CrimeStat IV 

Part VI: Crime Travel Demand Forecasting 
Chapter 25: 

Overview of Crime Travel Demand Modeling 

Ned Levine 

Ned Levine & Associates 

Houston, TX 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table of Contents 

Travel Demand Forecasting 25.1 

Need for More Complex Travel Model of Crime 25.2 

Crime Travel Demand Framework 25.5 

Crime Travel Definitions 25.8

 Crime Trip 25.8 

 Crime Travel Demand 25.10 

 Aggregate Volume/count Model 25.11 

 O-D Zone Pairs 25.11 

 Travel Mode 25.12 

 Estimating Travel Routes by Mode 25.12 

The CrimeStat Travel Demand Module 25.12 

Crime Travel Demand v. Journey-to-Crime 25.14 

Models v. Description 25.15 

Uses of a Crime Travel Demand Model 25.17 

Research Uses of a Crime Travel Demand Model 25.18 

 Utility for Policing and Law Enforcement 25.19 

References on Travel Demand Modeling 25.20 

References 25.22 
 

Chapter 25: 

Overview of Crime Travel Demand Modeling 

The next seven chapters present a module on crime travel demand modeling. Crime travel 

demand modeling is a framework for examining crime trips over an entire metropolitan area. In 

this chapter, an overview is presented. In this and the next five chapters, each of the separate 

components of crime travel demand modeling are presented. Finally, in Chapters 31 and 32, 

Richard Block and Dan Helms present case studies of the method applied to Chicago and Las 

Vegas crime data. 

Much of the theoretical background was discussed in Chapter 13 (Journey-to-crime). 

Readers would be advised to review that material before proceeding with the crime travel 

demand model. 

Travel Demand Forecasting 

Crime travel demand modeling is an application of travel demand forecasting (or travel 

demand modeling). It is used by transportation planners for examining travel patterns over an 

entire metropolitan area and for forecasting future trends. It is a model of transportation patterns 

in a metropolitan area and is used for both forecasting and the analysis of the likely effects of 

building new roadways or installing new transit facilities. In the United States, it is required by 

Federal law to be used in every metropolitan area greater than 50,000 population as a basis for 

making decisions on highway and transit expenditures (USDOT, 2003: 23CFR450). It is also 

used for transportation planning in the metropolitan areas of many other countries of the world 

(Field & MacGregor, 1987). 

The aim is to model travel over an urban area as a means for coordinating the 

approximately $36 billion dollars in transportation highway funds and $8.6 billion in transit 

funds that are spent every year in the U.S. (BTS, 2007). Rather than waste funds by building 

new roadways and transit facilities that will be little used, it is a lot more effective to first model 

the likely benefit of a new facility as a basis for making a decision to build it. In essence, 

Congress requires a transportation model be developed for every metropolitan area as part of an 

evaluation of the benefits to be obtained from particular transportation investments. 

The framework has emerged slowly since the 1950s and is now starting its ‘third 

generation’. For the ‘first generation’ - what is used by most Metropolitan Planning 

Organizations (MPO) today, modeling is conducted entirely at a zonal level. The ‘second 

generation’ involves modeling individual level choices in travel mode taken within a zonal 

25.1 
framework (Horowitz, Koppelman, & Lerman, 1986; McFadden, 2002), while a ‘third 

generation’ involves modeling individual-level trips in a framework known as ‘activity-based’ 

modeling (FHWA, 2009; Culp & Lee, 2005). In CrimeStat IV, we implement a modified ‘first 

generation’ model, primarily due to the lack of data on individual-level crime trips. In later 

versions, we may add individual-level choice components. 

Need for More Complex Travel Model of Crime 

Crime travel demand modeling is an application of travel demand theory targeted 

specifically to crime analysis. There are many reasons why such an approach is appropriate. 

First, current models of criminal travel behavior are too simple with respect to travel. As 

Chapter 13 discussed, journey-to-crime models assume that many offenders commit crimes in 

their neighborhoods. While this assumption is frequently empirically found, it is not a realistic 

model of modern day crime travel. Prior to World War II, Americans tended to live and shop 

almost exclusively in their residential community. Many people would grow up and live in a 

single community for most of their lives. Since World War II, however, American society has 

become very mobile. People move frequently, not just within metropolitan areas, but between 

metropolitan areas. For example, between 2009 and 2010, at the height of the Great Recession, 

28.5 million persons moved homes (U.S. Census, 2010a). This was down from 1999-2000 

where 43.4 million moved (Schacter, 2001) but still represented a substantial amount of 

movement. More than two-thirds of the households who moved (68%) stayed in the same 

county but 12 percent moved to a different State. 

Second, the almost universal use of personal automobiles has increased daily mobility. 

