form these places. The street profile method will be used to look at
and compare different values. In this study, only crime is used to
form the street profile. However, future research will compare
crime to other civic data such as transportation and pedestrian
traffic flow. This will allow for a more comprehensive under-
standing of crime in the urban domain.
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- Street profile analysis: A new method for mapping crime on major roadways
1. Introduction
2. Mapping framework
2.1. City infrastructure
2.2. Effectively mapping small places
2.3. Vizualizing the effect of crime attractors and crime generators
2.4. Conceptualizing urban directionality
3. Research study
3.1. Study area
3.2. Data
3.2.1. PIRS
3.2.2. GIS innovations data
3.3. Mapping methodology
3.3.1. Kernel density
3.3.2. Aggregate count to address
3.3.3. Aggregate count to street segment
3.3.4. Street profile
3.3.5. Line-transect methodology
4. Results
4.1. Kernel density
4.2. Aggregate count to address
4.3. Street segment crime density
4.4. Street profile
4.5. Transect analysis
5. Conclusion
References
Newton Crime Sci (2015) 4:30
DOI 10.1186/s40163-015-0040-7
R E S E A R C H
Crime and the NTE: multi-classification
crime (MCC) hot spots in time and space
Andrew Newton*
Abstract
This paper examines crime hot spots near licensed premises in the night-time economy (NTE) to investigate whether
hot spots of four different classification of crime and disorder co-occur in time and place, namely violence, disorder,
drugs and criminal damage. It introduces the concept of multi-classification crime (MCC) hot spots; the presence
of hot spots of more than one crime classification at the same place. Furthermore, it explores the temporal patterns
of identified MCC hot spots, to determine if they exhibit distinct spatio-temporal patterns. Getis Ord (GI*) hot spot
analysis was used to identify locations of statistically significant hot spots of each of the four crime and disorder clas-
sifications. Strong spatial correlations were found between licensed premises and each of the four crime and disorder
classifications analysed. MCC hot spots were also identified near licensed premises. Temporal profiling of the MCC hot
spots revealed all four crime types were simultaneously present in time and place, near licensed premises, on Friday
through Sunday in the early hours of the morning around premise closing times. At other times, criminal damage and
drugs hot spots were found to occur earlier in the evening, and disorder and violence at later time periods. Criminal
damage and drug hot spots flared for shorter time periods, 2–3 h, whereas disorder and violence hot spots were
present for several hours. There was a small spatial lag between Friday and Saturday, with offences occurring approxi-
mately 1 h later on Saturdays. The implications of these findings for hot spot policing are discussed.
Keywords: Policing, Licensed premises, Alcohol, Multi-classification crime (MCC) hot spots, Spatio-temporal analysis
© 2015 Newton. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://
creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided
you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate
if changes were made.
Background
There is a longstanding recognition that the locations
of alcohol consumption and crime co-occur (Gorman,
Speer, Gruenewald, & Labouvie, 2001; Home Office,
2003; Scott and Dedel, 2006; Newton and Hirschfield,
2009a). This often fuels the wider debate over the ‘causal’
versus ‘non-causal’ relationship between alcohol and
crime (Dingwall, 2013; Horvath and Le Boutillier, 2014).
A growing concern is the prevalence of clusters of crime,
termed hot spots, in urban areas with concentrations of
licensed premises, synonymous with the Night-Time
Economy (NTE). For the purposes of this paper licensed
premises are considered those selling alcohol for on and
or off premise consumption; examples include pubs,
bars, nightclubs, hotels, off licenses, supermarkets, con-
venience stores, restaurants, cafes, takeaways, cinemas
and social clubs. Sherman (1995, p 36) defines crime hot
spots as ‘small places in which the occurrence of crime
is so frequent that it is highly predictable, at least over
a 1-year period and this paper examines hot spots over
12–36 months. In addition to the known geographical
clustering of crime near licensed premises, NTE hot spot
areas also exhibit clear temporal patterns, especially on
Friday and Saturday evenings and early mornings, which
correspond with premise closing times (Block and Block,
1995; Newton and Hirschfield 2009b; Popova, Giesbre-
cht, Bekmuradov, & Patra, 2009; Uittenbogaard and Cec-
cato, 2012; Conrow, Aldstadt, & Mendoza, 2015). Thus
there are clear spatial and temporal patterns to NTE
crime hot spots.
There is a sound theoretical basis for the presence of hot
spots in the vicinity of licensed premises. Routine activity
theory (Cohen and Felson, 1979) and crime pattern the-
ory (Brantingham and Brantingham, 1993) contend that
persons, both potential offenders and victims, exhibit sys-
tematic movement patterns governed by their day to day
undertakings, termed routine activities. Certain places
Open Access
*Correspondence: a.d.newton@hud.ac.uk
The Applied Criminology Centre, The University of Huddersfield,
Queensgate, Huddersfield HD1 3DH, UK
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Page 2 of 12Newton Crime Sci (2015) 4:30
are frequented regularly, for example home, place of work
or leisure, termed activity nodes. The routes travelled
between nodes are known as paths. This movement devel-
ops a person’s awareness space, and crime is shown to be
more likely on the edges of these activity nodes (Bow-
ers, 2014). Places at which several offenders and victims
converge form multiple awareness spaces, which increase
the likelihood of crime. Eck, Clarke, and Guerette (2007)
identify a number of ‘risky facilities’ where concentrations
of crime are evident. Indeed, a small minority of facili-
ties contribute the majority of offences at all risky facili-
ties, termed the ‘iron law of troublesome places’ (Wilcox
and Eck, 2011: 476). Examples include shopping centres,
busy road junctions, hospitals, schools, train and bus sta-
tions, and entertainment districts. Places with clusters of
licensed premises represent recreational activity nodes,
where there is a convergence of people in time and space.
