Volume 1 Issue 4 | January 2009

Public Safety

Geography

A Quarterly Bulletin of Applied Geography for the Study of Crime & Public Safety

Districting and Resource 

Allocation: A Question of Balance 

By Christopher Bruce 

President, International Association of Crime Analysts

Danvers, Massachusetts

Early in my crime analysis career, I was introduced to the “Bud-Shell Method” of creating police districts. I believe the term was coined by Dr. William Stenzel of 

Northwestern University, and it describes a police administrator who sits down one night 

with a “six pack of Budweiser and a Shell station road map” and uses a magic marker to 

draw lines down major streets. If you have a major east-west artery and a major north-south 

artery—voila!—you have four districts! Never mind that one contains mostly upper-class 

residential housing and another contains a hospital, a high school, and a methadone clinic.

It would probably be too much to say that the “Bud-Shell Method” was the predominant 

method of districting during the first 90 percent of the 20th century—I think that most 

police agencies tried for some kind of geographic or workload balance—but until the 

advent of affordable desktop geographic information system (GIS) software, the task 

was too difficult to accomplish any other way. In a 1977 publication, Crime Analysis in 

Support of Patrol, the authors discuss “sophisticated approaches based on mathematical 

techniques [that] have been attempted to provide more realistic distributions of patrol 

forces,” but conclude that “there is no evidence in the literature that they have actually 

been implemented in any police department,” and fault, in part, the limitations of existing 

computer programs (pp. 13–14). 

Early resource allocation models focused on achieving a balance between calls for service 

and geographic area. Police administrators wanted to ensure that each patrol division had a 

relatively equal workload and could meet the agency’s response-time goals. But even early 

publications saw flaws in using calls for service as a measure of workload. “The demand 

for patrol services has been commonly assessed in terms of raw counts of incidents,” wrote 

G. Hobart Reinier. “However, for incident and manpower analysis,” he noted, “it is not 

how many but rather how much time is demanded for various levels of service” (Reinier 

et al., 1977, pp. 4-15–4-18). But computer-aided dispatch systems of the time could not 

make these calculations. Although some police agencies commissioned and implemented 

expensive and complicated allocation systems, the theory of the era generally surpassed the 

technology.

As GIS and other computer systems improved, “operations analysis”—the analysis of 

internal operations as a way of allocating police resources more effectively—began to 

achieve a higher status in crime analysis literature. At the same time, analysts began making 

recommendations for temporal and geographic allocation of patrol resources. 

Contents

1 Districting and Resource 

Allocation: A Question of 

Balance

3 Tucson Police Officers 

Redraw Division 

Boundaries to Balance 

Their Workload

6 Tactical Deployment: The 

Next Great Paradigm Shift in 

Law Enforcement?

8 Geospatial Technology Helps 

East Orange Crack Down on 

Crime

10 Technical Tips

13 Police Chief Magazine 

Focuses on Crime Mapping 

and Analysis

14 News Briefs

16 NIJ’s MAPS Projects

18 Geography and Public 

Safety Events
22

G&PS | January 2009

As an observer of the field for the last 15 years, 

I think that the majority of analysts have not 

internalized this role. Most of the literature 

on operations analysis has taken the form of 

case studies, and the profession still awaits 

a comprehensive, authoritative operations 

analysis text. Even training on the subject is 

rare. Hence, when analysts have set out to 

create allocation plans, they generally have 

invented their own formulas and processes, 

with varying degrees of success. Meanwhile, 

modern GIS systems and databases are sitting 

out there, waiting for us to use them. Today, 

the technology has surpassed the theory.

In Danvers, Massachusetts, the need for 

redistricting has become alarmingly apparent 

during the last 4 years. Continued commercial 

and residential development in one of our 

four patrol areas has resulted in an imbalanced 

workload. Area 1 officers have little to do for 

most of their shifts, while Area 4 officers have 

overwhelming workloads (see table).

Creating even basic statistics like these can 

involve answering a host of questions, such as 

the following:

 Should officer-initiated activities (e.g., 

motor vehicle stops, field interviews) be 

calculated as part of the service total? 

 What about directed patrols and selective 

enforcement assignments? 

 Should I exclude calls for service to the 

police department itself? 

 If an incident occurred in a different area 

than the one the call came from, toward 

which area should it count? Should we 

count it at all?

 Should we weight calls for service so that 

certain types of calls count more?

 Should call-for-service hours account for 

the total time of everyone who responded 

to each call, or just the patrol car from 

that area? 

 Should a supervisor’s time count?

 Should time spent by officers who 

technically are off duty on road details or 

private work count?

 Should time spent by mutual-aid units 

(i.e., cars brought from other cities) count?

To create more balanced officer workloads, I 

answered these questions and tried to create 

new districts that balanced total calls, call 

hours, and geographic area. These tasks cannot 

be completed mathematically because towns 

have natural divides like rivers, highways, 

and forests that put limitations on how 

commanders can redistrict. 

Districts should be shaped so that officers can 

travel easily from one end of the district to 

the other. The shape should be sensible, and 

officers should be able to memorize the district’s 

boundaries and roads so they know where they 

are located when they are on patrol. Some 

important questions to consider during this 

process include these:

 Should we divide hot spots among all 

districts or try to keep them within one 

district?

 Should boundaries go down the middle of 

major streets, or is it better to create them 

through backyards and open land?

 How important is neighborhood integrity? 

If an agency lacks the funds to purchase a 

comprehensive computer model, redistricting 

can become a lengthy process that involves 

testing, adjusting, and retesting before the ideal 

districting plan emerges.

In Tucson, Arizona, Crime Analyst Autumn 

Kistler went through the same process, but 

adopted a more scientific approach than I 

did, developing a model that assigns varying 

importance to total hours, call priority, street 

length, geographic area, population, and 

response times. In “Tucson Police Officers 

Redraw Division Boundaries to Balance Their 

Area 1 Area 2 Area 3 Area 4

Calls for Service 21 percent 27 percent 23 percent 29 percent

Call Hours 19 percent 25 percent 22 percent 34 percent

Danvers Police Workloads for Calendar Year 2007
3

Workload,” she discusses how she developed 

and implemented this model, which created 

a balanced distribution in her city. Phil 

Mielke’s tips on using ArcGIS’s redistricting 

tool will help analysts who want to achieve 

the same success in their own agencies.

While Autumn and I struggle with assigning 

districts, new technologies are challenging 

the concept of “districting” in the first place. 

In “Tactical Deployment: The Next Great 

Paradigm Shift in Law Enforcement?” Chief 

John Douglass of the Overland Park Police 

Department discusses how some agencies 

now use tactical deployment points, which 

have transcended the concept of districts—

dispatches are simply based on the closest 

unit, as determined by automatic vehicle 

location (AVL). East Orange, New Jersey, 

Crime Analyst Allison Mayer talks about 

further uses of an AVL tool in tactical and 

strategic deployment.

