Manual of 

Crime Analysis Map Production 
Manual of Crime Analysis Map Production 

Written by: 

Mary Velasco

Research Associate, Crime Mapping Laboratory 

and

Rachel Boba, Ph.D. 

Director, Crime Mapping Laboratory 

Police Foundation

November 2000 

Report to the Office of Community Oriented Policing Services

Cooperative Agreement #97-CK-WXK-004

The opinions, findings, and conclusions or recommendations expressed in this document are 

those of the authors and do not necessarily represent the official position or policies of the U.S. 

Department of Justice.

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Acknowledgements

The authors would like to thank Sean Bair, Program Manager of the Crime Mapping and 

Analysis Program, and Matthew Scheider, COPS Grant Monitor, for their careful review and 

comments.

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Table of Contents 

Page

I. Introduction ................................................................................................................................1

II. Guidelines for Crime Analysis Map Production.........................................................................2

Purpose.................................................................................................................................2

Audience ..............................................................................................................................2

Data Sources ........................................................................................................................3

Map Features and Symbols..................................................................................................3

Types of Maps......................................................................................................................6

Single Symbol Maps................................................................................................6

Graduated Size Maps ...............................................................................................7

Graduated Color Maps.............................................................................................7

Combination Maps...................................................................................................7

Design Elements ..................................................................................................................8

III. Examples of Crime Analysis Map Production.........................................................................10

Example 1: Residential Burglary Pattern...........................................................................11

Example 2: Top Auto Theft Locations ..............................................................................14

Example 3: Theft from Vehicle Citywide..........................................................................18

Example 4: Monthly Calls for Service by Geographic Area .............................................23

Example 5: Comparison of Citizens’ Fear of Crime and Reported Violent Crime...........26

IV. Conclusion ...............................................................................................................................30

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List of Maps 

Page

Map A: Point Features .....................................................................................................................4

Map B: Line Features.......................................................................................................................4

Map C: Polygon Features.................................................................................................................5

Map D: Layering of Map Features...................................................................................................6

Map E: Appropriate Map Design Elements.....................................................................................9

Example 1: Residential Burglary Pattern

Map 1.1: Single Symbol Point Map Depicting Land Parcels ............................................11

Map 1.2: Graduated Color Map Depicting Order of Occurrence ......................................12

Map 1.3: Final Map Depicting Residential Burglary Pattern and Relevant 

Field Contacts ........................................................................................................13

Example 2: Top Auto Theft Locations

Map 2.1: Graduated Size Point Map Depicting Auto Theft Citywide...............................14

Map 2.2: Graduated Size Point Map Depicting Top Auto Theft Locations ......................15

Map 2.3: Aerial Photo Depicting the Top Auto Theft Location........................................16

Map 2.4: Final Map Depicting the Top Ten Auto Theft Locations and a Detail of 

the Top Location....................................................................................................17

Example 3: Theft from Vehicle Citywide

Map 3.1: Single Symbol Point Map Depicting Theft from Vehicle Citywide ..................18

Map 3.2: Graduated Size Point Map Depicting Theft from Vehicle Citywide .................19

Map 3.3: Choropleth Map Depicting Theft from Vehicle by Census Tract ......................20

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Map 3.4: Choropleth Map Depicting Theft from Vehicle by Population..........................21

Map 3.5: Final Map Depicting Theft from Vehicle by Population....................................22

Example 4: Monthly Calls for Service by Geographic Area 

Map 4.1: Choropleth Map Depicting Calls for Service by Reporting District, Natural 

Breaks Classification .............................................................................................24

Map 4.2: Choropleth Maps Depicting Calls for Service by Reporting District, Natural 

Breaks Classification .............................................................................................24

Map 4.3: Final Map Depicting Calls for Service by Reporting District, Custom 

Classification Range ..............................................................................................25

Map 4.4: Final Map Depicting Calls for Service by Reporting District, Custom 

Classification Range ..............................................................................................25

Example 5: Comparison of Citizens’ Fear of Crime and Reported Violent Crime

Map 5.1: Graduated Size/Color Point Map Depicting Fear of Crime and Violent Crime 

Incidents.................................................................................................................27

Map 5.2: Choropleth Map Depicting Correlation Between Fear of Crime and 

Violent Crime Incidents.........................................................................................28

Map 5.3: Final Map Comparing Fear of Crime to Reported Violent Crime Incidents......29

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I. Introduction

Through discussion and comprehensive examples, this manual provides guidelines for 

introductory-level crime analysis mapping for use in a law enforcement environment. Mapping

is essential to both the analysis and presentation phases of crime analysis, as it facilitates spatial

depictions of crime and related law enforcement data. Crime analysis mapping is a valuable 

problem solving tool because it can lead to the identification of new problems facing law

enforcement, lend a visual perspective to an analysis, assist in the development of an effective

response, aid in the formation of partnerships by providing a common point of reference, and 

assist evaluation procedures. To produce accurate and effective crime analysis maps, there are 

three initial factors to consider: (1) the purpose of the map, (2) the audience of the map, and (3)

the types of data to include in the map. These considerations often dictate the type of map that 

will be used and the method of presentation. This document begins with a brief examination of 

these initial factors, follows with a discussion of the types of maps and design elements, and

concludes with five comprehensive examples that illustrate the process of crime analysis

mapping.

