Chapter 13: 

Journey-to-Crime Estimation 

Ned Levine 

Ned Levine & Associates 

Houston, TX 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table of Contents 

Location Theory 13.1

 Predicting Location from a Distribution 13.2 

Travel Demand Modeling 13.2

 Social Applications of the Gravity Concept 13.3 

 Intervening Opportunities 13.6 

 Urban Transportation Modeling 13.6 

 Alternative Distance Decay Functions 13.7 

Travel Behavior of Criminals 13.8

 Journey-to-crime Trips 13.8 

 Journey-to-crime trips by crime type 13.8 

 Personal characteristics and the journey-to-crime 13.9 

 Modeling the Offender Search Area 13.10 

Predicting the Location of Serial Offenders 13.11

 Geographic Profiling 13.11 

The CrimeStat Journey-to-crime Routine 13.12 

Journey-to-crime Estimation Using Mathematical Functions 13.16 

 Probability Distance Functions 13.16 

Linear 13.16 

 Negative exponential 13.18 

 Normal 13.18 

 Lognormal 13.19 

 Truncated negative exponential 13.19 

Calculating an Appropriate Probability Distance Function 13.20 

Example of Calibrating a Journey-to-crime Estimate with 

a Mathematical Function 13.21 

 Estimating Parameter Values Using Grouped Data 13.24 

Linear 13.24 

 Negative exponential 13.24 

 Normal 13.25 

 Lognormal 13.25 

 Truncated negative exponential 13.26 

 Example from Baltimore County, MD 13.28 

 Testing for Residual Errors in the Model 13.32 

Problems with Mathematical Distance Decay Functions 13.36 

Uses of Mathematical Distance Decay Functions 13.37 

Using the Routine with a Mathematical Function 13.38 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table of Contents (continued) 

Empirically Estimating a Journey-to-crime Calibration Function 13.38

 Calibrate Kernel Density Estimate 13.41 

 Data set definition 13.42 

 Kernel parameters 13.44 

 Saved calibration file 13.46 

Calibrate 13.46 

 Examples from Baltimore County, MD 13.46 

Journey-to-crime Estimation Using a Calibrated File 13.49

 Application of the Routine 13.56 

 Choice of Calibration Sample 13.58 

Sample Data Sets for Journey-to-crime Routines 13.61 

How Accurate are the Methods? 13.61 

 Test Sample of Serial Offenders 13.61 

 Identifying the Crime Type 13.62 

Identifying the Home Base and Incident Locations 13.63 

 Evaluated Methods 13.63 

 The Test 13.63 

 Measurement Error 13.64 

 Results of the Test 13.64 

 Search Area for a Serial Offender? 13.67 

Confirmation of These Results 13.69 

 Theoretical Limitations 13.69 

 Cautionary Notes 13.71 

Draw Crime Trips 13.72 

References 13.74 

Endnotes 13.83 

Attachments 13.84 

A. A Note on Alternative Journey-to-crime Models 

By Ned Levine 13.85 

B. Using CrimeStat for Geographic Profiling 

By Brent Snook, Paul J. Taylor, and Craig Bennell 13.93 

C. Using Journey-to-crime Routine for Journey-after-crime Analysis 

By Yongmei Lu 13.94 
 

 

Table of Contents (continued) 

D. Using Journey-to-crime Analysis for Different Age Groups of Offenders 

By Renato Assunção, Cláudio Beato, and Bráulio Silva 13.95 

E. Catching the Bad Guy 

By Bryan Hill 13.96 

F. Constructing Geographic Profiles Using the CrimeStat Journey-to-crime routine 

By Josh Kent and Michael Leitner 13.97 

G. Predicting Serial Offender Residence by Cluster in Korea 

By Kang Eun Kyoung 13.98 
Chapter 13: 

Journey-to-Crime Estimation 

The Journey-to-crime (Jtc) routine is a distance-based method that makes estimates about 

the likely residential location of a serial offender. It is an application of location theory, a 

framework for identifying optimal locations from a distribution of markets, supply 

characteristics, prices, and events. The following discussion gives some background to the 

technique. Those wishing to skip this part can go to page 13.12 for the specifics of the Jtc 

routine. 

Location Theory 

Location theory is concerned with one of the central issues in geography. This theory 

attempts to find an optimal location for any particular distribution of activities, population, or 

events over a region (Haggett, Cliff & Frey, 1977; Krueckeberg & Silvers, 1974; Stopher & 

Meyburg, 1975; Oppenheim, 1980, Ch. 4; Bossard, 1993). In classic location theory, economic 

resources were allocated in relation to idealized representations (Anselin & Madden, 1990). 

Thus, von Thünen (1826) analyzed the distribution of agricultural land as a function of the 

accessibility to a single population center (which would be more expensive towards the center), 

the value of the product produced (which would vary by crop), and transportation costs (which 

would be more expensive farther from the center). In order to maximize profit and minimize 

costs, a distribution of agricultural land uses (or crop areas) emerges flowing out from the 

population center as a series of concentric rings. Weber (1909) analyzed the distribution of 

industrial locations as a function of the volume of materials to be shipped, the distance that the 

goods had to be shipped, and the unit distance cost of shipping; consequently, industries become 

located in particular concentric zones around a central city. Burgess (1925) analyzed the 

distribution of urban land uses in Chicago and described concentric zones of both industrial and 

residential uses. Their theory formed the backdrop for early studies on the ecology of criminal 

behavior and gangs (Thrasher, 1927; Shaw, 1929). 

In more modern use, the location of persons with a certain need or behavior (the >demand=

side) is identified on a spatial plane and places are selected as to maximize value and minimize 

travel costs. For example, for a consumer faced with two retail shops selling the same product, 

one being closer but more expensive while the other being farther but less expensive, the 

consumer has to trade off the value to be gained against the increased travel time required. In 

designing facilities or places of attraction (the >supply= side), the distance between each possible 

facility location and the location of the relevant population is compared to the cost of locating 

near the facility. For example, given a distribution of consumers and their propensity to spend, 

13.1 
 

such a theory attempts to locate the optimal placement of retail stores, or, given the distribution 

of patients, the theory attempts to locate the optimal placement of medical facilities. 

Predicting Locations from a Distribution 

One can also reverse the logic. Given the distribution of demand, the theory could be 

applied to estimate a central location from which travel distance or time is minimized. One of 

the earliest uses of this logic was that of John Snow, who was interested in the causes of cholera 

in the mid-19th century (Cliff and Haggett, 1988). He postulated the theory that water was the 

major vector transmitting the cholera bacteria. After investigating water sources in the London 

metropolitan area and concluding that there was a relationship between contaminated water and 

cholera cases, he was able to confirm his theory by an outbreak of cholera cases in the Soho 

district of London. By plotting the distribution of the cases and looking for water sources in the 

center of the distribution (essentially, the center of minimum distance; see Chapter 4), he found a 

well on Broad Street that was, in fact, contaminated by seepage from nearby sewers. The well 

was closed and the epidemic in Soho receded. Incidentally, in plotting the incidents on a map 

and looking for the center of the distribution, Snow applied the same logic that had been 

followed by the London Metropolitan Police Department who had developed the famous >pin=

map in the 1820s. 

Theoretically, there is an optimal solution that minimizes the distance between demand 

and supply (Rushton, 1979). However, computationally, it is an almost impossible task to 

define, requiring the enumeration of every possible combination. Consequently in practice, 

approximate, though sub-optimal, solutions are obtained through a variety of methods (Everitt, 

2011, Ch. 4). 

Travel Demand Modeling 

A sub-set of location theory models the travel behavior of individuals. It actually is the 

converse. If location theory attempts to allocate places or sites in relation to both a supply-side 

and demand-side, travel demand theory attempts to model how individuals travel between places, 

given a particular constellation of them. One concept that has been frequently used for this 

purpose is that of the gravity function, an application of Newton=s fundamental law of attraction 

(Oppenheim, 1980). In the original Newtonian formulation, the attraction, F, between two 

bodies of respective masses Mi and Mj, separated by a distance d12, will be equal to: 

(13.1) 

13.2 
 

 

where g is a constant or scaling factor which ensures that the equation is balanced in terms of the 

measurement units (Oppenheim, 1980). As we all know, of course, g is the gravitational 

constant in the Newtonian formulation. The numerator of the function is the attraction term (or, 

alternatively, the attraction of M2 for M1) while the denominator of the equation, D2

, indicates 

that the attraction between the two bodies falls off as a function of their squared distance. It is an 

impedance term. 

Social Applications of the Gravity Concept 

The gravity model has been the basis of many applications to human societies and has 

been applied to social interactions since the 19th century. Ravenstein (1895) and Andersson 

(1897) applied the concept to the analysis of migration by arguing that the tendency to migrate 

between regions is inversely proportional to the squared distance between the regions. Reilly=s 

>law of retail gravitation= (1929) applied the Newtonian gravity model directly and suggested that 

retail travel between two centers would be proportional to the product of their populations and 

inversely proportional to the square of the distance separating them: 

(13.2) 

where Tij is the interaction between centers i and j, Pi and Pj are the respective populations, dij is 

the distance between them raised to the second power and α is a balancing constant. In the 

model, the initial population, Pi, is called a production while the second population, Pj, is called 

an attraction. 

Stewart (1950) and Zipf (1949) applied the concept to a wide variety of phenomena 

(migration, freight traffic, exchange of information) using a simplified form of the gravity 

equation: 

(13.3) 

where the terms are as in equation 13.2 but the exponent of distance is only 1. In doing so, they 

basically linked location theory with travel behavior theory. Given a particular pattern of 

interaction for any type of goods, service or human activity, an optimal location of facilities 

should be solvable. 

In the Stewart/Zipf framework, the two P=s were both population sizes and, therefore, 

their sums had to be equal. However, in modern use, it=s not necessary for the productions and 

attractions to be identical units (e.g., Pi could be population while Pj could be employment). 

13.3 
 

 

 

 

The total volume of productions (trips) from a single location, i, is estimated by summing 

over all destination locations, j: 

(13.4) 

where Ti is the number of trip originating from zone ݅, K is a constant, and L is the number of 

zones. 

Over time, the concept has been generalized and applied to many different types of travel 

behavior. For example, Huff (1963) applied the concept to retail trade between zones in an 

urban area using the general form of: 

(13.5) 

where Tij is the number of purchases in location j by residents of location i, Aj is the 

attractiveness of zone j (e.g., square footage of retail space), dij is the distance between zones i 

and j, β is the exponent of Sj, and λ is the exponent of distance, and α is a constant (Bossard, 

1993). The distance component, dij

-λ

 , is sometimes called an inverse distance function. This is a 

single constraint model in that only the attractiveness of a commercial zone is constrained, that is 

the sum of all attractions for j must equal the total attraction in the region. 

Again, it can be generalized to all zones by, first, estimating the total trips generated from 

one zone, i, to another zone, j: 

(13.6) 

where Tij is the interaction between two locations (or zones), Pi is productions of trips from 

location/zone i, Aj is the attractiveness of location/zone j, Dij is the distance between zones i and 

j, β is the exponent of Sj, ρ is the exponent of Hi, λ is the exponent of distance, and α is a 

constant. 

Second, the total number of trips generated by a location, i, to all destinations is obtained 

by summing over all destination locations, j: 

(13.7) 

13.4 
 

This differs from the traditional gravity function by allowing the exponents of the 

production from location i, the attraction from location j, and the distance between zones to vary. 

Typically, these exponents are calibrated on a known sample before being applied to a forecast 

sample and the locations are usually measured by zones. Thus, retailers in deciding on the 

location of a new store can use this type of model to choose a site location to optimize travel 

behavior of patrons. They will, typically, obtain data on actual shopping trips by customers and 

then calibrate the model on the data, estimating the exponents of attraction and distance. The 

model can then be used to predict future shopping trips if a facility is built at a particular 

location. 

This type of function is called a double constraint model because the balancing constant, 

K, has to be constrained by the number of units in both the origin and destination locations; that 

is, the sum of Pi over all locations must be equal to the total number of productions while the 

sum of Pj over all locations must be equal to the total number of attractions. Adjustments are 

usually required to have the sum of individual productions and attractions equal the totals 

(usually estimated independently). 

The equation can be generalized to other types of trips and different metrics can be 

substituted for distance, such as travel time, effort, or cost (Isard, 1960). For example, for 

commuting trips, usually employment is used for attractions, frequently sub-divided into retail 

and non-retail employment. In addition, for productions, median household income or car 

ownership percentage is used as an additional production variable. Equation 13.7 can be 

generalized to include any type of production or attraction variable (13.8 and 10.9): 

(13.8) 

(13.9) 

where Tij is the number of trips produced by location i that travel to location j, Pi is either a 

single variable associated with trips produced from a zone or the cross-product of two or more 

variables associated with trips produced from a zone, Aj is either a single variable associated 

with trips attracted to a zone or the cross-product of two or more variables associated with trips 

attracted to a zone, dij is either the distance between two locations or another variable measuring 

travel effort (e.g., travel time, travel cost), ρ, β, and λ are exponents of the respective terms, α1 is 

a constant associated with the productions to ensure that the sum of trips produced by all zones 

equals the total number of trips for the region (usually estimated independently), and α2 is a 

constant associated with the attractions to ensure that the sum of trips attracted to all zones 

equals the total number of trips for the region. Without having two constants in the equation, 

13.5 
usually conflicting estimates of K will be obtained by balancing the equation against productions 

or attractions. The summation over all destination locations, j (Equation 13.9), produces the total 

number of trips from zone i. 

Intervening Opportunities 

Stouffer (1940) modified the simple gravity function by arguing that the attraction 

between two locations was a function not only of the characteristics of the relative attractions of 

two locations, but of intervening opportunities between the locations. His hypothesis, A.assumes 

that there is no necessary relationship between mobility and distance.. that the number of persons 

going a given distance is directly proportional to the number of opportunities at that distance and 

inversely proportional to the number of intervening opportunities@(Stouffer, 1940, p. 846). This 

model was used in the 1940s to explain interstate and intercounty migration (Bright & Thomas, 

1941; Isbell, 1944; Isard, 1979). Using the gravity type formulation, we can write this as: 

(13.10) 

where Tji is the attraction of location j by residents of location i, Aj is the attractiveness of zone j, 

Ak is the attractiveness of all other locations that are intermediate in distance between locations i 

and j, dij is the distance between zones i and j, β is the exponent of Sj, ξ is the exponent of Sk, λ is 

the exponent of distance, and α is a constant. While the intervening opportunities are implicit in 

Equation 13.5 in the exponents, β and λ, and coefficient, K, Equation 13.10 makes the 

intervening opportunities explicit. The importance of the concept is that the interaction between 

two locations becomes a complex function of the spatial environment of nearby areas and not 

just of the two locations. 

Urban Transportation Modeling 

This type of model is incorporated as a formal step in the urban transportation planning 

process, implemented by most regional planning organizations in the United States and 

elsewhere (Stopher & Meyburg, 1975; Krueckeberg & Silvers, 1974; Field & MacGregor, 1987). 

The step, called trip distribution, is linked to a five step model. First, data are obtained 

on travel behavior for a variety of trip purposes. This is usually done by sampling households 

and asking each member to keep a travel diary documenting all their trips over a two or three day 

period. Trips are aggregated by individuals and by households. Frequently, trips by different 

purposes are separated. Second, the volume of trips produced by and attracted to zones (called 

traffic analysis zones) is estimated, usually on the basis of the number of households in the zone 

and some indicator of income or private vehicle ownership. Third, trips produced by each zone 

13.6 
are distributed to every other zone usually using a gravity-type function (Equation 13.9). That is, 

the number of trips produced by each origin zone and ending in each destination zone is 

estimated by a gravity model. The distribution is based on trip productions, trip attractions, and 

travel >resistance= (measured by travel distance or travel time). Fourth, zone-to-zone trips are 

allocated by mode of travel (car, bus, walking, etc); and, fifth, trips are assigned to particular 

routes by travel mode (i.e., bus trips follow different routes than private vehicle trips). The 

advantage of this process is that trips are allocated according to origins, destinations, distances 

(or travel times), modes of travel and routes. Since all zones are modeled simultaneously, all 

intermediate destinations (i.e., intervening opportunities) are incorporated into the model. 

Chapters 25-31 present a crime travel demand model, an application of travel demand modeling 

to crime. 

Alternative Distance Decay Functions 

One of the problems with the traditional gravity formulation is in the measurement of 

travel resistance, either distance or time. For locations separated by sizeable distances in space, 

the gravity formulation can work properly. However, as the distance between locations 

decreases, the denominator approaches infinity. Consequently, an alternative expression for the 

interaction has been proposed which uses the negative exponential function (Hägerstrand, 1957; 

Wilson, 1970): 

(13.11) 

where Aji is the attraction of location j for residents of location i, Sj is the attractiveness of 

location j, Dij is the distance between locations i and j, β is the exponent of Sj, e is the base of the 

natural logarithm (i.e., 2.7183..), and α is an empirically-derived exponent. Sometimes known as 

entropy maximization, the latter part of the equation includes a negative exponential function 

which has a maximum value of 1 (i.e., e-0 = 1). This has the advantage of making the equation 

more stable for interactions between locations that are close together. For example, Cliff and 

Haggett (1988) used a negative exponential gravity-type model to describe the diffusion of 

measles into the United States from Canada and Mexico. It has also been argued that the 

negative exponential function generally gives a better fit to urban travel patterns, particularly by 

automobile (Foot, 1981; Bossard, 1993; NCHRP, 1995). 

Other functions have also be used to describe the distance decay - negative linear, normal 

distribution, lognormal distribution, quadratic, Pareto function, square root exponential, and so 

forth (Haggett & Arnold, 1965; Taylor, 1970; Eldridge & Jones, 1991). Later in the chapter, we 

will explore several different mathematical formulations for describing the distance decay. One, 

13.7 
in fact, does not need to use a mathematical function at all, but could empirically describe the 

distance decay from a large data set and utilize the described values for predictions. 

The use of mathematical functions has evolved out of both the Newtonian tradition of 

gravity as well as various location theories which used the gravity function. A mathematical 

function makes sense under two conditions: 1) if travel is uniform in all directions; and 2) as an 

approximation if there is inadequate data from which to calibrate an empirical function. The first 

assumption is usually wrong since physical geography (i.e., oceans, rivers, mountains) as well as 

asymmetrical street networks make travel easier in some directions than others. As we shall see 

below, the distance decay is quite irregular for Journey-to-crime trips and would be better 

described by an empirical, rather than mathematical function. 

In short, there is a long history of research on both the location of places as well as the 

likelihood of interaction between these places, whether the interaction is freight movement, land 

prices or individual travel behavior. The gravity model and variations on it have been used to 

describe the interactions between these locations. 