This coming together may create unplanned but favoura-
ble crime opportunities, termed crime generators; or draw
in offenders to bars and localities with known opportuni-
ties for offending, termed crime attractors (Brantingham
& Brantingham, 1995). Within NTE areas both of these
eventualities are plausible.
A number of explanations exist for the occurrence of
crime in NTE areas (for good overviews see Finney, 2004;
Graham & Homel, 2008). These include: cultural factors,
relating to societies use and acceptance of alcohol; per-
son factors based on an individual’s responses and beliefs
about alcohol consumption; the psychopharmacologi-
cal properties of alcohol and their influence on an indi-
vidual’s behaviour; and contextual factors, the physical
and social circumstances of where and when alcohol is
consumed. Recently a focus for NTE research has been
on premise density and premise opening hours. Explana-
tions for crime have focussed on: NTE places deemed to
have ‘too many’ licensed premises, those saturated with a
high density of premises (Livingston, 2008; Pridemore &
Grubesic, 2013); and, premises open ‘too long’, with con-
cerns over the length of time premises can remain open
for, based around extensions granted in trading hours
(Chikritzhs & Stockwell, 2002; Holmes et al., 2014). What
is clear is the relationship between crime and alcohol is
multi-faceted. A useful explanation is offered by Elvins
and Hadfield (2003) who suggest a combination of fac-
tors are likely account for crime in NTE areas, including:
places with high densities of licensed premises in urban
areas; the convergence of large number of persons at
these places; crowding of persons within drinking ven-
ues in close proximity in confined spaces, often leading to
‘vertical drinking’; the consumption of alcohol, often in
large quantities; poor management of NTE places; and,
the cumulative build up of ‘environmental stresses’ over
the course of an evening.
Efforts to tackle problems of crime in the NTE have
predominantly but not exclusively focussed on: better
place management (Madensen & Eck, 2008); alcohol edu-
cation and awareness schemes; regulation of licensing,
legislation and enforcement (Hadfield and Newton 2010);
increasing the costs of unit prices of alcohol (Booth et al.,
2008); regulating the number of, and opening times of
premises (Chikritzhs & Stockwell, 2002); and high vis-
ibility police patrols. Whilst the merits of each approach
have and will continue to be debated in the literature (see
Graham & Homel, 2008; Humphreys & Eisner, 2014; Hol-
mes et al., 2014), the focus of this paper is on the use of
police patrols in NTE areas.
A recent movement in policing has been a resurgence
of hot spot policing, ‘targeted on foot patrols’, fuelled by
the willingness of a number of police forces to implement
randomised control trials (RCTs) of hot spot policing
effectiveness (Ratcliffe, Taniguchi, Groff, & Wood, 2011;
Braga, Papachristos, & Hureau, 2012; Groff et al., 2015).
Successes are evident for hot spot policing targeting bur-
glary, repeat calls for service, nuisance bars, drugs, and
violent crime, in particular when focussed on hot spots
defined tightly in both place and time. A caveat identi-
fied in the literature is that the effectiveness of the polic-
ing tactic used often is dependent on the type of hot spot
policed.
The process of hot spot policing involves identifying
hot spot areas, and then subsequently targeting patrols at
these places in a systematic fashion. It is contended here
that this reflects more general current trends in policing,1
of using evidence gleaned from crime analysis or crime
intelligence to inform police response. Many including
the author advocate a problem solving/evidence based
approach to policing and crime reduction. Two of the
most well know examples of this are Problem Orientated
Policing (Goldstein, 1990) and Intelligence Led Policing
(Ratcliffe, 2008). At the simplest level of explanation, the
analyst or police officer is encouraged to: firstly identify a
crime problem through some form of suitable analysis of
crime or other data; then, to further examine the identi-
fied problem to understand the mechanisms driving it
and the context of its setting; the next step is to identify
and implement possible solutions; and the final stage is to
monitor and or evaluate the effectiveness of the measure
implemented.
This paper focusses on the first stage of the pro-
cess, known as ‘scanning’ in the SARA model (Ashby
& Chainey, 2012) or ‘Intelligence’ in the 5Is approach
1 In the UK the College of Policing has recently launched the What Works
Crime Reduction Centre, http://whatworks.college.police.uk/Pages/default.
aspx; the US has a long standing Centre for Problem Orientated Policing
(POP) http://www.popcenter.org/about/?p=whatiscpop; and the Society of
Evidence Based Policing launched in 2012 http://www.sebp.police.uk/.
http://whatworks.college.police.uk/Pages/default.aspx
http://whatworks.college.police.uk/Pages/default.aspx
http://www.popcenter.org/about/?p=whatiscpop
http://www.sebp.police.uk/
Page 3 of 12Newton Crime Sci (2015) 4:30
(Ekblom, 2011). The process of identifying crime hot
spots for subsequent deployment of hot spot policing
tends to be atemporal. This is a reflection of both soft-
ware availability and analytical skills (Newton and Fel-
son, 2015). Furthermore, sample sizes are larger when
crime is not dissected by time of day, which increases the
robustness of hot spot analysis. Moreover, once a crime
hot spot has been identified, subsequent analysis by time
of day enables identification of when to implement hot
spot policing at detected hot spots. Perhaps an important
component of high crime places overlooked here is that
analysts are encouraged to be crime specific, and thus
tend to examine single crime classifications, for example
violent crime. This is not unexpected, the spatial patterns
of burglary will not closely resemble those of street rob-
bery, nor should they be expected to.