Only time will tell if AVL will eventually 

render the concept of districts obsolete, but 

one thing is certain: between GIS and AVL, 

the typical police department does not make 

the most of the available technology. The 

tools of operations analysis have matured. 

Let’s make sure the science follows.

References 

Gottlieb, S., S. Arenberg, and R. Singh. Crime 

Analysis: From First Report to Final Arrest. Montclair, 

California: Alpha Publishing, 1994.

Reinier, G.H., T.J. Sweeney, R.V. Waymire, F.A. 

Newton III, R.G. Grassie, S.M. White, and 

W.D. Wallace. Integrated Criminal Apprehension 

Program—Crime Analysis Operations Manual. 

Washington, D.C.: U.S. Department of Justice, 

National Institute of Justice, 1977. NCJ 43901.

Reinier, G.H., M.R. Greenlee, M.H. Gibbens, and 

S. Marshall. Crime Analysis in Support of Patrol—

National Evaluation Program, Phase 1 Report.

Washington, D.C.: U.S. Department of Justice, 

National Institute of Justice, 1976. NCJ 39887.

When Tucson, Arizona, officers’ workloads got out of whack, 

commanders decided to redraw the 

division boundaries.

The city of Tucson holds more than 500,000 

people and covers more than 200 square miles. 

Police work was originally divided into five 

operational divisions that varied in size and 

workload. But uneven urban growth within 

each division created an unbalanced workload 

for officers, leading to an imbalance in staffing, 

morale problems, and span-of-control issues. 

So in late 2005, Tucson Police Department 

(TPD) commanders began to redesign 

division boundaries to equalize the workload. 

Assessing the Workload 

Problem

Before beginning the project, the TPD 

examined its management strategies to assess 

whether different management practices 

could help resolve the problem. They 

examined the following concerns:

 Whether officers in all divisions were 

held accountable for productivity in field 

interviews, traffic stops, traffic citations, 

and primary-to-backup ratios.1

 Whether all divisions followed the same 

procedures (e.g., if calls are cleared after 

paperwork is complete, workload will 

appear higher than if they are cleared 

after the officer leaves the scene.) 

After assessing management strategies, 

staff examined what factors contribute 

to workload and developed a system to 

weigh factors and help with appropriate 

redistricting. A survey of existing literature 

on law enforcement redistricting showed 

that total hours worked, including all time 

assigned to a call from dispatch to clearance, 

provides the best measure of workload. 

Several other factors contribute to workload 

to a lesser degree. Total hours worked was 

given a weight of 75 percent, and the other 

factors were weighted as follows:

 Number of call responses (10 percent).

 Average response time (7 percent).

 Total length of all streets within the 

division (3 percent).

 Area (square miles) of the division 

(3 percent).

 Population (from the 2000 census) 

(2 percent).

Using Software to Create 

New Division Boundaries

TPD staff used a redistricting software 

program called Geobalance,2 in 

coordination with geographic information 

systems (GIS), to redraw boundary lines.

Creating Redistricting Cells

Using Geobalance software involves 

breaking the city down into cells that can be 

consolidated into divisions. Cells should be:

 Bordered by logical boundaries (streets, 

alleys, rivers, etc.).

 Large enough to contain meaningful data 

(e.g., hot spot locations, bar locations).

 Small enough that they can be combined 

in different ways to form divisions. 

The TPD used GIS mapping files, called 

shapefiles, to divide Tucson into numbered 

grid squares one-quarter of a square mile 

in size. Because most of the city’s streets are 

laid out on this grid, this was a natural fit. 

Tucson Police Officers Redraw Division 

Boundaries to Balance Their Workload

Autumn Kistler

Tucson Police Department

Tucson, Arizona
44

G&PS | January 2009

Populating the Cells with Data

Staff then populated the cells with workload 

data for a full calendar year’s worth of calls. 

An analyst determined the response time and 

total hours worked for each call, using SAS 

software. The software, which is designed for 

data mining, determined the time from the 

officer’s arrival on the scene to the officer’s 

clearance of the scene,3 and returned a total 

count of hours spent on each call.

An analyst used Environmental Systems 

Research Institute’s ArcMap software to create 

a map of calls for service based on the latitude 

and longitude recorded for each call. She 

added the redistricting cells to the map and 

used the “spatial join” function to append the 

cell number where each call occurred to the 

call’s record. 

She used the same process to assign cell 

numbers to street network segments and 

census blocks where the call occurred to 

the call’s record. The analyst used ArcMap’s 

“summarize” function for each shapefile to 

calculate totals for each of the six parameters 

(i.e., total hours worked, average response 

time, etc.) (see example in Figure 1). She then 

used the “attribute join” function to attach 

summary tables to the cell’s shapefile. This 

created a shapefile for the entire cell with six 

overall summary tables corresponding to the six 

parameters.4 

Populating cells with data can be the most 

difficult step of the redistricting process—the 

procedure is time-consuming and may require 

a fair amount of trial and error. The “join” 

and “summarize” procedures within ArcMap 

software produce many new files. Each file 

must be named separately to prevent confusion. 

Use a naming convention and keep a log with 

the name of each file, how it was created, and 

what it includes. 

Designing New Boundary Divisions 

After all the data were added to a master 

redistricting cell shapefile, the analyst put the 

master file into Geobalance, specified the six 

elements of data the software should read (each 

contributing factor), and assigned each a weight 

based on the percentage it contributes to an 

officer’s overall workload (described above). 

Geobalance allows the user to assign each cell 

on a map to a different division (see Figure 2). 

The software recalculates officers’ workloads 

each time a cell is added to a division. It 

produces running totals for the divisions, 

allowing the user to see immediately how 

moving cells from one division to another 

affects officer workload. 

Initially, the analyst assigned cells to the 

current division boundaries, creating a model 

to compare with new scenarios. She then 

created 12 new division scenarios with differing 

boundaries and team configurations. 

Figure 1. Redistricting grid cells that include data. The categories shown on the maps are for 

visual display only and were not used in the analysis.
5

Creating new scenarios. The challenge 

of redesigning division boundaries is to 

create divisions that have both comparable 

workloads and acceptable, logical 

boundaries. All cities have characteristics 

that affect where division boundaries can 

go. For example, most cities have areas with 

particularly high call loads, fast population 

growth, or limited access.5

For Tucson, some additional considerations 

included the boundaries of gang territories, 

city council wards, neighborhood 

associations, and the Davis-Monthan Air 

Force Base. The TPD also wished to keep 

the divisions compact to minimize travel 

time and to prevent establishing boundary 

divisions that would frustrate patrol officers. 