Travel Behavior of Criminals 

Journey-to-crime Trips 

The application of travel behavior theory to crime has a sizeable history as well. The 

analysis of distance for Journey-to-crime trips was applied in the 1930s by White (1932), who 

noted that property crime offenders generally traveled farther distances than offenders 

committing crimes against people, and by Lottier (1938), who analyzed the ratio of chain store 

burglaries to the number of chain stores by zone in Detroit. Turner (1969) analyzed delinquency 

behavior by a distance decay travel function showing how more crime trips tend to be close to 

the offender=s home with the frequency dropping off with distance. Phillips (1980) is, 

apparently, the first to use the term Journey-to-crime is describing the travel distances that 

offenders make though Harries (1980) noted that the average distance traveled has evolved by 

that time into an analogy with the journey to work statistic. 

Journey-to-crime trips by crime type 

Rhodes and Conly (1981) expanded on the concept of a criminal commute and showed 

how robbery, burglary and rape patterns in the District of Columbia followed a distance decay 

pattern. LeBeau (1987a) analyzed travel distances of rape offenders in San Diego by victimoffender relationships and by method of approach. Boggs (1965) applied the intervening 

opportunities model in analyzing the distribution of crimes by area in relation to the distribution 

of offenders. Other empirical descriptions of Journey-to-crime distances and other travel 

13.8 
behavior parameters have been studied by Blumin (1973), Curtis (1974), Repetto (1974), Pyle 

(1974), Capone and Nichols (1975), Rengert (1975), Smith (1976), LeBeau (1987b), and Canter 

and Larkin (1993). It has generally been accepted that property crime trips are longer than 

personal crime trips (LeBeau, 1987a), though exceptions have been noted (Turner, 1969). Also, 

it would be expected that average trip distances will vary by a number of factors: crime type; 

method of operation; time of day; and, even, the value of the property realized (Capone & 

Nichols, 1975). 

In more recent years, there have been more focused studies of travel behavior by types of 

crime: commercial robberies in the Netherlands (Van Koppen and Jansen, 1998); vehicle thefts 

in Baltimore County (Levine, 2005); robberies in Chicago and confrontations, burglaries, and 

vehicle thefts in Las Vegas (Block and Helms, 2005); residential burglaries in The Hague 

(Bernasco and Nieuwbeerta, 2005); homicides in Washington, DC (Groff and McEwen, 2005); 

bank robberies in Baltimore County (Levine, 2007); robberies in Chicago (Bernasco and Block, 

2009); and the trips of drunk drivers involved in crashes in Baltimore County (Levine & Canter, 

2011). These studies show substantial variability in crime trip lengths with many trips being 

long. 

Personal characteristics and the journey-to-crime 

In addition, there are several studies that have examined the how the personal 

characteristics of offenders effect their journey-to-crime. In terms of gender, Rengert (1975) 

found that female offenders were more likely to commit crimes within their own residential area 

than male offenders, hence making shorter trips, a result supported by Pettiway (1995) and by 

Groff and McEwen (2005). However, Phillips (1980) found that female offenders traveled 

longer distances, on average, than male offenders, a result supported by Fritzon (2001) who 

studied female arsonists. 

In terms of age of the offender, several studies (Groff & McEwen, 2005; Snook, Cullen, 

Mokros, & Harbort, 2005; Bernasco & Nieuwbeerta, 2005; Snook, 2004; Warren, Reboussin, 

Hazelwood, Cummings, Gibbs, & Trumbetta, 1998) have shown that generally juveniles make 

shorter trips. 

However, none of these studies attempted to control for myriad of factors that affect the 

journey-to-crime. In a more controlled study, Levine and Lee (2012) examined the interaction of 

gender and age group for offenders in Manchester, England and found distinct interactions 

between gender and age group. Juvenile male offenders had the shortest crime trips where adult 

male offenders had the longest. Female offenders, both juveniles and adults, had moderately 

long crime trips, though not as long as the adult males. However, a much higher proportion of 

crime trips by females went to commercial areas, in particular the town centre in Manchester. 

13.9 
Modeling the Offender Search Area 

Conceptual work on the type of model have been made by Brantingham and Brantingham 

(1981) who analyzed the geometry of crime and conceptualized a criminal search area, a 

geographical area modified by the spatial distribution of potential offenders and potential targets, 

the awareness spaces of potential offenders, and the exchange of information between potential 

offenders. In this sense, their formulation is similar to that of Stouffer (1940), who described 

intervening opportunities, though their=s is a behavioral framework. An important concept 

developed by the Brantingham=s is that of decreased criminal activity near to an offender=s home 

base, a sort of a safety area around their near neighborhood. Presumably, offenders, particularly 

those committing property crimes, go a little way from their home base so as to decrease the 

likelihood that they will get caught. This was noted by Turner (1969) in his study of delinquency 

in Philadelphia. Thus, the Brantingham=s postulated that there would be a small safety area (or 

>buffer= zone) of relatively little offender activity near to the offender=s base location; beyond that 

zone, however, they postulated that the number of crime trips would decrease according to a 

distance decay model (the exact mathematical form was never specified, however). 

Crime trips may not even begin at an offender=s residence. Routine activity theory 

(Felson, 2002; Cohen & Felson, 1979) suggests that crime opportunities appear in the activities 

of everyday life. The routine patterns of work, shopping, and leisure affect the convergence in 

time and place of would be offenders, suitable targets, and absence of guardians. Many crimes 

may occur while an offender is traveling from one activity to another. Thus, modeling crime 

trips as if they are referenced relative to a residence is not necessarily going to lead to better 

prediction. 

The mathematics of Journey-to-crime has been modeled by Rengert (1981) using a 

modified general opportunities model: 

(13.12) 

where Pij is the probability of an offender in location (or zone) i committing an offense at 

location j, Ui is a measure of the number of crime trips produced at location i (what Rengert 

called emissiveness), Vj is a measure of the number of crime targets (attractiveness) at location j, 

and f(Dij) is an unspecified function of the cost or effort expended in traveling from location i to 

location j (distance, time, cost). He did not try to operationalize either the production side or the 

attraction side. Nevertheless, conceptually, a crime trip would be expected to involve both 

elements as well as the cost of the trip. 

In short, there has been a great deal of research on the travel behavior of criminals in 

committing acts as well as a number of statistical formulations. 

13.10 
Predicting the Location of Serial Offenders 

The Journey-to-crime formulation, as in Equation 13.9, has been used to estimate the 

origin location of a serial offender based on the distribution of crime incidents. The logic is to 

plot the distribution of the incidents and then use a property of that distribution to estimate a 

likely origin location for the offender. Inspecting a pattern of crimes for a central location is an 

intuitive idea that police departments have used for a long time. The distribution of incidents 

describes an activity area by an offender, who often lives somewhere in the center of the 

distribution. It is a sample from the offender=s activity space. Using the Brantingham=s 

terminology, there is a search area by an offender within which the crimes are committed; most 

likely, the offender also lives within the search area. 

For example, Canter (1994) shows how the area defined by the distribution of the >Jack 

the Ripper= murders in the east end of London in the 1880s included the key suspects in the case 

(though the case was never solved). Kind (1987) analyzed the incident locations of the 

>Yorkshire Ripper= who committed thirteen murders and seven attempted murders in northeast 

England in the late 1970s and early 1980s. Kind applied two different geographical criteria to 

estimate the residential location of the offender. First, he estimated the center of minimum 

distance. Second, on the assumption that the locations of the murders and attempted murders that 

were committed late at night were closer to the offender=s residence, he graphed the time of the 

offense on the Y axis against the month of the year (taken as a proxy for length of day) on the X 

axis and plotted a trend line through the data to account for seasonality. Both the center of 

minimum distance and the murders committed at a later time than the trend line pointed towards 

the Leeds/Bradford area, very close to where the offender actually lived (in Bradford). 

There are several alternative models that have been proposed for Journey-to-crime 

modeling. The major ones are discussed in depth in Attachment A at the end of the chapter. 

Geographic Profiling 

Journey-to-crime estimation should be distinguished from geographical profiling. 

Geographical profiling involves understanding the geographical search pattern of criminals in 

relation to the spatial distribution of potential offenders and potential targets, the awareness 

spaces of potential offenders including the labeling of >good= targets and crime areas, and the 

interchange of information between potential offenders who may modify their awareness space 

(Brantingham & Brantingham, 1981). According to Rossmo: 

A..Geographic profiling focuses on the probable spatial behaviour of the offender within 

the context of the locations of, and the spatial relationships between, the various crime 

sites. A psychological profile provides insights into an offender=s likely motivation, 

13.11 
behaviour and lifestyle, and is therefore directly connected to his/her spatial activity. 

Psychological and geographic profiles thus act in tandem to help investigators develop a 

picture of the person responsible for the crimes in question@ (Rossmo, 1997). 

In other words, geographic profiling is a framework for understanding how an offender 

traverses an area in searching for victims or targets; this, of necessity, involves understanding the 

social environment of an area, the way that the offender understands this environment (the 

>cognitive map=) as well as the offender=s motives. 

On the other hand, Journey-to-crime estimation follows a much simpler logic involving 

the distance dimension of the spatial patterning of a criminal. It is a method aimed at estimating 

the distance that serial offenders will travel to commit a crime and, by implication, the likely 

location from which they started their crime >trip=. In short, it is a strictly statistical approach to 

estimating the residential whereabouts of an offender compared to understanding the dynamics 

of serial offenders. 

It remains an empirical question whether a conceptual framework, such as geographic 

profiling, can predict better than a strictly statistical framework. Understanding a phenomenon, 

such as serial murders, serial rapists, and so forth, is an important research area. We seek more 

than just statistical prediction in building a knowledge base. However, it does not necessarily 

follow that understanding produces better predictions. In many areas of human activity, strictly 

statistical models are better at predicting than explanatory models. I will return to this point later 

in the section. 

The CrimeStat Journey-to-crime Routine 

The Journey-to-crime (Jtc) routine is a diagnostic designed to aid police departments in 

their investigations of serial offenders. The aim is to estimate the likelihood that a serial 

offender lives at any particular location. Using the location of incidents committed by the serial 

offender, the program makes statistical guesses at where the offender is liable to live, based on 

the similarity in travel patterns to a known sample of serial offenders for the same type of crime. 

The Jtc routine builds on the Rossmo (1993a; 1993b; 1995) framework, but extends its modeling 

capability. 

1. A grid is overlaid on top of the study area. This grid can be either imported or 

can be generated by CrimeStat (see Chapter 3). The grid represents the entire 

study area. There is no optimal study area. The technique will model that which 

is defined. Thus, the user has to select an area intelligently. 

13.12 
2. The routine calculates the distance between each incident location committed by a 

serial offender (or group of offenders working together) and each cell, defined by 

the centroid of the cell. Rossmo (1993a; 1995) used indirect (Manhattan) 

distances. However, this would be appropriate only when a city falls on a 

uniform grid. The Jtc routine allows direct, indirect or network distances. These 

are defined on the Measurement Parameters page (see Chapter 3) In most cases, 

direct distances would be the most appropriate choice as a police department 

would normally locate origin and destination locations rather than particular 

routes that are taken (see below). 

3. A distance decay function is applied to each grid cell-incident pair and sums the 

values over all incidents. The user has a choice whether to model the travel 

distance by a mathematical function or an empirically-derived function. 

4. The resultant of the distance decay function for each grid cell-incident pair is 

summed over all incidents to produce a likelihood (or density) estimate for each 

grid cell. 

5. In both cases, the program outputs the two results: 1) the grid cell which has the 

peak likelihood estimate; and 2) the likelihood estimate for every cell. The latter 

output can be saved as a Surfer7 for Windows >dat=, ArcGIS Spatial Analyst8 >asc=, 

ASCII >grd=, ArcGIS7 >.shp=, MapInfo7 >.mif= , or as an Ascii grid >grd= file which 

can be read by many GIS packages (e.g., Vertical Mapper8 ). These files can also 

be read by other GIS packages (e.g., Maptitude). 

Figure 13.1 shows the logic of the routine and Figure 13.2 shows the Journey-to-crime 

(Jtc) screen. There are two parts to the routine. First, there is a calibration model that is used in 

the empirically-derived distance function based on a large sample of crime trips by offenders. 

Second, there is the Journey-to-crime (Jtc) model for estimating the likely origin location of a 

single serial offender. To estimate the function, the user can select either the already-calibrated 

distance function or the mathematical function. The empirically-derived function is, by far, the 

easiest to use and is, consequently, the default choice in CrimeStat. It is discussed below. 

However, the mathematical function can be used if there is inadequate data to construct an 

empirical distance decay function or if a particular form is desired. 

13.13 
Figure 13.1: 

Logic of Journey to Crime Interpolation Routin

Distance decay function 

Primary file: Crime locations 

Reference grReference gr
Figure 13.2: 

Journey-to-crime Screen 
 

1 

Journey-to-crime Estimation Using Mathematical Functions 

Let us start by illustrating the use of the mathematical functions because this has been the 

traditional way that distance decay has been examined. The CrimeStat Jtc routine allows the 

user to define distance decay by a mathematical function. 

Probability Distance Functions 

The user selects one of five probability density distributions to define the likelihood 

that the offender has traveled a particular distance to commit a crime. The advantage of having 

five functions, as opposed to only one, is that it provides more flexibility in describing travel 

behavior. The travel distance distribution followed will vary by crime type, time of day, method 

of operation, and numerous other variables. The five functions allow an approach that can 

simulate more accurately travel behavior under different conditions. Each of these has 

parameters that can be modified, allowing a very large number of possibilities for describing 

travel behavior of a criminal. 

Figure 13.3 illustrates the five types.1

 Default values based on Baltimore County have 

been provided for each. The user, however, can change these as needed. Briefly, the five 

functions are: 

Linear 

The simplest type of distance model is a linear function. This model postulates that the 

likelihood of committing a crime at any particular location declines by a constant amount with 

distance from the offender=s home. It is highest near the offender=s home but drops off by a 

constant amount for each unit of distance until it falls to zero. The form of the linear equation is: 

(13.14) 

There are, of course, many other types of mathematical functions that can be used to describe a declining 

likelihood with distance. However, the five types of functions presented here are commonly used. We 

avoided the inverse distance function because of its potential to distort the likelihood relationship: 

(13.13) 

where k is a power (e.g., 1, 2, 2.5). For large distances, this function can be a useful approximation of the 

lessening travel interaction with distance. However, for short distances, the function goes towards infinity 

as the distance approaches zero. In fact, for dij = 0, the function is unsolvable. Since many distances 

between reference cells and incidents will be zero or close to zero, the function becomes unusable. 

13.16 
Figure 13.3: 

Journey-to-crime Travel Demand Functions 

Five Mathematical Functions Five Mathematical Functions 
where f(dij) is the likelihood that the offender will commit a crime at a particular location, i, 

defined here as the center of a grid cell, dij is the distance between the offender=s residence and 

location i, A is a slope coefficient which defines the fall off in distance, and B is a constant. It 

would be expected that the coefficient B would have a negative sign since the likelihood should 

decline with distance. The user must provide values for A and B. The default for A is 1.9 and 

for B is -0.06. This function assumes no buffer zone around the offender=s residence. When the 

function reaches 0 (the X axis), the routine automatically substitutes a 0 for the function. 

Negative Exponential 

A slightly more complex function is the negative exponential. In this type of model, the 

likelihood is also highest near the offender’s home and drops off with distance. However, the 

decline is at a constant rate of decline, thus dropping quickly near the offender=s home until is 

approaches zero likelihood. The mathematical form of the negative exponential is 

(13.15) 

where f(dij) is the likelihood that the offender will commit a crime at a particular location, i, 

defined here as the center of a grid cell, dij is the distance between each reference location and 

each crime location, e is the base of the natural logarithm, A is the coefficient and B is an 

exponent of e. The user inputs values for A - the coefficient, and B - the exponent. The default 

for A is 1.89 and for B is -0.06. This function is similar to the Canter model (discussed in 

Attachment A) except that the coefficient is calibrated. Also, like the linear function, it assumes 

no buffer zone around the offender=s residence. 

Normal 

A normal distribution assumes the peak likelihood is at some optimal distance from the 

offender=s home base. Thus, the function rises to that distance and then declines. The rate of 

increase prior to the optimal distance and the rate of decrease from that distance is symmetrical 

in both directions. The mathematical form is: 

(13.16) 

(13.17) 

13.18 
where f(dij) is the likelihood that the offender will commit a crime at a particular location, i 

(defined here as the center of a grid cell), dij is the distance between each reference location and 

each crime location, ݀̅ is the mean distance input by the user, Sd is the standard deviation of 

distances, e is the base of the natural logarithm, and A is a coefficient. The user inputs values for 

݀̅, Sd, and A. The default values are 4.2 for the mean distance, ݀̅, 4.6 for the standard deviation, 

Sd, and 29.5 for the coefficient, A. 

By carefully scaling the parameters of the model, the normal distribution can be adapted 

to a distance decay function with an increasing likelihood for near distances and a decreasing 

likelihood for far distances. Choosing a standard deviation greater than the mean (e.g., ݀̅ = 1,Sd 

= 2) will skew the distribution to the left. The function becomes similar to the model postulated 

by Brantingham and Brantingham (1981) in that it is a single function which describes travel 

behavior. 

Lognormal 

The lognormal function is similar to the normal except it is more skewed, either to the 

left or to the right. It has the potential of showing a very rapid increase near the offender=s home 

base with a more gradual decline from a location of peak likelihood (see Figure 13.3). It is also 

similar to the Brantingham and Brantingham (1981) model. The mathematical form of the 

function is: 

(13.18) 

where f(dij) is the likelihood that the offender will commit a crime at a particular location, i, 

defined here as the center of a grid cell, dij is the distance between each reference location and 

each crime location, ݀̅ is the mean distance, Sd is the standard deviation of distances, e is the 

base of the natural logarithm, and A is a coefficient. The user inputs ݀̅, Sd , and A. The default 

values are 4.2 for the mean distance, ݀̅, 4.6 for the standard deviation, Sd, and 8.6 for the 

coefficient, A. They were calculated from the Baltimore County data (see Table 13.3). 

Truncated Negative Exponential 

The truncated negative exponential is a joined function made up of two distinct 

mathematical functions - the linear and the negative exponential. For the near distance, a 

positive linear function is defined, starting at zero likelihood for distance 0 and increasing to dp, a 

location of peak likelihood. Thereupon, the function follows a negative exponential, declining 

quickly with distance. The two mathematical functions making up this spline function are 

13.19 
 

 

2 

 Linear: (13.19) 

Negative

 Exponential: (13.20) 

where dij is the distance from the home base, B is the slope of the linear function and for the 

negative exponential function A is a coefficient and C is an exponent. Since the negative 

exponential only starts at a particular distance, dp, A, is assumed to be the intercept if the Y-axis 

were transposed to that distance. Similarly, the slope of the linear function is estimated from the 

Cutoff distance, dp, by a peak likelihood function. The default values are 0.4 for the Cutoff 

distance, dp, 13.8 for the peak likelihood, and -0.2 for the exponent, C. Again, these were 

calculated with Baltimore County data. 