For example, in the 2010 census, 91% of households owned at least one motor vehicle, an 

increase over 2000 where it was 86% (NHMC, 2012; U.S. Census, 2003; Aizcorbe & StarrMcCluer, 1996). For certain metropolitan areas, particularly in the west and in the south, motor 

vehicle ownership was greater than 92%. Even in cities with lower vehicle ownership, more than 

half the population do own vehicles (e.g., New York City had 56% of households with one or 

more vehicles in 2000; Wikipedia, 2012a). 

Further, per capita vehicle travel has consistently increased over time. Since at least 1960, 

and probably before, the growth in vehicle miles traveled (VMT) has increased at a much faster 

rate than population, a trend that does not seem to be abating (NAP, 2009; BTS, 2003; FHWA, 

1996). Essentially, automobile use has become almost ubiquitous. There is no reason to think 

that offenders would not be affected by these trends. Since there is no data available that could 

test whether offenders are less likely to own an automobile than non-offenders, it has to be 

assumed that more and more offenders have access to an automobile for the use of committing a 

crime. Clearly, the existence of an automobile makes crime travel much more fluid and difficult 

to model. While offenders will probably commit crimes in locales for which they are familiar, 

25.2 
25.3 

there is no reason to think that those locales will necessarily be the communities in which they 

live. 

Third, the widespread availability of motor vehicles has allowed major shifts in intraurban travel patterns. In the last census (2010), approximately half the U.S population lived in 

areas that would normally be called ‘suburbs’, even though the U.S. Census Bureau does not use 

this nomenclature (They use Outlying Counties within a Metropolitan Statistical Area type of 

Core Based Statistical Areas; Wikipedia, 2012b; OMB, 2010). Within metropolitan areas, 

approximately two-thirds of the population lives in suburban areas. 

Much of the community-oriented crime patterns that were described by the so-called 

‘Chicago School of Criminology’ in the 1920s and 1930s are no longer true (Burgess, 1925; 

Thrasher, 1927). Crimes have decreased in both the central cities and in the suburbs throughout 

the U.S. between 1990 and 2008 (Kneebone & Raphael, 2011). However, the differentials 

between the central city and the suburbs have decreased over time. In 1990, for example, the 

differential in violent crime between the central cities and the suburbs was 2.8 times whereas it 

was 2 times in 2008. Similarly, the differential in property crimes between the central cities and 

the suburbs decreased from 2 times to 1.7 times over that period. The researchers noted that this 

gap decreased in nearly two-thirds of metropolitan areas in the U.S. These decreases are 

associated with a dramatic drop in crime within the central cities but a more gradual drop in the 

suburbs. 

While crime has been decreasing in most metropolitan areas within the U.S., the travel 

patterns of offenders has become quite complex. Figure 25.1 below shows a sample of 200 

crime trips in Baltimore County that occurred between 1993 and 1997. As seen, there is a 

complex pattern. Some of the trips are short; for some, the origin and destination are the same 

location. But, for other trips, the travel distances are substantial. In other words, there is a 

complex pattern of crime trips in Baltimore County which is not easily modeled by a simple 

distance decay-type function. 

Fourth, an empirical examination of travel patterns shows considerable temporal 

variation. There are hourly variations, daily variations, and seasonal variation in crimes. Some 

of this can be understood as reflecting existing travel patterns in congested metropolitan areas. 

For example, in Baltimore County, crime travel distances were generally shorter during the peak 

afternoon ‘rush hours’ (4-7 PM) than at other times. Such a pattern suggests an adaptation to 

traffic by offenders, a not unreasonable assumption given the difficulties of traversing a 

metropolitan area during peak travel times. 

Fifth, crime travel behavior represents a complex pattern in itself. Especially for personal 

crimes, there is an interaction in the travel patterns of offenders and victims that is very difficult 
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Figure 25.1: 

Baltimore County Crime Trips: 1993-1997

Origins and Destinations

Sample of 200 Crime Trips 
 

to even describe, least of all model. Many crimes are committed by multiple offenders and the 

existence of intermediate locations (e.g., ‘fences’ for the distribution of stolen goods, auto theft 

drop locations) makes crime travel even more of a complex pattern to be understood. 

In short, American society has become very mobile, leading to larger travel distances, 

more frequent trips, and more complex trips. Again, offenders are going to be affected by these 

trends. Because of this, there is a need to understand crime patterns in terms of the complexity 

of travel rather than continue to rely on overly simple models of travel ‘distance decay’. 

Crime Travel Demand Framework 

Crime travel demand theory is a framework for understanding this complexity. There 

are two phases: 

1. An inventory (or data gathering) phase; and 

2. A modeling phase. 

The data gathering involves putting together the necessary data to estimate the model. 

This involves selecting an appropriate zone system (since the model is estimated at the zonal 

level), obtaining data on crime ‘trips’ and allocating it to the zones, obtaining zonal variables that 

will predict trips (both on the production side and on the attraction side), creating possible policy 

or policing interventions, and obtaining one or more modeling networks.1 

The modeling phase involves four distinct modeling steps (or stages) that represent a 

logical ‘causative’ pattern: 

1. Trip generation - separate models are produced of crime trip productions (i.e., 

the number of crime trips that originate in each zone) and crime trip attractions 

(i.e., the number of crime trips that occur in each zone). The model may include 

policy or intervention variables as predictors as well as socio-economic variables. 