However, areas with concentrations of licensed prem-
ises are known to be highly criminogenic and not just for
violence. Associations have been demonstrated between
licensed premises and a number of crime types, most
notably violence and aggression, but also criminal dam-
age, disorder, and drug use (Scott & Dedel, 2006; Graham
& Homel, 2008; Newton and Hirschfield, 2009b). Indeed
Yang (2010) demonstrated longitudinally that correlations
in time and place exist between violence and disorder.
Furthermore, offenders have been shown to be versatile
in the types of crime they commit (Roach & Pease, 2014),
and indeed police may overestimate the specialised nature
of offending. Thus, if offenders are known to commit sev-
eral types of crime, and several types of crimes have been
shown to be related to NTE places, should analysis of crime
at these places be focussed on single crime classifications?
This discussion has demonstrated that: particular NTE
places experience more than one crime type; offenders
are known to be versatile in the types of crime they com-
mit, and that one of the limitations of spatio-temporal
analysis is that segmenting data in both time and place
can substantially reduce sample size. Combing several
‘related’ crime types into a single analysis is a possible
solution here. Therefore, this research aims to investi-
gate whether multi-classification crime (MCC) hot spots
exist near licensed premises, and if so, do they exhibit
distinctive spatio-temporal patterns. More specifically, it
examines four crime types known to be associated with
licensed premises, namely violence against the person,
criminal damage, drugs, and disorder incidents (anti-
social behaviour), to ascertain how these crimes manifest
in NTE hot spots both in time and place. The following
research questions were formulated for this study.
Research questions:
• Is there spatial correspondence between the locations
of hot spots for different crime and disorder classi-
fications near licensed premises (violence, criminal
damage, disorder and drugs)?
• Do MCC hot spots correspond temporally, that is to
say, when a place is a hot spot for violence, is it also a
hot spot for criminal damage?
• Do MCC hot spots fluctuate over time, for example
does a place experience criminal damage, and then
later in the day or a different day of the week experi-
ence violence against the person?
Methods
Data
This study used crime and disorder data for an
anonymised case study area in England. Its residential
population is approximately 1.5 million persons and
includes a mixture of large towns and several rural vil-
lages, covering a geographical area of approximately
600 km2. Offence data were obtained for the 3 years
period 1st January 2007 to 31st December 2009 for
crimes categorised as violence against the person
(VAP), criminal damage (CD), and drugs; based on
the UK Home Office 2010 counting rules for recorded
crime. Incident data for calls for service for disorder
(non-crimed) were also obtained for the 12 month
period 1st January to 31st December 2007. An addi-
tional dataset used was a licensed premise database
for the case study area, and 6047 premises were iden-
tified as ‘open’ during the considered time period
(2007–2009).
Data processing
The crime and disorder data were cleaned to include
only those containing a known time of offence, and
those with geo-spatial references outside of the case
study area were also excluded. This resulted in a sam-
ple of: 64,440 VAP offences; 83,159 CD offences; 18,270
drugs offences, and 346,022 disorder incidents. A Geo-
graphical Information Science (GIS) software program
was used to calculate the distance from each offence
or incident to the nearest licensed premise, and the
results of this are shown in Table 1. This demonstrates
that for all crime and disorder types the mean distance
to a licensed premise was approximately 130–170 m.
Median distances ranged from 80 to 125 m. Considering
these distances and other studies using buffer analysis
to examine crime near licensed premises (Newton and
Hirschfield, 2009b; Ratcliffe, 2012), a 250 m thresh-
old was selected as an appropriate distance to repre-
sent crime and disorder ‘near’ licensed premises in
this study. As shown in Table 2, for all crime and dis-
order types analysed, 50–65 % of all crime and disorder
offences (varying by crime or disorder classification)
occurred within 250 m of a licensed premise.
Page 4 of 12Newton Crime Sci (2015) 4:30
The temporal nature of offences
It was previously identified that NTE hot spots exhibit
distinct spatial and temporal patterns, with crime peaks
evident on Friday and Saturday evening, or the early
hours of Saturday and Sunday morning, around premise
closing times. In order to examine this further the time of
all crime and disorder in NTE hot spots (within 250 m)
were re-coded with a value representing both the time of
day and day of week (termed week-hour, ‘WH’ for this
study). There are a total of 168 h in a week, and thus each
crime and disorder incident was assigned a WH2 value
from 6 to 173.