Narrowing the list. Command staff 

analyzed the options available and narrowed 

the list to four designs (see Figure 3). An 

analyst created a mapping file for each and 

regenerated the six data parameters for those 

files without using the redistricting cells. 

This refined the data and made them easier 

to understand. The commanders then made 

their final selection. 

Implementing the New 

Divisions 

After the final design for the new 

divisions was chosen, the TPD planned 

implementation. This involved doing the 

following:

 Reallocating sergeants, patrol officers, 

and vehicles.

 Making modifications to the TPD’s 

computer-aided dispatching and record 

management systems. 

The morning of the implementation date 

passed seamlessly and without incident. 

For a year afterwards, the TPD evaluated 

call load and hours worked in each patrol 

division on a monthly basis. The divisions 

maintained workload ratios that were 

almost identical to those projected during 

the redistricting study, never fluctuating 

more than 3 percent. To date, these 

division boundaries have been successful 

in balancing team workload and ensuring 

efficient and effective law enforcement. 

Notes

1

This ratio describes the rate a primary officer is on 

call compared to a backup officer.

2

For more information, see www.coronasolutions.

com/products/geobalance-features.shtml.

3

Clearance of the scene is the point at which the 

officer is finished processing the scene (i.e., is no 

longer at the scene and has finished filing all reports).

4

TPD used shapefiles of streets, census data, grid 

cells, and calls for service. These shapefiles were 

already maintained locally. If law enforcement 

agencies do not have mapping files, they 

should check with other local agencies, such as 

transportation and planning offices, to find out if 

such files are available. If not, the agency should 

build extra time into the redistricting study. If the 

skills to create the shapefiles are not available in 

house, the agency may need to contract with an 

outside provider.

5

For instance, rural communities where officers have 

limited access to roads in winter weather.

Figure 2. A screenshot of the Geobalance software shows how cells 

can be allocated to divisions

Figure 3. A sample of the data that the analyst presented to the commanders, which allowed 

them to choose from among the four final options.
66

G&PS | January 2009

Overview

Before World War II, car radios—available 

to some of the larger law enforcement 

organizations—experienced limited use 

nationwide. Telephones were the communication 

technology of choice. Call boxes in major cities 

formed the functional communication network 

of their time, and beat officers were strategically 

placed to take advantage of these stationary 

communication devices. 

Following the technological breakthroughs of the 

war, municipal law enforcement in the United 

States tentatively began to embrace the use of 

portable and transient radios in their day-to-day 

activities, though it would take nearly a decade 

to filter down to most police departments. 

Despite this evolution in police communications, 

the advent of the radio in the mobile environment 

would be structured for many years around 

the fixed-grid structure of deployment, most 

notably the police district. This method of 

deployment stemmed from certain erroneous 

beliefs such as these: 

 Given enough previous information, 

workloads and calls for service could be 

structured into efficient and dynamic 

police districts.

 Assigning these police districts would 

guarantee that the closest car got the call.

 Officers assigned to specific districts would 

become familiar with those who lived, 

worked, and shopped in their districts and, 

thus, would be best-suited to fight crime in 

these areas. 

Unfortunately, this blueprint for service 

deployment proved to be unworkable in 

practice. Any officer with more than a year 

of experience knows that within the first halfhour of reporting for duty the call-for-service 

carousel begins. You leave your district to 

answer a call in another district; while you 

are gone, another officer from another district 

is sent to answer a call in your assigned area. 

And so it begins, one call leading to another 

until, shortly, no officers are close to their 

assignments.

For most of my career, districts were created or recreated every 2 or 3 years. We prided ourselves on 

doing sophisticated call-load analysis to frame the 

exact boundaries of that district, assuming that we 

would get the most efficient productivity as a result. 

We failed to take into consideration, however, 

that crime and criminals move rapidly from one 

opportunity to another. Consequently, our system 

of service delivery proved inadequate in tackling 

the day-to-day criminal activities and public safety 

needs of the people within our districts.

The Turning Point: Desert Storm 

Brings Change

The tactics and technologies employed in 

Operation Desert Storm and the use of the 

smart bomb and Global Positioning System 

(GPS) munitions changed the way we looked 

at service delivery; the military had begun using 

sensors and geological information to identify 

and target specific bomb sites. Law enforcement 

faced a parallel problem: we needed to use the 

maximum resources needed to tackle a problem 

swiftly and effectively without wasting resources. 

It became apparent that information systems 

could also be used to enhance police service 

delivery. 

Though this idea was not entirely new, we 

previously lacked the technology to make it 

happen. With the advancement of computeraided dispatch (CAD) and record management 

systems (RMS) however, officers began to 

perform sophisticated strategic and tactical 

crime analysis. This kind of analysis provides 

real-time information, which allows law 

enforcement to virtually locate crimes as they 

occur and respond with the resources necessary 

to make a difference. 

Tactical Deployment: The Next Great 

Paradigm Shift in Law Enforcement?

John Douglass

Overland Park Police Department

Overland Park, Kansas
7

Overland Park, Kansas: 

Real-Time Policing in Action

The transition from dispatch model to 

incident-command model using hot-spotbased technology was relatively simple in 

Overland Park. 

We started with a comprehensive 

CompStat-like program called ABSTRAT1

and leveraged the expertise of our 

extremely competent Crime Analysis 

Unit to develop tactical-level information 

and analysis for operations and watch 

commanders, sergeants, and officers. This 

information is delivered to them on a 

nearly real-time basis—less than 24 hours 

after a crime occurs. 

Next, we replaced the traditional district 

model with three sectors, each with a 

sergeant and corresponding staff. Equipped 

with the crime analysis information, the 

sergeants, operations commanders, and 

watch commanders make specific resource 

assignments to the sector where the crime is 

occurring. 

Finally, operations commanders were moved 

from their offices in administration to a frontrow seat in dispatch, from which they could 

direct all department operations in real time. 

Interestingly, this hot-spot-based use of 

technology in law enforcement became 

common in multiple areas of the country 

almost simultaneously. This most likely 

occurred because we all watched the same 

strategies being employed successfully by the 

military and had access to the same tools.

The Future of Tactical 

Deployment

The use of geographic information systems 

to guide patrol efforts is only 6 or 7 years 

old at most, and most initiatives are still 

being defined and structured. As with most 

change, the transition has not been easy, 

and officers are still adjusting to this new 

format. Nonetheless, preliminary results 

are encouraging. 

There are numerous stories of improved 

apprehension opportunities nationwide 

and the occasional account of how law 

enforcement has been able to predict 

and prevent a crime with the help of 

this technology. This, however, is just the 

beginning.