One of the major uses of the model is to explore how different interventions might 

alter the number of trips taken. 

2. Trip distribution - a model that predicts the number of crime trips that will begin 

in every production zone and will end in every attraction zone. 

In the usual travel demand modeling framework, data gathering is called a land use inventory and involves 

estimating population and employment by different land uses, particularly retail trade and several other 

types of industry. 

25.5 

1 
 

3. Mode split - a model that predicts, for each production-attraction zone pair, 

which travel modes will be taken (e.g., walking, bicycle, driving, bus). 

4. Network assignment - a model that predicts, for each production-attraction zone 

pair by travel model, which route is liable to be taken. 

The modeling is typically sequential following these steps. The output from each stage is 

then used as an input for the subsequent stage. Figure 25.2 below shows the sequence. 

One can think of the model as a plausible behavioral representation. First, someone 

decides to make a trip (e.g., an offender decides to commit a robbery to get some money to 

purchase drugs). That would be the first stage. Second, that individual decides where to go to 

commit the robbery. This is the second stage. Third, the individual decides how to travel to that 

location (walk, drive, or take the bus). This is the third stage. Finally, the individual chooses a 

route; in the case of walking, biking, or driving, that is a deliberate choice whereas in the case of 

transit trips, it is dependent on the actual bus or rail network. This is the fourth stage. 

However, the analogy to behavioral decisions quickly breaks down as alternative 

behavioral sequences can be generated (e.g., the offender first makes a trip and then decides to 

commit a crime; the offender first decides to commit a crime and chooses a destination, but then 

commits a crime at an intermediate location in the trip). As a behavioral model, this type of 

framework is actually not very accurate for predicting individual behavior as a number of studies 

have suggested (Ortuzar &Willumsen, 2001; Domencich & McFadden, 1975). 

Consequently, it is important to understand this framework as a zonal model, rather than 

a behavioral explanation. The data are aggregated at the zonal level and the model is applicable 

to that level. The model is good at predicting total trips in a metropolitan area and for predicting 

the major trip links, and should be used only at that level. 

Note in Figure 25.2 that there is feedback from the network assignment stage to the mode 

split stage. This is a function of transit use since the choice of travel mode is dependent on the 

availability of an appropriate network (e.g., one cannot have train trips if there are no trains 

nearby).2 

In classic travel demand modeling, there are several feedback loops. One is from the network to the mode 

choice, as in the crime travel demand version. A second is from the network to both mode choice and trip 

distribution stage. If a particular route becomes very congested (having a traffic volume-to-capacity ratio 

greater than 1.0), it has been noted alternative destinations become more attractive. For example, people 

will often travel farther and more out of the way to avoid congested corridors. In short, there are a variety 

of feedbacks from later stages to earlier stages, and the model is quite flexible in being able to 

accommodate the different sequences. 

25.6 

2 
Crime Inventory

Data gathering

Mdloe

Possible scenarios

Contextual variables

Possible scenarios Netw

Netw

Trip generation

Trip distribution

Mode split

Network Assignment

Figure 25.2: 

Crime Travel Demand Model 
 

 

 

Also, crime travel demand modeling is a framework, rather than a specific theory. There 

is more than one way to implement the framework. In transportation modeling, there are many 

variations of the model and transportation planning organizations implement it in slightly 

different ways. For this reason, it is best thought of as a framework. 

In this version of CrimeStat, we implement one particular version of the framework. It is 

a framework that is consistent and appears to produce reasonable predictions of crime travel 

behavior. But, clearly, it is not the only way that this could have been implemented. 

The ‘second-‘ and ‘third-generation’ travel demand models represent alternative ways of 

modeling travel in a metropolitan area. In the following chapters, these alternatives will be 

mentioned where appropriate. Nevertheless, the type of framework implemented in this version 

should be seen as a first step in developing a more realistic model of crime travel behavior. 

Crime Travel Definitions 

Let us start with two definitions. 

Crime Trip 

In the CrimeStat implementation, a crime trip is a round-trip journey from an offender’s 

residence that includes a committed crime at a specified location. From a modeling perspective, 

the offender’s residence will be considered the origin of the trip and the crime location will be 

considered the destination. Note that there may be intermediate trips between the origin and the 

destination, as Figure 25.3 below illustrates. But, at some point, the offender will probably 

return home to the initial origin. Defining a crime trip in this way avoids the issue of identifying 

the actual origin of the trip. As mentioned in Chapter 13, routine activity theory suggests that 

many crimes are committed while offenders are involved in other activities. The possibilities can 

become quite complex (e.g., an offender stays overnight at some other location than his/her 

residence and commits a crime as a part of that stay rather than while en route to home). 