Crime analysis mapping is a dynamic process, and the decision to utilize a certain type of map or 

design element may change based on the purpose of the map, the audience, and/or the available 

data. In this document, five situations with a particular purpose and audience are posited. The

readers are then guided through the process of basic crime analysis map production. The

examples in this manual do not represent every possible method of map production and design

because, in reality, the possibilities are endless. The examples are not software specific, but they 

are intended to exemplify relevant practical situations to develop a general conceptual 

framework for the production of informative and effective crime analysis maps. 

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II. Guidelines for Crime Analysis Map Production 

Purpose

Crime analysis mapping can be used to assist all stages of the problem solving process. If the 

purpose of a crime analysis map is to assist in the identification of a particular problem, selected 

data may be mapped to identify patterns of activity that have been previously undetected. For

example, to determine if a pattern of commercial burglary incidents is occurring, all commercial

burglaries occurring in the city for the past three months may be mapped. By analyzing clusters 

of activity, a group of incidents occurring in a proximate area may be identified. In this instance, 

the purpose of crime analysis mapping is to identify a problem; that is, a short-term increase in 

commercial burglary activity in a particular area.

Another purpose may be that if a particular issue is identified, crime analysis mapping can be 

used to conduct a comprehensive spatial analysis of the problem. In other words, the purpose of 

the map is to study the problem more closely by examining it from various perspectives and/or 

by using a variety of data. For example, after a significant increase of daytime residential

burglaries has been identified, crime analysis mapping can be used to analyze the problem by 

determining where the activity has increased the most. In addition, other types of data may be 

mapped along with the crime data such as the locations of schools, truancy information, and 

known offenders as well as demographic information to determine if areas with the most increase 

are similar to one another. Using various types of maps and data, crime analysis mapping can be 

used to examine an issue more closely.

Lastly, once a problem has been identified and analyzed, presentation of the map to an audience 

becomes important, as the purpose of the map may be to develop a response to the problem and 

to ultimately evaluate that response. Maps for presentation can range from simple to complex,

depending on the levels of detail and analysis that are necessary. Monthly maps of a town that 

depict reported crime are useful for providing general information to the public about changes in 

hotspots of reported crime; however, a detailed map of each hotspot location may be necessary 

for patrol officers and investigators who need specific information in order to focus their 

problem solving efforts. In either case, it is important to prepare maps that are appropriate to the 

purpose.

Audience

When determining the purpose of a crime analysis map, it is also necessary to consider the 

audience, i.e. the users of the information. The audience can include police department 

personnel, the general public, community groups, or city council members, to name a few. By

gauging the needs, expectations, and skill level of these individuals, maps that are appropriate for

each group can be developed. For example, a map of the residences of all known gang members 

that includes names and case numbers may be useful to gang enforcement officers compiling

intelligence information. However, a map with this level of detail would not be disseminated to 

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the public as it may violate an individual’s privacy rights or compromise an investigation. It

may be useful to include a brief notation on the map, such as “law enforcement use only” or 

“external use” to indicate the intended audience. When producing a crime analysis map, the 

needs of users of the information need to be balanced with political, legal, and ethical concerns.

Data Sources 

Crime analysis maps are typically composed of two types of data - tabular and geographic.

Tabular data sources contain information about phenomena that are not primarily geographic but 

often have a geographic component. Crime data and calls for service data are types of tabular 

data most frequently mapped in law enforcement. For example, these data contain information 

primarily about crime incidents and calls for service activity such as the type of activity, date, 

time, priority, and disposition, yet both types have at least one geographic component, such as 

address, beat, and/or district. Additional law enforcement tabular data sources include arrests, 

accidents, known offenders, field information relative to criminal incidents, citizen survey results 

indicating victimization or fear of crime, and the locations of probationers/parolees. Other nonlaw enforcement tabular data sources include, but are not limited to, school information, risk and 

protective factors, census data, and land use information. 