At Overland Park, plans include the creation 

of a desktop application that would allow 

a patrol sergeant to view the hot spots 

identified by our crime analysis unit and use 

the application’s “drag-and-drop” function 

to deploy patrol units to these areas. These 

deployments could change from place to 

place and hour to hour based on the volume 

of criminal activity. As a result, a patrol 

officer could be assigned to any number of 

deployments throughout his or her shift. 

Additionally, the crime analysis unit will 

have the capability to retrieve information 

on a given deployment using data from 

our CAD, RMS, and GPS systems. This 

information would include the amount 

of time an officer was within his or her 

deployment area, the number of calls for 

service answered, the number of citizen 

contacts made, the number of reports 

written, all field contacts recorded, the 

number of traffic citations issued, the 

number of arrests made, and any other 

action the officer took that might have had 

an impact on the problem. The officers will 

also be able to enter comments pertaining 

to a particular assignment—including 

observations made, actions taken, and 

any recommendations—directly into the 

deployment application. 

After analyzing these data, the crime 

analysis unit will adjust or redefine hot 

spot areas as needed to target criminal 

activity better. Overland Park’s crimeanalysis-supported enforcement deployment 

strategies are presented at weekly meetings 

where appropriate response strategies 

are developed, discussed, and assigned. 

It is of great importance that the Crime 

Analysis Unit’s research and recommended 

deployment opportunities receive high 

priority during these sessions. 

Although long-term statistical analysis 

of the new deployment techniques may 

not be possible for another decade, one 

thing is for certain—the paradigm has 

shifted and an increasing number of law 

enforcement agencies across the nation 

are turning to geographic analysis and 

advanced technology to make decisions 

about how best to use police resources to 

fight problems as they occur. There will be 

no turning back, and it is essential to apply 

the necessary resources to make tactical 

deployment a success. 

Notes

1

ABSTRAT (Accountability Based Strategic 

Deployment) is Overland Park’s version of 

CompStat. CompStat was founded in 1994 by New 

York Police Commissioner William Bratton. The 

primary focus of both programs is to provide timely, 

accurate, and useful intelligence information to the 

officers on the street and to follow that information 

with an accountability process to track success.

Figure 1. Captain Bruce Veatch, Operations Commander, directs the department’s tactical operations.
88

G&PS | January 2009

Geospatial 

Technology Helps 

East Orange Crack 

Down on Crime

Allison Mayer

East Orange Police Department

East Orange, New Jersey

East Orange, New Jersey, has become a national leader in reducing crime. Its 

success may come as a surprise—East Orange 

was named one of the “urban 15” cities in 

New Jersey that historically account for onethird of the state’s criminal activity. Recent 

policing strategies reduced criminal incidents 

from nearly 7,300 serious crimes in 2003 to 

only 3,200 in 2008, a rate the city had not 

seen in more than 25 years. The success is due 

largely to the use of a geographical technology 

called the Tactical Automatic Vehicle Locator 

(TAC–AVL).

Like many police departments, East Orange 

employed a traditional, reactive policing 

approach when dealing with gang and drug 

violence and quality-of-life problems. But 

with the staggering crime statistics observed in 

2003, the department knew that it had to revise 

its strategies. It began to operate proactively 

rather than reactively and started using policing 

technologies like TAC–AVL. 

TAC–AVL has been a successful and reliable 

initiative. It is a real-time Geographic 

Positioning System (GPS) tool that allows 

commanders to see their crime-prevention 

plans working at any time of day, at any 

location in the city. It provides commanders 

with a real-time map of the city and displays all 

patrol car locations, which allows commanders 

to check whether vehicles are at the appropriate 

place at the appropriate time. It also has a 

playback feature, which allows officers to learn 

from their mistakes and compile a series of 

best practices. TAC–AVL is accessible on all 

computers at department headquarters and in 

supervisors’ cars, so that activity in the city can 

be monitored instantaneously at all times.

Becoming a Proactive Force 

In an effort to reduce crime and to police 

proactively, the East Orange Police Department 

reorganized its command structure in 2006. 

One important change involved the way 

neighborhoods were patrolled.

In the past, if an incident happened in a police 

zone,1

 a car in the area would respond and 

others in neighboring areas would provide 

backup. This is called “pulling” because 

deployment “pulled” the backup cars outside 

their areas of responsibility. This method can 

create problems because a car responding to a 

backup call may get a call for service in its own 

zone. As a result, that vacant zone may have to 

“pull” another car from a nearby zone, which 

creates a domino effect—when a car leaves its 

zone, the area becomes more susceptible to 

criminal incidents. 

In response to this conundrum, East Orange 

created a set of Impact,2

 Resource,3 Response4

and Conditions5 cars. These cars are not 

assigned to patrol a specific zone; they remain 

at a home base and respond to backup 

calls. These units are then “pushed” into the 

neighborhoods that need them—as backup, or 

as a first responder to a call while the primary 

zone car is at another call. 

“Pushing” has a number of positive effects:

 It ensures zone integrity.6

 It alleviates call “stacking” (i.e., where one 

car answers call after call).

 It decreases response time.

 It increases officer effectiveness.7

 It decreases officer stress and fatigue. 

Depending on the shift (i.e., midnight, day, 

or evening tour) the number of supplemental 

units can fluctuate based on the typical 

number of calls for service at that time. Each 

car responds to certain priorities of calls for 

service.9

 With this constant coverage, no zone 

is ever left vulnerable to an influx of crime 

because of the absence of patrol units. 

TAC–AVL lets dispatchers, commanders, and 

decision-makers ensure that “pushing” occurs 

into an area. If an area has no uniformed police 

support, a nearby supplemental patrol unit can 

be sent into the zone immediately for coverage. 

As a result, the response time for calls decreases 

and the efficiency of the dispatch center 

increases. 
9

Using TAC–AVL to Enhance 

Crime Prevention 

Commanding officers can use TAC–AVL to 

ensure that their weekly and daily tactical 

crime plans are in conjunction with the 

current crime problem and that they are 

followed. 

For example, a commander may want to 

increase police presence on the north side of 

the city because of an influx of car break-ins. 

From her in-car computer (or mobile data 

terminal [MDT]), a supervisor can access 

TAC–AVL to see the following:

 What type of call an officer is on.

 How long the officer has been on the call.

 The result of the call. 

 If nearby zones are understaffed. 

If nearby zones are understaffed, the 

commanding officer can send an Impact, 

Resource, Response, or Conditions car to the 

area, shaping patrol plan outcomes on the fly.

Discovering Best Practices 

Commanding officers can use TAC–AVL’s 

playback feature to discover best practices 

and learn from their mistakes. TAC–AVL 

is often used during CompStat meetings 

to view a specific incident. By selecting a 

certain call, commanders can see where 

every AVL-enabled unit throughout the city 

was at the time of the incident, the officer’s 

response to the problem, and whether 

a commander should have altered the 

response efforts. 