This function is the closest approximation to the Rossmo model (see Attachment A). 

However, it differs in several mathematical properties. First, the >near home base= function is 

linear (Equation 13.19), rather than a non-linear function. It assumes a simple increase in travel 

likelihoods by distance from the home base, up to the edge of the safety zone.2 Second, the 

distance decay part of the function (Equation 13.20) is a negative exponential, rather than an 

inverse distance function (see Attachment A); consequently, it is more stable when distances are 

very close to zero (e.g., for a crime where there is no >near home base= offset). 

Calibrating an Appropriate Probability Distance Function 

The mathematics are relatively straightforward. However, how does one know which 

distance function to use? The answer is to get some data and calibrate it. It is important to 

obtain data from a sample of known offenders where both their residence at the time they 

committed crimes as well as the crime locations are known. This is called the calibration data 

set. Each of the models are then tested against the calibration data set using an approach similar 

There are, of course, many other types of mathematical functions that can be used to describe a declining 

likelihood with distance. However, the five types of functions presented here are commonly used. We 

avoided the inverse distance function because of its potential to distort the likelihood relationship: 

(13.21) 

where k is a power (e.g., 1, 2, 2.5). For large distances, this function can be a useful approximation of the 

lessening travel interaction with distance. However, as the distance between the reference cell location and 

an incident location becomes very small, approaching zero, then the likelihood estimate becomes very 

large, approaching infinity. In fact, for dij = 0, the function is unsolvable. Since many distances between 

reference cells and incidents will be zero or close to zero, the function becomes unusable. 

13.20 
 

 

 

to that explained below. An error analysis is conducted to determine which of the models best 

fits the data. Finally, the >best fit= model is used to estimate the likelihood that a particular serial 

offender lives at any one location. Though the process is tedious, once the parameters are 

calculated they can be used repeatedly for predictions. 

Because every jurisdiction is unique in terms of travel patterns, it is important to calibrate 

the parameters for the particular jurisdiction. While there may be some similarities between 

cities (e.g., Eastern Acentralized@ cities v. Western Aautomobile@ cities), there are always unique 

travel patterns defined by the population size, historical road pattern, and physical geography. 

Consequently, it is necessary to calibrate the parameters anew for each new city. Ideally, the 

sample should be a large enough so that a reliable estimate of the parameters can be obtained. 

Further, the analyst should check the errors in each of the models to ensure that the best choice is 

used for the Jtc routine. However, once it has been completed, the parameters can be re-used for 

many years and only periodically re-checked. 

Example of Calibrating a Journey-to-crime Estimate with a Mathematical Function

 I will illustrate the calibration of a journey-to-crime probability estimate using a 

mathematical function with data from Baltimore County, MD. The steps in calibrating the Jtc 

parameters were as follows: 

1. 49,083 matched arrest and incident records from 1992 through 1997 were 

obtained in order to provide data on where the offender lived in relation to the 

crime location for which they were arrested.3 

2. The data set was checked to ensure that there were X and Y coordinates for both 

the arrested individual=s residence location and the crime incident location for 

which the individual was being charged. The data were cleaned to eliminate 

duplicate records or entries for which either the offender=s residence or the 

incident location were missing. The final data set had 41,424 records. There 

were many multiple records for the same offender since an individual can commit 

There are several sources of error associated with the data set. First, these records were arrest records prior 

to a trial. Undoubtedly, some of the individuals were incorrectly arrested. Second, there are multiple 

offenses. In fact, more than half the records were for individuals who were listed two or more times in the 

database. The travel pattern of repeat offenders may be slightly different than for apparent first-time 

offenders (see Figure 13.22). Third, many of these individuals have lived in multiple locations. 

Considering that many are young and that most are socially not well adjusted, it would be expected that 

these individuals would have multiple homes. Thus, the distribution of incidents could reflect multiple 

home bases, rather than one. Unfortunately, the data we have only gives a single residential location, the 

place at which they were living when arrested. 

13.21 

3 
 

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

more than one crime. In fact, more than half the records involved individuals who 

were listed two or more times. The distribution of offenders by the number of 

offenses for which they were charged is seen in Table 13.1. As would be 

expected, a small proportion of individuals account for a sizeable proportion of 

crimes; approximately 30% of the offenders in the database accounted for 56% of 

the incidents. 

3. The data were imported into a spreadsheet, but a database program could equally 

have been used. For each record, the direct distance between the arrested 

individual=s residence and the crime incident location was calculated. Chapter 3 

presented the formulas for calculating direct distances between two locations and 

are repeated in endnote i. 

Table 13.1 

Number of Offenders and Offenses in Baltimore County: 1993-97 

(Journey-to-crime Database) 

Number of Number of Percent of Number of Percent of 

Offenses Individuals Offenders Incidents Incidents 

1 18,174 70.0% 18,174 43.9% 

2 4,443 17.1% 8,886 21.5% 

3 1,651 6.4% 4,953 12.0% 

4 764 2.9% 3,056 7.4% 

5 388 1.5% 1,940 4.7% 

6-10 482 1.9% 3,383 8.2% 

11-15 61 0.2% 757 1.8% 

16-20 10 <0.0% 175 0.4% 

21-25 3 <0.0% 67 0.2% 

26-30 0 <0.0% 0 0.0% 

30+ 1 <0.0% 33 <0.0% 

25,977 41,424 

4. The records were sorted into sub-groups based on different types of crimes. Table 

13.2 presents the categories with their respective sample sizes. Of course, other 

sub-groups could have been identified. Each sub-group was saved as a separate 

file. The same records can be part of multiple files (e.g., a record could be 

included in the >all robberies= file as well as in the >commercial robberies= file). 

All records were included in the >all crimes= file. 

13.22 
 

 

Table 13.2: 

Baltimore County Files Used for Calibration: 1993-97 

Crime Type Sample Size 

All crimes 41,426 

Homicide 137 

Rape 444 

Assault 8,045 

Robbery (all) / 3,787 

Commercial robbery 1,193 

Bank robbery 176 

Burglary 4,694 

Motor vehicle theft 2,548 

Larceny 19,806 

Arson 338 

5. For each type of crime, the file was grouped into distance intervals of 0.25 miles 

each. This involved two steps. First, the distance between the offender=s 

residence and the crime location was sorted in ascending order. Second, a 

frequency distribution was conducted on the distances and grouped into 0.25 mile 

intervals (often called bins). The degree of precision in distance would depend on 

the size of the data set. For 41,426 records, quarter mile bins were appropriate. 

6. For each type of crime, a new file was created which included only the frequency 

distribution of the distances broken down into quarter mile distance intervals, di. 

7. In order to compare different types of crimes, each of which will have different 

frequency distributions, two new variables were created. First, the frequency in 

the interval was converted into the percentage of all crimes of in each interval by 

dividing the frequency by the total number of incidents, N, and multiplying by 

100. Second, the distance interval was adjusted. Since the interval is a range with 

a starting distance and an ending distance but has been identified by spreadsheet 

program as the beginning distance only, a small fraction, representing the 

midpoint of the interval, is added to the distance interval. In our case, since each 

interval is 0.25 miles wide, the adjustment is half of this, 0.125. Each new file, 

therefore, had four variables: the interval distance, the adjusted interval distance, 

the frequency of incidents within the interval (the number of cases falling into the 

interval), and the percentage of all crimes of that type within the interval. 

13.23 
8. Using the OLS regression program in the regression module (see Chapter 15), a 

series of regression equations was set up to model the frequency (or the 

percentage) as a function of distance. In this case, I used our routines, but other 

statistical packages could equally have been used. Again, because comparisons 

between different types of crimes were of interest, the percentage of crimes (by 

type) within an interval was used as the dependent variable (and was defined as a 

percentage, i.e., 11.51% was recorded as 11.51). Five equations testing each of 

the five models were set up. 

Estimating Parameter Values Using Grouped Data 

The parameters of the function can be estimated from the grouped data. 

Linear

For the linear function, the test is: 

(13.22) 

where Pcti is the percentage of all crimes of that type falling into interval i, di is the distance for 

interval i, A is the intercept, and B is the slope. A and B are estimated directly from the 

regression equation. 

Negative Exponential 

For the negative exponential function, the variables have to be transformed to estimate 

the parameters. The function is: 

(13.23) 

A new variable is defined which is the natural logarithm of the percentage of all crimes of 

that type falling into the interval, ln(Pcti). This term was then regressed against the distance 

interval, di: 

(13.24) 

(13.25) 

However, since the original equation has been transformed into a log function, B is the 

coefficient and A can be calculated directly from: 

13.24 
(13.26) 

If the percentage in any bin is 0 (i.e., Pcti = 0), then a value of -16 is taken since the 

natural logarithm of 0 cannot be solved (it approximates -16 as the percentage approaches 

0.0000001). 

Normal 

For the normal function, a more complex transformation must be used. The normal 

function in the model is: 

ௌ೏√ଶగ

First, a standardized Z variable for the distance, di, is created: 

(13.27) 

(13.28) 

where ݀̅ is the mean distance and ܵௗ is the standard deviation of distance. These are calculated 

from the original data file (before creating the file of frequency distributions). Second, a normal 

transformation of Z is constructed with: 

(13.29) 

Finally, the normalized variable is regressed against the percentage of all crimes of that type 

falling into the interval, Pcti with no constant 

(13.30) 

A is estimated by the regression coefficient. 

Lognormal 

For the lognormal function, another complex transformation must be done. The 

lognormal function for the percentage of all crimes of a type for a particular distance interval is: 

13.25 
 

(13.31) 

The transformation can be created in steps. First, create L: 

(13.32) 

Second, create M: 

(13.33) 

Third, create O: 

(13.34) 

Fourth, create P by raising e to the Oth power. 

(13.35) 

Fifth, create the lognormal conversion, Lnormal: 

(13.36) 

Finally, the lognormal variable is regressed against the percentage of all crimes of that type 

falling into the interval, Pcti with no constant: 

(13.37) 

A is estimated with the regression coefficient. 

Truncated Negative Exponential 

For the truncated negative exponential function, two models were set up. The first 

applied to the distance range from 0 to the distance at which the percentage (or frequency) is 

highest, Maxdi. The second applied to all distances greater than this distance: 

Linear: for (13.38) 

13.26 
 

Negative 

Exponential: for ݀௜ ൐ ݋ݐݑܥ݂݂ ݀) 13.39 (

To use this function, the user specifies the distance at which the peak likelihood occurs 

(Cutoff d) and the value for that peak likelihood, P (the peak likelihood). For the negative 

exponential function, the user specifies the exponent, C. 

In order to splice the two equations together (the spline), the CrimeStat truncated 

negative exponential routine starts the linear equation at the origin and ends it at the highest 

value. Thus, 

A = 0 (13.40) 

(13.41) 

where P is the peak likelihood and Cutoff d is the cutoff distance at which the probability is 

highest. 

The exponent, C, can be estimated by transforming the dependent variable, Pcti, as in the 

negative exponential above (Equation 13.23) and regressing the natural log of the percentage 

(ln(Pcti) against the distance interval, di, only for those intervals that are greater than the Cutoff 

distance. I have found that estimating the transformed equation with a coefficient, A in: 

(13.42) 

(13.43) 

gives a better fit to the equation. However, the user need only input the exponent, C, in the Jtc 

routine as the coefficient, A, of the negative exponential is calculated internally to produce a 

distance value at which the peak likelihood occurs. The formula is: 

where P is the peak likelihood,

(13.44) 

is the distance for the peak likelihood, C is an exponent 

(assumed to be positive) and di is the distance interval for the histogram. 

9. Once the parameters for the five models have been estimated, they can be 

compared to see which one is best at predicting the travel behavior for a particular 

13.27 
type of crime. It is to be expected that different types of crimes will have 

different optimal models and that the parameters will also vary. 

Example from Baltimore County, MD 

Let us illustrate with the Baltimore County, MD data. Figure 13.4 shows the frequency 

distribution for all types of crime in Baltimore County. As can be seen, at the nearest distance 

interval (0 to 0.25 miles with an assigned >adjusted= midpoint of 0.125 miles), about 6.9% of all 

crimes occur within a quarter mile of the offender=s residence (it can be seen on the Y-axis). 

However, for the next interval (0.25 to 0.50 miles with an assigned midpoint of 0.375 miles), 

almost 10% of all crimes occur at that distance (9.8%). In subsequent intervals, however, the 

percentage decreases, a little less than 6% for 0.50 to 0.75 miles (with the midpoint being 0.625 

miles), a little more than 4% for 0.75 to 1 mile (the midpoint is 0.875 miles), and so forth. 

The best fitting statistical function was the negative exponential. The particular equation 

was: 

(13.45) 

This is shown with the solid line. As can be seen, the fit is good for most of the 

distances, though it underestimates at close to zero distance and overestimates from about a half 

mile to about four miles. There is only slight evidence of decreased activity near to the location 

of the offender. 

However, the distribution varies by type of crime. With the Baltimore County data, 

property crimes, in general, occur farther away than personal crimes. The truncated negative 

exponential generally fit property crimes better, lending support for the Brantingham and 

Brantingham (1981) framework for these types. For example, larceny offenders have a definite 

safety zone around their residence (Figure 13.5). Fewer than 2% of larceny thefts occur within a 

quarter mile of the offender=s residence. However, the percentage jumps to about 4.5% from a 

quarter mile to a half. The truncated negative exponential function fits the data reasonably well 

though it overestimates from about 1 to 3 miles and underestimates from about 4 to12 miles. 

Similarly, motor vehicle thefts show decreased activity near the offender=s resident, 

though it is less pronounced than larceny theft. Figure 13.6 shows the distribution of motor 

vehicle thefts and the truncated negative exponential function which was fit to the data. The fit 

is reasonably good though it tends to underestimate middle range distances (3-12 miles). 

13.28 
Figure 13.4: 

Journey-to-crime Distances: All Crimes 

Negative Exponential Distribution Negative Exponential Distribution 
Figure 13.5: 

Journey-to-crime Distances: Larceny

Truncated Negative Exponential Function Truncated Negative Exponential Function 
Figure 13.6: 

Journey-to-crime Distances: Vehicle Theft 

Truncated Negative Exponential Function Truncated Negative Exponential Function 
Some types of crime, on the other hand, are very difficult to fit. Figure 13.7 shows the 

distribution of bank robberies. Partly because there were a limited number of cases (N=176) and 

partly because it is a complex pattern, the truncated negative exponential gave the best fit, but 

not a particularly good one. As can be seen, the linear (>near home=) function underestimates 

some of the near distance likelihoods while the negative exponential drops off too quickly; in 

fact, to make this function even plausible, the regression was run only up to 21 miles (otherwise, 

it underestimated even more). 

For some crimes, it was very difficult to fit any single function. Figure 13.8 shows the 

frequency distribution of 137 homicides with three functions being fitted to the data - the 

truncated negative exponential, the lognormal, and the normal. As can be seen each function fits 

only some of the data, but not all of it. 

Testing for Residual Errors in the Model 

In short, the five mathematical functions allow a user to fit a variety of distance decay 

distributions. Each of the models will predict some parts of the distribution better than others. 

Consequently, it is important to conduct an error analysis to determine which model is >best=. In 

an error analysis, the residual error is defined as: 

(13.46) 

where Yi is the observed (actual) likelihood for distance i and E(Yi) is the likelihood predicted by 

the model. If raw numbers of incidents are used, then the likelihoods are the number of incidents 

for a particular distance. If the number of incidents are converted into proportions (i.e., 

probabilities), then the likelihoods are the proportions of incidents for a particular distance. 

The choice of >best model= will depend on what part of the distribution is considered most 

important. Figure 13.9, for example, shows the residual errors on vehicle theft for the five fitted 

models. That is, each of the five models was fit to the proportion of vehicle thefts by distance 

intervals (as explained above). For each distance, the discrepancy between the actual percentage 

of vehicle thefts in that interval and the predicted percentage was calculated. If there was a 

perfect fit, then the discrepancy (or residual) was 0%. If the actual percentage was greater than 

the predicted (i.e., the model underestimated), then the residual was positive; if the actual was 

smaller than the predicted (i.e., the model overestimated), then the residual was negative. 

13.32 
Figure 13.7: 

Journey-to-crime Distances: Bank Robbery 

Truncated Negative Exponential Function Truncated Negative Exponential Function 
Figure 13.8: 

Journey-to-crime Distances: Homicide

Normal Lognormal and Truncated Negative Exponential FuncNormal, Lognormal, and Truncated Negative Exponential Func
Figure 13.9: 

Residual Error for Jtc Mathematical Models 

Vehicle Theft Vehicle Theft 
As can be seen in Figure 13.9, the truncated negative exponential fit the data well from 0 

to about 5 miles, but then became poorer than other models for longer distances. The negative 

exponential model was not as good as the truncated for distances up to about 5 miles, but was 

better for distances beyond that point. The normal distribution was good for distances from 

about 10 miles and farther. The lognormal was not particularly good for any distances other than 

at 0 miles, nor was the linear. 

The degree of predictability varied by type of crime. For some types, particularly 

property crimes, the fit was reasonably good. I obtained R2

 in the order of 0.86 to 0.96 for 

burglary, robbery, assault, larceny, and auto theft. For other types of crime, particularly violent 

crimes, the fit was not very good with R2 values in the order of 0.53 (rape), 0.41 (arson) and 0.30 

(homicide). These R2 values were for the entire distance range; for any particular distance, 

however, the predictability varied from very high to very low. 

In modeling distance decay with a mathematical function, a user has to decide which part 

of the distribution is the most important as no simple mathematical function will normally fit all 

the data (even approximately). In these cases, I assumed that the near distances were more 

important (up to, say, 5 miles) and, therefore, selected the model which >best= fit those distances 

(see Table 13.2). However, it was not always clear which model was best, even with that limited 

criterion. 

Problems with Mathematical Distance Decay Functions 

There are several reasons that mathematical models of distance decay distributions, such 

as illustrated in the Jtc routine, do not fit data very well. First, as mentioned earlier, few cities 

have a completely symmetrical grid structure or even one that is approximately grid-like (there 

are exceptions, of course). Limitations of physical topography (mountains, oceans, rivers, lakes) 

as well as different historical development patterns make travel asymmetrical around most 

locations. 

Second, there is population density. Since most metropolitan areas have much higher 

intensity of land use in the center (i.e., more activities and facilities), travel tends to be directed 

towards higher land use intensity than away from them. For origin locations that are not directly 

in the center, travel is more likely to go towards the center than away from it. 

This would be true of an offender as well. If the person were looking for either persons 

or property as >targets=, then the offender would be more likely to travel towards the metropolitan 

center than away from it. Since most metropolitan centers have street networks that were laid 

out much earlier, the street network tends to be irregular. Consequently, trips will vary by 

location within a metropolitan area. One would expect shorter trips for offenders living close to 

13.36 
the metropolitan center than one living farther away, living in more built-up areas than in lower 

density areas, living in mixed use neighborhoods than in strictly residential neighborhoods; and 

so forth. Thus, the distribution of trips of any sort (in our case, crime trips from a residential 

location to a crime location), will tend to follow an irregular, distance decay type of distribution. 