Nevertheless, by referencing all trips with respect to the offender’s residence, a consistent 

set of estimates can be obtained. Since intermediate trips are almost never known, it is a 

hypothetical question whether modeling origins from offender residences will produce better 

estimates than modeling origins from other locations.3 

If it were possible to obtain data on intermediate locations during crime trips by offenders, then it would be 

possible to test whether modeling the origin with respect to these intermediate locations produces more 

stable and clearer predictions than with respect to the residences of the offenders. But, until that data is 

obtained, the question is speculative. 

25.8 

3 
Figure 25.3: 

Origin-Destination Links 

There is an origin (residence)There is a destination (crime location)There may be intermediate links

I3 I2

O D

I1
 

 

In the usual travel demand forecasting framework, transportation modelers usually 

distinguish productions and attractions from origins and destinations. The reason is that 

origins are asymmetrical in time. For example, for a home-to-work (commuting) trip, the origin 

location in the morning is the residence while the destination is the work location. On the return 

trip, however, the origins and destinations are reversed (i.e., the work location is the origin while 

the home location is the destination). The models are referenced in the same way that is done 

here, namely from the residence location and the trips are assumed to be reciprocal. Thus, the 

production end of a trip is always the residence location and the attraction end of a trip is always 

the work location. The round-trip journey can be broken into different time sequences (e.g., 

morning home-to-work trips; afternoon work-to-home trips), but the production and attraction 

ends are always the same. 

In crime travel demand modeling, there is usually data on intermediate trips. 

Consequently, some of the finer analysis cannot be done. Therefore, we adopt a similar logic, 

but with a slightly different terminology. As with the usual travel demand modeling, the 

production end is always the home location and the attraction end is always the crime location. 

However, we use origin and destination interchangeably with production and attraction since we 

cannot document the return part of a crime trip. 

Crime Travel Demand 

Crime travel demand is the number of offenders per unit time that are expected to travel 

on a given segment of the transportation system under a set of socioeconomic, land-use, and 

environmental conditions. That is, the final model output is an estimate of the number of trips 

(or offenders) that travel on any given segment of the transportation system at a given time under 

a given set of conditions: 

Number of trips = number of offenders traveling to crime (25.1) 

First, as mentioned above, the model is estimated sequentially. In the first stage, trip 

generation, there is a prediction of the number of crime trips that originate from each origin zone 

and the number of crime trips that occur (end) in each destination zone. In this case, a crime trip 

is equated with an offender because of the nature of arrest records from which these estimates 

come. With most arrest records, there is a single record for each crime that an individual 

commits. Thus, the origin is the residence location of the offender while the destination is the 

crime location. If the individual committed more than one crime, there will be a separate record 

for each crime (or, at least, those that are known). If two individuals commit a single crime and 

both are arrested, then there will be two records in the data base. In other words, the nature of 

the data equates a crime trip with a single offender. Thus, the total number of crime trips 

25.10 
 

 

estimated (whether from the production or attraction end) is equivalent to the number of 

offenders. 

Aggregate Volume/count Model 

Second, by a ‘set of socioeconomic, land-use, and environmental conditions’ is meant 

correlates of crime trips. At the aggregate level of a zone, predictors of crime trips (whether 

productions or attractions) are correlates of those trips. Since the number of trips are being 

predicted, the model estimates volumes (or counts), not rates.4

 That is, the number of crime trips 

originating in a zone or ending in a zone is a count of events. Aggregate counts, in turn, tend to 

be related to other aggregates, particularly population. Thus, in developing a predictive model, 

population is almost always one of the dominant variables. Sometimes it can be a sub-set of 

population, such as number of households, number of vehicles, or number of males aged 16-25. 

But, since the number of incidents is usually a function of the size, there can be difficulties in 

inferring individual characteristics from ecological models.5 It is important to keep this 

distinction in mind and not make inferences about individuals. 

In addition to population, variables that predict crime trips are also ecological variables - 

employment, retail space, number of bars, number of pawn shops, existence of a freeway, 

number of arterial lane miles, and so forth. 

O-D Zone Pairs 

In the second stage, trip distribution, a model is estimated of the number of crime trips 

that occur from any particular origin zone to any particular destination zone. Since the input to 

the second stage is the number of predicted crimes originating in each origin zone and the 

number of predicted crimes ending in each destination zone, the second stage estimates how 

many trips will be distributed from each origin zone to each destination zone. The result is an 

4 Some agencies have actually used it to predict rates. Since a rate is an event relative to a baseline, 

population is factored into the dependent variable. It is possible to apply the model as a rate, though the 

user needs to ensure that all the predictor variables are also rates. 5 The question of whether an ecological inference is valid or not has been studied extensively. Sometimes it 

holds and sometime it does not. An ecological inference occurs when data are aggregated with a grouping 

variable (e.g., state, county, city, census tract; see Freedman, 1999; Langbein & Lichtman, 1978). The 

relationship is often called an ecological fallacy, but that is an oversimplification. Typically, if the 

between-group variance (i.e., differences) is greater than those within groups, then the ecological 

relationship will be a lot stronger than at an individual level. Conversely, if the within-group variance is 

greater than the between-group variance, a relationship that holds at the individual level will not be seen at 

the aggregate level. There are other ecological characteristics that account for typically higher R2

 at the 

aggregate level - spatial autocorrelation, skewness in the dependent variable, and hetereoscedasticity 

(unequal estimation errors around a statistical estimate). 

