Chapter 31: 

Case Studies in 

Crime Travel Demand Modeling: 

I - Travel Patterns of Chicago Robbery Offenders 

Richard Block 

Professor Emeritus 

Loyola University Chicago 

Chicago, IL 
 

 

 

 

 

 

 

 

 

 

 

 

Table of Contents 

Case Study I: Travel Patterns of Chicago Robbery Offenders 31.1

 Two Models: Econometric and Opportunistic 31.1 

 Crime Travel Demand Modeling in Chicago 31.3 

Data for the Chicago Study 31.4

 Incident and Arrest Files 31.4 

 Traffic Analysis Zones 31.5 

 Chicago’s Road Network 31.5 

Trip Generation 31.6 

Trip Distribution 31.9

 Gravity Model of Chicago Robbers 31.11 

Predicting 1998 Trips from 1997 Trips 31.14 

 Predicting Overnight Robbery Trips 31.14 

Mode Split 31.17 

Network Assignment 31.17

 Shortest Time or Shortest Distance? 31.19 

 Overnight robbery trip load 31.19 

Conclusions 31.22 

Feasibility & Advantages of Crime Travel Demand Modeling 31.22 

Limitations to Crime Travel Demand Modeling 31.23 

References 31.26 
 

 

Chapter 31: 

Case Studies in 

Crime Travel Demand Modeling: 

I - Travel Patterns of Chicago Robbery Offenders 

In this chapter, a case study of the crime travel demand model for Chicago, IL, robbery is 

discussed. Originally written in 2004, it is presented to illustrate the application of the model to 

a compact city with substantial transit services. 

Travel Patterns of Chicago Robbery Offenders 

Some neighborhoods are dangerous others are safe. Crime clusters in specific areas. 

So too do criminals. Criminologists, police, and civilians have known this for nearly 150 years. 

However, relatively little research has been done on the travel patterns of offenders. Using a 

modification of standard transportation models, CrimeStat IV allows police and researchers to 

describe and predict travel patterns based on four sequential models. 

The object of research presented here is to test the usefulness and feasibility of 

CrimeStat=s Crime Travel Demand model utilizing police reports of all robberies occurring in 

Chicago in 1997 and 1998 that had at least one known offender who lived in the city. In sum, 

the objectives of this study of robbery in Chicago are: 

1. To test the CrimeStat IV crime travel demand model in a mature central city; 

2. To describe the travel patterns of robbery offenders based upon offenders home 

and location of incident; 

3. To predict the travel patterns of robbers in 1998 based upon characteristics of the 

offender's resident neighborhood and the incident neighborhood and a gravity 

model of the relationship between the two; 

4. To predict the travel patterns of robbers in 1998 based upon the patterns of 1997; 

and 

5. To assess the quality of the predictions and their value to the police. 

31.1 
 

 

Two Models: Econometric and Opportunistic 

As outlined in Chapter 25, a travel demand model is a four-step sequential model. The 

first stage is trip generation, whereby the number of crimes originating in a neighborhood and the 

number of crimes ending in a neighborhood are modeled. The second stage is trip distribution 

which summarizes the number of trips that go from each origin zone to each destination zone. 

The third stage is mode split, which models the number of trips for each zone pair (origin zone 

and destination zone) that travels by a particular mode of travel. The fourth, and final stage, is 

network assignment which models the likely routes taken by offenders in traveling between 

particular zone pairs. 

This mapping of links assumes that travel decisions are based upon minimizing costs to 

get to a valued destination. When I go to work, I weigh costs and benefits. I choose the route 

that will get me there quickest with the fewest problems. Early theories of criminology 

assumed that criminal activity was no different than other behavior. It was determined 

rationally. By extension, travel routes and crime locations are also determined rationally. 

Trips of offenders are similar to any repeated activity. Most of our activities occur near 

where we live or work or on the path in between. This is our knowledge space. Trips within it 

maximize our efficiency and minimize costs. Daily purchases occur close to home with a rapid 

fall off with distance. But major purchases are an exception. They may occur far away. This 

distance decay can be generalized to travel cost decay. The more expensive in time, money, 

and distance, the less likely a trip will occur. Applied to robbery, most incidents occur close to 

home, but a bank robber might incur greater costs to find a good target. In addition most 

previous research has found that predatory criminals avoid incidents too close to home for fear 

that they will be recognized. Combined with distance decay, this creates a buffer zone of few 

criminal incidents (Rossmo, 2000). 