Figure 1 shows the weekly temporal distribution of
each crime and disorder type and reveals distinctive pat-
terns in the WH of VAP, CD, drugs and disorder. For all
crime and disorder types there are clear peaks during the
evening and early hours of the morning on all days. How-
ever, there are some differences in the patterns observed;
the highest peaks for disorder are on Friday evening fol-
lowed by Saturday evening, with lower peaks from Sun-
day though to Thursday; VAP peaks on Saturday evening,
followed by Sunday, Saturday, and Monday, with lower
peaks Tuesday to Thursday; drug offences peak on Satur-
day evenings, followed by Friday and Sunday, with more
2 A value of 6 represents the time period 6.00 a.m. to 6.59 a.m. on a Sunday
morning; 23 represents 11.00 p.m. to 11.59 p.m. on a Sunday evening; 24
represents midnight to 0.59 a.m. on a Monday morning; 47 represents 11.00
p.m. to 11.59 p.m. on a Monday evening; 48 is midnight to 0.59 a.m. on a
Tuesday; and so forth. A look up reference for this is provided in Additional
file 1: Appendix S1.
irregular peaks during the rest of the week; for CD the
highest peaks are Sunday evening, followed by Saturday
and Friday; peaks during the rest of the week are again
lower, but the reduction is less than that of other crime
types. Disorder, CD and drugs also exhibit two separate
peaks during Saturday evenings which are not evident for
VAP. CD tends to have two distinct peaks in the evening
most days of the week, unlike disorder and VAP which
have single evening peaks all days except Saturday. Over-
all, there are clear and distinct temporal patterns evident
for each crime type.
It is possible that using 3 years of data may skew the
results as the temporal patterns of each crime may have
changed over time. In order to test this the WH val-
ues for each time period were compared by year, thus
WH values for 2007 were compared with those of 2008
(2007–2008), and WH values for 2008 compared with
those of 2009 (2008–2009). Mann–Whitney tests were
used to compare the means (non-parametric independ-
ent samples). The results were as follows: for VAP 2007–
2008, z = − 0.253, p = 0.8; for VAP 2008–2009 z = − 0.7,
p = 0.48; for CD 2007–2008 z = − 0.35, p = 0.25; for
CD 2008–2009 z = −0.18, p = 0.6, for drugs 2007–2008
z = −1.5, p = 0.12, and for drugs 2008–2009 z = −0.46,
p = 0.09. This suggests that there were no significant dif-
ferences in WH crime times for VAP, CD or drugs over
any of the comparative time periods, and therefore that
the WH temporal patterns of each of the three crime
types remained stable over the 3 years period. As only
12 months of data were available for disorder, tests for
this were not conducted. However, it is assumed that
these are also likely to have remained stable, based on the
stability of the recorded crime results.
Identifying hot‑spots
A range of methods can be used to identify crime hot
spots including thematic mapping, kernel density estima-
tions, nearest neighbourhood hierarchical clustering, and
the Getis Ord GI* statistic (Eck, Chainey, Cameron, &
Wilson, 2005; Chainey & Ratcliffe, 2005; Levine, 2015).
For this analysis the Getis-Ord GI* method (Getis & Ord,
1992; Ratcliffe, 2010; Chainey, 2014) was used to identify
significant hot spot areas of crime around licensed prem-
ises. The advantage of this method over other hot spot
mapping techniques is that it identifies small grid areas
that are statistically significant, and returns a z3 score that
measures the strength or intensity of the clustering and
its significance. This method also produces tightly
defined hot spot areas appropriate for hot spot policing.
3 The higher the z score the greater the clustering, and a z score equal to or
above 1.960 is significant at the 95 % confidence level, and equal to or above
2.576 significant at the 99 % level.
Table 1 Average distances of offences to licensed prem-
ises (metres)
Offence/incident N Distance to nearest licensed
premise (m)
Mean Median SD
Disorder 346,022 167.5 119.5 197.7
Violence against person 64,640 132.4 84.2 173.4
Criminal damage 83,159 163.4 124.6 178.6
Drugs 18,270 149.1 85.4 225.6
Table 2 Percentage of offences and incidents near licensed
premises (within 250 m)
Offence/incident N < 250 m Percentage Total N
Disorder 188,756 54.6 346,022
Violence against person 41,538 64.3 64,640
Criminal damage 44,570 53.6 83,159
Drugs 11,870 65.0 18,270
Page 5 of 12Newton Crime Sci (2015) 4:30
Using the GIS software a 250 m grid matrix was gener-
ated across the study area resulting in 104,958 grids. A
GIS was used to count the number of crimes in each grid
repeated for VAP, CD drug offences, and disorder inci-
dents. This analysis used all crimes within the case study
area. An alternative approach would be to only select
crimes within 250 m of premises, but this may skew the
hot spot generation. For each of the four classifications of
crime and disorder, GI* hot spots were calculated4 using
ArcGIS spatial statistics toolbox. Figure 2 shows the case
study area, the 250 m grids, and the location of licensed
premises. The results of the hot spot analysis are shown
in Fig. 3a–d, which maps the location of hot spots. Note
in these maps only grids which are clustered with 99 %
confidence or greater (z ≥ 2.576) are displayed, with hot
spots superimposed by the locations of licensed premises
4 The parameters for this were to use a fixed distance band, with a threshold
(spatial lag) of 355 m (based on 250 m grids).
in the case study area. The images are rotated for
anonymity.
There are distinct spatial hot spots evident in Fig. 3,
which correlate with urban areas containing high densi-
ties of licensed premises. Upon first glance similar hot
spot patterns are apparent for VAP, CD, disorder and
drugs. However a more detailed visual inspection reveals
subtle differences. The extent of the hot spots around
urban centres is greater for VAP and disorder, and more
tightly concentrated for drugs and CD. Towards the bot-
tom of the case study area there are hot spots of VAP, CD
and disorder, but not for drug offences. Towards the right
of the map there is an area with large concentrations
of VAP, drugs, disorder, and CD, but close inspection
reveals the extent of this is much more spread for VAP
than the other three crime types. On these maps only
grid cells that are significant hot spots at 99 % confidence
interval are displayed. There were 2970 such cells, and
these cells are now examined further.