25.11 
 

estimate of crime trips between zone pairs (an origin zone and a destination zone). There are 

different names that are used for this combination - zone-to-zone trips; zone pairs; zone-to-zone 

links, O-D links (for origin-destination links), O-D pairs, but in all cases the term refers to the 

number of trips that start in any one origin zone that go to any one destination zone. 

Travel Mode 

In the third stage, mode split, the number of trips by any O-D combination are then split 

into different travel modes - walking, biking, driving, bus (if available) or train (if available). In 

the usual travel demand modeling done by transportation modelers, some of these modes are 

broken down very finely (e.g., drive alone trips, car pooling trips, park-and-ride trips). There is 

no logical reason why mode split cannot be defined in multiple ways. For our purposes in 

modeling crime trips, simple choices are probably adequate because of a lack of data that would 

allow finer distinctions to be made. 

Estimating Travel Routes by Mode 

Finally, in the fourth stage, the number of trips from any origin to any destination by 

separate travel modes are assigned to a route on the transportation network. Thus, if the trip is 

by walking, biking, or driving, the model may predict a different route than if the trip is by 

transit since a transit system is limited to particular bus or rail routes. Hence, the final stage is an 

estimate of the total number of crimes that occur on any segment of a transportation network by 

separate travel mode. 

The CrimeStat Crime Travel Demand Module 

The CrimeStat crime travel demand module follows this logic fairly closely, but adapts it 

to the nature of crime data. Figure 25.4 below shows a screen image of the module. There are 

five main sections (tabs). Four of them correspond to the four stages. Each of the four sections 

has several routines associated with them. These will be explained in the subsequent chapters. 

In addition, there is a ‘File worksheet’ section. This allows the user to save the file 

names in order to keep track of them. The module is complicated and there are a lot of files used 

- 38 of them, many used multiple times. In addition, there are a variety of parameters that are 

used for the different files. The result is complex because not only is the model tested 

sequentially but there are multiple options available for each stage. The subsequent chapters, the 

file worksheet tab, and the online help menu will try to make the routines easy to understand. 

But, the user has to realize that it will take time to gather the data and to construct the model. 

25.12 
Figure 25.4: 

Crime Travel Demand Module 
Crime Travel Demand v. Journey-to-crime 

A distinction should be made between crime travel demand and journey-to-crime. Crime 

travel demand modeling is not journey-to-crime modeling. Journey-to-crime modeling (and its 

use in geographical profiling) is a much simpler system. Research on journey-to-crime has been 

conducted since the 1930s (see Chapter 13). For the most part, journey-to-crime modeling is a 

descriptive framework. Estimates are made of the distance that offenders travel during particular 

crime trips. A distance decay-type function is estimated from these trips and comparisons are 

made between different types of crime or the same type of crime for different time periods. 

There is very little in the way of theory for this type of model. Crime trips are a function 

of distance plus some other characteristics, such as the crime type or whether there is or is not a 

‘buffer zone; around the offender’s residence (see Chapter 13). Most of the journey-to-crime 

studies have compared different types of crime by distance traveled, whether measured as 

average distance or by type of function as was used in Chapter 13. Almost exclusively, the key 

variable is travel distance. There are very few studies that have looked at travel time (see Kent, 

Leitner & Curtis, 2004 for an exception). 

In other words, journey-to-crime modeling is a single-stage model, essentially a 

description, with the primary variable being distance. It is also ‘non-adjustable’ in the sense that 

the conditions cannot be varied since there is no model that predicts distance other than crime 

type (or buffer zone, for which we did not find evidence; see Chapter 13). There is very little in 

the way of predictions that the model can make other than to estimate the likely origin location 

of an offender (for events committed by a single offender). 

Crime travel demand modeling, on the other hand, is a predictive framework. Crime trips 

are a function of productions, attractions, and impedance. Productions are a function of some 

socio-economic and policy variables. Attractions are a function of some other socio-economic 

and policy variables. Impedance is a function of cost and availability variables. Each of these 

components is predicted by different variables. Hence, the model can be adjusted (e.g., by 

adding or subtracting a policy intervention variable). One of its benefits is the ability to adjust 

conditions. For example, if it can be shown that the amount of policing in a zone impacts the 

number of crimes that either originate or end in that zone, then a subsequent run can ‘re-assign’ 

police personnel to impact crimes in other zones. 

The model is multi-stage since it is estimated sequentially and, therefore, can be used for 

prediction. Thus, once the model is estimated on one data set, it can be used on another set. 