Geographic data sources are composed of data describing phenomena that are primarily 

geographic. Even though these types of data may include non-geographic information, the 

primary focus is the geographic features. For example, census tract data contains information on 

population and other demographic variables; however, these data are based on the geographic

boundaries of the census tracts. Other examples of geographic data sources include land parcels, 

streets, streams, highways, block groups, and city boundaries. Examples of geographic data 

specific to law enforcement may include gang territorial boundaries, beat boundaries, and district 

boundaries.

Tabular and geographic data are brought together through the process called "geocoding,"

whereby tabular data are represented on a map based on a common geographic unit of analysis.

(Geocoding is described at length in the report, “Geocoding in Law Enforcement,” produced by 

the Police Foundation’s Crime Mapping Laboratory and available on the COPS Web site at 

www.usdoj.gov/cops.) Once tabular data have been located on a map, symbol styles, sizes, and 

colors may need to be adjusted according to the data they are being used to represent and the

complexity of the map.

Map Features and Symbols

In addition to considering the types and availability of data sources, it is important to determine

the best method for representing these data on a map. Maps are composed of point, line, and 

polygon features. Point features are usually designated by a symbol [ ] and represent discrete 

locations with unique latitude and longitude coordinates. Locations of crime, traffic accidents,

and schools are all examples of point features. Lines [

Streets are the most common line feature; however, line segments can also represent rivers,

] are used to represent linear features.

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pathways, and utility lines. Polygons, which are multi-sided, closed features [ ], can be 

used to represent geographic areas such as land parcels, patrol beats, or city boundaries. 

The appearance of point, line, and polygon symbols should be specific to the data they are being 

used to represent as well as to the purpose of the map. When producing crime analysis maps and 

adding specific point features such as the locations of crime, calls for service, or known 

offenders to the map, it is important to differentiate between different types of activity. Map A 

depicts single incidents of robbery locations as point features. Different symbol styles are used 

depending on the type of robbery (firearm, knife, strong arm). 

Map A: Point Features

Map B depicts several different line features including streets, railroads, and rivers. Each line 

feature is represented by a unique symbol so that it can be differentiated from others. 

Map B: Line Features

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Map C depicts different polygon features, including a city boundary, census tracts, and block 

groups. A unique symbol style has been used to represent each feature so that each polygon can 

be distinguished from the others. 

Map C: Polygon Features

Similarly, the order in which point, line, and polygon features are placed on a map affects the 

appearance as well as the overall usefulness of the map. Features such as buildings, streets,

police districts, and aerial photographs are all distinct layers of data that are stacked on one 

another to create a map. It is important to consider the order in which these layers are placed on 

a map, as closed polygon features or images such as aerial photographs will cover point and line 

features if they are placed on top. Generally, background features should be placed on the map 

first while point and line features should be placed on top so that they can be clearly 

differentiated, as illustrated by Map D. 

5
 Map D: Layering of Map Features

Line

feature

Background

Point

feature

Polygon

feature

Additionally, only those features that are essential to the purpose of the map should be visible. 

While the use of orthophotography may enhance the map’s visual appeal, this level of detail may 

not be realistic for viewing a map of the entire city.

Types of Maps 

After selecting the appropriate symbols to represent basic map features, it is necessary to 

determine what type of map will allow for the most accurate analysis and presentation. Four

basic types of maps are discussed below: single symbol maps, graduated size maps, graduated

color maps, and combination maps. These types were chosen as basic types of maps often used 

is law enforcement. Illustrations of the types of maps are not shown here, but each type of map 

is used in the examples following this section. 

Single Symbol Maps. Similar to the wall maps and push pins that have historically been used by 

police agencies to map crime, single symbol maps use individual symbols to represent point 

features such as the locations of crime or traffic accidents, line features such as streets or 

pathways, and polygon features such as land parcels or police districts. Single symbol maps 

allow for a detailed analysis, and they are used quite frequently in law enforcement to depict 

incident locations for small amounts of data. A drawback to using single symbol mapping is that 

if two or more incidents share a geographic attribute such as the same address, subsequent 

symbols are placed on top of the original. Thus, it is difficult and time-consuming to determine 

if more than one incident is occurring at the same address. Additionally, maps of large data sets 

can become cluttered if too many features are added. 

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Graduated Size Maps. In a graduated size map, data are summarized so that symbols (point or 

line features) are altered in size to reflect the frequencies in the data (see next section for 

classification methods). A graduated size point map reflects more incidents at an individual 

location with a larger symbol. A graduated size line map reflects more incidents along that line 

segment with a thicker line. Graduated size maps are valuable for depicting multiple events of 

the same type at the same location, such as traffic accidents. However, it is difficult to use 

graduated size maps to depict different types of incidents, e.g. burglaries and thefts, as they too 

are placed directly on top of one another. In addition, graduated size maps become cluttered if a 

large number of incidents are mapped, and oftentimes, larger symbols may conceal smaller ones, 

depending on the map’s scale. 