Fig. 1 Weekly-hourly2 crime frequencies (Sunday to Saturday) four each of four crime types (a–d). CD criminal damage, VAP violence against person
Page 6 of 12Newton Crime Sci (2015) 4:30
Results
The first research question was to examine the degree
to which hot spots of different crime classifications co-
exist spatially, in other words occur at the same place.
Analysis of all grids in the study area using Spearman’s
Rank revealed strong statistically significant correlations
for each crime and disorder type (Table 3) with the loca-
tion of licensed premises; the strongest relationship was
between premises and disorder, followed by CD, VAP,
and drugs. All crime and disorder types were correlated
with premises at R > 0.7, p < 0.01 which indicates a high
degree of correlation between the location of licensed
premises, and crime and disorder events in the case study
area.
Further analysis was undertaken using only grids sig-
nificant at the 99 % level (2970) which contained a sig-
nificant hot spot for at least one of the four crime and
disorder classifications examined. 2435 grids contained
a licensed premise, and unsurprisingly all of these grids
were identified as a statistically significant hot spot for
at least one crime type. Further analysis revealed 2485
grids of the 2970 were hot spots for VAP (83 %), 2385
for CD (80 %), 2160 for disorder (72.7 %), and 1307 for
drugs (44 %). Each grid could contain a hot spot for one,
two, three, or all four crime types, and a Conjunctive
Case Analysis (CCA, Miethe, Hart, & Regoeczi, 2008)
was used to examine the 256 (44) possible combinations
here.5 The results of this are presented in Table 4. This
found 1214 grids, 40 % of the significant crime hot spot
grids, were statistically significant hot spots for all four
crime classifications. A further 663 grids (22 %) were
significant hot spots for at least three types of crime.
This shows strong evidence of an overlap in the location
of hot spots for VAP, disorder, CD and drugs near
licensed premises and suggests strong evidence in the
case study area that MCC hot spots are present near
licensed premises.
Profiling the ‘hottest’ hot spots
The research has thus far demonstrated that MCC hot
spots are present spatially, thus hot spots of VAP are also
hot spots of CD for example. The purpose of research
questions two and three are to further examine the MCC
hot spots temporally, to ascertain whether the different
crime types found in the MCC hot spots occur at the
same time, at different times of day, or different days of
the week. Therefore the top twenty hot spot grids were
identified for further profiling. To determine these top
twenty cells, the ‘hottest hot spots’, cells that were statisti-
cally significant hot spots for all four types of crime and
disorder (VAP, CD, drugs and disorder) were identified.
There were 1214 of these cells. Cells with the highest
combined z scores6 were selected to represent the twenty
‘hottest’ hot spots. A profile of each of these cells is pro-
vided in Table 5. At these twenty 250 m grid cells over the
3 years period (12 months for disorder) there were a high
number of crime and disorder incidents ranging from: 78
to 802 for VAP; 252 to 1736 for disorder; 37 to 182 for
CD; and 8 to 265 for drugs. The number of license prem-
ises in each grid ranged from a minimum of 3 to a maxi-
mum of 96. In order to examine the temporal profiles of
these cells, the WH values of each crime type for each
cell was calculated, and the results of this are presented
in Fig. 4. The frequencies of offences by time of day were
divided into five equal quintiles, and these are colour
coded as per the table key. Those in red represent the
20 % of times with the highest levels of crime for each
classification, VAP, CD, disorder and drugs.
Figure 4 shows the temporal profiles of the 20 hot-
test MCC hot spots. There were seven WH time periods
(each WH is 1 h of the week) that had high levels (col-
oured red in Figure) of crime and disorder for all four
crime and disorder categories at the same time and
same place: Thursday 2.00 a.m. to 2.59 a.m.; Friday 1.00
5 An alternative here may be the use of Multiple Classification Analysis
(MCA), also known as factorial ANOVA. However, as this is used for linear
data, and spatial crime data often follows a negative binomial distribution,
this was not considered appropriate here.
6 Calculated as combined z score of each of four crime classifications from
GI* analysis.
Fig. 2 Case study area with 250 m grids and licensed premises
Page 7 of 12Newton Crime Sci (2015) 4:30
a.m. to 2.59 a.m.; and Saturday midnight to 02.59 a.m.
There were some further distinctive temporal patterns
identified in the MCC hot spots. Disorder is prevalent
Wednesday through Sunday evenings; on Sunday peaks
were at 7.00 p.m., 9.00 p.m., and from midnight to 2.59
a.m.; on Wednesday from 1.00 a.m. to 2.59 a.m.; on
Thursday from midnight to 3.59 a.m.; on Friday from 6.00
p.m. until 2.59 a.m.; and then on Saturday from 7.00 p.m.
until 3.59 a.m. Thus there is an extended period of disor-
der on Friday and Saturday, which last for several hours.