Thus, it represents a calibration against a known data set. For example, one could calibrate the 

model on one year’s worth of data and then use the estimated coefficients and parameters on a 

second year’s worth of data. This, in fact, is how it is used in transportation modeling. The 

25.14 
model is calibrated on a current year and then applied to a future year to make a forecast of 

future travel demand. 

In short, crime travel demand is a theory of travel behavior whereas journey-to-crime 

modeling is but a simple description. In many ways, crime travel demand modeling is a quantum 

leap in complexity and analysis, requiring gathering a lot more data and calibrating many 

individual steps. Nevertheless, that complexity allows a far greater use of the model than the 

traditional journey-to-crime. 

Models v. Description 

A key distinction in the crime travel demand framework is that of an empirical 

description versus a model. The framework can be applied both as an empirical description and 

as a model, assuming that data can be obtained. An empirical description describes the data that 

have been collected. For example, for trip generation, it is a count of the number of crimes that 

originate in each zone and the number of crimes that occur in each zone. For trip distribution, it 

is the actual number of trips that go from each origin zone to each destination zone. For mode 

split, assuming that data could be obtained on travel mode, it is a count of the number of trips for 

each origin-destination pair that are taken by each travel mode. Finally, for network assignment, 

again assuming that data could be obtained on the actual routes taken, it is a documentation of 

the actual routes that are taken and a count of the number of trips on each segment of the 

transportation network. In other words, an empirical description is a count of the number of 

offenders, whether by origin location, destination location, O-D pair, travel mode, or route. 

A model, on the other hand, is a simplified set of relationships that approximate the most 

important features of the actual count. The model is not reality, but is a rough approximation to 

it. Because it is rough, a model inevitably makes errors. Consequently, there always will be a 

difference between a model and the actual events to which the model approximates. 

The two differ on other dimensions as well. A model has only a few variables whereas 

the actual events have many (perhaps hundreds). A model has a simplified set of relationships 

among the variables whereas the actual events have very complex relationships among the 

variables, often too complex to describe properly. By simplifying the relationships, a model 

produces, what Herbert Simon and Allen Newell called, an analogy to the actual situation, 

whereas the actual events are literal (Simon & Newell, 1963; Newell, Shaw & Simon, 1957). 

The CrimeStat crime travel demand routines can be applied both to empirical data as well 

as modeled relationships. In fact, two of the routines are directly concerned with the differences 

between the model and the actual data. Both sets of endeavors have value in their own right, but 

they differ. An empirical description is most relevant to the present. For a police department 

25.15 
 

 

 

 

trying to mitigate crime and catch offenders, the empirical description is probably of more use 

than an abstract model. As will be seen, the empirical description of crime trips will always be 

more complex than the estimated model. If the only purpose is to describe the actual patterns 

that are occurring, then a model is not needed. 

On the other hand, a model has definite advantages that a description does not. First, it 

can be used for prediction. If a model is calibrated against a known data set, that model can then 

be applied to a new data set. For example, one could create an estimate of crime trip productions 

based on existing socioeconomic and land use data. Then, one could apply that model to a 

forecast data set of future socioeconomic and land use conditions. The result is a prediction of 

future crime levels. Of course, since the model was never completely correct in the first place, it 

will inevitably make errors.6

 Further, since there is no guarantee that past relationships will 

necessarily hold in the future, there is no certainty about whether the most important part of the 

predicted relationships will actually hold. Nevertheless, there has been enough success in 

demographic, economic, and transportation modeling that new fields of forecasting have 

emerged as legitimate research activities. 

A second advantage of a model is that it can be manipulated. Variables can be modified 

to explore their effect. Distributions can be re-arranged to, again, understand their effect. For 

example, if relationships can be established between the number of crimes produced or attracted 

to zones, on the one hand, and the number of police personnel in a zone or to the existence of a 

large shopping mall, or to the existence of a drug treatment center, on the other hand, then 

scenarios could be run that explore the different arrangements. These ‘What if?’ types of 

scenarios can be very useful. For example, if a relationship exists between shopping malls and 

crime trips, what is liable to happen when a new shopping mall is built? One could take the 

model, add the new shopping mall (or the retail employment or acreage associated with the mall) 

and run the model to make a prediction about its likely impact. Or to take another example, if it 

can be shown that there is a negative relationship between the number of beat police officers and 

the number of crimes originating in zones, then it would be possible to evaluate the likely 

consequences of re-arranging police personnel across different beats. 

In short, a model is a very powerful tool for evaluating policy or intervention type 

strategies. Rather than speculate or gather evidence from other metropolitan areas on their 

experience (which is valuable, of course), a model can be used to simulate the likely 

Simon and Newell described two kinds of errors: 1) errors of commission (Type I errors); and 2) errors of 

omission (Type II errors). The first kind represents relationships and predictions that do not exist (to use 

our terminology) while the second kind represents the failure to detect relationships that do exist. Any 

model will have both sets of errors. The point to keep in mind is whether a model captures the most 

important relationships and does not make too many Type I errors (Simon & Newell, 1963; Newell, Shaw, 

& Simon, 1957). 