For instance, if three car break-ins occur 

in the same neighborhood in 15 minutes, 

commanders can query the TAC–AVL 

to find the location of the officer in the 

corresponding zone. The result could 

answer why the break-ins happened or raise 

new questions, such as: Was the officer on 

another call? Was the Conditions car on the 

opposite side of the neighborhood? Did these 

decisions have a negative impact on the entire 

neighborhood’s situation? Was the response 

taken the best course of action? Questions 

like these and the answers that come out help 

shape outcomes and form a series of best 

practices that officers can replicate. 

A 2-hour playback feature allows officers to 

view all patrol car activity 2 hours before 

and after an incident of interest. Activities 

are color coded so that officers can easily 

recognize whether they took place before 

or after the incident under review. If units 

were not near the crime scene during this 

time, platoon commanders must review and 

address the issue. Any outstanding issues 

must be addressed at daily roll calls, so that 

officers learn that they must be in crimeprone areas at hot times to prevent crime. 

Ultimately, TAC–AVL allows police 

commanders to monitor patrol cars and 

make immediate adjustments to their day-today strategies based on the situations that 

arise. If they make mistakes or find they are 

successful, they can review their decisions 

and choose whether to incorporate their 

ideas into future strategies. In East Orange, 

the use of TAC–AVL has been a prominent 

factor in reducing the city’s crime rates. 

And commanders are touting the results. 

“By using TAC–AVL,” says Sergeant Chris 

DeHagara, “I have a quick and effective 

way of finding out what my officers are 

doing and where. Gone are the days where 

I find out what my troops did after their 

shift. Now I have a real-time tool that tells 

me everything I need to know at the click 

of a mouse and allows me to shift resources 

when needed!”

Figure 1. An aerial display in TAC–AVL, which has a zooming feature that shows the location of a 

patrol car and what type of call it is responding to.

Figure 2. This map displays all directed patrol cars targeting motor vehicle crimes in a single day. 

By clicking on a patrol car, commanders can view the officers’ actions and determine whether 

the unit is available. The legend on the bottom right indicates the status of all units in service. 
1010

G&PS | January 2009

Environmental Systems Research Institute’s (ESRI) districting tool1 is a free extension 

for ESRI’s ArcGIS Desktop that allows analysts 

to create new police districts in a city or region. 

The redistricting tool initially was designed to 

help governments draw congressional district 

lines or create school districts, but has been 

co-opted to help police departments outline 

new beats. It helps them draw boundaries 

and review the outcomes (e.g., predict new 

workloads and crime rates) for the new districts. 

The tool provides summary statistics for every 

boundary selection. It does not automatically 

create districts from preexisting data; rather, it 

can be used to aid police communication and 

make geographic information systems (GIS) a 

central part of the redistricting process. 

Preparing the Data 

The ESRI districting tool requires an agency 

to have GIS data entered as a polygon feature 

class2 with attribute data. These polygons must 

be smaller than the intended districts. Analysts 

should use polygons that coincide with naturally 

occurring boundaries and main thoroughfares. 

Police departments must divide a city or region 

into discrete boundaries to balance service and 

distribute responsibilities into organizational 

branches. This task requires departments to join 

years of incident data (the point feature class) 

to the chosen polygon feature class. This helps 

ensure that a proportional number of incidents 

is allotted to each geographic region. 

The process of joining incident data to 

polygons in ArcGIS to achieve a count of 

incidents in each region is the same process that 

an analyst would use to create a choropleth3

map that depicts different incident counts. To 

join incident data to a polygon feature class, an 

analyst should do the following:

1. Right-click the polygon layer, hover over 

“Joins and Relates,” and click “Join.”

2. In the following dialog box (Figure 1), 

select “Join data from another layer based 

on spatial location.”

3. If the project contains the incident data 

that should be joined to the polygon, use 

the dropdown menu to choose that layer. 

If not, load the incident data by selecting 

the button. This join will produce a 

field in the polygon layer with a count of 

incidents within each polygon.

4. Do not check any summarization boxes. 

5. Using join produces a new feature class. 

Specify where it should be saved. 

Using ESRI’s Districting Tool in Policing

Philip Mielke

Redlands Police Department

Redlands, California

Notes

1

East Orange has nine police zones (i.e., districts), and 

one patrol car (zone car) is responsible for each.

2

Impact cars perform typical patrol duties such 

as answering calls for service, conducting crimeprevention-directed patrols, addressing quality-of-life 

concerns, and addressing traffic issues. They respond 

only to priority 1, 2, and 3 calls for service. 

3

Resource cars respond to priority 1 or 2 calls for service 

when a zone car is unavailable, and back up zone cars in 

addition to conducting crime-prevention-directed patrols.

4

Response cars supplement Resource cars when crime 

or quality-of-life concerns require additional resources. 

They respond only to priority 1 calls for service when 

zone cars are unavailable and they back up zone cars. 

5

Officers in Conditions cars perform duties in civilian 

attire and focus on crime-prevention-directed patrols. 

They also respond to priority 1 calls for service when 

zone cars are unavailable.

6

Zone integrity is when a police unit (car) remains in 

its zone of responsibility. This enhances the officer’s 

familiarity with the residents and crime issues 

within the zone. It also allows officers to maintain 

responsibility for their zone—officers will not let 

anything occur in their zone because they are the ones 

held solely responsible for it. 

7

By having supplemental patrol cars in a police zone, the 

zone officer has more time for proactive policing.

8

Impact, Resource, Response, and Conditions cars are 

referred to as supplemental units.

9

Calls for service are calls made from a person or agency 

to police headquarters requesting police response.

Priority levels alter based on the level or urgency 

(i.e., shooting [priority 1] versus an animal-control 

complaint [a lower level priority]).
11

Analysts should now see a “Count” field in 

the resulting polygon that can be used in the 

districting tool. 

Installing the Districting 

Extension 

The districting extension is free and 

available at www.esri.com.

4

 The download 

contains a simple executable file that will 

add the extension to ArcGIS Desktop. A 

“help” file and tutorial data are included 

with the installation. 

Analysts should add a toolbar for the 

districting extension to the project and must 

turn on the extension. They should follow 

these instructions:

1. Open ArcMap and select “Tools-> 

Extensions.”

2. Check the box for the extension labeled 

“Districting.”

3. Add the Districting toolbar by rightclicking empty toolbar space and 

checking “Districting.”

Creating a New Districting Plan 

To create a new districting plan, analysts 

should do the following:

1. Click “Districting->Districting 

Admin-> Create New Plan.”

2. Click “Create a New Workspace” and 

specify a new geodatabase file name. 