Simple mathematical models will not fit the data very well and will make many errors. 

Third, the selection of a best mathematical function is partly dependent on the interval 

size used for the bins. In the above examples, an interval size of 0.25 miles was used to calculate 

the frequency distribution. With a different interval size (e.g., 0.5 miles), however, a slightly 

different distribution is obtained. This affects the mathematical function that is selected as well 

as the parameters that are estimated. For example, the questoin of whether there is a safety zone 

near the offender=s residence from which there is decreased activity or not is partly dependent on 

the interval size. With a small interval, the zone may be detected whereas with a slightly larger 

interval the subtle distinction in measured distances may be lost. On the other hand, having a 

smaller interval may lead to unreliable estimates since there may be few cases in the interval. 

Having a technique depend on the interval size makes it vulnerable to misspecification. 

Uses of Mathematical Distance Decay Functions 

Does this mean that one should not use mathematical distance functions? I would argue 

that under most circumstances, a mathematical function will give less precision than an 

empirically-derived one (see below). However, there are two cases when a mathematical model 

would be appropriate. First, if there is either no data or insufficient data to model the empirical 

travel distribution, the use of a mathematical model can serve as an approximation. If the user 

has a good sense of what the distribution looks like, then a mathematical model may be used to 

approximate the distribution. However, if a poorly defined function is selected, then the selected 

function may produce many errors. 

A second case when mathematical models of distance decay would be appropriate is in 

theory development or application. Many models of travel behavior, for example, assume a 

simple distance decay type of function in order simplify the allocation of trips over a region. 

This is a common procedure in travel demand modeling where trips from each of many zones are 

assigned to every other zone using a gravity type of function (Stopher & Meyburg, 1975; Field & 

MacGregor, 1987). Even though the model produces errors because it assumes uniform travel 

behavior in all directions, the errors are corrected later in the modeling process by adjusting the 

coefficients for allocating trips to particular roads (traffic assignment). The model provides a 

simple device and the errors are corrected down the line. Still, I would argue that an empiricallyderived distribution will produce fewer errors in allocation and, thus, require less adjustment 

later on. Errors can never help a model and it is better to get it more correct initially than to have 

to adjust it later on. Nevertheless, this is common practice in transportation planning. 

13.37 
 

Using the Routine with a Mathematical Function 

The Jtc routine which allows mathematical modeling is simple to use. Figure 13.10 

illustrates how the user specifies a mathematical function. The routine requires the use of a grid 

which is defined on the reference file tab of the program (see chapter 3). Then, the user must 

specify the mathematical function and the parameters. In the figure, the truncated negative 

exponential is being defined. The user must input values for the peak likelihood, the Cutoff 

distance, and the exponent (see equations 10.43 and 10.44 above). In the figure, since the serial 

offenses were a series of 18 robberies, the parameters for robbery have been entered into the 

program screen. The peak likelihood was 9.96% (entered as a whole number - i.e., 9.96); the 

distance at which this peak likelihood occurred was the second distance interval 0.25-0.50 miles 

(with a mid-point of 0.38 miles); and the estimated exponent was 0.177651. As mentioned 

above, the coefficient for the negative exponential part of the equation is estimated internally. 

Table 13.3 gives the parameters for the >best= models which fit the data for the 11 types of 

crime in Baltimore County. For several of these (e.g., bank robberies), two or more functions 

gave approximately equally good fits. Note that these parameters were estimated with the 

Baltimore County data. They will not fit any other jurisdiction. If a user wishes to apply this 

logic, then the parameters should be estimated anew from existing data. Nevertheless, once they 

have been calibrated, they can be used for predictions. 

The routine can be output to ArcGIS, MapInfo, Atlas*GIS, Surfer for Windows, Spatial 

Analyst, and as an Ascii grid file which can be read by many other GIS packages. All but Surfer 

for Windows require that the reference grid be created by CrimeStat. 

Empirically Estimating a Journey-to-crime Calibration Function 

An alternative to mathematical modeling of distance decay is to empirically describe the 

Journey-to-crime distribution and then use this empirical function to estimate the residence 

location. CrimeStat has a two-dimensional kernel density routine that can calibrate the distance 

function if provided data on trip origins and destinations. The logic of kernel density estimation 

was described in chapter 10, and will not be repeated here. Essentially, a symmetrical function 

(the >kernel=) is placed over each point in a distribution. The distribution is then referenced 

relative to a scale (an equally-spaced line for two-dimensional kernels and a grid for threedimensional kernels) and the values for each kernel are summed at each reference location. See 

chapter 8 for details. 

13.38 
Figure 13.10: 

Jtc Mathematical Distance Decay Function 
 

 

 

 

 

 

 

 

Table 13.3: 

Journey-to-crime Mathematical Models for Baltimore County 

Parameter Estimates for Percentage Distribution 

(Sample Sizes in Parentheses) 

ALL CRIMES

 Negative Exponential: Coefficient: 5.575107 

 Exponent: 0.229466 

HOMICIDE

 Truncated 

 Negative Exponential: Peak likelihood 14.02%

 Cutoff distance 0.38 miles

 Exponent 0.064481 

RAPE

 Lognormal: Mean 3.144959

 Standard Deviation 4.546872

 Coefficient 0.062791 

ASSAULT

 Truncated 

 Negative Exponential: Peak likelihood 27.40%

 Cutoff distance 0.38 miles

 Exponent 0.181738 

ROBBERY

 Truncated 

 Negative Exponential: Peak likelihood 9.96% 

 Cutoff distance 0.38 miles

 Exponent 0.177651 

COMMERCIAL ROBBERY

 Truncated 

 Negative Exponential: Peak likelihood 4.9455% 

 Cutoff distance 0.625 miles 

 Exponent 0.151319 

13.40 
 

 

 

 

 

 

 

Table 13.3: (continued) 

BANK ROBBERY

 Truncated 

 Negative Exponential: Peak likelihood 9.96% 

 Cutoff distance 5.75 miles

 Exponent 0.139536 

BURGLARY

 Truncated 

 Negative Exponential: Peak likelihood 20.55%

 Cutoff distance 0.38 miles

 Exponent 0.162907 

AUTO THEFT

 Truncated 

 Negative Exponential: Peak likelihood 4.81% 

 Cutoff distance 0.63 miles

 Exponent 0.212508 

LARCENY

 Truncated 

 Negative Exponential: Peak likelihood 4.76% 

 Cutoff distance 0.38 miles

 Exponent 0.193015 

ARSON

 Truncated 

 Negative Exponential: Peak likelihood 38.99%

 Cutoff distance 0.38 miles

 Exponent 0.093469 

Calibrate Kernel Density Estimate

 The CrimeStat calibration routine allows a user to describe the distance distribution for a 

sample of Journey-to-crime trips. The requirements are that: 

1. The data set must have the coordinates of both an origin location and a destination 

location; and 

13.41 
 

2. The records of all origin and destination locations have been populated with 

legitimate coordinate values (i.e., no unmatched records are allowed). 

Data set definition 

The steps are relatively easy to run the routine. First, the user defines a calibration data 

set with both origin and destination locations. Figure 13.11 illustrates this process. As with the 

primary and secondary files, the routine reads Excel ‘xls’ and ‘xlsx’, ArcGIS >shp=, dBase >dbf=, 

Ascii >txt=, and MapInfo >dat= files. For both the origin location (e.g., the home residence of the 

offender) and the destination location (i.e., the crime location), the names of the variables for the 

X and Y coordinates must be identified as well as the type of coordinate system and data unit 

(see Chapter 3). In the example, the origin locations has variable names of HomeX and HomeY 

and the destination locations has variable names of IncidentX and IncidentY for the X and Y 

coordinates of the two locations respectively. However, any name is acceptable as long as the 

two locations are distinguished. 

The user should specify whether there are any missing values for these four fields (X and 

Y coordinates for both origin and destination locations). By default, CrimeStat will ignore 

records with blank values in any of the eligible fields or records with non-numeric values 

(e.g.,alphanumeric characters, #, *). Blanks will always be excluded unless the user selects 

<none>. There are 8 possible options: 

1. <blank> fields are automatically excluded. This is the default 

2. <none> indicates that no records will be excluded. If there is a blank field, 

CrimeStat will treat it as a 0 

3. 0 is excluded 

4. B1 is excluded 

5. 0 and B1 indicates that both 0 and -1 will be excluded 

6. 0, -1 and 9999 indicates that all three values (0, -1, 9999) will be excluded 

Any other numerical value can be treated as a missing value by typing it (e.g., 

99)Multiple numerical values can be treated as missing values by typing them, separating each 

by commas (e.g., 0, -1, 99, 9999, -99). 

The program will calculate the distance between the origin location and the destination 

location for each record. If the units are spherical (i.e., lat/lon), then the calculations use 

spherical geometry; if the units are projected (either meters or feet), then the calculations are 

Euclidean (see chapter 3 for details). 

13.42 
Figure 13.11: 

Jtc Calibration Data Input 
 Kernel Parameters 

Second, the user must define the kernel parameters for calibration. There are five choices 

that have to be made (Figure 13.12): 

1. The method of interpolation. As with the two-dimensional kernel technique 

described in Chapter 10, there are five possible kernel functions: 

A. Normal (the default); 

B. Quartic; 

C. Triangular (conical); 

D. A negative exponential (peaked); and 

E. A uniform (flat) distribution. 

2. Choice of bandwidth. The bandwidth is the width of the kernel function. For a 

normal kernel, it is the standard deviation of the normal distribution whereas for 

the other four kernels (quartic, triangular, negative exponential, and uniform), it is 

the radius of the circle defined by the kernel. As with the two-dimension kernel 

technique, the bandwidth can be fixed in length or made adaptive (variable in 

length). However, for the one-dimensional kernel, the fixed bandwidth is the 

default since an even estimate over an equal number of intervals (bins) is 

desirable. If a fixed bandwidth is selected, the interval size must be specified and 

the units defined (in miles, kilometers, feet, meters, and nautical miles). The 

default is 0.25 mile intervals. If the adaptive bandwidth is selected, the user must 

identify the minimum sample size that the bandwidth should incorporate; in this 

case, the bandwidth is widened until the specified sample size is counted. 

3. The number of interpolation bins. The bins are the intervals along the distance 

scale (from 0 up to the maximum distance for a Journey-to-crime trip) and are 

used to estimate the density function. There are two choices. 

A. The user can specify the number of intervals (the default choice with 100 

intervals). In this case, the routine calculates the maximum distance (or 

longest trip) between the origin location and the destination location and 

divides it by the specified number of intervals (e.g., 100 equal-sized 

intervals). The interval size is dependent on the longest trip distance 

measured. 

13.44 
Figure 13.12: 

Jtc Calibration Kernel Parameters 
B. Alternatively, the user can specify the distance between bins (or the 

interval size). The default choice is 0.25 miles, but another value can be 

entered. In this case, the routine counts out intervals of the specified size 

until it reaches the maximum trip distance. 

4. The output units. The user specifies the units for the density estimate (in units per 

mile, kilometer, feet, meters, and nautical miles). 

5. The output calculations. The user specifies whether the output results are in 

probabilities (the default) or in densities. For probabilities, the sum of all kernel 

estimates will equal 1.0. For densities, the sum of all kernel estimates will equal 

the sample size. 

Saved calibration file 

Third, the user must define an output file to save the empirically determined function. 

The function is then used in estimating the likely home residence of a particular function. The 

choices are to save the file as a >dbf= or Ascii text file. The saved file then can be used in the Jtc 

routine. Figure 13.13 illustrates the output file format. 

Calibrate 

Fourth, the calibrate button runs the routine. A calibration window appears and indicates 

the progress of the calculations. When it is finished, the user can view a graph illustrating the 

estimated distance decay function (Figure 13.14). The purpose is to provide quick diagnostics to 

the user on the function and selection of the kernel parameters. While the graph can be printed, 

it is not a high quality print. If a high quality graph is needed, the output calibration file should 

be imported into a graphics program. 

Examples from Baltimore County, MD 

I will illustrate this method by showing the results for the same data sets that were 

calculated above in the mathematical section (Figures 13.4-13.8). In all cases, the normal kernel 

function was used. The bandwidth was 0.25 miles except for the bank robbery data set, which 

had only 176 cases, and the homicide data set, which only had 137 cases; because of the small 

sample sizes, a bandwidth of 0.50 miles was used for these two data sets. The interval width 

selected was a distance of 0.25 miles between bins (0.5 miles for bank robberies and homicides) 

and probabilities were output. 

13.46 
Figure 13.13: 

Jtc Calibration Output File 
Figure 13.14: 

Jtc Calibration Graphic Output 
 

Figure 13.15 shows the kernel estimate for all crimes (41,426 trips). A frequency 

distribution was calculated for the same number of intervals and is overlaid on the graph. It was 

selected to be comparable to the mathematical function (see Figure 13.4). Note how closely the 

kernel estimate fits the data compared to the negative exponential mathematical function. The fit 

is good for every value but the peak value; that is because the kernel averages several intervals 

together to produce an estimate. 

Figure 13.16 shows the kernel estimate for larceny thefts. Again, the kernel method 

produces a much closer fit as a comparison with Figure 13.5 will show. Figure 13.17 shows the 

kernel estimate for vehicle thefts. Figure 13.18 shows the kernel estimate for bank robberies and 

Figure 13.19 shows the kernel estimate for homicides. An inspection of these graphs shows how 

well the kernel function fits the data, compared to the mathematical function, even when the data 

are irregularly spaced (in vehicle thefts, bank robberies, and homicides). Figure 13.20 compares 

the distance decay functions for homicides committed against strangers compared to homicides 

committed against known victims. 

In short, the Jtc calibration routine allows a much closer fit to the data than any of the 

simpler mathematical functions. While it=s possible to produce a complex mathematical function 

that will fit the data more closely (e.g., higher order polynomials), the kernel method is much 

simpler to use and gives a good approximation to the data. 

Journey-to-crime Estimation Using a Calibrated File 

After the distance decay function has been calibrated and saved as a file, the file can be 

used to calculate the likelihood surface for a serial offender. The user specifies the name of the 

already-calibrated distance function (as a >dbf= or an Ascii text file) and the output format. As 

with the mathematical routine, the output can be to ArcGIS, MapInfo, Atlas*GIS, Surfer for 

Windows, Spatial Analyst, and as an Ascii grid file which can be read by many other GIS 

packages. All but Surfer for Windows require that the reference grid be created by CrimeStat. 

The result is produced in three steps: 

1. The routine calculates the distance between each reference cell of the grid and 

each incident location; 

2. For each distance measured, the routine looks up the calculated value from the 

saved calibration file; and 

3. For each reference grid cell, it sums the values of all the incidents to produce a 

single likelihood estimate. 

13.49 
Figure 13.15: 

Journey-to-crime Distances: All Crimes 

Kernel Density Estimate by Percent of Crimes Estimate by Percent of Crimes 
Figure 13.16: 

Journey-to-crime Distances: Larceny

Kernel Density Estimate by Percent of Crimes Estimate by Percent of Crimes 
Figure 13.17: 

Journey-to-crime Distances: Vehicle Theft 

Kernel Density Estimate by Percent of Crimes Estimate by Percent of Crimes 
Figure 13.18: 

Journey-to-crime Distances: Bank Robbery 

Kernel Density Estimate by Percent of Crimes Estimate by Percent of Crimes 
Figure 13.19: 

Journey-to-crime Distances: Homicide

Kernel Density Estimate by Percent of Crimes Estimate by Percent of Crimes 
Figure 13.20: 

Journey-to-crime Distances: Homicide by Victim Rela

Kernel Density Estimate by Percent of Crimes Estimate by Percent of Crimes 
Application of the Routine 

To illustrate the techniques, the results of the two methods on a single case are compared. 

The case has been selected because the routines accurately estimate the offender=s residence. 

This was done to demonstrate how the techniques work. In the next section, I will ask the 

question about how accurate these methods are in general. 

The case involved a man who had committed 24 offenses. These included 13 thefts, 5 

burglaries, 5 assaults, and one rape. The spatial distribution was varied; many of the offenses 

were clustered but some were scattered. Since there were multiple types of crimes committed by 

this individual, a decision had to be made over which model to use to estimate the individual=s 

residence. In this case, the theft (larceny) model was selected since that was the dominant type 

of crime for this individual. 

For the mathematical function, the truncated negative exponential was chosen from Table 

13.3 with the parameters being: 

Peak likelihood 4.76% 

Cutoff distance 0.38 miles 

Exponent 0.193015 

For the kernel density model, the calibrated function for larceny was selected (see Figure 13.16). 

Figure 13.21 shows the results of the estimation for the two methods. The output is from 

Surfer for Windows (Golden Software, 2008). The left pane shows the results of the 

mathematical function while the right pane shows the results for the kernel density function. The 

incident locations are shown as circles while the actual residence location of the offender is 

shown as a square. Since this is a surface model, the highest location has the highest predicted 

likelihood. 

In both cases, the models predicted quite accurately. The discrepancy (error) between the 

predicted peak location and the actual residence location was 0.66 miles for the mathematical 

function and 0.36 miles for the kernel density function. For the mathematical model, the actual 

residence location (square) is seen as slightly off from the peak of the surface whereas for the 

kernel density model the discrepancy from the peak cannot be seen. 

Nevertheless, the differences in the two surfaces show distinctions. The mathematical 

model has a smooth decline from the peak likelihood location, almost like a cone. The kernel 

density model, on the other hand, shows a more irregular distribution with a peak location 

followed by a surrounding >trough= followed a peak >rim=. This is due to the irregular distance 

13.56 
Figure 13.21: 

Predicted and Actual Location of Serial Thief 

Man Charged with 24 Offenses in Baltimore County Man Charged with 24 Offenses in Baltimore County 

Predicted with Mathematical and Kernel Density Models for Larceny 

Residence = Crime locatCrime locat

Mathematical Model: Kernel Density Model Truncated Negative Exponential 
 

decay function calibrated for larceny (see Figure 13.16). But, in both cases, they more or less 

identify the actual residence location of the offender. 

Choice of Calibration Sample 

The calibration sample is critical for either method. Each method assumes that the 

distribution of the serial offender will be similar to a sample of >like= offenders. Obviously, 

distinctions can be made to make the calibration sample more or less similar to the particular 

case. For example, if a distance decay function of all crimes is selected, then a model (of either 

the mathematical or kernel density form) will have less differentiation than for a distance decay 

function from a specific type of crime. Similarly, breaking down the type of crime by, say, mode 

of operation or time of day will produce better differentiation than by grouping all offenders of 

the same type together. This process can be taken on indefinitely until there is too little data to 

make a reliable estimate. An analyst should try to match a calibration sample to the actual as is 

possible, given the limitations of the data. 