Environmental criminology assumes that most activity occurs in a knowledge space that 

includes nodes of residence work and play and the routes between these (Brantingham & 

Brantingham, 1984; 1990). However, the components of travel for criminals may not be the 

same as other people. For example, for someone with a full time job, getting to work as quickly 

as possible is important; time is money. For a jobless criminal, time may be less important. 

Routine activities theory assumes that both targets and offenders choose their activities 

based on a weighing of costs and benefits. Offenders seek out targets in locations where they are 

likely to congregate (e.g. bars at closing time, rapid transit stations). A crime occurs when an 

offender and a target converge in the absence of a capable guardian (Felson, 2002). The routine 

activities of offenders may mostly be hanging out rather than rationally seeking targets. What is 

the basis of convergence? Chance or the decisions of offenders? Any potential robber’s 

31.2 
 

 

 

decision is effected by both chance and cost. Time and distance are both measures of cost. 

However, within a short distance of home time and distance costs are near to zero. 

An alternative hypothesis is that robbers do not weigh costs and benefits of travel. 

Rather, they may see an opportunity for crime and take it. Because much of their day to day 

activity is near home, many incidents occur near the robber’s home. Travel patterns are 

irrelevant for these crimes. The number of robberies decline with distance from the offender's 

home because fewer of the robber's daily activities occur far from home. On the other hand, more 

professional robbers may seek out specific areas or locations where lucrative targets are found 

and may be willing to travel great distances. 

In Chicago, an opportunistic robber=s knowledge of good targets may be limited to the 

isolated area around his residence. In addition, trips within the area cost almost nothing, although 

other costs, such as risk of capture may be relatively high. The difference between Chicago and 

Baltimore County (or between Chicago and its suburbs) has to do as much with knowledge of the 

distribution of opportunities as with the cost of travel. Chicago=s neighborhoods are so isolated 

that some offenders may have little knowledge of opportunities outside their resident area. The 

crime travel demand model holds that in the aggregate offenders appear to weigh costs and 

benefits. However, the data analyzed here says nothing about individual decisions. Decisions 

may be made with other factors not captured by shortest distance or time. 

In one of the few studies of non-arrested robbers Wright and Decker (1997) found that 

most St. Louis robbers are opportunistic and rob close to home. Rationality and careful cost 

calculation have little to do with their decisions. These are people who have a need for quick 

money. If they saw an opportunity near home, they would take it. Opportunities were most 

likely to occur as the potential offender and victim went about their daily routine activities. 

Most of these happened close to home. Therefore, robbery occurred close to home. 

The closer to an offender's home that an incident occurs, the more likely the incident has 

resulted from a chance meeting. The further away that it occurs the more likely that it was 

planned. Part of the planning is transportation costs. It is difficult to calculate this for 

offenders. The best we can do is estimate travel time. 

Crime Travel Demand Modeling in Chicago 

The Offender Travel Model is a new application of the Travel Demand Model. The travel 

demand model has been in development since the 1950's. It is used in every metropolitan area 

in the United States. CrimeStat's crime travel demand model was outlined in Chapter 25. 

As applied to robbery in Chicago, description is as important as prediction. While the 

Chicago Police Department (CPD) has long collected information on the location of the incident 

31.3 
 

 

 

and residence of the offender, these were not linked in any systematic way. In meetings with the 

department, credible descriptive maps proved to be the most convincing reason to use the new 

CrimeStat travel demand module. Before a new technique is tested, its potential credibility 

must be demonstrated. Therefore, the last phase, in the Chicago Travel Demand Model 

emphasized both the predicted travel demand model and the observed travel of offenders. 

Analysis of Chicago's Crime Travel Demand proceeds in three stages. The first step 

(trip generation) is a prediction of variables associated with the number of crimes originating in 

each zone and the number of crimes ending in each zone. 

The second step is the prediction of links between zones based on zonal characteristics of 

incident locations and offender residences and a measure of the attraction between the two zones. 

These predictive models are compared to the observed links and trips and the previous year=s 

trips used as a prediction. 

The mode split step was not run because of the lack of data. Unfortunately, the Chicago 

police data does not permit an analysis by different modes of transportation (see Chapter 29). 

Data on whether the offender drove, walked, or rode rapid transit to the incident are not 

collected. 

The final step is the description of probable travel routes from the offender=s home zone 

to the incident zone based on shortest time or distance along a transportation network. The 

links modeled in the second step can be converted to a probable route between home and 

incident zones over a road network or a summary network load which aggregates travel of all 

offenders along a transportation network. 