There are also some disorder peaks on Tuesday afternoon
not found for other crime types. VAP followed similar
patterns to that of disorder. However, the length of the
peaks was shorter, occurring slightly later on Sunday until
3.59 a.m., and generally VAP starts later in the evening
Fig. 3 GI* hot spot maps of crime and licensed premises by each of four crime types (a–d) (>99 % significant hot spots shown). CD criminal dam-
age, VAP violence against person
Table 3 Correlations between licensed premises and crime
hot spots (250 m grid based analyses)
Spearman’s Rho correlation
with licensed premises
VAP CD Drugs Disorder
N 10,948 10,948 10,948 10,948
P 0.805 0.913 0.712 0.937
Sig 0.001 0.001 0.001 0.001
Page 8 of 12Newton Crime Sci (2015) 4:30
than disorder. The corresponding periods of disorder and
violence also seem to occur 1 h later on a Saturday than
they do on a Friday. Drugs followed a more unusual pat-
tern; offences occurred on Thursday to Sunday evenings
correlating with VAP and disorder, and there were some
unique peaks early Friday morning at 9.00 a.m. and 11.00
a.m. Drug offence peaks tended to be for 1 h only with
the exception of Thursday through Sunday. CD tended
to occur at much earlier time periods during the day, for
example: on Sunday between 6.00 p.m. and 8.00 p.m., and
then 10.00 p.m. to midnight; at 5.00 p.m. on a Monday
and Thursday; and 5.00 p.m. and 7.00 p.m. on a Saturday.
Discussion of findings
The top 20 ‘hottest’ hot spots identified (based on 250 m
grid cells) accounted for less than half a percent of all the
Table 4 Hot spot grids (99 % significance) and crime and disorder types
Crime type VAP CD Disorder Drugs Total
Number of grids 2485 2385 2160 1307 2970
Percentage of grids (%) 83.7 80.3 72.7 44.0 100.0
CCA analysis of grids by hot spot types
VAP CD Disorder Drugs Number
of cells
Presence (1) or absence of a hot
spot (0) of a hot spot
1 1 1 1 1214
1 1 1 0 609
1 0 1 1 25
0 1 1 1 16
1 1 0 1 5
1 0 1 1 5
0 1 1 1 3
Table 5 Top 20 grid profiles (the hottest hot spots)
Z score based on Getis Ord (GI*) hot spot significance (>2.576 = 99 % significant)
Grid_ID Premises (N) VAP (N) VAP
(z score)
Disorder (N) Disorder
(z score)
CD (N) CD
(z score)
Drugs (N) Drugs
(z score)
Total z All Crime
(N)
54124 63 530 106.86 784 79.88 143 53.27 115 88.87 4920.74 1602
54125 17 146 110.88 800 85.18 58 71.53 42 98.01 7206.67 1056
54126 5 92 53.64 338 54.96 85 60.76 28 51.21 3220.20 553
54417 19 187 92.54 532 64.97 37 36.61 39 78.63 3035.92 809
54418 44 756 126.20 1736 94.52 172 58.94 187 109.04 6647.91 2871
54419 35 468 120.32 876 90.04 182 71.75 129 103.21 7615.46 1685
54420 3 126 55.78 384 53.56 126 57.49 54 52.79 3143.88 704
54712 9 224 103.58 498 83.26 101 55.67 53 92.81 5353.51 887
54713 49 78 95.60 266 74.95 67 66.00 22 85.40 5807.23 439
54714 8 87 50.04 252 41.41 56 52.54 26 49.79 2707.54 427
55006 75 124 56.56 472 50.91 90 43.30 27 62.40 2809.36 718
55007 96 83 54.28 348 50.54 72 51.74 30 53.89 2893.05 538
55301 48 205 58.52 266 56.72 103 60.62 49 49.79 3133.43 635
55595 7 96 54.69 338 50.27 79 57.71 8 46.01 2760.01 527
62448 16 202 78.72 542 57.60 93 36.24 56 73.11 2786.24 910
62449 8 181 88.94 642 68.99 63 43.52 83 87.14 3950.09 981
62450 4 100 73.80 436 62.44 49 41.63 28 81.31 3520.88 622
62742 11 185 80.64 458 61.20 66 38.65 35 81.78 3302.21 756
62743 22 802 90.40 1234 76.17 182 49.05 265 94.39 4796.07 2539
62744 23 166 77.13 1018 64.33 78 41.48 42 82.57 3566.55 1319
Totals 562 4838 12,220 1902 1318 20,578
Page 9 of 12Newton Crime Sci (2015) 4:30
grids that contained a crime or disorder incident (6165
cells), yet contained over 5 % of all crime and disor-
der incidents analysed across the entire case study area.
Moreover, a 7 h time window (Thursday 2.00 a.m. to 2.59
a.m., Friday 1.00 a.m. to 2.59 a.m., and Saturday midnight
to 02.59 a.m.), which represented 4 % of the 168 WH
intervals over a week), accounted for nearly 15 % of all
crimes at these top 20 hot spots alone. Therefore crime
is highly concentrated at these times in these places. This
7 h time frame is important as at these times MCC hot
spots co-existed both in time and space, for all four crime
classifications examined. The most plausible explanations
for this are the high volumes of persons likely to be pre-
sent at these times and places create multiple opportuni-
ties for crime, supported by crime pattern theory, routine
activity theory, and the non-specialised nature of many
offenders. Indeed conterminously at the same places and
locations there may be suitable targets and lack of capa-
ble guardians in these micro places for drugs, criminal
damage, disorder and violence. At these time periods hot
spot policing may require a range of tactics, due to the
diverse nature of multiple crime types prevalent.