Graduated Color Maps. In a graduated color map, either point, line, or polygon features are 

shaded according to a statistical formula, custom setting, or unique value. There are several 

approaches to classifying, or grouping information when creating graduated color maps. (Note: 

these classifications are also used to produce graduated size maps described above.) Each

classification method is briefly described below: 

Natural breaks: Identifies natural breakpoints inherent in the data by using a 

statistical formula.

Equal area: Finds breakpoints between polygon features so that the total area 

of the features in each classification range is roughly the same.

Equal interval: Divides the entire range of values into classifications based on 

equal sized sub-ranges.

Quantile: Divides each class into the same number of features.

Standard deviation: Identifies the mean value and classifies values above or below the 

mean based on the standard deviation. 

Custom: Classes are determined by the user. 

Unique Value: Classes are based on unique values in the data. 

With the exception of custom ranges, which are determined by the user, all of these classification 

methods are dependent upon the data; that is, for each data set, the values in the ranges will be 

different depending on the distribution and values within the data. 

A graduated color map of polygon features such as police beats or census tracts is referred to as a 

choropleth map. Choropleth maps are used regularly in law enforcement and are most useful for 

representing large data sets, such as annual calls for service by police reporting area or crime rate 

per population. 

Combination Maps. As the name implies, combination mapping involves the use of more than 

one type of map, such as a graduated size and a graduated color map, to depict multiple data

sources on the same map. Basic combination maps are most useful for analyzing more than one 

type of data at the same time.

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Design Elements

The issues previously discussed include considerations and decisions made in the analysis

process. Oftentimes, during the analysis process, five or more maps are produced to find the one 

that is most appropriate for representing the analysis. Thus, the final step in the crime analysis

mapping process is preparing the map to be presented to others. There are five fundamental 

design elements to include in the presentation of a crime analysis map: 

Title: Describes the tabular data sources used in the map and the time span of the 

data.

Legend: Lists the tabular and geographic data sources used in the map and 

indicates the symbols that are used to represent each data source. 

Scale bar: Describes the distance units used in the map. 

North arrow: Indicates the geographic orientation of the map. 

Credits/date: Indicates who created the map and when. 

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Although it may be appropriate to include a brief explanation of the analysis, it is important to 

design crime analysis maps so that they can stand alone. Once a map is disseminated, additional 

pages that contain text describing the map may be lost or the map may be photocopied, and this 

information may be lost. Thus, the text describing the map should be included on the map, and it 

should also be brief. This ensures that information will not be lost and that maps can be easily 

interpreted without requiring a great deal of explanation. Map E is an example of a final map 

containing all of these components. 

Title

9

Map E: Appropriate Map Design Elements

Scale bar

North arrow 

Legend

Credits/Date
III. Examples of Crime Analysis Map Production 

To follow are five comprehensive examples of crime analysis maps that are commonly used in

law enforcement. These examples are designed to walk readers through the process of creating

different types of crime analysis maps depending on the situation, e.g. deciding which data are 

relevant, which type of map to use, and how to present the map considering the purpose and 

audience. Since the maps in the following examples are meant to depict different stages of the

analysis process, labels such as street names and design elements such as legends and scale bars 

have not been included. At the end of each example, a final map with all of the appropriate

design elements is included to illustrate how each map would ultimately be presented based on 

the purpose, audience, and data. 

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Example 1: Residential Burglary Pattern

Situation: Patrol officers have noticed that several residential burglaries have occurred in the 

Oak St./Elm Rd. neighborhood recently. The patrol bureau has requested 

information on all residential burglary incidents as well as any field contacts 

involving suspicious persons/activity that have occurred in the general area during 

the last six months. 

Purpose: To inform police personnel about proximate residential burglary incidents and 

relevant field contacts in the area to determine if a crime pattern exists and if any

potential investigative leads have been contacted. 

Audience: Patrol officers, detectives, and other police personnel 

Data: Geographic - land parcels 

Tabular - crime data, relevant field contact information 

A search of residential burglary data for the Oak St./Elm Rd. neighborhood yielded ten incidents. 

Because of this small number of burglaries in a relatively small geographic area, a map depicting

land parcels has been used (see Map 1.1). Points placed in the center of each parcel - circles to 

symbolize the burglary incidents and a star to denote the field contact, represent the tabular data, 

residential burglaries.