Data for the Chicago Study 

Incident and Arrest Files 

The analysis presented here merged information from many sources. This research is 

based on incident and arrest records from the CPD. Excluding O=Hare Airport, the city of 

Chicago is divided into 946 traffic analysis zones. Incidents are assigned to these zones for 

both residence location (the origin) and the crime location (the destination). These include all 

Chicago robberies in 1997 and 1998 that had at least one known offender who lived in Chicago. 

These were geocoded by the address of the incident and the home address of all known 

offenders. Offenders who traveled longer distances were probably under-represented (Block, 

2007). About 20% of all reported robberies were included. In 1997, there were 25,000 

robberies reported to the police. Of these robberies, 4,636 resulted in the arrest of at least one 

Chicago resident. Including robberies with multiple offenders, there were 6,643 crime trips. 

31.4 
 

Traffic Analysis Zones 

These incidents and offenders are counted in 946 Traffic Analysis Zones (TAZ). 

O=Hare Airport is excluded. Chicago's traffic analysis zones are mostly based on a uniform grid 

of 1/2 mile squares. These are not based on census tracts or other city divisions. However, 

some census data was available for these zones along with information on employment. About 

100 of them had no census population and therefore were unlikely to include the residence of an 

offender. Land use, employment, population, and robbery incident and offender residence 

counts were available for all zones. Land use goes beyond the standard census measures to 

include characteristics from many data sources that might be related to crime. Among these are 

code violations, vacant parcels, fires, liquor licenses, pawn shops, entertainment venues, distance 

from the central business district and other potentially criminogenic characteristics.1

 These 

traffic analysis zones were the unit of analysis. Trips were defined from the center of a zone. 

Chicago=s Road Network 

The base of Chicago's road network is a grid with 1/8 mile between blocks, a feeder street 

every half mile, and a main street every mile. Layered on top of this grid is a series of diagonal 

streets that tend to be major shopping streets and a relatively small number of expressways that 

converge at the edge of the central city. A semi-expressway, Lake Shore Drive, runs along the 

lakefront for 25 miles. Chicago has a well developed rapid transit system that, unfortunately, 

could not be included in the current analysis. 

Two street networks were available for analysis: 

1. Modified TIGER Line File: A mostly complete map of all streets and rail 

lines. Following police practice, the modified TIGER file allows for geo-coding 

in non-addressed areas, such as parks, by extending the base grid. All public 

streets are included, but one-way streets are not taken into account and the 

shortest distance may be on a route that no one would travel. Some areas of the 

city were not well mapped. 

2. Modeling network: This includes Expressways, principal arterials and collector 

roads. Each road segment is uni- (or single-) directional; that is, it expresses travel 

in only one direction. Thus, for a two-way road, there will be two records for 

every segment, one in each direction. This has the advantage that one-way 

streets can examined since there will not be an opposite direction pair. On the 

In contrast to many cities, Chicago has a large population living in the central business district and lacks a 

ring of impoverished communities surrounding downtown. 

31.5 

1 
other hand, a modeling network is less complete since minor streets are ignored. 

This type of map is useful for capturing trips that occur over a mile or more, but is 

not very useful for the many trips of less than 1/2 mile that occur in Chicago. It 

does take into account one-way streets. Using distance, the network will 

over-emphasize surface diagonal streets and will under-emphasize expressways. 

One of the advantages of the modeling network is that street segments can be weighted 

by speed or travel time, rather than just distance. There are eight distinct time periods with the 

travel time on each segment by period being indicated. Each street segment can be weighted by 

its travel time in minutes during a specific time period (e.g.; 7- 9 AM) to allow a more realistic 

description of travel behavior. Further, travel in opposite directions can be treated differently 

since travel times can be different for each direction. During rush hour, travel in one direction 

may be much quicker than travel in the other direction. Weighting by travel time will allow 

larger arterial roads and expressways to be chosen more because travel speeds will generally be 

faster on the larger capacity roads. This network tends to be most realistic for longer trips but, 

again, is not useful for very short >local= trips since the local, neighborhood road network is not 

included. A greater percentage of the travel is on expressways. 

Trip Generation 

Using the arrest data, events were aggregated to the TAZ=s for both the origins and the 

destinations. As expected, the distribution of crimes by origin zone and by destination zone 

were highly skewed. For example, 419 zones had no robberies originate in them while one 

zone had 27 and another had 24 originate in them. A similar condition held for the number of 

crimes by destination. For example, no robberies occurred in 409 zones while one zone had 24 

robberies and two had 23. 