3. Enter a title for the districting project. 

4. Click “Next.”

5. In this wizard screen, check the box 

that says “Source geography has 

attribute data” and then click the “Add 

New” button.

6. Browse to find the polygon file with 

spatially joined incident data. 

7. Select “Count” as the universal field 

in the attribute data field selection 

screen. If there is a secondary value 

to be summed, select that field in the 

optimal-value field.

8. Click “Next.”

9. Select the number of districts.

10. Click “Next.”

11. Accept the default target optimal value. 

12. Click “Next.”

13. An option to automatically calculate 

the sum and percentage of the universal 

field to involve other statistical counts 

by boundary will appear. For the 

sake of demonstration, leave these 

unchecked.

14. Select the districting dropdown and 

select “Start Editing.”

Once a districting plan has been created and 

the editing process begins, the districting 

toolbar becomes active and smaller polygons 

can be grouped into districts. Redistricting 

should be an iterative process by which 

decision-makers can gather input and create 

temporary selections, examining statistical 

output for each scenario. Although analysts 

should focus on grouping these polygons, 

they can examine other pertinent layers 

(e.g., facilities, aerial photography) if they 

need them as references. 

TECHINCAL TIPS

Figure 1. The dialog box for joining spatial data.
1212

G&PS | January 2009

Assigning Polygons to Districts

Districting can be accomplished by selecting a 

district from the dropdown list (see Figure 2). 

Notice that the number of districts prescribed 

earlier in the wizard is the same number of 

districts as in the dropdown menu. 

Simple selection tools can be used to allot 

smaller polygon groups to a district. Mousing 

over the toolbar gives a short description of 

each tool, and this tool ( ) selects polygons. 

Right-clicking on the selection and clicking 

“Assign Selection” allots the selection to the 

district number in the dropdown menu. 

The “Undo Assignment” tool ( ) puts the 

selection back to where it was. Two buttons on 

the right bring the charts and statistics windows 

into view as the analyst assigns polygons to 

districts (see Figure 3). 

Figure 2. A districting toolbar.

Figure 3. Charts and statistics correspond to district selections.
13

Advanced Use of the 

Districting Tool 

The districting tool was developed to 

assist law enforcement agencies with 

demographics analysis. Agencies, however, 

should recognize that other data fields can 

help them make decisions about district 

boundaries. The “Create Report” button 

( ) gives a breakdown of all attribute data 

fields supported in the new district plan. 

Analysts should examine which other 

data fields can be joined to the polygon 

and consider what statistical breakdowns 

are required for the command staff to 

make decisions. For example, if parolees 

are a focus, consider joining their home 

addresses to the smaller polygon list. 

Or, if some incidents require officers to 

remain on scene for a longer period, the 

agency may wish to district on the basis of 

time spent in the field rather than on raw 

incident numbers. 

Ultimately, analysts must interpret and explain 

data and map the real world on the basis of 

the information available to them. Decisionmakers rely on analysts to help them find 

the appropriate focus in a pool of otherwise 

overwhelming data. The ESRI districting tool 

allows the analyst to become a liaison between 

the system and police administration by 

modeling the effects of districting decisions. 

Notes

1

For more information, see www.esri.com/software/

arcgis/extensions/districting/index.html.

2

The representation of a geographic feature. When 

referring to geographic features, feature classes 

include point, line, area, and surface.

3

A cloropleth map is a descriptive map that indicates 

through some form of shading or pattern the 

relative size of a particular phenomenon within a 

defined geographical area.

4

To download the districting extension, go to 

www.esri.com/software/arcgis/extensions/

districting/index.html.

The September 2008 issue of Police Chiefmagazine contains several articles meant 

to educate police executives about the power 

of crime mapping and analysis. 

In “Making Smarter Decisions: Connecting 

Crime Analysis with City Officials,” Carl 

Peed, Ronald Wilson, and Nicole Scalisi 

discuss applications of crime mapping 

and analysis beyond the organizational 

limitations of a police department. Crime 

analysis, they argue, can help build 

partnerships with communities, interpret 

threats and opportunities in urban 

development, inform municipal planning, 

and develop municipal policy. They suggest 

that crime analysts can be invaluable 

resources for helping local neighborhoods 

respond to crime and public disorder. 

In “Closing the Gap between Analysis and 

Response,” Christopher Bruce and Neil 

Ouellette target a complaint common 

among mappers and analysts—that no 

one uses their work. Bruce and Ouellette 

demonstrate how police agencies can make 

better use of crime analysis by giving their 

analysts proper resources and training, 

getting them out of their offices to connect 

with the community, and involving them 

in crime response and planning. They also 

suggest that agencies should establish formal 

tactic- and strategy-development processes 

like CompStat. “Analysts can do great work 

for an agency,” the authors conclude. “It 

should not be allowed to go to waste.”

“Finding Out What You Don’t Know: 

Tips on Using Crime Analysts,” by Kevin 

Paletta and Stacy Belledin, covers how 

a mid-sized police agency can use crime 

analysis. The article offers tips on proper 

administration of a crime analysis unit. 

The authors describe the importance of 

integrating analysis into the agency’s mission 

and training programs, keeping analysts 

free from administrative tasks, and exposing 

them to the department’s daily operations. 

Finally, in “Beyond Arrest: Using Crime 

Analysis to Prevent Crime,” Police Chief 

Tom Casady summarizes the significant 

technological advancements in crime 

analysis and mapping during the last 2 

decades and asks whether police have taken 

advantage of the available technologies. 

Casady argues that practice has not caught 

up with technology. He encourages police 

to move away from traditional arrest-based 

approaches to crime reduction and embrace 

long-term problem solving. “Crime analysts 

(and their supervisors),” he writes, “need to 

recognize that crime-prevention efforts can 

be much more effective than investigative 

strategies.”

The articles are available online at 

www.policechiefmagazine.org. 

Police Chief Magazine Focuses 

on Crime Mapping and Analysis

Christopher Bruce

President, International Association of Crime Analysts

Danvers, Massachusetts
1414

G&PS | January 2009

Austin Redistricting 

Increases Efficiency, 

Reduces Crime

When the Austin (Texas) Police Department 

changed its district boundaries, it saw 

immediate results—officers could respond 

to crime faster and still maintain a constant 

presence in high-crime areas.

Each of Austin’s nine districts has a standard 

set of four subdistricts, which helps equalize 

commander workload. The new strategy gives 

commanders more flexibility to move officers 

within the district, ensuring that each district 

area is patrolled at all times and shortening 

response times. Austin also uses “district 

representatives” who handle nonemergency calls 

and sustain community partnerships. 

Austin’s changes have given law enforcement 

time to increase its focus on crime control 

and community improvement. Increased 

patrols and interactions with neighborhood 

organizations have helped officers target and 

reduce crime.