Map 1.1: Single Symbol Point Map Depicting Land Parcels

Depending on the proximity of the events, parcel maps are valuable tools that, unlike centerline

street maps, allow for detailed spatial analyses of a small area. Using a parcel map to analyze

these residential burglary incidents shows that alleys border several of the homes (as indicated in 

Map 1.1). Further analysis of the method of entry in these incidents may indicate that entry was 

gained through the alley and into the rear of the residence. 

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A single symbol point map has been used in this instance because there are only ten incidents. In

this case, the data were quickly scanned to ensure that none of the incidents occurred at the same 

location. If more than one incident had occurred at a particular address, a graduated size point 

map could have been used. 

Another method of representing the tabular data is by shading the entire parcel to indicate the 

occurrence of an incident, as shown in Map 1.2. Additionally, by shading the parcels according 

to the date of each incident and/or placing numbers next to each incident to indicate the order of 

occurrence, an overall temporal analysis can be done simultaneous to the spatial analysis.

Map 1.2: Graduated Color Map Depicting Order of Occurrence

As these maps illustrate, point maps are useful for analyzing crime patterns and hotspots and

assisting short-term problem solving efforts, as they give patrol officers and investigators a 

visual perspective of an area so that they can direct patrol and/or prevention efforts. In addition, 

maps depicting land parcels allow for a detailed analysis of specific plots of land and 

thoroughfares. Although not shown here, maps that depict the specific locations of buildings

within each parcel would be useful for analyzing patterns in the point and/or method of entry.

Map 1.3 is the final map that would be accompanied by a detailed summary of the incidents, 

including information such as case numbers, dates, times, addresses, and modus operandi. 

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Map 1.3: Final Map Depicting Residential Burglary Pattern and Relevant Field Contacts 

13
Example 2: Top Auto Theft Locations

Situation: The police department would like to conduct an auto theft education and 

prevention campaign. Since they have a limited amount of resources, they would 

like to focus their efforts and target the prevention campaign at the top locations

to assist in an overall reduction of the city’s auto theft problem. 

Purpose: To determine the top auto theft locations and their relationship to one another so 

that police personnel can focus their education and prevention efforts. 

Audience: Crime prevention personnel, auto theft investigators

Data: Geographic - centerline streets, city boundary 

Tabular - crime data 

A centerline street map has been used in this instance since the map includes a large number of 

incidents that have occurred throughout the city. The city boundary serves to acquaint the 

audience with the city’s borders. Since the purpose of this analysis is to generally determine the 

top auto theft locations, a more descriptive map, such as one depicting land parcels, has not been 

used.

Map 2.1 is a graduated size point map that serves to distinguish the locations with multiple auto 

thefts, which are depicted by the larger red symbols. This map is a first attempt at mapping all 

the auto theft incidents that occurred during the past year. However, since the goal of the 

education and prevention campaign is to target the top locations only, this map contains too 

much information that is not discernable. The map is cluttered with graduated points, and it is 

difficult to determine how many incidents have occurred at a particular location. 

Map 2.1: Graduated Size Point Map Depicting Auto Theft Citywide

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Map 2.2 is a depiction of only the ten locations with the highest frequencies of auto theft. Since

it is more realistic for police personnel to focus their time and resources at specific locations, this 

map can be used to generally inform prevention personnel and auto theft investigators about the 

most problematic locations for auto theft during the past year. It is important to include a chart, 

as shown in this example, or to label each hotspot with the corresponding rank or number of 

incidents so that users can determine how many auto thefts were reported at each location. 

Map 2.2: Graduated Size Point Map Depicting Top Auto Theft Locations 

Since Maps 2.1 and 2.2 lack geographic detail in that they show the approximate street location 

of the auto thefts, not the specific locations, aerial photographs may be used to present a more indepth picture of a location. They can be used to analyze exit routes and determine whether 

environmental characteristics, such as poor visibility or maintenance, contribute to the auto theft 

problem at a particular location. 

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Map 2.3 depicts the most frequent location for auto theft during the past year, a mall parking lot. 

Analysis of the police reports indicates that the far corner of the parking lot is the most frequent 

location for auto theft. Examining the aerial map and conducting an environmental assessment 

of the mall parking lot may reveal that a lack of surveillance and a convenient exit route 

contribute to the auto theft problem at this location. The aerial map can be used to help 

determine the placement of surveillance personnel or a bait car. 