At other times of the day MCC hot spots were also
evident but not for all crime types. On Friday and Satur-
day afternoons disorder was evident from 6.00 p.m. until
the early hours of the morning, whereas violent offences
tended to occur after midnight. This may be reflective of
a number of factors, perhaps disorder is a signal crime of
later violence (similar to the Innes, 2004). Alternatively
later in the evening, the number of persons in the NTE
settings may increase, but to fewer locations; cumula-
tively more alcohol is consumed, and the result that dis-
order may escalate into more serious violence. Criminal
damage offences occur earlier in the evening than vio-
lence. An interesting finding is the apparent spatial lag
between Friday and Saturday; both have similar patterns
but offences are approximately 1 h earlier on Fridays.
This may reflect cultural difference and routines; those
who partake in the NTE on Friday’s may do so straight
from work, whereas those who go out on Saturdays may
have constrained activities on Saturday afternoons, or go
out with different friend groups or their partners, thus
drinking in the NTE may start slightly later on Saturdays.
There are a number of limitations to this study. Police
recorded crime and disorder data is known to be sub-
ject to both underreporting and errors in the accuracy
of geo-coding (Chainey and Ratcliffe, 2005; Newton and
Hirschfield, 2009a). It would be useful to supplement this
data with hospital accident and emergency data (A & E)
or ambulance data. According to Shepherd, Ali, Hughes,
and Levers (1993) six in seven of those attending A & E
for violent injuries are not in recorded crime statistics.
However, health data does not always contain location
specific information on when and where crime occurs,
and this data is not always available to the police. It is
suggested a more robust future analysis incorporating
A & E data is likely to confirm the presence of MCC hot
spots near licensed premised.
There are limitations in the arbitrary 250 m buffer
distance, and the use of the GI* statistic. Analysis using
Time of Day 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 1 2 3 4 5
Sunday Disorder 22 30 18 22 26 26 46 48 64 48 50 50 80 92 74 96 74 72 112 110 128 54 60 20
VAP 4 1 8 2 4 6 9 6 15 34 24 22 31 29 43 37 62 97 71 113 90 74 35 10
CD 4 5 2 1 4 1 6 5 6 8 6 19 22 25 15 17 22 15 27 14 14 18 21 5
Drugs 0 2 0 0 2 0 2 5 2 4 4 7 9 9 11 15 8 41 16 17 10 9 1 0
Monday Disorder 4 10 12 30 34 44 76 58 50 72 50 78 76 68 64 66 62 74 60 88 80 48 16 4
VAP 6 3 6 6 5 9 12 11 21 19 27 21 23 19 19 27 36 69 56 58 57 61 21 3
CD 1 4 3 7 1 1 9 6 6 10 12 30 18 18 16 16 17 27 14 23 12 16 8 6
Drugs 0 0 0 0 2 5 2 4 7 10 11 8 7 6 4 9 6 13 19 20 6 8 2 0
Tuesday Disorder 6 6 26 24 32 64 60 94 102 64 104 80 72 84 74 64 52 64 56 68 76 58 20 6
VAP 1 3 2 3 10 12 10 17 21 29 20 18 15 28 22 25 41 37 37 48 50 40 20 1
CD 0 0 3 5 3 5 6 7 9 6 12 27 20 11 9 18 15 13 16 14 12 10 6 5
Drugs 2 0 1 0 4 0 6 1 9 9 6 2 7 3 11 7 16 9 28 12 0 1 6 0
Wednesday Disorder 4 12 20 30 58 52 74 84 90 86 66 54 72 62 82 88 68 74 74 94 100 60 22 8
VAP 2 2 1 7 6 5 9 17 24 33 21 15 21 29 27 29 24 50 35 44 51 39 16 5
CD 1 3 0 4 4 7 6 5 6 12 9 18 23 9 11 17 15 20 14 18 16 7 8 0
Drugs 0 0 0 8 4 5 9 5 5 8 4 2 3 8 13 7 8 3 4 8 5 3 4 0
Thursday Disorder 6 2 14 32 44 50 52 80 72 68 82 66 82 66 62 66 58 80 132 154 138 166 80 22
VAP 4 0 4 7 13 10 14 7 13 22 25 16 18 10 21 23 26 37 73 82 90 83 37 7
CD 1 2 4 2 3 0 3 4 3 3 13 27 8 19 10 10 8 13 18 19 22 24 12 3
Drugs 0 0 1 7 3 6 5 9 7 2 3 7 0 4 8 4 2 11 52 22 18 5 4 3
Friday Disorder 6 6 22 28 40 60 42 54 74 88 90 68 92 122 128 140 140 172 226 228 190 150 84 30
VAP 1 5 1 4 5 15 17 10 18 20 17 19 15 10 20 29 37 48 92 112 101 90 45 6
CD 1 4 4 5 5 1 10 5 11 5 14 22 19 11 17 15 17 23 21 31 23 18 10 6
Drugs 0 0 2 14 6 12 5 8 3 2 2 4 4 7 2 4 10 22 24 42 16 20 10 2
Saturday Disorder 22 14 26 22 48 50 70 66 92 82 96 118 82 106 108 104 146 202 286 338 312 244 142 54
VAP 2 1 4 5 5 8 11 7 20 21 16 28 17 25 32 44 37 65 123 178 173 121 58 17
CD 2 1 2 1 0 4 6 8 6 5 8 30 16 26 10 19 22 15 33 35 28 28 26 14
Drugs 0 0 0 3 0 5 0 4 4 4 5 5 6 12 11 11 13 36 45 76 51 27 11 6
Fig. 4 The ‘Hottest’ hot spot profiles by time of day and crime type (MCC hot spots): values indicate crime counts
Page 10 of 12Newton Crime Sci (2015) 4:30
alternative buffers (100 m, 400 m) found no discernible
differences in patterns of crime observed. A possible lim-
itation of the GI* is it identifies too many hot spot areas
significant at 99 %. Future analysis could compare the use
of a corrected Bonferonni approach rather than Gausian
for determining Z-score (Chainey, 2014). This technique
also identifies cells that have low crime counts, as it is
based on neighbourhoods surrounding cells rather than
just inside a cell in its calculation; alternative hot spot
techniques should be used explored and compare MCC
hot spots.