25.16 

6 
 

consequences of an action on crime levels. In transportation planning, the travel demand model 

is used all the time to evaluate the likely consequences of implementing particular projects. This 

does not mean that it is the only factor considered in making a decision or even the most 

important factor; clearly, politics, financing, and community support are also major components 

of any decision. Nevertheless, the travel model is a very important input into any decisions 

about future investments. 

Uses of a Crime Travel Demand Model 

Table 25.1 illustrates some possible uses of the crime travel demand model, assuming 

that data could be obtained. 

Table 25.1: 

Possible Uses of Crime Travel Demand Model 

Trip

Generation

Trip

Distribution

Mode

Split

Network

Assignment

Description Identify

correlates 

of crimes 

Identify

crime trip 

links 

Identify

crime travel 

models 

Identify

routes taken 

by offenders 

Calibration Estimate

coefficients

of predictor 

variables 

for crime 

origins & 

destinations 

Estimate

origin-todestination 

coefficients 

for crime 

trips 

Estimate

formula

for travel 

modes used 

by offenders 

Estimate

model for

routes taken 

by offenders 

 

Prediction Predict future 

crime levels 

Predict future 

crime trip 

links 

Predict future

crime travel 

modes 

Predict future 

routes used

by offenders

The model could be used for description, calibration, or prediction. In description, the 

emphasis is on describing the travel behavior of offenders. For trip generation, it involves 

identifying the correlates of crimes, both by origin zone and by destination zone. For trip 

distribution, it involves describing the actual crime trips taken between specific origin zones and 

25.17 
specific destination zones. For mode split, it involves identifying the different modes that 

offenders are using, describing the proportion of each mode that are used, as well as describing 

the modes used for particular origin-destination links. Finally, network assignment involves 

describing the actual routes taken by offenders. In other words, the emphasis on description is 

identifying the specifics used in crime trips. 

On the other hand, calibration involves selecting variables that can approximate the 

description and estimating coefficients for their use. The emphasis is on finding a limited 

number of general variables and coefficients that can produce a reasonable approximation to the 

actual travel behavior. Thus, in trip generation, the aim is to find a few variables that can predict 

reasonably accurately the number of crimes by origin zone and destination zone. In trip 

distribution, the aim is to estimate coefficients that can approximately describe the trips that are 

taken from particular origin zones to particular destination zones. In mode split, the aim is to 

develop coefficients that can approximate the travel modes used while in network assignment, 

the aim is to find an algorithm that approximates the actual travel routes used by offenders. The 

result of a calibration is a model that can be generalized whereas a description cannot be 

generalized. 

Finally, in prediction, the calibration models are applied to other data, either forecast 

values of future levels of the predictive variables or data from other jurisdictions to see the 

similarities or differences. The existence of a model (ideally calibrated against a real data set) 

allows a forecast to be made whereas a description cannot be forecast. 

Research Uses of a Crime Travel Demand Model 

For research, a crime travel demand model has many different uses, only some of which 

are explored in the next five chapters. First, it organizes crime travel information in a systematic 

manner. The model is logical and proceeds in a systematic way. As opposed to a journey-tocrime-type model, which is just a description, the crime travel demand model systematically 

steps through the four stages in an understandable way. It is a very good way to organize 

information on crime travel, though, clearly, it is not the only way. 

Second, compared to the journey-to-crime literature, it is a more realistic model of 

offender travel. For one thing, it incorporates information about origin locations. This helps 

answer the question of why certain areas produce more crimes than others (remember, it is not a 

behavioral explanation, but an ecological model). For another thing, it incorporates information 

about destination locations and helps answer the question of why certain areas attract more 

crimes than others. For a third thing, it models travel choice in a more complex manner. Instead 

of assuming that all offenders will travel to a crime in exactly the same way (e.g., by walking), 

the model allows the separation of different travel modes. For journey-to-crime models, distance 

25.18 
 

is the only impedance variable, whereas for crime travel demand modeling, travel time and travel 

cost are often better predictors of travel behavior, especially in relationship to an available 

network. In short, it is a much more complex, yet realistic, representation of crime travel 

behavior. 

Third, it is a dynamic analysis of travel behavior. Crime trips are seen as a product of 

neighborhood production factors, attractions, and travel costs (impedance). Since these change 

by various hours of the day, so too do the travel patterns change. The ability to model travel at 

different times of the day is one of the strengths of the travel demand type of framework. 