Separate models of these incident were developed at the zone level. The regression 

analysis tools in CrimeStat are excellent, but choosing regression predictors requires both skill 

and theory. Many explanatory variables were tested. The independent variables chosen for 

analysis were based on those previously found to be important predictors of violent crime in 

Chicago. Significant variables were: 

1. POP2000 The most important was the 2000 population because the dependent 

variable was a predicted count of origins or destination. Other variables that 

were included were: 

2. ETHNICPER The percentage of the dominant racial or ethnic group within the 

TAZ. Recent research (Sampson & Raudenbush, 2001) has found that racial 

isolation and poverty predicted high community levels of violence. 

31.6 
 

 

3. POVPERCENT The percent of the households below the poverty level. 

Sampson and Raudenbush (2001) found this to be a dominant variables 

explaining community disorder. 

4. VENUE The number of entertainment venues (clubs, theaters, bowling allies) in a 

TAZ. This is information gathered from the MetroMix and the Reader in 2002. 

It was negatively related to the residence of the offender and was probably more a 

measure of perceived neighborhood safety than availability of targets. 

5. PAWNSHOP The number of pawnshops is included in several regressions. A 

pawnshop is both a focus for potential targets and a good place to get cash. 

6. VACANT: Count of vacant buildings in the TAZ. Perhaps this is an indicator of 

general neighborhood dilapidation (Broken Windows). 

The variables that were not significantly related to origins or destinations included many 

that are typically related to travel demand including employment and distance from the central 

business district. In addition, variables that are often associated with robbery, such as counts of 

drug arrests, convenience stores, liquor licenses, banks and currency exchanges were unrelated to 

origins or destinations after poverty and population were accounted for. Few TAZ characteristics 

that might attract an offender to commit a crime were significantly related to the number of 

robbery incidents in a TAZ. In general the results of the regression models and the resulting 

travel demand matrix supported the depiction of robbery in Chicago as occurring in or near the 

offender=s relatively isolated home neighborhood. 

Poisson regression models for origin and destination zone counts for overnight trips were 

similar in 1997 and 1998. Table 31.1 presents the final Poisson regression model for the resident 

zone of robbers in 1998. 

The Likelihood Ratio was good and an analysis of the residual errors did not reveal any 

major outliers. Given the large number of zones (n=946) the regression predicted variations in 

the count of origins fairly well. 

31.7 
 

 

 

 

 

 

 

 

 

 

 

Predictor 

 CONSTANT 

DF Coefficient Stand Error 

1 -2.072610 0.170828 

Tolerance z-value 

 . -12.132746 

 p-value 

0.001 

 POP2000 1 0.000235 0.000011 0.876420 22.156415 0.001 

ETHNICPER 1 0.015786 0.001746 0.909463 9.042151 0.001 

POVPERCENT 1 0.037134 0.002144 0.872974 17.321707 0.001 

 VACANT 1 0.016970 0.002528 0.835809 6.712064 0.001 

 VENUE 1 -0.115182 0.033458 0.933336 -3.442566 0.001 

 

 

----------------------------------------------------------------------------- 

---------------------------------------------------------------------------- 

Table 31.1: 

Overnight 1998 Robbery Origin Model

 Data file: Chicago TAZ with Time.dbf 

 Type of model: Origin

DepVar: Robbery Origins 8PM-5:59AM

N: 946 

Df: 940 

Type of regression model: Poisson with over-dispersion correction

 Log Likelihood: -2,011.35 

 Likelihood ratio(LR): 2,962.73 P-value of LR: 0.0001 

AIC: 4,034.71 

SC: 4,063.82 

 Dispersion multiplier: 1.00 

Similarly with the destination model (Table 31.2), the Likelihood Ratio of the destination 

model was reasonably good, though not as strong as with the origin model. There were not any 

apparent major outliers. Given the large number of zones (n=946) the regression predicted 

variations in the count of destinations fairly well. 

In both regression models, population had a positive relationship to the number of crimes. 

Similarly, the poverty variable and the ethnic homogeneity variable were positively related to the 

number of crimes, both origins and destinations. 