Map 2.3: Aerial Photo Depicting the Top Auto Theft Location 

Example 2 illustrates that graduated size maps may not be useful for depicting a large number of 

incidents, but may be useful when the data are truncated; in this case, the total number of auto 

thefts occurring at the top ten locations in the city. Aerial maps are useful because they allow for 

a more detailed analysis of specific locations. These maps assist the problem solving process in 

that they help locate and spatially relate problematic locations for auto theft and inform

prevention personnel about where they may need to concentrate their efforts. Map 2.4 includes 

the final graduated size point map as well as an aerial map of the top location as it might be 

distributed to police personnel. This information could be supplemented with a report that lists 

the most frequent times/days of the week for auto theft and/or breaks down the most frequently

stolen vehicle makes and models. 

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Map 2.4: Final Map Depicting the Top Ten Auto Theft Locations and a Detail of the Top Location 

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Example 3: Theft from Vehicle Citywide 

Situation: Theft from vehicle was the most reported crime in the city during the past year,

accounting for approximately 10% of all reported offenses. Police administrators 

have requested information to determine which areas of the city have the most 

reported thefts from vehicle. This information will be used by community

policing officers to educate residents about how to prevent vehicle crime. It will 

also be used by patrol officers to increase surveillance in these areas.

Purpose: To determine the most problematic areas for theft from vehicle in order to initiate

an education campaign.

Audience: Police administrators, community policing officers, patrol

Data: Geographic - centerline streets, census tracts, city boundary 

Tabular - crime data 

A map of the entire city depicting centerline streets has been used in Map 3.1 since the purpose 

of the map is to determine which areas of the city have the most thefts from vehicle. Similar to 

Example 2, the purpose of the analysis is relatively general and at this point, it is not necessary to 

use more detailed maps, such as parcel or aerial maps. In fact, the centerline street map may 

even be too specific for this analysis, since the point symbols cover many of the streets. 

Map 3.1: Single Symbol Point Map Depicting Theft from

Vehicle Citywide

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Map 3.1 is a single symbol map depicting all reported thefts from vehicle for the last year. This

map is cluttered with points, and it is difficult to distinguish the high frequency areas, which is 

the reason for creating the map. Additionally, simple point maps are not recommended for a 

large number of incidents since point data that are geocoded to the same location will be stacked

on top of one another. In Map 3.1, there is no way to determine if more than one theft from 

vehicle occurred at a particular location. Given the limitations of this map, it would most likely 

not be disseminated since it does not accurately depict the theft from vehicle problem nor does it

meet the needs of the audience since it is nearly impossible to distinguish one incident from 

another.

Map 3.2: Graduated Size Point Map Depicting Theft from

Vehicle Citywide

Map 3.2 is a graduated size point map depicting all thefts from vehicle for the past year. The use 

of a graduated size map has solved the problem of determining whether multiple crimes have

been reported at a single location; however, the map is still cluttered. In addition, it is nearly 

impossible to distinguish between crimes occurring at proximate locations, preventing a 

substantive analysis. At this point in the analysis, it is important to consider the purpose and the 

audience for the map. The analysis should help determine what part of the city has experienced

the most thefts from vehicle so that police personnel can concentrate their education and 

19
enforcement efforts. Obviously, with such a high incidence of thefts from vehicle during the 

past year, a point map does not seem appropriate or necessary for representing these data. 

Map 3.3 is a choropleth map that depicts the total number of thefts from vehicle by census tract. 

Census tracts have been used instead of centerline street maps because they allow for analysis of 

general areas. Although choropleth maps may incorrectly imply that the activity is occurring 

uniformly throughout the census tract, a choropleth map is useful in this example since the 

purpose of the map is to merely highlight problem areas, rather than specific locations. Since

police administrators and community policing officers plan to focus their efforts at general areas 

rather than single locations, census tracts are more appropriate than the preceding centerline

street maps. In this example, census tracts were used; however, depending on the purpose and 

availability of geographic layers, block groups (smaller census areas), precincts, or reporting 

districts can also be used. 

Map 3.3: Choropleth Map Depicting Theft from Vehicle by Census Tract 

Map 3.3 is more closely suited to the purpose of the analysis, as the darkly shaded census tracts 

depict the high frequency areas for thefts from vehicle. Although census tracts are used in this 

example, block groups could also be used to depict theft from vehicle over a more precise 

geographic area. While this map generally depicts the census tracts with the most theft from

vehicle activity, it does not take into account factors such as the size or population of each tract. 

20
As one would expect, larger census tracts are likely to have more vehicles and subsequently,

more vehicle crime, as are those with a higher population. These factors are not taken into 

consideration in Map 3.3. As a result, a map depicting frequencies may not accurately represent 

theft from vehicle crime unless a common characteristic, such as land use or population, is used. 