For example, in our calibration data set, there were 4,694 burglary incidents where both 

the offender=s home residence and the incident location were known. The approximate time of 

the offense for 2,620 of the burglaries was known and, of these, 1,531 occurred at night between 

6 pm and 6 am. Thus, if a particular serial burglar for whom the police are interested in catching 

tends to commit most of his burglaries at night, then choosing a calibration sample of nighttime 

burglars will generally produce a better estimate than by grouping all burglars together. 

Similarly, of the 1,531 nighttime burglaries, 409 were committed by individuals who had a prior 

relationship with the victim. Again, if the analysts suspect that the burglar is robbing homes of 

people he knows or is acquainted with, then selecting the subset of nighttime burglaries 

committed against a known victim would produce even better differentiation in the model than 

taking all nighttime burglars. However, eventually, with further sub-groupings there will be 

insufficient data. 

This point has been raised in a recent debate. Van Koppen and De Keijser (1997) argued 

that a distance decay function that combines multiple incidents committed by the same 

individuals could distort the estimated relationship compared to selecting incidents committed by 

different individuals.4

 This result has been supported by Smith, Bond and Townsley (2009) and 

They also argued that the combination of incidents - which they called >aggregation=, would distort the 

relationship between distance and incidence likelihood because of the ecological fallacy. To my mind, they 

are incorrect on this point. Data on a distribution of incidents by distance traveled is an individual 

characteristic and is not >ecological= in any way. An ecological inference occurs when data are aggregated 

with a grouping variable (e.g., state, county, city, census tract; see Langbein and Lichtman, 1978). A 

frequency distribution of individual crime trip distances is an individual probability distribution, similar, for 

example, to a distribution of individuals by height, weight, income or any other characteristic. Of course, 

there are sub-sets of the data that have been aggregated (similar to heights of men v. heights of women, for 

13.58 

4 
 

 

 

Townsley and Sidebottom (2010). Rengert, Piquero and Jones (1999) argued that such a 

distribution is nevertheless meaningful. In our language, these are two different sub-groups - 

persons committing multiple offenses compared to persons committing only one offense. 

Combining these two sub-groups into a single calibration data set will only mean that the result 

will have less differentiation in prediction than if the sub-groups were separated out. 

Actually, there is not much difference, at least in Baltimore County, a result that we also 

found in Baltimore County, MD (Levine & Lee, 2012). From the 41,426 cases, 18,174 were 

committed by persons who were only listed once in the database while 23,251 offenses were 

committed by persons who were listed two or more times (7,802 individuals). Categorizing the 

18,174 crimes as committed by >single incident offenders and the 23,251 crimes as committed by 

>multiple incident offenders=, the density distance decays functions were calculated using the 

kernel density method (Figure 13.22). 

The distributions are remarkably similar. There are some subtle differences. The 

average Journey-to-crime trip distance made by a single incident offender is longer than for 

multiple incident offenders (4.6 miles compared to 4.0 miles, on average); the difference is 

highly significant (p#.0001), partly because of the very large sample sizes. However, a visual 

inspection of the distance decay functions shows they are similar. The single incident offenders 

tend to have slightly more trips near their home, slightly fewer for distances between about a 

mile up to three miles, and slightly more longer trips. But, the differences are not very large. 

There are several reasons for the similarity. First, some of the >single incident offenders=

are actually multiple incident offenders who have not been charged with other incidents. 

Second, some of the single incident offenders are in the process of becoming multiple incident 

offenders so their behavior is probably similar. Third, there may not be a major difference in 

travel patterns by the number of offenses an individual commits, certainly compared to the major 

differences by type of crime (see graphs above). In other words, the distinction between a single 

offender crime trip and a multiple offender crime trip is just another sub-group comparison and, 

apparently, not that important. Nevertheless, it is important to choose an appropriate sample 

example). Clearly, identifying sub-groups can make better distinctions in a distribution. But, it is still an 

individual probability distribution and does not produce bias in estimating a parameter, only variability. 

For example if a particular distance decay function implies that 70% of the offenders live within, say, 5 

miles of their committed incidents, then 30% do not live within 5 miles. In other words, because the data 

are individual level, then a distance decay function, whether estimated by a mathematical or a kernel 

density model, is an individual probability model (i.e., an attempt to describe the underlying distribution of 

individual travel distances for Journey-to-crime trips). See the following discussion (Wikipedia, 2010a; 

2010b; Friedman, 1999). 

13.59 
Perc

ent of all homicides

(by offender type) 

Figure 13.22: 

Journey-to-crime Distances

Kernel Density Estimate for Single and Multiple Incident Offe

Estimate for Single and Multiple Incident Offe
from which to estimate a likely home base location for a serial offender. The method depends on 

a similar sample of offenders for comparison. 

Sample Data Sets for Journey-to-crime Routines 

Three sample data sets from Baltimore County have been provided for the Journey-tocrime routine. The data sets are simulated and do not represent real data. The first file - 

JtcTest1.dbf, are 2000 simulated robberies while the second file - JtcTest2.dbf, are 2500 

simulated burglaries. Both files have coordinates for an origin location (HomeX, HomeY) and a 

destination location (IncidentX, IncidentY). Users can use the calibration routine to calculate the 

travel distances between the origins and the destinations. A third data set - Serial1.dbf, are 

simulated incident locations for a serial offender. Users can use the Jtc estimation routine to 

identify the likely residence location for this individual. In running this routine, a reference grid 

needs to be overlaid (see chapter 3). For Baltimore County, appropriate coordinates for the 

lower-left corner are -76.910

 longitude and 39.190 latitude and for the upper-right corner are -

76.320 longitude and 39.720 latitude. 

How Accurate are the Methods? 

A critical question is how accurate are these methods? The Journey-to-crime model is 

just that, a model. Whether it involves using a mathematical function or an empirically-derived 

one, the assumption in the Jtc routine is that the distribution of incidents will provide information 

about the home base location of the offender. In this sense, it is not unlike the way most crime 

analysts will work when they are trying to find a serial offender. A typical approach will be to 

plot the distribution of incidents and routinely search a geographic area in and around a serial 

crime pattern, noting offenders who have an arrest history matching case attributes (MO, type 

weapon, suspect description, etc.). Because a high proportion of offenses are committed within a 

short distance of offender residence=s, the method can frequently lead to their apprehension. But, 

in doing this method, the analysts are not using a sophisticated statistical model. 

Test Sample of Serial Offenders 

To explore the accuracy of the approach, a small sample of 50 serial offenders was 

isolated from the database and used as a target sample to test the accuracy of the methods. The 

50 offenders accounted for 520 individual crime incidents in the database. To test the Jtc method 

systematically, the following distribution was selected (Table 13.4). The sample was not 

random, but was selected to produce a balance in the number of incidents committed and to, 

roughly, approximate the distribution of incidents by serial offenders. Each of the 50 offenders 

was isolated as a separate file so that each could be analyzed in CrimeStat. 

13.61 
 

 

 

 

 

 

Table 13.4: 

Serial Offenders Used in Accuracy Evaluation 

Number of 

Offenders 

4

 4

 4

 4

 4

 4

 3

 3

 3

 2

 2

 2

 2

 1

 1

 1

 1

 1

 1

 1

 1

 1

 _________ 

50 

Identifying the Crime Type 

Number of Crimes

Committed by 

Each Person

 3

 4

 5

 6

 7

 8

 9

 10

 11

 12

 13

 14

 15

 16

 17

 18

 19

 20

 21

 22

 24

 33

 _________ 

520 

Each of the 50 offenders was categorized by a crime type. Only two of the offenders 

committed the same crime for all their offenses; most committed two or more different types of 

crimes. Arbitrarily, each offender was typed according to the crime type that he/she most 

frequently committed; in the two cases where there was a tie between two crime types, the most 

severe was selected (i.e., personal crime over property crime). While I recognize that there is 

arbitrariness in the approach, it seemed a practical solution. Any error in categorizing an 

offender would be applicable to all the methods. The crime types for the 50 offenders 

13.62 
approximately mirrored the distribution of incidents: larceny (29); vehicle theft (7); burglary (5); 

robbery (5); assault (2); bank robbery (1); and arson (1). 

Identifying the Home Base and Incident Locations 

In the database, each of the offenders was listed as having a residence location. For the 

analysis, this was taken as the origin location of the Journey-to-crime trip. Similarly, the 

incident location was taken as the destination for the trip. Operationally, the crime trip is taken as 

the distance from the origin location to the destination location. However, it is very possible that 

some crime trips actually started from other locations. Further, many of these individuals have 

moved their residences over time; we only have the last known residence in the database. 

Unfortunately, there was no other information in the digital database to allow more accurate 

identification of the home location. In other words, there may be, and probably are, numerous 

errors in the estimation of the Journey-to-crime trip. However, these errors would be similar 

across all methods and should not affect their relative accuracy. 

Evaluated Methods 

Eleven methods were compared in estimating the likely residence location of the 

offenders. Four of the methods used the Jtc routines and seven were simple spatial distribution 

methods (Table 13.5). 

The mean center and center of minimum distance are discussed in chapter 4. The center 

of minimum distance, in particular, is more or less the geographic center of distribution in that it 

ignores the values of particular locations; thus, locations that are far away from the cluster 

(extreme values) have no effect on the result. When the center of minimum distance is 

calculated on a road network in which each segment is weighted by travel time or speed, the 

result is the center of minimum travel time, the point at which travel time to each of the incidents 

is minimized. The directional mean, triangulated mean, geometric and harmonic means are 

discussed in chapter 4. 

The Test 

Each of these eleven methods were tested with the the files created for the serial 

offenders. For the seven >means= (mean center, geometric mean, harmonic mean, directional 

mean, triangulated mean, center of minimum distance, center of minimum travel time), the mean 

was itself the best guess for the likely residence location of the offender. For the four Journeyto-crime functions, the grid cell with the highest likelihood estimate was the best guess for the 

likely residence location of the offender. 

13.63 
Table 13.5: 

Comparison Methods for Estimating the Home Base of a Serial Offender

 Journey-to-crime Methods 

1. Mathematical model for all crimes 

2. Mathematical model for specific crime type 

3. Kernel density model for all crimes 

4. Kernel density model for specific crime type

 Spatial Distribution Methods 

5. Mean center 

6. Center of minimum distance 

7. Center of minimum travel time 

 (calculated on road network weighted by travel time) 

8. Directional mean (weighted) calculated with >lower left corner= as origin 

9. Triangulated mean 

10. Geometric mean 

11. Harmonic mean 

Measurement of Error 

For each of the 50 offenders, error was defined as the distance in miles between the >best 

guess= and the actual location. For each offender, the distance between the estimated home base 

(the >best guess=) and the actual residence location was calculated using direct distances. Table 

13.6 presents the results. The data show the error by method for each of the 50 offenders. The 

three right columns show the average error of all methods and the minimum error and maximum 

errors obtained by a method. The method with the minimum error is boldfaced; for some cases, 

two methods are tied for the minimum. The bottom three rows show the median error, the 

average error and the standard deviation of the errors for each method across all 50 offenders. 

Results of the Test 

The results point to certain conclusions. First, the degree of precision for any of these 

methods varies considerably. The precision of the estimates vary from a low of 0.0466 miles 

(about 246 feet) to a high of 75.7 miles. The overall precision of the methods is not very high 

and is highly variable. There are a number of possible reasons for this, some of which have been 

discussed above. Each of the methods produces a single parameter from what is, essentially, a 

probability distribution whereas the distribution of many of these incidents are widely dispersed. 

13.64 
Table 13.6: 

Accuracy of Methods for Estimating Serial Offender Residences 

(N= 50 Serial Offenders) 

Number Primary * Mean Center of Mini- Triangulated Geometric Harmonic Jtc Kernel: Jtc Kernel: Jtc Math: Jtc Math: * 

of Crime * Center mum Distance Mean Mean Mean All Crimes Crime Type All Crimes Crime Type * Av

Dataset Crimes Type * Error (miles) Error (miles) Error (miles) Error (miles) Error (miles) Error (miles) Error (miles) Error (miles) Error (miles) * Er

------------------- -------------------- --------------------- * ------------------------------------------------------------ ---------------------------------------------------------- ----------------------- ----------------------------- -------------------------- ---------------------- ------------------------- * ---

3A 3 Larceny * 31.5991 32.4477 32.4109 31.5995 31.6000 32.7824 32.7880 32.7824 32.7880 * 

3B 3 Larceny * 13.2303 12.1683 24.1531 13.2311 13.2319 10.7526 14.4929 10.7526 11.2501 * 

3C 3 Bank robbery * 2.8348 0.9137 2.7767 2.8335 2.8322 0.6775 5.8416 0.6775 6.0946 * 

3D 3 Burglary * 2.9733 3.2603 6.1013 2.9728 2.9724 4.6038 3.3883 3.3882 3.7931 * 

4A 4 Vehicle theft * 4.2436 4.2670 3.8217 4.2436 4.2436 4.2527 4.2364 4.2527 4.2590 * 

4B 4 Larceny * 1.9618 0.3100 2.0563 1.9621 1.9623 0.3125 0.2018 0.3125 0.2784 * 

4C 4 Larceny * 4.4733 4.4733 4.6789 4.4733 4.4733 4.9681 4.3563 4.2637 4.3563 * 

4D 4 Assault * 0.2925 0.1905 0.0466 0.2925 0.2926 0.0703 0.0703 0.0703 0.4560 * 

5A 5 Larceny * 17.3308 16.6459 17.8985 17.3292 17.3276 15.9738 17.8655 15.9739 16.4526 * 

5B 5 Larceny * 1.3609 0.2481 1.7733 1.3586 1.3564 0.2068 0.6974 0.5140 0.6974 * 

5C 5 Larceny * 2.2458 2.6832 16.4518 2.2450 2.2442 2.7886 2.4205 2.7886 3.0922 * 

5D 5 Larceny * 0.9169 0.2250 0.2371 0.9171 0.9174 0.1577 0.4267 0.1577 0.4267 * 

6A 6 Larceny * 5.1837 5.2081 7.9621 5.1837 5.1837 5.1271 4.8554 4.9393 5.2256 * 

6B 6 Vehicle theft * 1.3720 1.1869 0.9625 1.3710 1.3700 3.1126 2.3800 1.3566 2.0831 * 

6C 6 Larceny * 1.3199 0.3157 1.7928 1.3192 1.3184 0.2580 0.5272 0.2580 0.5272 * 

6D 6 Larceny * 3.2458 2.3324 6.5209 3.2431 3.2405 1.2506 2.6253 1.9718 1.9718 * 

7A 7 Larceny * 3.9023 3.4185 2.3176 3.9022 3.9021 2.7419 3.0532 3.1364 3.0532 * 

7B 7 Larceny * 12.4100 9.2973 14.8293 12.4107 12.4115 8.5357 8.6148 8.5357 8.8275 * 

7C 7 Burglary * 5.0501 7.1477 10.8567 5.0481 5.0460 7.9975 7.9975 7.9975 7.6274 * 

7D 7 Larceny * 2.2686 0.7733 75.7424 2.2684 2.2682 0.0892 0.7191 0.0892 0.7191 * 

8A 8 Larceny * 6.0298 6.0165 6.2653 6.0264 6.0229 8.4210 6.2962 6.2022 6.1166 * 

8B 8 Larceny * 1.0041 1.1437 2.1776 1.0042 1.0042 1.7475 1.3510 1.5298 1.3510 * 

8C 8 Larceny * 1.3059 1.6944 1.3684 1.3043 1.3027 2.1513 1.2020 2.1513 1.8707 * 

8D 8 Vehicle theft * 3.5794 2.3780 5.5915 3.5809 3.5825 0.5900 1.3340 1.9133 1.3340 * 

9A 9 Robbery * 5.2527 5.7156 4.8574 5.2529 5.2532 7.8257 7.1961 6.2520 5.9265 * 

9B 9 Larceny * 8.1923 10.6555 6.9916 8.1886 8.1850 12.4578 10.3957 12.4578 12.0514 * 

9C 9 Robbery * 3.7778 3.8454 11.0042 3.7758 3.7738 4.9015 5.1862 4.6206 4.3445 * 

10A 10 Larceny * 0.9358 0.5159 1.1003 0.9355 0.9353 0.0606 0.3720 0.2601 0.7172 * 

10B 10 Larceny * 2.8581 3.4940 14.2219 2.8536 2.8491 6.4051 6.5709 10.3095 6.4758 * 

10C 10 Larceny * 0.8052 0.7251 5.5938 0.8050 0.8049 0.9059 0.8404 0.9060 1.2786 * 

11A 11 Vehicle theft * 2.9127 3.2715 3.1192 2.9130 2.9134 3.6936 3.4335 3.4282 3.2087 * 

11B 11 Robbery * 0.3250 0.3250 0.2513 0.3250 0.3250 0.4235 0.2263 0.4235 0.7011 * 

11C 11 Vehicle theft * 1.2689 1.7157 1.4750 1.2709 1.2729 2.8945 0 6984. 2.8945 2.2049 * 

12A 12 Larceny * 3.3881 4.2334 10.9241 3.3867 3.3852 6.4050 3.2639 5.5843 5.2132 * 12B 12 Larceny * 0.5562 0.5361 2.8003 0.5562 0.5562 0.7897 0.6709 0.7897 0.9631 * 

13A 13 Larceny * 6.3282 7.2857 6.0244 6.3248 6.3213 7.6438 7.4607 7.6438 7.9915 * 

13B 13 Assault * 1.4943 1.4943 1.5279 1.4944 1.4944 1.6501 1.5954 1.6501 2.0824 * 

14A 14 Larceny * 1.9363 0.8706 1.4498 1.9365 1.9368 0.3434 0.6058 0.2596 0.7631 * 

14B 14 Arson * 0.6898 0.3727 0.8086 0.6899 0.6900 0.3359 0.3359 0.3359 0.6213 * 15A 15 Vehicle theft * 0.7282 0.7189 0.3362 0.7277 0.7271 0.8155 0.4855 0.8155 1.5128 * 

15B 15 Robbery * 0.4914 0.4914 0.8254 0.4914 0.4914 0.6468 0.5693 0.6468 0.6546 * 

16A 16 Vehicle theft * 2.1107 2.0995 8.2311 2.1107 2.1107 1.5957 1.6404 2.5911 2.4033 * 

17A 17 Burglary * 1.6484 0.3093 1.0227 1.6461 1.6438 0.2879 0.2879 0.2879 0.5268 * 

18A 18 Larceny * 0.6308 0.4196 1.0876 0.6329 0.6349 0.2132 0.3383 0.2132 0.6985 * 19A 19 Larceny * 8.6462 9.4195 8.6772 8.6486 8.6511 10.2869 9.2708 9.7022 9.5548 * 

20A 20 Burglary * 6.3520 5.7969 28.3094 6.3486 6.3452 0.5934 0.8673 0.5934 0.7945 * 

21A 21 Burglary * 1.2396 0.8861 1.2776 1.2393 1.2390 0.5243 0.5243 1.0253 0.4965 * 

22A 22 Larceny * 3.6828 2.6232 2.0949 3.6803 3.6777 2.4937 2.8944 2.4937 2.8944 * 

24A 24 Larceny * 1.7959 0.5892 2.3033 1.7975 1.7991 0.2658 0.3574 0.4222 0.6587 * 

33A 33 Robbery * 3.9901 5.0481 7.2505 3.9940 3.9979 7.9485 7.6939 8.1907 7.9439 * 

------------------- -------------------- --------------------- * ------------------------------------------------------------ ---------------------------------------------------------- ----------------------- ----------------------------- -------------------------- ---------------------- ------------------------- * ----

Median Error = * 2.5517 2.2159 3.4704 2.5509 2.5502 1.9494 2.0102 2.0615 2.1440 

Mean Error = * 4.0434 3.8441 7.6472 4.0429 4.0424 4.0395 4.0305 4.0163 4.1467 * 

SD Error = * 5.2166 5.3845 12.0642 5.2166 5.2166 5.5678 5.6237 5.5398 5.4177 * 
Few of the offenders had such a concentrated pattern that only a single location was 

possible. Since these are probability distributions, not everyone follows the >central tendency=. 