31.8 
Data file: Chicago TAZ with Time.dbf 

 Type of model: Destination 

DepVar: Robbery Destinations 8PM-5:59AM

N: 946 

Df: 941 

Type of regression model: Poisson with over-dispersion correction 

 Log Likelihood: -2,041.56 

 Likelihood ratio(LR): 2,661.30 P-value of LR: 0.0001 

AIC: 4,093.11 

SC: 4,117.37 

 Dispersion multiplier: 1.00 

 

 

 

 

 

 

----------------------------------------------------------------------------- 

----------------------------------------------------------------------------- 

Table 31.2: 

Overnight 1998 Robbery Destination Model 

Predictor DF Coefficient Stand Error Tolerance 

CONSTANT 1 -1.946591 0.032370 . 

POP2000 1 0.000218 0.000008 0.898680 

ETHNICPER 1 0.015913 0.000874 0.944910 

PAWNSHOP 1 0.335678 0.029184 0.954563 

POVPERCENT 1 0.035707 0.001888 0.989400 

z-value

-60.135432 

26.418877 0.001 

 p-value 

0.001 

18.201093 0.001 

11.501940 0.001 

18.913079 0.001 

Trip Distribution 

After the two predicted models were developed, the trip distribution was predicted, in 

other words the modeled number of trips that went from each origin zone to each possible 

destination zone was estimated (trip distribution). The inputs were the predicted origins and 

predicted destinations for robberies in 1998 from Tables 31.1 and 31.2. 

The test of CrimeStat=s crime travel demand module began with an analysis of 1997. 

Preparatory analysis indicated that 29% of robbery trips occurred in the offender=s home zone. 

While the number of intra-zonal trips can be mapped and predicted, travel within a zone cannot 

be described. 

Using observed crime trips, the actual number of trips from each zone to every other zone 

was calculated. Figure 31.1 depicts the volume of observed inter- and intra-zonal trip links in 

1997. The zone shadings indicate the number of intra-zonal trips. The width of the links indicates 

the frequency of trip links for zones with 3 or more links. 

31.9 
Figure 31.1: 
ഊ

 

 

 

 

Impoverished areas of the west and south side dominate this analysis. Most inter-zonal 

links are quite short (Figure 31.2). Many begin in zones that also have many intra-zonal trips. 

In Las Vegas and Baltimore County many links are associated with specific sites such as 

shopping malls or entertainment areas. Within the City of Chicago, the links lack a clear focal 

zone for incidents. However, few robbery trips are made to the central business district. 

From a police perspective, even the distribution of crime trips can be of value for tactical 

purposes and for planning interventions. However, the description of 1998 night time robberies 

south side dominate this analysis. Most inter-zonal links are quite short. Many begin in zones 

that also have many intra-zonal trips. In Las Vegas and Baltimore County many links are 

associated with specific sites such as shopping malls or entertainment areas. Within Chicago, 

the links lack a clear focal zone for incidents. However, few robbery trips are made to the 

central business district. 

A trip distribution analysis includes both inter- and intra-zonal trips in a single analysis. 

The analysis is not of travel from home to destination, but from a home zone to a destination 

zone. For transportation planners inter-zonal trips are more important than intra-zonal trips 

because these predict changing transportation needs. The volume of within zone travel can be 

predicted but not specific routes. However, many Chicago robberies (29% in 1997, 26% in 

1998) are intra-zonal. 

Therefore, two techniques were tested to account for the many intra-zonal trips. First, 

both inter- and intra-zonal overnight robberies trips were included in the same model. Second, 

to see whether different variables were predicting incidents close to the offender=s home address 

from those further away, inter- and intra-zonal trips were analyzed separately. Ultimately, I 

concluded that there was little to be gained by separating the two types of trips. 

Gravity Model of Chicago Robbers 

The gravity model that underlies CrimeStat=s trip distribution model assumes that travel 

between or within zones is dependent upon the offender pool, opportunities, and costs. 

Conceptually, this can be written as: 

(31.1)

where ܶ௜௝ is the number of trips from zone ݅ to zone ݆, Pi is the number of offenders in zone ݅

(the offender pool), Aj is the number of attractions or opportunities in zone ݆, ܥ௝௜

ఒ is cost of travel 

from zone ݅ to zone ݆, α and β are coefficients and λ is an exponent. The impedance (or >cost=) 

31.11 
Figure 31.2: 
 

 

component is modeled with a mathematical function. After experimentation, I found that the 

best impedance function was a lognormal distribution with a mean of 2 miles and a standard 

deviation of 5. The resulting model fit the actual trip length distribution quite well. 