Map 3.4 is a graduated color map that depicts census tracts shaded by the total number of thefts 

from vehicle per population. Simply shading the census tracts by the frequency of thefts may not 

provide an accurate picture of the activity, as larger census tracts with a high population would 

tend to have more thefts from vehicle. Rather than simply shading the census tracts by 

frequency, the number of thefts from vehicle per census tract is normalized by the population of 

that tract so that the data are classified according to a common characteristic. Map 3.4 shows 

that there are two census tracts (shaded the darkest) with high rates of thefts from vehicle per 

population where police personnel can focus their problem solving efforts. 

Map 3.4: Choropleth Map Depicting Theft from Vehicle by Population

Another effective method for mapping thefts from vehicle may be to normalize the number of 

thefts by the number of registered vehicles or by land use, since theft from vehicle often occurs 

in commercial areas.

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Map 3.5 is the final map that might be supplemented with a report that indicates the total number 

of thefts from vehicle that occurred per census tract during the past year as well as by month. All

of this information can be used to determine the scope of the city’s theft from vehicle problem 

and to focus education and prevention efforts at the most problematic areas. 

Map 3.5: Final Map Depicting Theft from Vehicle by Population

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Example 4: Monthly Calls for Service by Geographic Area 

Situation: The patrol division would like to scan calls for service data on a monthly basis to 

determine increases and/or decreases in calls for service and to identify hotspot

areas for call activity by geographic area (reporting districts). 

Purpose: To spatially analyze monthly calls for service data in order to scan for geographic

patterns and increases and/or decreases in call activity.

Audience: Patrol bureau, police administrators 

Data: Geographic - reporting districts, city boundary 

Tabular - calls for service data

The geographic features used in this example are police reporting districts and the city boundary 

since patrol administrators are trying to generally determine the number of calls occurring in 

each reporting district for comparison purposes. Since there are typically between 1,000 and 

1,500 calls for service occurring in the city each month, it is not reasonable to map calls for 

service data on a centerline street map or to use a single symbol point map. 

Maps 4.1 and 4.2 (see next page) are choropleth maps that depict the frequency of calls for 

service shaded by police reporting district for the months of January and February. Choropleth

maps are ideal for conducting a comprehensive analysis of a large amount of data and for 

producing regular maps on a monthly and/or yearly basis to alert administrators to general, longterm patterns of activity, thereby assisting strategic problem solving efforts. Since these monthly 

calls for service maps will be produced on a regular basis, guidelines need to be established to 

ensure that the appropriate classification method is used each time the map is produced. 

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Maps 4.1 and 4.2:Choropleth Maps Depicting Calls for Service by Reporting District, 

Natural Breaks Classification

Map 4.1 Map 4.2 

The default classification method in most mapping applications is natural breaks, whereby a 

statistical formula is used to assign classification ranges based on natural breakpoints in that 

particular data set. Since each data set is different, a map that uses the natural breaks 

classification method will vary each time a map based on different data is produced. For

example, in Map 4.1, the darkest areas symbolize reporting districts that had between 62 and 103 

calls for service whereas this same color represents between 45 and 79 calls in Map 4.2. 

Because the distribution of calls for service will not be the same from month to month, the 

classification ranges will depict different ranges each month, which could lead to 

misinterpretation of the map.

A custom classification range should be developed for maps that compare data across time so 

that the same range is used each time the map is produced, as shown in Maps 4.3 and 4.4. This

will allow for comparisons across time, as the colors and/or symbols will represent the same

range each month. For example, the darkest areas will represent 61 or more calls for service 

each time the map is produced, regardless of the data that the map is based upon. 

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Maps 4.3 and 4.4: Final Maps Depicting Calls for Service by Reporting District, Custom 

Classification Range 

Map 4.3 Map 4.4 

Maps 4.3 and 4.4 are the final maps in this example. They show two months of calls for service 

data whereby both maps use the same classification range. Analysis of these maps reveals that 

the reporting districts receiving the most calls for service are located in the northeast portion of 

the city. Another method for mapping these data may be to map calls for service per square 

mile, which would compare police reporting districts by land area, thereby normalizing the data

according to a common characteristic. By combining this monthly map with a supplemental 

statistical report that lists the top calls in each reporting district and compares call types and 

totals in each reporting district with the rest of the city, patrol administrators have a mechanism

to scan for problems on a monthly basis. 

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Example 5: Comparison of Citizens’ Fear of Crime and Reported Violent Crime

Situation: The city council has requested a report comparing citizens’ fear of crime with 

reported violent crimes in the city by district (since council members are 

responsible for different areas of the city). The police department had conducted 

a comprehensive citizen survey designed to gauge public attitudes concerning 

issues such as fear of crime, victimization, and satisfaction with police services. 