Conclusions
This paper has presented strong evidence for the pres-
ence of MCC hot spots near clusters of premises,
known to be particularly criminogenic places. This is
not surprising, given the literature on crime oppor-
tunity, crime pattern theory, routine activities, risky
facilities, and crime attractors and generators. How-
ever, what this research does begin to question is the
conventional wisdom of hot spot analysis and hot spot
policing being wholly crime specific, using single crime
classifications at highly criminogenic places. Hot spots
of VAP, CD, drugs and disorder were identified at the
same locations in the study area, near to licensed prem-
ises. Moreover, the results show that at particular time
periods (seven hourly periods of a 168 h week) all four
crime and disorder types occurred conterminously in
both time and space. At other times only one or two hot
spots were present, and at some times of the day hot
spots were not found. This has clear implications for hot
spot policing in terms of tactics used and when best to
target resources. Further exploration and explanation
of these patterns is warranted to assist in effective hot
spot policing deployment and tactics at MCC hot spot
locations.
A range of methods could be incorporated to refine
future analysis. In particular more statistical time based
analysis should test: whether MCCs are clustered in time
and space; if the space–time clustering occurs continu-
ously or within defined time periods; or if there is a space
time interaction (Levine, 2015). Suggested tests here are
to use the Knox and Mantel tests to examine the interac-
tions between licensed premises and the MCC hot spots
identified. Furthermore circular statistics could be incor-
porated, for example the use of Rayleigh’s test to exam-
ine significant clustering by time of day, or the Watsons
U test to examine for differences in two temporal data-
sets (Wuschke, Clare, & Garis, 2013) by month, season
or year.
As observed by Townsley (2008) characteristics of
crime hot spots can alter over time, with periods of emer-
gence, persistence, and decline. Therefore any future
analysis that is developed should also consider how
MCC hot spots may emerge and dissipate over time near
licensed premises, and whether they are stable hot spots
or occur more sporadically. Moreover, there are seasonal
variations in crime patterns and discretionary routines
influenced by daylight hours and temperature (Tompson
& Bowers, 2015) and this may influence MCC hot spots
near licensed premises.
At present there are a number of studies using predic-
tive crime mapping or crime forecasting (Chainey, 2014).
Perhaps predicting MCC hot spots should form part of
this research. Indeed, Shekhar, Mohan, Oliver, and Zhou
(2012) attempt to do similar, by testing for the emergence
of crime trends with multiple crime types. MCC hot
spots have been identified near licensed premises, but
perhaps alternatives exist, for example: burglary hot spot
analysis could also consider patterns of theft of, and theft
from vehicle; the locations of street robbery could be
compared with pickpocketing and theft from person; at
drug locations a number of crimes associated with illicit
trade could be examined. In other places known to be
criminogenic, it may be important to identify alternative
configurations of MCC hot spots.
VAP, CD, drugs and disorder have all been shown to
relate to licensed premises, but more detailed informa-
tion on types of premises, density and opening hours
should also be taken into account before prioritising
hot spot policing. Indeed a final question that remains
is the implications of this research for hot spot polic-
ing and resource targeting. It is possible to continue to
police hot spots based on single crime types effectively.
It is not known if focussing on the places and times of
MCC hot spots is likely to be more effective in reduc-
ing crime, as theoretically more offenders are likely to
be present at MCC than single crime hot spots, thus
police may be more likely to deter or apprehend offend-
ers at MCC hot spots. However, tactically it may be
more difficult to police MCC areas, targeting multiple
types of crime may require several concurrent tactics
that may conflict. MCC hot spots have been shown to
contain different crime types over time, criminal dam-
age and disorder earlier in the day and violence at later
times. It is not known if early intervention here would
reduce crime at later times of the day, or if police would
need to remain at these MCC hot spots for longer
time periods. It is suggested an RCT of MCC hots spot
patrols near licensed premises may shed some light on
this question.
Additional file
Additional file 1. Appendix 1: Look up table for ‘WH’ weekly-hour
values in Fig. 1.
http://dx.doi.org/10.1186/s40163-015-0040-7
Page 11 of 12Newton Crime Sci (2015) 4:30
Abbreviations
CD: criminal damage; GIS: geographical information science; MCC: multi-clas-
sification crime; NTE: night-time economy; VAP: violence against the person;
WH: week hour.
Competing interests
The author declares no competing interests.
Received: 3 July 2015 Accepted: 30 September 2015
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- c.40163_2015_Article_40_25877
Crime and the NTE: multi-classification crime (MCC) hot spots in time and space
Abstract
Background
Methods
Data
Data processing
The temporal nature of offences
Identifying hot-spots
Results
Profiling the ‘hottest’ hot spots
Discussion of findings
Conclusions
Competing interests
References