Predicting 1998 Trips From 1997 Trips 

Can the 1998 distribution be successfully predicted from the 1997 model? In time series 

analysis, the best prediction of one period is generally the period that immediately preceded it. In 

spatial analysis, this is also likely to be true, especially in a mature city. However, while 

neighborhood characteristics change slowly in Chicago, they do change. During the late 1990's 

many public housing projects were emptied and most were torn down. While few 

neighborhoods deteriorated, many gentrified. Any of these might cause a change in the 

distribution of robbery trips. 

Nevertheless, to test the model, the 1997 observed robbery travel matrix was used to 

predict observed travel in 1998 (Figure 31.3). CrimeStat IV, in conjunction with a GIS and a 

statistical package, provides several comparison tools. Comparing 1997 and 1998, the fit was 

quite good. Including street segments that had no trips in either year, 55% of the trip links in 

1998 were predicted by the trip links in 1997. The coincidence ratio of .86 for 1998 and the 

distance distribution in Figure 31.2 above indicated a high degree of similarity. However, a 

comparison of the top 300 trip links illustrated that, while zones with many intra-zonal incidents 

were fairly well predicted, inter-zonal trips were not as well predicted. Mapping these made 

clear that 1997 inter-zonal links did not accurately predict specific 1998 links (Figure 31.4). 

However, specific links may be less important from a police perspective than knowledge of the 

frequency of offender travel on specific streets. The coincidence ratio was about the same for 

both the 1997 and 1998 comparisons (Figure 31.2 for night time robbery trips). 

In figure 31.3, predicted and observed overnight robbery trips in 1998 are pictured. To 

graphically indicate the trips, straight lines are used to indicate links between zones and widths to 

indicate volume. An inspection of Figure 31.3 shows that many specific links were not well 

predicted. In general, the prediction underestimated very short trips but overestimated middle 

distance trips (2-4 miles). 

Predicting Overnight Robbery Trips 

After selecting only those 1998 robberies that occurred from 8 PM to 5:59 AM, a zone to 

zone matrix was constructed. This matrix included both intra-zonal (31.5% of the total) and 

inter-zonal trips. As shown in Figure 31.5, zones with many intra-zonal overnight trips also had 

31.13 
Figure 31.3 
Figure 31.4: 
Figure 31.5: 
 

 

 

 

many inter-zonal trips. Intra-zonal links were widely dispersed throughout the city with an area 

of concentration on the west side, but there was no clear pattern. 

Mode Split 

Because of the lack of information about travel mode, the mode split model was not run. 

It is hoped that, with better information, this type of model could be run in the future. 

Network Assignment 

The third, and final, step in the analysis was to examine the likely routes taken as well as 

the total demand placed on the road network. Network assignment is an especially useful tool 

for police work because it can suggest possible locations for intervention. Because it is based 

on the actual street network, it is more concrete than a depiction of links. Therefore, I tested 

several ways to depict network assignment for 1997 robbery travel before proceeding to the 1998 

analysis. 

The network assignment routine in CrimeStat IV outputs two results: 

1. The shortest routes on a street network. For each zone-to-zone pair, the shortest 

path was calculated. 

2. The Network load. Network load counts the number of trips over each street 

segment regardless of origin or destination and sums these. 

Both the shortest routes and the total network load can be based on time or cost rather 

than distance. 

First, all inter-zonal robberies in 1997 were mapped along Chicago=s street network by 

shortest distance (Figure 31.6). The 4000 trips were counted along each of Chicago=s 51,000 

street segments and mapped as a network load (see Chapter 30). As the width and color 

changes from blue to red in Figure 31.6, the number of trips that passed over a segment 

increased. However, this map is difficult to interpret and lacks credibility. Much of the load 

is along small side-streets. Diagonal streets are emphasized and expressways are ignored 

because they usually are not the shortest route in terms of distance. Also, travel in the wrong 

direction on a one-way street is possible since only distance was used to calculate the shortest 

path. The CPD did not believe this to be a useful map. 

31.17 
Figure 31.6: 
 

 

 

The same inter-zonal links were mapped again along using the Chicago modeling 

network, but weighting segments only by distance (Figure 31.7). While this resulted in a 

greatly simplified map, it still lacked some credibility. Expressways are rarely the shortest 

distance, therefore, their use is under emphasized. The algorithm resulted in an over emphasis 

on diagonal main streets. Some connected segments looked like a stair case following along 

Chicago=s grid of main and secondary streets from one high incident neighborhood to another on 

the west and southwest sides. 

Distance did not seem to be a good representation of travel routes. Given that police 

records include time of incident and travel time along Chicago=s road network is available, and 

that CrimeStat allows for analysis by travel time, I re-conceptualized travel cost as shortest time 

rather than distance. 