For more information, see www.impactnews.

com/northwest-austin/local-news/1273-

redistricting-improves-police-response-time.

UCrime.com 

Works to Make 

Colleges Safer

When university students are worried about 

crime on campus, UCrime.com can give them 

fast answers.

The web site provides maps of crime committed 

on college campuses across the nation since 

April 21, 2008. Crimes are reported to the 

system by on-campus voluntary reporters. 

UCrime.com allows students to sign up for 

automated alerts by e-mail or cell phone that 

notify them when a crime is reported within a 

certain radius of a given address.

Find out more at www.ucrime.com.

Charlotte’s 

Redistricting 

Creates Positive 

Results

Six weeks after redistricting, the CharlotteMecklenburg (North Carolina) Police 

Department’s geographic information systembased redistricting was already improving police 

response times and effectiveness. 

City police reported that it had improved 

response times to both emergency and 

nonemergency calls. Certain divisions were 

better able to focus on specific crime problems, 

such as traffic violations and illegal gun 

seizure—one division increased its illegal gun 

seizures by 500 percent. 

Additionally, police have been able to focus 

more readily on neighborhood partnerships, 

including safe community forums, and 

reductions in street-level crimes.

For more information about the department, 

see www.charmeck.org.

San Bernardino 

Redraws District 

Boundaries 

In an effort to save money and increase 

efficiency, the San Bernardino (California) 

Police Department redrew district boundaries.

The department’s new strategic efforts cut $2.9 

million from the city budget. It eliminated 

its central district and divided the city into 

two districts. The department also closed six 

underused community service offices. And 

officers’ workloads will change—officers now 

have smaller beats and more shifts.

The department hopes that these changes will 

decrease response times and lower the number 

of crimes. 

NEWS BRIEFS
15

“It’s an evolution of our community 

policing model,” Police Chief Michael 

Billdt told the San Bernardino Sun, “and 

I believe by approaching a community’s 

crime problem through the beat plan and 

using technology to deploy where hot spots 

are, we can be more effective in delivering 

quality police service to the community.”

For more information, see: www.sbsun.com/

ci_10899973.

Arlington Police 

Department Adds 

Fourth District, 

Balances Workload 

When shifts in the west district of 

Arlington, Texas, became overwhelming, the 

police department decided to add a fourth 

district to balance the workload.

Officers in Arlington’s west district received 

high numbers of calls for service. The 

district was handling about 40 percent 

of the city’s workload, causing major 

inefficiencies. 

When officers cannot handle a call for 

service on their beat, a dispatcher sends 

a nearby officer. These backup calls can 

send them far away from their regular 

assignments and take up time that could be 

spent monitoring for crimes or getting in 

touch with community members. 

With the addition of a fourth district, 

Arlington will go back to equal workload 

divisions. A new office in south Arlington 

will provide residents with more law 

enforcement presence and a greater sense 

of safety. And officers will be equipped to 

respond quickly to calls for service, while 

maintaining a strong presence in their beat.

For more information, see: www.startelegram.com/235/story/1033271.html.

#2 Crime Prevention

Research Review

Police Enforcement Strategies to 

Prevent Crime in Hot Spot Areas is 

the second in the Crime Prevention 

Research Review series produced 

by the COPS Office. Summarizing the 

findings of rigorous academic studies 

evaluating such strategies, the report 

shows that focusing efforts on places 

with high crime and calls for service 

can help prevent crime in those 

locations. Fewer calls for service and 

other reductions in crime and disorder 

measures were noted in most studies. 

Displacement of crime resulting from 

enforcement efforts was not prevalent 

in studies that measured displacement. 

The publication is available online 

at www.cops.usdoj.gov/RIC/

ResourceDetail.aspx?RID=456, or can 

be ordered by calling the COPS Office 

Response Center at 800.421.6770. 
1616

G&PS | January 2009

Geography can be applied to most social science research questions, helping 

researchers better understand human activity. 

Several crime theories—including defensible 

space, situational crime prevention, routine 

activity theory, and crime pattern theory—

have used geographic principles as a means 

to understand how crime occurs and how 

to prevent it. NIJ’s Geography and Crime 

project will use geographic principles to 

analyze the impact of ecology, economics, 

and demographics on crime trends at the 

local and regional levels, looking at eight 

different regions in the United States. 

The Geography and Crime Project applies the 

theory of social disorganization to local and 

regional crime trends. Social disorganization 

theory says that social disorganization can 

influence behavior. Related research uses 

geography to examine spatial distribution and 

can be applied to settlement patterns (i.e., 

where people choose to live). The theory was 

later extended to examine the interactions 

and interrelationships between adjacent 

neighborhoods. While the theory helps provide 

a contextual understanding of why crime occurs 

where it does, crime analysts also consider the 

context of the local environment. 

Researchers apply geographic theory in two 

types of crime analysis:

1. They analyze crime using spatial statistics, 

which provide an analysis of the spatial 

characteristics of the crime incidents, 

including how incidents are related to each 

other and distributed across space. 

2. They analyze the incidents and the 

characteristics of the environments in 

which crime occurs. The results show what 

factors are related to crime. 

The Geography and Crime Project will 

use this second type of analysis, examining 

eight metropolitan regions across the U.S. 

Researchers will analyze the geographic 

characteristics of ecology, economics, 

demographics, and their relationships to 

crime. The project will also examine historical 

conditions of these geographic characteristics 

and determine how they have influenced crime 

and led to the current conditions in each 

region. 

Additionally, researchers will assess 

metropolitan events that may have influenced 

crime in each area. They will ask the following 

questions:

 Do regional economic and business trends 

affect crime at the local level?

 Do local socioeconomic factors affect 

regional crime trends? 

 If yes, then to what scale do the observed 

effects permeate? 

NIJ predicts the project may help state and 

local governments create more informed 

public policy decisions.

The Geography and Crime Project: 

Understanding Place and Its Influence

on Crime

Ronald E. Wilson

Mapping and Analysis for Public Safety Program and Data Resources

National Institute of Justice

NIJ’s MAPS Projects

The MAPS program engages in intramural research on crime, geography, 

and public safety. Two current projects are described below.
17

This project provides a comprehensive analysis of three families of hot spot 

techniques. It will determine which 

statistical techniques best predict future 

clusters of crime. Researchers will use data 

sets from multiple jurisdictions across the 

United States and assess changes in urban 

geography from metropolitan area to 

metropolitan area. 

The research determines the accuracy of 

the statistical techniques. Each technique 

is used to analyze four different measures 

of crime as it relates to geography. These 

measures include the following:

1. The Prediction Accuracy Index (PAI), 

which provides a ratio of incidents in 

each geographic area.

2. A repeatability index (RI), which provides 

the ratio of the PAI over 2 years.