Also, some of these offenders may have moved during the period indicated by the incidents, 

thereby shifting the spatial pattern of incidents and making it difficult to identify the last 

residence. 

A second conclusion is that, for any one offender, the methods produce similar results. 

For many of the offenders the difference between the best estimate (the minimum error) and the 

worst estimate (the maximum error) is not great. Thus, the simple methods are generally as good 

(or bad) as the more sophisticated methods. 

Third, across all methods, the center of minimum travel time, which is calculated on a 

road network (see chapters 3 and 30), and it=s distance-based >cousin= - the center of minimum 

travel time, had the lowest average error. Thus, the approximate geographic center of the 

distribution where travel time to each of the incidents was minimal produced as good an estimate 

as the more sophisticated methods. However, it was not particularly close (3.84 miles, on 

average). The worst method was the triangulated mean which had an average error of 7.6472 

miles. The triangulated mean is produced by vector geometry and will not necessarily capture 

the center of the distribution. Other than this, there were not great differences. This reinforces the 

point above that the methods are all, more or less, describing the central tendency of the 

distribution. For offenders that don=t live in the center of their distribution, the error of a method 

will necessary be high. 

Looking at each of the 50 offenders, the methods vary in their efficacy. For example, the 

Jtc kernel function for all crimes was the best or tied for best for 17 of the offenders, but was also 

the worst or tied for worst for 9. Similarly, the Jtc kernel function for the specific crimes was 

best or tied for best for 8 of the offenders, but worse for 4. Even the most consistent was best 

for 4 offenders, but also worst for one. On the other hand, the triangulated mean, which had the 

worst overall error, produced the best estimate for 9 of the individuals while it produced the 

worst estimate for 25 of the individuals. Thus, the triangulated mean tends to be very accurate or 

very inaccurate; it had the highest variance, by far. 

Fourth, the median error is smaller than the average error. That is, the median is the point 

at which 50% of the cases had a smaller error and 50% had a larger error. Overall, most of the 

cases were found within a shorter distance than the average would indicate. This indicates that 

several cases had very large errors whereas most had smaller errors; that is, they were outliers. 

Over all methods, the Jtc kernel approach for all crimes had the lowest median error (1.95 miles). 

In fact, all four Jtc methods had smaller median errors than the simple centrographic methods. In 

other words, they are more accurate than the centrographic methods most of the time. The 

problem in applying this logic in practice, however, is that one would not know if the case being 

13.66 
 

studied is typical of most cases (in which case, the error would be relatively small) or whether it 

was an outlier. In other words, the median would define a search area that captured about 50% 

of the cases, but would be very wrong in the other 50%. If we could somehow develop a 

method for identifying when a case is >typical= and when it isn=t, increased accuracy will emerge 

from the Jtc methods. But, until then, the simple center of minimum travel time will be the most 

accurate method. 

Fifth, the amount of error varies by the number of incidents. Table 13.7 below shows the 

average error for each method as a function of three size classes: 1-5 incidents; 6-9 incidents; and 

10 or more incidents. As can be seen, for each of the ten methods, the error decreases with 

increasing number of incidents. In this sense, the measured error is responsive to the sample size 

from which it is based. It is, perhaps, not surprising that with only a handful of incidents no 

method can be very precise. 

Sixth, the relative accuracy of each of these methods varies by sample size. The method 

or methods with the minimum error are boldfaced. For a limited number of incidents (1-5), the 

Jtc mathematical function for all crimes (i.e., the negative exponential with the parameters from 

Table 13.5) produced the estimate with the least error, followed by the Jtc kernel function for all 

crimes; the was the third best. The differences in error between these were not very great. For 

the middle category (6-9 incidents), the center of minimum distance produced the least error 

followed by the Jtc mathematical function for the specific crime type. For those offenders who 

had committed ten or more crimes, the Jtc kernel function for the specific crime type produced 

the best estimate, followed by the center of minimum distance. The two mathematical functions 

produced the least accuracy for this sub-group, though again the differences in error are not very 

big (2.2 miles for the best compared to 2.7 miles for the worst). In other words, only with a 

sizeable number of incidents does the Jtc kernel density approach for specific crimes produce a 

good estimate. It is better than the other approaches, but only slightly better than the simple 

measure of the center of minimum distance. 

Search Area for a Serial Offender? 

A number of researchers have been interested in the concept of a search area for the 

police (Rossmo, 2000; Canter, 2003). The concept is that the Journey-to-crime method can 

define a small search area within which there is a higher probability of finding the offender. The 

average or median error discussed above can be used to define such a search area if treated as a 

radius of a circle. While intuitive, this does not necessarily represent a meaningful statistic. For 

example, taking the average error of the center of minimum distance (3.84 miles) would produce 

a search area of 46.4 square miles, not exactly a small area in which to find a serial offender. 

Even if we take the median error of 1.94 miles from the Jtc kernel approach for all crimes (1.94 

13.67 
Table 13.7: 

Method Estimation Error and Sample Size

 Average Error of Method by Number of Incidents (miles) 

* Jtc Jtc Jtc Jtc 

Number of * Mean Center of Mini- Triangulated Geometric Harmonic Kernel: Kernel: Math: Math: 

Incidents * Center mum Distance Mean Mean Mean All Crime types All Crime type-------------- * ----------- -------------------------------------------- ---------------- --------------- ----------- ------------------- ----------- --------------

3-5 * 6.9553 6.4861 9.3672 6.9160 6.9545 6.4622 7.2321 6.3278 6.9

* 

6-9 * 4.2596 4.0753 10.6160 4.3331 4.2576 4.4805 4.2489 4.2274 4.2

* 

10+ * 2.3832 2.3149 4.8136 2.4575 2.3827 2.4880 2.2176 2.6725 2.6

-------------- * ----------- -------------------------------------------- ---------------- --------------- ----------- ------------------- ----------- --------------
miles) will still produce a search area of 11.9 square miles, and it would be correct only half the 

time. These methods are still very imprecise. 

Confirmation of These Results 

This analysis was first conducted in 2000 with version 1.1 of CrimeStat (Levine, 2000). 

Since then, it has been confirmed with several studies. Snook, Zito, Bennell, and Taylor (2005) 

found a similar result with 16 serial burglars who had committed 10 or more incidents in the 

United Kingdom. Simple measures did as well as the complex measures. 

Snook, Canter and Bennell (2002) compared the journey-to-crime method with the 

judgment of student volunteers and found that the journey-to-crime method was not significantly 

more accurate than the student judgments. A subsequent study found that simple training of 

geographic principles improved the predictive accuracy of police officers in predicting the 

residence location of 36 serial offenders and were as accurate as the journey-to-crime method 

(Bennell, Snook, Taylor, Corey, & Keyton, 2007). 

Paulsen (2006) conducted an analysis of 247 serial offenders from Baltimore County and 

found that simple centrographic measures were more accurate than the more complex journey-tocrime method. He also compared four different software packages in terms of their accuracy. 

He found that all packages had about the same degree of accuracy, that simple centrographic 

approaches were generally more accurate, that there were substantial differences in the accuracy 

by different crime types, but, most importantly, none of the methods were very accurate. 

Bennell, Taylor , and Snook (2007) examined a number of studies of geographic profiling 

and argued that simple heuristics can provide as much accuracy as more sophisticated methods, 

with much less effort and cost to a police department. 

In other words, in several independent tests of the accuracy of the journey-to-crime 

approach to geographical profiling, simple measures, particularly the center of minimum 

distance, do as well as, if not better than, the journey-to-crime approach. 

Theoretical Limitations 

There are also some theoretical problems in journey-to-crime analysis which limits the 

method’s ability to predict the origin location of a serial offender. First, the method is entirely 

based on distance traveled from a theoretical origin that will be estimated by the method. The 

means for assigning the distance is the journey-to-crime function that has been chosen (normal, 

quartic, exponential etc). However, transportation modelers usually conceptualize travel 

13.69 
distance not as an independent variable but the result of predispositions, attractions, and 

networks (Domencich & McFadden, 1975; Ortuzar & Willumsen, 2001; Culp, 2002). Different 

individuals have predispositions to travel that vary by gender as well as by age (Levine & Lee, 

2012). 

Second, the distance function in a journey-to-crime model is assumed to operate in any 

direction. In reality, there is a large amount of asymmetry in the direction of travel because 

attractions are more concentrated towards the center of a metropolitan area (FCCDR, 1994; 

Bruegmann, R.,2008; Bertaud, 2009; SCTLC 2009). For example, an offender who lives in a 

suburb is more likely to travel towards the center of a metropolitan area than away from it 

because there are more opportunities in the center than farther away. Similarly, offenders in a 

high crime neighborhood of a metropolitan area are more likely to travel to other high crime 

neighborhoods and not just in any direction. In addition travel is restrained by physical and 

social barriers (Bernasco & Block, 2009). The journey-to-crime approach assumes a uniform 

cost function that applies to everyone. 

Third, criminal opportunities (or attractions) are never measured, but are inferred from 

the pattern of crime incidents. That is, the crime location is assumed to represent the opportunity 

for the offender, but the attraction for the offender is never measured. Therefore, the distance 

traveled is assumed to represent the likelihood of travel by the offender without any 

differentiation by place, crime type, type of person, or environment. As a pragmatic tool for 

informing a police search, one could argue that this is not important. However, in a different 

location or crime set, the distance function is liable to differ substantially. 

Fourth, it is not clear whether knowing an offender=s >cognitive map= will help in 

prediction. There have been no evaluations that have compared a strictly statistical approach 

with an approach that utilizes information about the offender as he or she understands the 

environment. It cannot be assumed that integrating information about the perception of the 

environment will aid prediction. In most travel demand forecasts that transportation engineers 

and planners make, cognitive information about the environment is not utilized except in the 

definition of trip purpose (i.e., what the purpose of the trip was). The models use the actual trips 

by origin and destination as the basis for formulating predictions, not the understanding of the 

trip by the individual. Understanding is important from the viewpoint of developing theory or 

for ways to communicate with people. But, it is not necessarily useful for prediction. In short, 

understanding and prediction are not the same thing. 

In short, journey-to-crime methodology is limited both theoretically and empirically. 

Theoretically, it ignores the distribution of opportunities and focuses only on the cost of travel. 

Empirically, the method has a substantial amount of error and cannot even do as well as simple 

measures in terms of prediction. Finally, existing journey-to-crime methodologies assume that 

13.70 
the awareness space of serial offenders surrounds the offender’s anchor point. But, there are 

offenders who commit crimes far from where they reside or from their anchor point, so called 

‘Commuter’ offenders ( Paulson, 2006). 

Cautionary Notes 

There are certain cautions that must be considered in using either of these Journey-tocrime methods (the mathematical or the empirical). First, a simple technique, such as the center 

of minimum distance, may be as good as a more sophisticated technique. It does not always 

follow that a sophisticated method will produce any more accuracy than a simple one. 

Second, there are other limitations to the technique. The model must be calibrated for 

each individual jurisdiction. Further, it must be periodically re-calibrated to account for changes 

in crime patterns. For example, in using the mathematical model, one cannot take the parameters 

estimated for Baltimore County (Table 13.3) and apply them to another city or if using the kernel 

density method take the results found at one time period and assume that they will remain 

indefinitely. The model is a probability model, not a guarantee of certainty. It provides guesses 

based on the similarity to other offenders of the same type of crime. In this sense, a particular 

serial offender may not be typical and the model could actually orient police wrongly if the 

offender is different from the calibration sample. It will take insight by the investigating officers 

to know whether the pattern is typical or not. 

Third, as a theoretical model, the Journey-to-crime approach is quite simple. It is based 

on a distribution of incidents and an assumed travel distance decay function. As mentioned 

above, the method does not utilize information on the distribution of target opportunities nor 

does it utilize information on the travel mode and route that an offender takes. It is purely a 

statistical model. 

The research area of geographic profiling attempts to go beyond statistical description 

and understand the cognitive maps that offenders use as well as how these interact with their 

motives. This is good and should clearly guide future research. But it has to be understood that 

the theory of offender travel behavior is not very well developed, certainly compared to other 

types of travel behavior. Further, some types of crime trips may not even start from an 

offender=s residence, but may be referenced from another location, such as vehicle thefts 

occurring near disposal locations. Routine activity theory would suggest multiple origins for 

crimes (Cohen & Felson, 1979). 

The existing models of travel demand used by transportation planners (which have 

themselves been criticized for being too simple) measure a variety of factors that have only been 

marginally included in the crime travel literature - the availability of opportunities, the 

13.71 
 

concentration of offender types in certain areas, the mode of travel (i.e., auto, bus, walk), the 

specific routes that are taken, the interaction between travel time and travel route, and other 

factors. It will be important to incorporate these elements into the understanding of Journey-tocrime trips to build a much more comprehensive theory of how offenders operate. Travel 

behavior is very complicated and we need more than a statistical distance model to adequately 

understand it. 

In the next chapter, a Bayesian approach to journey-to-crime modeling will be discussed 

in which additional information about the origin location for the offender is introduced into the 

model in order to improve the distance estimate. As we shall see, the method is more accurate 

and more precise than the journey-to-crime function. 

Draw Crime Trips 

The Journey-to-crime module also includes one utility that can help visualize the pattern 

before selecting a particular estimation model. This is a Draw Crime Trips routine that simply 

draws lines between the origin and destination of individual crime trips. The X and Y 

coordinates of an origin and destination location are input and the routine draws a line in ArcGIS 

>shp=, MapInfo >mif=, Google Earth ‘kml’ or various Ascii formats. 

Figure 13.23 illustrates the drawing of the known travel distances for 444 rape cases for 

which the residence location of the rapist was known. Of the 444 cases, 113 (or 25.5%) occurred 

in the residence of the rapist. However, for the remaining 331 cases, the rape location was not 

the residence location. As seen, many of the trips are of quite long distances. This would 

suggest the use of a Journey-to-crime function that has many trips at zero distance but with a 

more gradual decay function. 

13.72 
Figure 13.23: 
References 

Andersson, T. (1897). Den Inre Omflyttningen. Norrland: Mälmo. 

Anselin, L. & Madden, M. (1990). New Directions in Regional Analysis. Belhaven Press: New 

York. 

Bennell, C., Snook, B., Taylor, P. J., Corey, S. & Keyton, J. (2007). It’s no riddle, choose the 

middle: The effect of number of crimes and topographical detail on police officer predictions of 

serial burglars’ Home Locations. Criminal Justice And Behavior, 34 (1), 119-132. 

Bennell, C., Taylor, P. J., &Snook, B. (2007). Clinical versus actuarial geographic profiling 

approaches: A review of the research. Police Practice and Research, 8(4), 335-345. 

Bernasco, W. & Block, R. (2009). Where offenders choose to attack: A discrete choice model of 

robberies in Chicago. Criminology 47(1): 93-130. 

Bernasco, W. & Nieuwbeerta, P. (2005). How do residential burglars select target areas?. British 

Journal of Criminology 44: 296-315. 

Bertaud, A. (2009). The Spatial Structure of Cities: International Examples of the Interaction of 

Government, Topography, and Markets. Alain-Bertaud.com. 

http://AB_China_course_part3_PPT.ppt 

Blumin, D. (1973). Victims: A Study of Crime in a Boston Housing Project. City of Boston, 

Mayor=s Safe Street Act, Advisory Committee: Boston. 

Boggs, S. L. (1965). Urban crime patterns, American Sociological Review, 30, 899-908. 

Bossard, E. G. (1993). RETAIL: Retail trade spatial interaction. In Klosterman, R. E. Brail, R. 

& Bossard, E. G. Spreadsheet Models for Urban and Regional Analysis. Center for Urban 

Policy Research, Rutgers University: New Brunswick, NJ, 419-448. 

Brantingham, P. L. & Brantingham, P. J. (1981). Notes on the geometry of crime. In 

Brantingham, P. J. & Brantingham, P. L, Environmental Criminology. Waveland Press, Inc.: 

Prospect Heights, IL, 27-54. 

Bright, M. L. & Thomas, D. S. (1941). Interstate migration and intervening opportunities, 

American Sociological Review, 6, 773-783. 

13.74 
References (continued 

Bruegmann, R. (2008). Driving works. Forbes Magazine. July 29, 

http://www.forbes.com/2008/07/29/commuting-suburbs-future-lead-commuting08-

cx_rb_0729bruegmann.html 

Burgess, E. W. (1925). The growth of the city: an introduction to a research project. In R. E. 

Park, R. E. Burgess, E. W. & Mackensie, R. D. (ed), The City. University of Chicago Press: 

Chicago, 47-62. 

Canter, D. (2003). Dragnet: A Geographical Prioritisation Package. Center for Investigative 

Psychology, Department of Psychology, The University of Liverpool: Liverpool, UK. 

http://www.i-psy.com/publications/publications_dragnet.php. 

Canter, D. (1994). Criminal Shadows: Inside the Mind of the Serial Killer. Harper Collins 

Publishers: London. 

Canter, D. V, Coffey, T., Huntley, M., & Missen, C. (2000). Predicting serial killers’ home base 

using a decision support system. Journal of Quantitative Criminology, 16, 457-478. 

Canter, D. & A. Gregory (1994). Identifying the residential location of rapists, Journal of the 

Forensic Science Society, 34 (3), 169-175. 

Canter, D. & Larkin, P. (1993). The environmental range of serial rapists, Journal of 

Environmental Psychology, 13, 63-69. 

Canter, D. V., & Snook, B. (1999). Modelling the home location of serial offenders. Paper 

presented at the meeting of the Crime Mapping Research Center, Orlando, FL. December. 

Canter, D. & Tagg, S. (1975). Distance estimation in cities, Environment and Behaviour, 7, 59-

80. 

Capone, D. L. & Nichols Jr, W. W. (1975). Crime and distance: an analysis of offender 

behaviour in space, Proceedings, Association of American Geographers, 7, 45-49. 