Shortest Time or Shortest Distance? 

What does distance measure? Traveling ten miles during Chicago's evening rush is 

quite different than at midnight. However, the two blocks from my house to the nearest 

convenience store is unaffected by the time of day and little effected by the mode of 

transportation. While distance appears to be a straightforward measure, it is not. For close 

distances, it specifies knowledge space or the location of routine activities. Further from home, 

it is related to a lack of knowledge but is also an inaccurate measure of the cost of travel. Better 

measures than distance are often available. All U.S. major metropolitan areas map travel time 

by time of day on major streets, feeder streets, and expressways using modeling networks (see 

chapter 30). These maps along with police data on time of incident can be combined to 

realistically describe shortest travel time rather than shortest distance. 

The Chicago Area Transportation Survey (CATS) divides the day into eight time periods 

based on travel demand. Whether a crime trip was intra- or inter-zonal was unaffected by time 

of day (χ

2

=7.07 sig=.421 in 1998). Not surprisingly, the robber's daily travel cycle was 

different than the general population. In 1998, robbers showed little demand for travel in the 

morning rush hour period (6 AM to 10 AM). Of the remaining trips, about half (46% in 1998) 

occurred from 8 PM and 5:59 AM. These overnight trips are the subject of the analysis 

presented here. 

Overnight robbery trip load 

Overnight network load was mapped on Chicago's arterial roads and expressways 

according to both shortest distance (Figure 31.8 left) and shortest time (Figure 31.8 right). 

31.19 
Figure 31.7: 
Figure 31.8 
 

 

 

 

The two maps are very different. Expressways are rarely included in the shortest 

distance between zones. Much of the travel is on diagonal surface streets. However, if travel 

time is taken into account, many of the trips are on expressways and on Lake Shore Drive. This 

is probably a more realistic description of longer distance trips. 

In moving from a complete street network to a simplified network using distance as an 

impedance to a time-based network, the description moves from an unrealistic and probably 

un-interpretable map to one that probably corresponds to the routes taken by offenders. Does 

this add to police knowledge? Of the 10,763 mapped segments in the network, 65.1% had no 

predicted trips assigned to them. Two percent of the road segments, those with 15 or more 

trips, contributed 20.2% of the 16,162 robber's movements across road segments. These were 

typically arterial roads or expressways. By identifying these streets as those most likely to carry 

crime trips, these >hot street= segments could become a focus for police patrol or for intervention 

to prevent crime. 

Conclusions 

Feasibility & Advantages of Crime Travel Demand Modeling 

The police already collect information on the location and time of incidents and the home 

address of arrested offenders. Can this information be utilized to describe and predict the travel 

patterns of Chicago robbers? CrimeStat's trip distribution module was used to describe zonal 

patterns of travel for all known 1997 Chicago robbery offenders. Around 30% of Chicago 

robberies were committed near to the offender's home. For these a zonal model cannot predict 

travel patterns. For other robberies, a time-weighted travel pattern resulted in a more credible 

description than one based on distance. 

The key to analyzing the robber's travel pattern is to reconsider the meaning of distance. 

Close to home or work, distance represents a knowledge space and an opportunity space, a place 

the offender knows in which he or she spends a lot time. This is an area where the benefits of 

knowledge may outweigh the costs of possible capture or it may simply be where the offender 

hangs out.. Further away, shortest distance is a poor representation of travel cost. In major 

metropolitan areas, a better representation is shortest travel time. Combining travel time of day 

with time of incident, results in a more realistic travel pattern. 

These intra- and inter-zonal links are a new way to look at the relationship between 

offender and incident. However, they need some representation before they are useful to the 

police for tactical analysis or crime prevention. In my discussion with the Chicago Police 

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Department, a network load map seemed to be most useful. Network load summarizes the 

number of crime trips that passed over each segment in a road network. 

Limiting analysis to robberies occurring overnight (8PM to 5:59 AM), 1997 travel 

patterns were a good predictor of travel distances, intra-zonal robberies, and network load in 

1998. However, 1997 travel patterns only weakly predicted specific links between traffic 

analysis zones. For 1998 incidents, a trip distribution model (using Poisson regression of the 

zonal count of robbers' homes and incident locations, and an impedance function) modeled the 

overnight travel links between home and incident. Substituting a lognormal impedance function 

that better matched the observed overnight robbery pattern resulted in predictions that were 

nearly as good as the 1997 observed travel patterns. A combination of these predictions with 

analysis of travel patterns over several years might eventually result in an excellent zonal 

prediction of crime travel patterns. 