3. The percentage of all incidents 

captured by the hot spots.

4. A Gini-type coefficient that graphs the 

cumulative percentage of the cases (X 

axis) against the cumulative percentage 

of the study area (Y axis). 

Analysis will determine which techniques 

perform best within each family and 

which performs best overall. Additionally, 

researchers will perform an Analysis of 

Variance (ANOVA) on the PAI, RI, and 

the percentage of all incidents captured to 

determine if the unique geographies matter 

or do not. 

The results from the PAIs will help 

determine whether the techniques 

accurately predict hot spots. Overall 

study results will improve the accuracy of 

geographic modeling and clarify which 

factors influence whether a location 

becomes a crime hot spot.

A Multigeographic Analysis of Hot Spot Techniques

Ronald E. Wilson

Mapping and Analysis for Public Safety Program and Data Resources

National Institute of Justice

COPS Grant Information at your fingertips

The COPS Office Response Center provides information 

on the following:

•	 COPS Grants

•	 Grant Applications

•	 Grant Management

•	 COPS Online, passwords, financial status reports, etc.

•	 Community policing training available from the 

Regional Community Policing Institutes

•	 COPS Publications

•	 Contacts for your state Grant Program Specialist 

or Staff Accountant. 

To contact the COPS 

Office Response Center:

Phone: 800.421.6770

Fax: 202.616.8594

E-mail: askcopsrc@usdoj.gov

Mailing address:

COPS Office Response Center

1100 Vermont Avenue, N.W.

Washington, DC 20530

Hours of Operation:

Monday to Friday, 9:00–5:00 eastern time

After-hours voice mail—call returned next business day

Information available 24 hours a day at www.cops.usdoj.gov

Ask us for materials to address a specific problem 

in your community.
1818

G&PS | January 2009

Geography and Public Safety Events

Dealing with crime problems in a local law enforcement agency sometimes means reaching 

out to other local agencies to come up with a solution. The events listed here are good 

opportunities to learn what mapping professionals and those in related areas are doing, get 

new ideas, and present your work. 

2009 CA/HI/NV Regional User Group 

(CAHINVRUG) Conference

February 3–5, 2009

in Sacramento, California

www.esri.com/events/cahinvrug/

index.html

2009 Indiana GIS Conference

February 17–18, 2009

in Bloomington, Indiana

www.in.gov/igic/conference/index.html

2009 ESRI Federal User Conference 

(FedUC) February 18–20, 2009

in Washington, D.C.

www.esri.com/events/feduc/index.html

2009 North Carolina GIS Conference

February 19–20, 2009

in Raleigh, North Carolina

www.cgia.state.nc.us/Default.

aspx?alias=www.cgia.state.nc.us/

ncgis2009

2009 National States Geographic 

Information Council (NSGIC) 

Midyear Conference

February 22–25, 2009

in Annapolis, Maryland

www.nsgic.org/events/future.cfm

NEMA 2009 Mid-Year Conference 

and 35th Anniversary Celebration

March 6–10, 2009 

in Alexandria, Virginia

www.nemaweb.org/?2068

Association of American 

Geographers (AAG) 2009

Annual Meeting

March 22–27, 2009

in Las Vegas, Nevada

www.aag.org/annualmeetings

/2009/index.htm

California GIS Conference 2009

April 6–9, 2009

in Sacramento, California

http://calgis.org/

2009 Southeast Regional User 

Group (SERUG) Conference

April 27–29, 2009

in Jacksonville, Florida

www.esri.com/events/serug/index.html

Pennsylvania GIS Conference 2009

May 19–20, 2009

in Grantville, Pennsylvania

www.pagisconference.org/

GEOTEC Event 2009

June 1–4, 2009 in Vancouver, 

British Columbia, Canada

www.geoplace.com/ME2/dirmod.asp

?sid=F1E958ECB4E84C1C97324D48

51580DDB&type=gen&mod=Core+P

ages&gid=104ED2C8722E405C96D6

F83446767961

Tenth Crime Mapping 

Research Conference

August 2009 

in New Orleans, Louisiana

www.ojp.usdoj.gov/nij/maps/

2009 National States Geographic 

Information Council (NSGIC) 

Annual Conference

October 4–8, 2009 in Cleveland, Ohio

www.nsgic.org/events/future.cfm

GEOINT 2009

October 18–21, 2009 

in San Antonio, Texas

www.geoint2008.com/

2009 Applied Geography Conference

October 28–30, 2009 

in Baton Rouge, Louisiana

appliedgeog.binghamton.edu/
Solutions to crime and public safety problems involve a combination of research, 

practice, technology, and policy. Law enforcement agencies now often use 

geography to help solve crime problems.

Advancements in geographic technologies have helped law enforcement agencies 

understand crime more completely, deploy public safety resources better, and 

examine criminal justice policies critically. Geographic research leads to better 

practice and technological advances. Research provides a theoretical foundation. 

Practice applies technology to put theories into operation. Research and practical 

demonstrations help focus policy decisions. 

The Crime Mapping Research Conference is about understanding crime and public 

safety and their effect on communities. It presents a range of research findings, 

practical applications, technology demonstrations, and results that can shape future 

policies.

Presentations and workshops for the 10th Conference will highlight geographic 

principles in research, geographic applications in practice, technology 

demonstrations, and solutions to crime and public safety problems that can lead to 

new policies.

For information about submittting a presentation, go to: www.ojp.usdoj.gov/nij/

events/events/maps/welcome.htm.

Mapping and Mapping and Analysis for Public Safety Program: 

A Call for Presentations and Workshops

The Tenth Crime Mapping Research Conference

Conference Dates: Wednesday, August 19–Saturday, August 22, 2009 

Sheraton Hotel, New Orleans, Louisiana

Submissions Due: Friday, January 30, 2009, before 7:00 p.m., e.s.t.

Solving Problems with Geography and Technology
U.S. Department of Justice

Office of Community Oriented Policing Services

1100 Vermont Avenue, N.W.

Washington, DC 20530

To obtain details on COPS programs, call the 

COPS Office Response Center at 800.421.6770

Visit COPS Online at www.cops.usdoj.gov

COPS Editorial Staff 

Nicole Scalisi

Research Analyst

Office of Community Oriented Policing Services

Judith Beres

Editor

Office of Community Oriented Policing Services

Sandra Sharpe

Production

Office of Community Oriented Policing Services

MAPS Editorial Staff 

Ron Wilson

Program Manager/Social Science Analyst 

National Institute of Justice

Timothy Brown

Research Associate (Contractor)

National Institute of Justice

Ariel Whitworth

Communications Editor

National Criminal Justice Reference Service

e01097182

Visit NIJ Online at www.ojp.usdoj.gov/nij