Cliff, A. D. & Haggett, P. (1988). Atlas of Disease Distributions. Blackwell Reference: Oxford. 

Cohen, L.E. & Felson, M. (1979) Social change and crime rate trends: a routine activity 

approach, American Sociological Review, 44: 588-608. 

13.75 
References (continued 

Culp, M. (2005). Travel Model Improvement Program Annual Report FY04. Federal Highway 

Administration, U.S. Department of Transportation: Washington, DC. 

http://www.fhwa.dot.gov/planning/tmip/about/annual_reports/fy2004/. Access April 1, 2012. 

Curtis, L. A. (1974). Criminal Violence. Lexington Books: Lexington, MA. 

Demographia (1999). U.S. Central Cities and Suburban Crime Rates Ranked: 1999. Wendell 

Cox Consultancy: Belleville, IL. http://www.demographia.com/db-crime99r.htm. 

Demographia (1998). U. S. Metropolitan Areas: 1998 Central City and Suburban Population. 

Wendell Cox Consultancy: Belleville, IL. http://www.demographia.com/db-usmsacc98.htm. 

Domencich, T. & McFadden, D. (1975). Urban Travel Demand: A Behavioral Analysis. North 

Holland Publishing Company: Amsterdam and Oxford (republished in 1996). Also found at 

http://emlab.berkeley.edu/users/mcfadden/travel.html. Accessed April 1, 2012. 

Eldridge, J. D. & Jones, J. P. (1991). Warped space: a geography of distance decay, Professional 

Geographer, 43 (4), 500-511. 

Everitt, B. S. (2011). Cluster Analysis (5th edition). J. Wiley: London. 

FCCDR (1994). Sustainable Community Design Principles. Tampa, FL: Florida Center for 

Community Design and Research. 

http://www.fccdr.usf.edu/upload/projects/tlushtml/tlus100.htm. Accessed April 1, 2012. 

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

Field, B. & MacGregor, B. (1987). Forecasting Techniques for Urban and Regional Planning. 

UCL Press, Ltd: London. 

Foot, D. (1981). Operational Urban Models. Methuen: London. 

Freedman, D. A. (1999). Ecological inference and ecological fallacy. International 

Encyclopedia of the Social and Behavioral Sciences, Technical Report No. 549, October. 

http://www.stanford.edu/class/ed260/freedman549.pdf. Accessed March 26, 2012. 

Fritzon, K. (2001). An Examination of the Relationship between Distance Travelled and 

Motivational Aspects of Firesetting Behaviour. Journal of Environmental Psychology, 21, 45-60. 

13.76 
References (continued 

Golden Software. 2008. Surfer® for Windows (Ver. 10). Golden Software, Inc.: Golden, CO. 

Groff, E. R. & McEwen, J. T (2005). Disaggregating the Journey to Homicide. In Wang, F. 

(ed.), Geographic Information Systems and Crime Analysis. Idea Group Publishing: Hershey, 

PA. 

Hägerstrand, T. (1957). Migration and area: survey of a sample of Swedish migration fields and 

hypothetical considerations on their genesis. Lund Studies in Geography, Series B, Human 

Geography, 4, 3-19. 

Haggett, P. & Arnold, E. (1965). Locational Analysis in Human Geography (1st edition). 

Edward Arnold: London. 

Haggett, P., Cliff, A. D. & Frey, A. (1977). Locational Analysis in Human Geography (2nd 

edition). Edward Arnold: London. 

Harries, K. (1980). Crime and the Environment. Charles C. Thomas Press: Springfield. 

Hodge, S. & Canter, D. (1998) Victims and Perpetrators of Male Sexual Assault. Journal of 

Interpersonal Violence, 1 (April), 222-239. 

Huff, D. L. (1963). A probabilistic analysis of shopping center trade areas. Land Economics, 39, 

81-90. 

Isard, W. (1979). Location and Space-Economy: A General Theory Relating to Industrial 

Location, Market Areas, Land Use, Trade, and Urban Structure (originally published 1956). 

Program in Urban and Regional Studies, Cornell University: Ithaca, NY. 

Isard, W. (1960). Methods in Regional Analysis. John Wiley & Sons: New York. 

Isbel, E. C. (1944). Internal migration in Sweden and intervening opportunities, American 

Sociological Review, 9, 627-639. 

Kind, S. S. (1987). Navigational ideas and the Yorkshire Ripper investigation. Journal of 

Navigation, 40 (3), 385-393. 

Krueckeberg, D. A. & Silvers, A. L. (1974). Urban Planning Analysis: Methods and Models. 

John Wiley & Sons: New York. 

13.77 
References (continued 

Langbein, L. I. & Lichtman, A. J. (1978). Ecological Inference. Sage University Paper series on 

Quantitative Applications in the Social Sciences, series no. 07-010. Beverly Hills and London: 

Sage Publications. 

LeBeau, J. L. (1987a). The journey to rape: geographic distance and the rapist’s 

method of approaching the victim, Journal of Police Science and Administration, 15 (2), 129-

136. 

LeBeau, J. L. (1987b). The methods and measures of centrography and the spatial dynamics of 

rape, Journal of Quantitative Criminology, 3 (2), 125-141. 

Levine, N. (2007). Crime travel demand and bank robberies: Using CrimeStat III to model bank 

robbery trips. Social Science Computer Review, 25(2), 239-258. 

Levine, N. (2000). CrimeStat: A Spatial Statistics Program for the Analysis of Crime Incident 

Locations (version 1.1). Ned Levine and Associates, Annandale, VA.; National Institute of 

Justice, Washington, DC. August 2000. Update chapter. 

Levine, N. & Lee, P. (2012). Journey-to-crime by Gender and Age Group in Manchester, 

England. In Leitner, Michael (ed), Crime Modeling and Mapping Using Geospatial 

Technologies, Springer. In press. 

Levine, N. & Canter, P. (2011). Linking origins with destinations for DWI motor vehicle 

crashes: An application of crime travel demand modeling. Crime Mapping, 3, 7-41. 

Lottier, S. (1938). Distribution of criminal offences in metropolitan regions, Journal of Criminal 

Law, Criminology, and Police Science, 29, 37-50. 

NCHRP (1995). Travel Estimation Techniques for Urban Planning. Project 8-29(2). National 

Cooperative Highway Research Program, Transportation Research Board: Washington, DC. 

http://www4.trb.org/trb/crp.nsf/0/647c1cb3a6b6bfe285256748005619fa?OpenDocument 

Oppenheim, N. (1980). Applied Models in Urban and Regional Analysis. Prentice-Hall, Inc.: 

Englewood Cliffs, NJ. 

Ortuzar, J. D. & Willumsen, L. G. (2001). Modeling Transport (3rd edition). New York: John 

Wiley and Sons. 

13.78 
References (continued 

Paulsen, D. (2006). Connecting the dots: assessing the accuracy of geographic profiling 

software. Policing: An International Journal of Police Strategies and Management. 29 (2), 306-

334. 

Pettiway, L. E. (1995). Copping crack: The travel behaviour of crack users. Justice Quarterly, 

12(3), 499-524. 

Phillips, P. D. (1980) Characteristics and typology of the journey to crime. In Georges-Abeyie, 

D. E. & Harries, K. D. (eds), Crime: A Spatial Perspective, Columbia Univ. Press: New York, 

156-166. 

Pyle, G. F. (1974). The Spatial Dynamics of Crime. Department of Geography Research Paper 

No. 159, University of Chicago: Chicago. 

Ravenstein, E. G. (1885). The laws of migration. Journal of the Royal Statistical Society. 48. 

Reilly, W. J. (1929). Methods for the study of retail relationships. University of Texas Bulletin, 

2944. 

Rengert, G., Piquero, A. R., & Jones, P. R. (1999). Distance decay re-examined, Criminology, 

37 (2), 427-445. 

Rengert, G. F (1981). Burglary in Philadelphia: a critique of the opportunity structure model. In 

Brantingham, P. J. & Brantingham, P. L., Environmental Criminology. Waveland Press, Inc.: 

Prospect Heights, IL, 189-202. 

Rengert, G. F. (1975). Some effects of being female on criminal spatial behavior. The 

Pennsylvania Geographer, 13 (2), 10-18. 

Repetto, T. A. (1974). Residential Crime. Ballinger: Cambridge, MA. 

Rhodes, W. M. & Conly, C. (1981). Crime and mobility: an empirical study. In Brantingham, P. 

J. & Brantingham, P. L., Environmental Criminology. Waveland Press, Inc.: Prospect Heights, 

IL, 167-188. 

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

13.79 
References (continued 

Rossmo, D. K. (1997). Geographic profiling. In Jackson, J. L. & Bekerian, D. A., Offender 

Profiling: Theory, Research and Practice. John Wiley & Sons: Chicester, 159-175. 

Rossmo, D. K. (1995). Overview: multivariate spatial profiles as a tool in crime investigation. In 

Block, C. R., Dabdoub, M & Fregly, S., Crime Analysis Through Computer Mapping. Police 

Executive Research Forum:Washington, DC. 65-97. 

Rossmo, D. K. (1993a). Multivariate spatial profiles as a tool in crime investigation. In Block, 

C. R. & Dabdoub, M. (eds), Workshop on Crime Analysis Through Computer Mapping: 

Proceedings. Illinois Criminal Justice Information Authority and Loyola University Sociology 

Department: Chicago. (Library of Congress HV7936.C88 W67 1993). 

Rossmo, D. K. (1993b). Target patterns of serial murderers: a methodological model. American 

Journal of Criminal Justice, 17, 1-21. 

Rushton, G. (1979). Optimal Location of Facilities. COMPress: Wentworth, NH. 

SPSS, Inc. (1999). SPSS 9.0 for Windows. SPSS, Inc.: Chicago. 

SCTLC. (2009). Patterns of Development. Sonoma County, CA: Sonoma County Transportation 

and Land Use Coalition, http://www.sonomatlc.org/LandUse/Patterns.htm. Accessed April 1, 

2012. 

Shaw, C. R. (1929). Delinquency Areas. University of Chicago Press: Chicago. 

Smith, T. S. (1976). Inverse distance variations for the flow of crime in urban areas. Social 

Forces, 25(4), 804-815. 

Smith, W. Bond, J. W., & Townsley, M. (2009). Determining how journeys-to-crime vary: 

Measuring inter- and intra-offender crime trip distributions. In Weisburd, D., Bernasco, W., & 

Bruinsma, G. (eds.), Putting Crime in its Place: Units of Analysis in Spatial Crime Research. 

New York: Springer. 

Snook, B. (2004). Individual differences in distance travelled by serial burglars. Journal of 

Investigative Psychology and Offender Profiling, 1, 53-66. 

Snook, B., Cullen, R. M., Mokros, A., & Harbort, S. (2005). Serial murderers’ spatial decisions: 

factors that influence crime location choice. Journal of Investigative Psychology and Offender 

Profiling, 2, 147-164. 

13.80 
 

References (continued 

Snook, B., Zito, M., Bennell, C. & Taylor, P. J. (2005). On the complexity and accuracy of 

geographic profiling strategies. Journal of Quantitative Criminology, 21 (1), 1-26. 

Snook, B., Canter, D. V., & Bennell, C. (2002). Predicting the home location of serial offenders: 

A preliminary comparison of the accuracy of human judges with a geographic profiling system. 

Behavioural Sciences and The Law, 20, 109-118. 

Snyder, J. P. (1987). Map Projections - A Working Manual. U.S. Geological Survey 

Professional Paper 1395. U. S. Government Printing Office: Washington, DC. 

Stewart, J. Q. (1950). The development of social physics. American Journal of Physics, 18, 239-

53. 

Stopher, P. R. & Meyburg, A. H. (1975). Urban Transportation Modeling and Planning. 

Lexington, MA: Lexington Books. 

Stouffer, S. A. (1940). Intervening opportunities: a theory relating mobility and distance. 

American Sociological Review, 5, 845-67. 

Taylor, P. J. (1970). Interaction and Distance: An Investigation into Distance Decay Functions 

and a Study of Migration at a Microscale. PhD thesis, University of Liverpool: Liverpool. 

Thrasher, F. M. (1927). The Gang, University of Chicago Press: Chicago. 

Townsley, M. & Sidebottom, A.. (2010). All offenders are equal, but some are more equal than 

others: Variations in Journeys to Crime between offenders. Criminology, 48 (3), 897-917. 

Turner, S. (1969). Delinquency and distance. In Wolfgang, M. E. & Sellin, T. (eds), 

Delinquency: Selected Studies. John Wiley & Sons: New York. 

U.S. Census Bureau (2000). All across the USA: Population distribution, 1999, In Population 

Profile of the United States: 1999. Bureau of the Census, U. S. Department of Commerce: 

Washington, DC., chapter 2. 

van Koppen, P. J. & de Keijser, J. W. (1997). Desisting distance decay: on the aggregation of 

individual crime trips. Criminology, 35 (3), 505-516. 

13.81 
References (continued 

von Thünen, J. (1826). The Isolated State in Relation to Agriculture and Political Economy. 

English edition, van Suntum, Ulrich. Palgrave Macmillan:Houndsmills, Basingstoke, Hampshire, 

England, 2009. 

Warren, J., Reboussin, R., Hazelwood, R. R., Cummings, A., Gibbs, N., & Trumbetta, S. 

(1998). Crime scene and distance correlates of serial rape. Journal of 

Quantitative Criminology, 14(1), 35-59. 

Weber, A. (1909). Über den Standort der Industrien (Theory of Location of Industries). 

White, R. C. (1932). The relationship of felonies to environmental factors in Indianapolis. 

Social Forces, 10 (4), 488-509. 

Wikipedia (2010a). Ecological Correlation. http://en.wikipedia.org/wiki/Ecological_correlation. 

Accessed March 26, 2012. 

Wikipedia (2010b). Ecological Fallacy. http://en.wikipedia.org/wiki/Ecological_fallacy. 

Accessed March 26, 2012. 

Wilson, A. G. (1970). Entropy in Urban and Regional Planning. Leonard Hill Books: 

Buckinghamshire. 

Zipf, G. K. (1949). Human Behaviour and the Principle of Least Effort. Cambridge. 

13.82 
 

 

 

 

 

 

Endnotes 

i. If the coordinate system is projected with the distance units in feet, meters or miles, then the distance 

between two points is the hypotenuse of a right triangle using Euclidean geometry: 

repeat (3.1) 

where each location is defined by an X and Y coordinate in feet, meters, or miles. If the coordinate system 

is spherical with units in latitudes and longitudes, then the distance between two points is the Great Circle 

distance. All latitudes and longitudes are converted into radians using: 

repeat (3.2) 

repeat (3.3) 

Then, the distance between the two points is determined from: 

repeat (3.4) 

with all angles being defined in radians (Snyder, 1987, p. 30, 5-3a). 

13.83 
Attachments 

13.84 
A Note on Alternative Journey-to-crime Models 

Ned Levine 

Ned Levine & Associates 

Houston, TX 

There are several alternative journey-to-crime models that have been developed in 

addition to the CrimeStat model. This is a brief note on two of them, the Rossmo model and the 

Canter model. The citations are listed in the reference section above. 

Rossmo Model 

Rossmo (1993a; 1995) has adapted location theory, particularly travel behavior modeling, 

to serial offenders. In a series of papers (Rossmo, 1993a; 1993b; 1995; 1997) he outlined a 

mathematical approach to identifying the home base location of a serial offender, given the 

distribution of the incidents. The mathematics represent a formulation of the Brantingham and 

Brantingham (1981) search area model, discussed above in which the search behavior of an 

offender is seen as following a distance decay function with decreased activity near the 

offender=s home base. He has produced examples showing how the model can be applied to 

serial offenders (Rossmo, 1993a; 1993b; 1997). 

The model has four steps (what he called criminal geographic targeting): 

1. First, a rectangular study area is defined that extends beyond the area of the 

incidents committed by the serial offender. The average distance between points 

is taken in both the Y and X direction. Half the Y coordinate inter-point distance 

is added to the maximum Y value and subtracted from the minimum Y value. 

Half the X coordinate inter-point distance is added to the maximum X value and 

subtracted from the minimum X value. These are based on projected 

coordinates; presumably, the directions would have to be adjusted if spherical 

coordinates were used. The rectangular study defines a grid from which columns 

and rows can be defined. 

2. For each grid cell, the Manhattan distance to each incident location is taken (see 

Chapter 3 for definition). 

3. For each Manhattan distance from a grid cell to an incident location, MDij, one of 

two functions is evaluated: 

13.85 
A. If the Manhattan distance, , is less than a specified buffer zone radius, 

B, then: 

(13.47) 

where Pij is the resultant of offender interaction for grid cell i; with 

incident j, c is the incident number, summing to T; φ = 0; k is an 

empirically determined constant; g is an empirically determined exponent; 

and f is an empirically determined exponent. 

The Greek letter, Π, is the product sign, indicating that the results for each 

grid cell-incident distance, Mdij, are multiplied together across all 

incidents, c. This equation reduces to: 

(13.48) 

(13.49) 

Within the buffer region, the function is the ratio of a constant, k, times 

the radius of the buffer, B, raised to another constant, g-f, divided by the 

difference between the diameter of the circle, 2B, and the critical 

Manhattan distance, Mdij, raised to a constant, g. This is a non-linear 

function that is increasing within the buffer zone. 

4. If the Manhattan distance, , is greater than a specified buffer zone 

radius, B, then 

(13.50) 

where Pij is the resultant of offender interaction for grid cell, i, and 

incident location, j; c is the incident number, summing to T; φ = 1; k is an 

empirically determined constant (the same as in Equation 13.47 above); 

and f is an empirically determined exponent (the same as in Equation 

13.47 above). 

13.86 
Again, the Greek letter, Π, indicates that the results for each grid cellincident distance, , are multiplied together across all incidents, c. This 

equation reduces to: 

(13.51) 

(13.52) 

Outside of the buffer region, the function is a constant, k, divided by the 

distance, , raised to an exponent, f. It is an inverse distance function 

and drops off rapidly with distance. 

4. Finally, for each grid cell, i, the functions evaluated in step 3 above are summed 

over all incidents. 

For both the >within buffer zone= (near to home base) and >outside buffer zone= (far from 

home base) functions, the coefficient, k, and exponents, f and g, are empirically determined. 

Though he does not discuss how these are calculated, they are presumably estimated from a 

sample of known offender locations where the distance to each incident is known (e.g., arrest 

records). 

The result is a surface model indicating a likelihood of the offender residing at that 

location. He describes it as a probability surface, but it is actually a density surface. Since the 

probability of interaction between any one grid cell, i, and any one incident, j, cannot be greater 

than 1, the surface actually indicates the product of individual likelihoods that the offender uses 

that location as the home base. To be an actual probability function, it would have to be re-scaled 

so that the sum of the grid cells was equal to 1. 