Fourth, and finally, a crime travel demand model can allow comparisons between 

different types of crimes in the productions, the attractions, and the costs. So, too, can journeyto-crime models be used to compare different type of crimes. But those comparisons are unidimensional, essentially comparing different distance decay functions. The crime travel demand 

model can explain the ‘distance decay’ function and hence allow a more structural interpretation 

than was previously possible. For example, in comparing data sets from Baltimore, Chicago, and 

Las Vegas, Richard Block, Dan Helms and myself are finding that there may be very little 

difference in the cost function used for different types of crime trips, but that differences in these 

trips are more a function of the distribution of opportunities (attractions). To link this up to the 

early theme of this chapter, American society has become so mobile and the automobile so 

ubiquitous that distances are not as much a barrier to offenders as they used to be. In other 

words, the distribution of opportunities appears to be the more dominant factor predicting types 

of crimes than the limitations of neighborhoods and small communities. If this turns out to be 

true, then we are in for a major shift in the type of crimes that our society will experience over 

the next few decades. Mobility may replace neighborhood as a determining factor in crime 

behavior. In other words, the local ‘community-based’ offender is morphing into a metropolitanwide and, perhaps, regional offender, a not very desirable prospect. The Kneebone and Raphael 

(2011) study may indicate that this has already started to occur. 

Crime travel demand modeling allows for a more complex, more interventionist and, 

perhaps, deeper understanding of crime travel than previous types of model, particularly the 

journey-to-crime and serial walk type of model (see Chapter 13). 

Utility for Policing and Law Enforcement 

For police department and other law enforcement agencies, crime travel demand 

modeling has some advantages as well. First, it can be used to model different policing 

strategies, as suggested above. For example, it could be used to evaluate the likely effect of 

shifting patrol deployment. The ‘What if?’ nature of crime travel demand modeling makes it 

useful to explore alternative arrangements before they are actually implemented. 

25.19 
Second, it could be used for forecasting. As mentioned, if a model has been calibrated on 

one set of data, then it could be applied to another set to predict, for example, the distribution of 

crimes five or ten years later. Typically, police departments have not done forecasting, but they 

are often expected to be able to anticipate changes. This type of model can be useful for that 

purpose since Councils of Governments (COG) and Metropolitan Planning Organizations (MPO) 

systematically make forecasts of future population and employment levels. 

Third, it can be used for modeling interventions. Aside from modeling different policing 

strategies, a range of land use and communities changes could be explored. For example, what 

would be the effect of introducing more drug treatment centers or more ‘weed and seed’ 

adolescent facilities? The logic is similar to forecasting. A model is calibrated against one data 

set. But, in addition to socioeconomic and land use variables, variables on facilities are added to 

the equation as predictors. If it can be shown that they have any effect (which we hope they do), 

then these can be used as variables in a modeling scenario. 

Fourth, these types of models can be used for anticipating changes in the community. 

Again, this is similar to the forecasting purpose mentioned above. But, it is slightly different in 

that it anticipates structural changes. An example was given of anticipating changes from new 

shopping malls. In Baltimore County, for example, shopping malls were shown to be the 

strongest attractors of crime trips. In that context, what would happen if a new mall was built? 

This type of model can be used to model this scenario. Conversely, a lack of employment 

opportunities appears to be correlated with crime productions, at least in Baltimore County. 

What would happen to crime if local employment was increased in certain zones? Again, this 

type of model is useful for exploring that type of question. 

Again, going back to an earlier point, there is, of course, a difference between a model 

and reality (an actual situation). Reality is complex; models are not, or are a lot simpler. Still, 

models as analogies can provide insight into mechanisms and allow police, law enforcement, and 

the policy community as a whole to try to simulate changes without having to commit to 

expensive, and perhaps disastrous, changes with little information. In other words, modeling in 

general, and crime travel demand modeling in particular, is a tool that may have wide utility for 

the law enforcement community. 

References on Travel Demand Modeling 

In this final section, some sources on travel demand forecasting are listed. There are a 

large number of sources, though there are few actual textbooks. A very good textbook on the 

subject is by Ortuzar and Willumsen (2001), while an older, out of print book is by Stopher and 

Meyburg (1975). There are several major handbooks on the topic (Hensher & Button, 2003; 

25.20 
 

ITE, 2003). Some good chapters on the subject are found in Beimborn (1995), Field and 

MacGregor (1987, Ch. 6) and by Engelen (1986, Ch 17). Discussions of ‘second’ generation 

models can be found in Domencich and McFadden (1975) and Ben-Akiva and Lerman (1985). 

However, probably the best source for articles on the subject are found on the Federal 

Highway Administration (http://www.fhwa.dot.gov) and other web sites. Among the 

articles/presentations that can be found on that site are FHWA (2012), FHWA (2009), Jeannotte, 

Sallman, Margiotta, and Howard (2009), and McKeever and Griesenbeck (2009). Of particular 

interest is a study of bicycle and pedestrian travel modeling (Turner, Shunk, and Hottenstein, 

1998), which may be relevant for crime analysis, and ‘third’ generation models. 

Older sources, which are still good are by Oppenheim (1975, ch. 4) and Krueckeberg and 

Silvers (1974, Ch. 10), aside from the Stopher and Meyburg text mentioned above. 

25.21 
 

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25.25