Crime travel demand analysis is complex and time consuming and requires a relatively 

powerful PC with a large memory capacity. Is it worth it? Yes. Information on crime trips 

is automatically gathered by the police, but it is not fully utilized. However, unlike 

transportation planners, police are generally concerned with the short term and with acute rather 

than chronic problems. They work on an existing street network rather than planning for the 

future. Crime travel demand models may better serve the police as short term descriptions 

rather than long term predictions and can probably be used to describe the effect of specific 

police interventions such as road blocks or drug interdictions. The crime travel demand model 

along with a GIS can identify hot street segmentsBthose segments that are most likely to be on 

the travel routes of offenders and most useful for intervention to prevent crime. 

For researchers, on the other hand, a crime travel demand model is a good way to ask 

long-term, structural questions. If the travel patterns remain relatively constant over time, then 

these relationships can be modeled using a limited number of variables. The result is a way to 

compare different metropolitan areas as well as a way to look at the same metropolitan area over 

different time periods. It is a framework for analysis that is broader than just a 

journey-to-crime type of description. 

Limitations to Crime Travel Demand Modeling 

There are also limitations to the model: 

1. Only crimes with at least one known offender are analyzed. To the extent that 

offender travel patterns in unsolved crimes are different than those with known 

offenders, travel patterns will be misrepresented. 

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2. The model works best if records are gathered in such a way that the address of an 

offender home can be linked to the address of an incident. 

3. The travel demand model assumes that the offender=s home address is accurate. 

Offenders may not have a stable address or may give a false address. 

4. The travel demand model assumes that offenders travel directly from home 

neighborhood to incident neighborhood; many probably do not. 

5. The crime travel demand model is an aggregate model, not a individual one. It 

predicts travel from the center of one zone to the center of another. It cannot 

predict specific trips or the behavior of specific offenders and cannot predict 

travel within a zone. 

6. The model must be crime and city specific. Chicago robbers were much more 

likely to attack close to home than those in Baltimore County (Chapter 28) or Las 

Vegas (Chapter 32). Because these homes were distributed throughout the city, 

the travel patterns of Chicago robbers were much less focused on single target 

zones than in the other test sites. 

7. The study of Chicago was limited to incidents that occurred in the city of 

Chicago. It does not model travel patterns of incidents occurring outside the city 

and can say nothing about them. 

8. The data available from the Chicago Police Department did not allow for a test of 

travel mode used. It cannot be assumed that criminal trips use the same modes 

of transportation as non-criminal trips. 

Chicago is a city of isolated neighborhoods. Even nearby neighborhoods may be terra 

incognita. Crime travel follows the pattern of neighborhoods. In Chicago, many robberies 

occur very close to the home address of the offender. The crime travel demand model cannot 

analyze these crime trips because each zone is represented by a single point. In some 

impoverished neighborhoods, robbery is very common. An offender can opportunistically 

attack on any block. Even when offenders travel they tend to stay nearby their home 

neighborhood. The isolation of robbery in the a few neighborhoods results in a downtown that 

is relatively free of incidents and crime trips that are relatively short. 

Chicago is a mature city. Neighborhoods change slowly. Large scale changes in 

housing, poverty, or attractors do occur and include the destruction of public housing, 

widespread gentrification and the replacement of rail yards with upscale housing. With these 

31.24 
 

changes come new opportunities for crime and changing crime travel patterns. These may be 

predicted with the crime travel demand module. 

31.25 
References 

Block, R. Brice, D, & Galary, A. (2007) The Journey to Crime: Victims and Offenders Converge 

in Violent Index Offenses in Chicago. Security Journal, April 2007. 

Brantingham, P. & Brantingham, P. J. (1984). Patterns in Crime. Macmillan Publishing: New 

York. 

Brantingham, P. & Brantingham, P.J. (1990). Environmental Criminology. Waveland Press: 

Long Grove IL. 

Felson, M. (2002). Crime & Everyday Life (3rd Ed). Sage: Thousand Oaks, CA. 

Rossmo, D. Kim (2000). Geographic Profiling. CRC Press: Boca Raton Fl. 

Sampson & Raudenbush, (2001). Disorder in Urban Neighborhoods: Does it Lead to Crime? 

National Institute of Justice, Washington D.C. 

Wright, R. T. & Decker, S. H. (1997). Armed Robbers in Action: Stickups and Street Culture. 

Northeastern University Press, Boston 

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