Purpose: To conduct an analysis of survey respondents’ fear of crime in comparison to 

reported violent crimes by city council district. 

Audience: City council, police administrators, community 

Data: Geographic - block groups, city council districts 

Tabular - survey results, violent crime data 

The geographic data include block groups within one council district. Since each city council 

member represents a different area of the city, maps that compare fear of crime with reported

violent crime would ultimately be produced for each council district. Block groups have been 

included to give council members a general idea of the areas where violent crimes are occurring 

in their districts as well as where survey respondents reside. 

Map 5.1 is a graduated size map that depicts the locations of violent crime incidents as well as 

the locations of the respondents’ residences. The red graduated symbols represent violent crime 

incidents (homicide, sexual assault, robbery, aggravated assault) that have occurred during the 

past year. The points indicating the residences of survey respondents have been shaded 

according to their responses to a question asking them to rate their chances of becoming the 

victim of a violent crime in their neighborhoods.

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 Map 5.1: Graduated Size/Color Point Map Depicting Fear of Crime

and Violent Crime Incidents

Violent crime data have been included in Map 5.1 to generally depict how many violent crimes 

occur in each block group and where. Survey respondents’ addresses and fear of crime levels 

have also been included in this map to determine if fear of crime correlates to the proximity of 

respondents’ homes to violent crime incidents. Although a great deal of valuable information 

has been included in this map, a combination graduated size and graduated color point map may 

not be the most appropriate method for representing all of these data. Given the large number of 

points combined with the thematic shading, it is difficult to draw meaningful conclusions about 

the relationship between fear of crime and reported violent crime from this map. 

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Map 5.2 is a choropleth map whereby block groups are shaded according to the statistical 

correlation between citizens’ fears of becoming the victim of a violent crime and the number of 

violent crimes occurring by block group. One of the goals of this analysis is to compare levels of 

fear with the number of reported violent crime incidents. For example, if survey respondents 

indicated a high fear of crime, yet they reside in a block group that experienced a relatively low 

number of violent crimes, a strong negative correlation (closer to -1) would exist between these 

two variables. If respondents indicating a high fear of crime reside in block groups with a high

number of violent crimes, a strong positive correlation (closer to +1) would exist.

Map 5.2: Choropleth Map Depicting Correlation Between Fear of Crime and

Violent Crime Incidents

Although Map 5.2 uses an effective measure to determine the relationship between fear of crime 

and actual reported crime, it may not be the appropriate method for representing this analysis

considering the purpose and audience. It may be difficult and time-consuming to explain this 

method of analysis to the city council in a brief presentation; therefore, a simpler method for 

representing these data may be more appropriate. 

Map 5.3, a combination map that includes data on citizens’ fears of becoming the victim of a 

violent crime and reported violent crime incidents for the past year, is the final map in this 

example. Map 5.3 is appropriate for the city council in that it is fairly easy to understand and 

does not require a great deal of explanation. Additionally, it does not reveal sensitive 

information such as the home addresses of survey respondents or the locations of violent crime 

incidents.

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Combining these data sources reveals that, for several areas, a high fear of crime does not 

correspond with a high frequency of reported crime. For example, some of the dark block 

groups in the center of the map where survey respondents indicated a high fear of crime have 

none or not as many violent crimes as other nearby neighborhoods. This may indicate that the 

opinions of citizens living in these block groups are influenced by the activity in nearby areas. 

Alternatively, violent crime may occur in these areas, yet citizens might be reluctant to report it 

for fear of retaliation. The police may not be aware of this problem were it not for this 

innovative use of mapping.

Map 5.3: Final Map Comparing Fear of Crime to Reported Violent Crime Incidents 

Additional analysis may be conducted by including demographic information about the block 

groups to help illuminate the analysis. For example, it is widely known that elderly people have 

a higher fear of crime but are less likely to be victims of violent crime; thus, computing a fear of 

crime by percentage of persons over 65 in a block group may help to explain the difference in 

fear of crime and actual reported crime. However, including too many variables in an analysis

for this particular audience should be done cautiously as it may not add to the analysis but only 

confuse it. 

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IV. Conclusion

This manual outlines a number of fundamental concepts concerning the production of crime 

analysis maps and seeks to establish basic guidelines for introductory crime analysis mapping in 

law enforcement. The five examples are designed to walk readers through the process of crime

analysis mapping and to illustrate the importance of considerations such as purpose, audience,

available data, and type of map to the crime analysis mapping process. These examples are not 

meant to instruct law enforcement practitioners and researchers on how to create maps, but rather

to help them think critically about the crime analysis mapping process so that they can produce 

accurate and informative maps.

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