The second function - >outside the buffer zone= (Equation 13.52) is a classic gravity 

function, similar to Equation 13.5 except there is no attraction definition. It is the distance decay 

part of the gravity function. The first function, Equation 13.49, is an increasing curvilinear 

function designed to model the area of decreased activity near the offender=s home base. 

Strengths and Weaknesses of the Rossmo model 

The Rossmo model has both strengths and weaknesses. First, the model has some 

theoretical basis utilizing the Brantingham and Brantingham (1981) framework for an offender 

search area as well as the mathematics of the gravity model and distinguishes two types of travel 

behavior - near to home and farther from home. Second, the model does represent a systematic 

13.87 
 

approach towards identifying a likely home base location for an offender. By evaluating each 

grid cell in the study area, an independent estimate of the likelihood is obtained, which can then 

be integrated into a continuous surface with an interpolation graphics routine. 

There are problems with the particular formulation, however. First, the exclusive use of 

Manhattan distances is questionable. Unless the study area has a street network that follows a 

uniform grid, measuring distances horizontally and vertically can lead to overestimation of travel 

distances; further, the more the layout differs from a north-south and east-west orientation, the 

greater the distortion. Since many urban areas do not have a uniform grid street layout, the 

method will necessarily lead to overestimation of travel distances in places where there are 

diagonal or irregular streets.5 

Second, the use of a product term, Π, complicates the mathematics. That is, the 

technique evaluates the distance from a particular grid cell, i, to a particular incident location, j. 

It then multiplies this result by all other results. Since the P values are actually densities, which 

can be greater than 1.0, the process, if strictly applied, would be a compounding of probabilities 

with overestimation of the likelihood for grid cells close to incident locations and 

underestimation of the likelihood for grid cells farther away. In the description of the method, 

however, Rossmo actually mentions summing the terms. Thus, the substitution of a summation 

sign, Σ, for the product sign would help the mathematics. 

A third problem is in the distance decay function (Equation 13.52). The use of an inverse 

distance term has problems as the distance between the grid cell location, i, and the incident 

location, j, decreases. For some types of crimes, there will be little or no buffer zone around the 

offender=s home base (e.g., rapes by acquaintances). Consequently, the buffer zone radius, B, 

would approach 0. However, this would cause the model to become unstable since the inverse 

distance term will approach infinity. 

Fourth, the use of a mathematical function to describe the distance decay, while easy to 

define, probably oversimplifies actual travel behavior. A mathematical function to describe 

distance decay is an approximation to actual travel behavior. It assumes that travel is equally 

likely in each direction, that travel distance is uniformly easy (or difficult) in each direction, and 

that, similarly, opportunities are uniformly distributed. For most urban areas, these conditions 

would not be true. Few cities form a perfect grid (there are exception, such as Salt Lake City), 

though most cities have sections that are grided. Both physical geography limit travel in certain 

directions as does the historical street structure, which is often derived from earlier communities. 

It should also be pointed out that the use of direct distances will underestimate travel distances particularly 

if the street network follows a grid. 

13.88 

5 
A mathematical function does not consider this structure, but rather assumes that the >impedance=

in all directions is uniform. 

This latter criticism, of course, would be true for all mathematical formulations of travel 

distance. There are corrections that can be made to adjust for this. For example, in the urban 

travel demand type model, trip distribution between locations is estimated by a gravity model, 

but then the distributed trips are constrained by, first, the total number of trips in the region 

(estimated separately), second, by mode of travel (bus v. single driver v. drivers plus passengers 

v. walk, etc.), and, third, by the route structure upon which the trips are eventually assigned 

(Krueckeberg & Silvers, 1974; Stopher & Meyburg, 1975; Field & MacGregor, 1987). 

Calibration at all stages against known data sets ensures that the coefficients and exponents fit 

>real world= data as closely as possible. It would take these types of modifications to make the 

travel distribution type of model postulated by Rossmo and others be a more realistic 

representation. 

Fifth, the model imposes mathematical rigidity on the data. While there are two different 

functions that could vary from place to place, the particular type of distance decay function 

might also vary. Specifying a strict form for the two equations limits the flexibility of applying 

the model to different types of crime or to places where the distance decay does not follow the 

form specified by Rossmo. 

A sixth problem is that opportunities for committing crimes - the attractiveness of 

locations, are never measured. That is, there is no enumeration of the opportunities that would 

exist for an offender nor is there an attempt to measure the strength of this attraction. Instead, 

the search area is inferred strictly from the distribution of incidents. Because the distribution of 

offender opportunities would be expected to vary from place to place, the model would need to 

be re-calibrated at each location. In this sense, both the Canter model (described below) and my 

Journey-to-crime model (described in the chapter) also share this weakness. It is understandable 

in that victim/target opportunities are difficult to define a priori since they can be interpreted 

differently by individuals. Nevertheless, a more complete theory of Journey-to-crime behavior 

would have to incorporate some measure of opportunities, a point that both Brantingham and 

Brantingham (1981) and Rengert (1981) have made. 

Finally, the >buffer zone= concept is but one interpretation of the tendency of many crimes 

not to be committed close to the home location. There are other interpretations that are 

applicable. For example, the distribution of crime opportunities is often not close to the home 

location, either. Many crimes occur in commercial areas. In most American and British cities, 

residential areas are not located in commercial areas. Thus, there will usually be a distance 

between a residential location and a nearby crime opportunity. This does not imply anything 

about a >safety zone= for the offender but, instead, may illustrate the distribution of crime 

13.89 
opportunities. If we could map the travel distance of, say, shopping trips, we would probably 

find a similar distribution to that seen in most of Journey-to-crime studies (and illustrated 

below). 

The concept of a >buffer zone= is a hypothesis, not a certainty. The language of it is so 

appealing that many people believe it to be true. But, to demonstrate the existence of a >buffer 

zone= would require interviewing offenders (or offenders who have been arrested) and 

demonstrating that they did not commit crimes near their residence even though there were 

opportunities (i.e., they valued safety over opportunity). Otherwise, one cannot distinguish 

between the >buffer zone= hypothesis and the distribution of available opportunities. They may 

very well be the same thing. 

Canter Model 

Canter=s group in Liverpool and, more recently, Huddersfield (Canter & Tagg, 1975; 

Canter & Larkin, 1993; Canter & Snook, 1999; Canter, Coffey, Huntley, & Missen, 2000) have 

modified the distance decay function for Journey-to-crime trips by using a negative exponential 

term, instead of the inverse distance. Their Dragnet program uses the negative exponential 

function: 

(13.53) 

where Y is the likelihood of an offender traveling a certain distance to commit a crime,, dij is the 

distance (from a home base location to an incident site), α is an arbitrary constant, β is the 

coefficient of the distance, P is a normalization constant, and e is the base of the natural 

logarithm. The model is similar to Equation 13.52 except, like Rossmo, it does not include the 

attractiveness of the location. 

Using the logic that most crimes are committed near the offender=s home base, Canter, 

Coffey, Huntley, and Missen (2000) use a five step process to estimate a search strategy: 

2. The study area is defined by a rectangle that is 20% larger in area than that 

defined by the minimum and maximum X/Y points. A grid cell structure of 13, 

300 cells is imposed over the rectangle. Each grid cell is a reference location, i. 

3. A decay coefficient is selected. In Equation 13.53, this would be the coefficient, 

β, for the distance term, dij, both of which are exponents of e. Unlike Rossmo, 

Canter uses a series of decay coefficients from 0.1 to 10 to estimate the sensitivity 

13.90 
of the model. The equation indicates the likelihood with which any location is 

likely to be the home base of the offender based on one incident. 

4. Because different offenders have different search areas, the measured distances 

for each cell are divided by a normalization coefficient, P, that adjusts all offenses 

to a comparable range. Canter uses two different types of normalization function: 

1) Mean inter-point distance between all offenses (across a group of offenders); 

and 2) The QRange, which is an index that takes into account asymmetry in the 

orientation of the incidents. 

5. For each reference cell, i, the distance between each grid cell and each incident 

location is evaluated with the function and the standardized likelihoods are 

summed to yield an estimate of location potential. 

6. A search cost index is defined by the proportion of the study area that has to be 

searched to find the offender. By calibrating the model against known cases, an 

estimate of search efficiency is obtained. 

Additional modifications can be added to the functions to make them more flexible 

(Canter, Coffey, Huntley & Missen, 2000). For example, >steps= are distances near to home 

where offenders are not likely to act while >plateaus= are constant distances near to home where 

there is the highest likelihood of acting. For example, Canter and Larkin (1993) found an area 

around serial offenders= homes of about 0.61 mile in radius within which they were less likely to 

commit crimes. 

Canter and Snook (1999) provide estimates of the search cost (or efficiency) associated 

with various distance coefficients. For example, with the known home base locations of 32 

burglars, a β of 1.0 yielded a mean search cost of 18.06%; that is, on average, only 18.06% of the 

study area had to be searched to find the location of 32 burglars in the calibration sample. 

Clearly, for some of them, a larger area had to be searched while for others a smaller area; the 

average was 18.06%. Conversely, the mean search cost index for 24 rapists was 21.10% and for 

37 murderers 28.28%. They further explored the marginal increase in locating offenders by 

increasing the percentage of the study area that had to be searched. They found for their three 

samples (burglary, rape, homicide) that more than half the offenders could be located within 15% 

of the area searched. 

The Canter model is different from the Rossmo model is that it suggests a search strategy 

by the police for a serial offender rather than a particular location. The strength of it is to 

indicate how narrow an area the police should concentrate on in order to optimize finding an 

offender. Clearly, in most cases, only a small area needs be searched. 

13.91 
Strengths and Weaknesses of the Canter model 

The model has both strengths and weaknesses. First, the model provides a search 

strategy for law enforcement. By examining which type of function best fits a certain type of 

crime, police can target their search efforts more efficiently. The model is relatively easy to 

implement and is practical. Second, the mathematical formulation is stable. Unlike the inverse 

distance function in the Rossmo model, Equation 13.49 will not have problems associated with 

distances that are close to 0. Further, the model does provide a search strategy for identifying an 

offender. It is a useful tool for law enforcement officers, particularly as they frame a search for a 

serial offender. 

There are also weaknesses to the model. First, it lacks a theoretical basis. Canter=s 

research has provided a great deal in terms of understanding the activity spaces of serial 

offenders (Canter & Larkin, 1993; Canter & Gregory, 1994; Canter, 1994; Hodge & Canter, 

1998). However, the empirical model used is strictly pragmatic. Second, mathematically, it 

imposes the negative exponential function without considering other distance decay models. In 

the Dragnet program, the decay function is a string of 20 numbers so that, in theory, any function 

can be explored. However, the default is a negative exponential. The negative exponential has 

been used in many travel behavior studies (Foot, 1981; Bossard, 1993), but it does not always 

produce the best fit. While the model can be adapted to be more flexible by different exponents 

and including steps and plateaus, for example, it is still tied to the negative exponential form. 

Thus, the model might work in some locations, but may fail in others; a user can=t easily adjust 

the model to make it fit new data. 

Third, the coefficient of the negative exponential, α, is defined arbitrarily. In the Dragnet 

program, it is usually set as 0.5. While this ensures that the result never exceeds 1.0 for any one 

incident, there is a limit on the location potential summation since the total potential is a function 

of the number of incidents (i.e., it will be higher for more incidents). It would have been better if 

the coefficient were calibrated against a known sample. 

Fourth, and finally, also similar to the Rossmo model (and to my journey-to-crime 

model), criminal opportunities (or attractions) are never measured, but are inferred from the 

pattern of crime incidents. As a pragmatic tool for informing a police search, one could argue 

that this is not important. However, in a different location, the distance coefficient is liable to 

differ as is the search cost index. It would need to be re-calibrated each time. 

Nevertheless, the Canter model is a useful tool for police department and can help shape 

a search strategy. It is different from the other location models in that it is not focused so much 

on the best prediction for a location of an offender (though the summation discussed above in 

step 4 can yield that) as it does in defining where the search should be optimized. 

13.92 
 

1 

0 

Using CrimeStat for Geographic Profiling 

Brent Snook, Memorial University of Newfoundland, 

Paul J. Taylor, University of Liverpool, Liverpool 

Craig Bennell, Carleton University, Ottawa 

A challenge for researchers providing investigative support is to use information 

about crime locations to prioritize geographic areas according to how likely they are to 

contain the offender’s residence. One prescient solution to this problem uses probability 

distance functions to assign a likelihood value to the activity space around each crime 

location. A research goal is to identify the function that assigns the highest likelihood to 

the offender’s actual residence, since this should prove more efficient in future 

investigations. 

CrimeStat was used to test of the effectiveness of two functions for a sample of 68 

German serial murder cases, using a measure known as error distance. The top figures 

below illustrate the two functions used and the bottom figures portray the corresponding 

effectiveness of the functions by plotting the percentage of the sample ‘located’ by error 

distance. A steeper effectiveness curve indicates that home locations were closer to the 

point of highest probability and that, consequently, the probability distance function was 

more efficient. In this particular test, no difference was found between the two functions 

in their ability to classify geographic areas. 

Truncated Negative Negative Exponential 

Exponential 

Probability of Locating Offender's Home 0.9 

0.8 

0.7 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

1 

0.9 

0.8 

Probability of Location Offender's Home 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 

Distance from Crime 

0.7 Distance Decay Model 

0.6 

0.5 

0.4 

0.3 

0.2 

0.1 

0 

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 

Distance from Crime 

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 800 

Cumulative Percentage of Sample 

0 

10 

20 

30 

40 

50 

60 

70 

80 

90 

100 

0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 800 

Cumulative Percentage of Sample 

Cumulative Error 

Distance 

Error Distance Error Distance 

Original Article: Taylor, P.J., Bennell, C., & Snook B. (2002) Problems of Classification in Investigative Psychology. Proceedings of 

the 8th Conference of the International Federation of Classification Societies, Krakow, Poland 
Using Journey-to-crime Routine for Journey-after-crime Analysis 

Yongmei Lu 

Department of Geography 

Southwest Texas State University 

San Marcos, TX 

The study of vehicle theft recovery locations can fill a gap in the knowledge about 

criminal travel patterns. Although the journey-to-crime routine of CrimeStat was 

designed to analyze the distance between offense location and offender’s residential 

location, it can be used to describe the distance between vehicle theft location and the 

corresponding recovery location. 

There were more than 3000 vehicle thefts in the City of Buffalo in 1998. 

Matching the offenses with vehicle recoveries in the same year, 1600 location pairs were 

identified for a journey-after-vehicle-theft analysis. To evaluate the randomness of the 

distances, 1000 groups of simulations were conducted. Every group contains 1600 

simulated trips of journey-after-vehicle-theft. The results indicate that 1) short distances 

dominate journey-after-vehicle-theft, and 2) the observed trips are significantly shorter 

than the random trips given the distribution of possible vehicle theft and recovery 

locations. 

0.00 

0.01 

0.02 

0.03 

0.04 

0.05 

0.06 

0.07 

0.08 

0.09 

0.00

1.59

3.17

4.76

6.34

7.93

9.51 

11.10 

12.68 

14.27 15.85 17.44 19.02 20.61 22.19 

distance from vehicle theft location (miles) 

probability of recovery 

0.00 

0.01 

0.01 

0.02 

0.02 

0.03 

0.03 

3.083.103.123.143.173.193.213.233.253.283.303.323.343.373.393.413.43 

distance from theft location (miles) 

probability of means 

0.00 

0.20 

0.40 

0.60 

0.80 

1.00 

1.20 

cumulative probability 

Probability of recovering a stolen

vehicle by distance from vehicle

theft location 

Distribution of mean distances of 

simulated vehicle theft-recovery

location pairs. 
Using Journey-to-crime Analysis for Different Age Groups of Offenders 

Renato Assunção, Cláudio Beato, Bráulio Silva 

CRISP, Universidade Federal de Minas Gerais , Brazil 

CrimeStat offers a method for analysing the distance between the crime scene and 

the residence of the offender within the spatial modeling module. We analysed homicide 

incidents in Belo Horizonte, a Brazilian city of 2 million inhabitants, for the period 

January 1996 – December 2000. We used 496 homicide cases for which the police 

identified an offender who was living in Belo Horizonte, and for which both the crime 

location and offender residence could be identified. The cases were divided into three 

groups according to the offender‘s age: 1) 14 to 24 (N=201); 2) 25 to 34 (N=176); and 3) 

35 or older (N=119). The journey-to-crime calibration routine was used to produce a 

probability curve P(d) that gives the approximate chance of an offender travelling 

approximately distance d to commit the crime. 

We used the normal kernel, a fixed bandwidth of 1000 meters, 100 output bins, 

and the probability (or proportion of all points) option, rather than densities. This is to 

allow comparisons between the three age groups since they have different number of 

homicides. We tested for each age group separately and directed the output to a text file 

to analyse the three groups simultaneously. 

The green, blue, and purple curves are associated with the 14-24, 25-34, 35+ year 

olds respectively. There are more similarities than differences between the groups. Most 

homicides are committed near to the residence of the offenders with between 60% t o 

70% closer than one mile from their home. However, the curve does not vanish totally 

even for large distances because there are around 15% of offenders, of any age group, 

travelling longer than 3 miles to commit the crime. The oldest offenders travel longer 

distances, on average, followed by the youngest group, with the 25-34 year olds 

travelling the shortest distances. 

Journey to homicide probabilities in Belo Horizonte, Brazil 

0.0 0.02 0.04 0.06 0.08 0.10 

probability 

0 1 2 3 4 

distance travelled (in miles) 
Catching the Bad Guy 

Bryan Hill 

Glendale Police Department 

Glendale, AZ 

The City of Glendale, Arizona recently had a string of auto thefts committed by 

the same individual. The map shows known auto theft suspects and their home address. 

The red area in the map shows the most probable home address. Prior to the analysis, the 

Phoenix Police Department’s Crime Analysis Unit was able to calibrate the CrimeStat Jtc 

routine with known offender robbery suspect data. 

Monthly citation data was used to search for anyone that lived within the area 

identified by the routine who also drove a red Saturn. A suspect with a felony warrant 

was identified and proved to be also the suspect in a series of armed robberies and a 

homicide that occurred in the Phoenix and Glendale jurisdictions. When he was arrested 

for the felony warrant at his home, evidence of the robberies and homicide were found. 
Constructing Geographic Profiles 

Using the CrimeStat Journey-to-crime Routine 

Josh Kent, 

Michael Leitner, 

Louisiana State University 

Baton Rouge, LA 

The map below shows a geographic profile constructed from nine crime sites 

associated with a Baton Rouge serial killer, Sean Vincent Gillis, who was apprehended 

on April 29, 2004 at his residence in Baton Rouge. Eight of the nine are body dump sites 

and the ninth is a point of fatal encounter. All crime sites were located in the City of 

Baton Rouge and surrounding parishes. Gillis’s hunting style can best be described as 

that of a typical ‘localized marauder’. 

The Journey-to-crime routine, implemented in CrimeStat , was applied to simulate 

the travel characteristics of Gillis to and from the known crime sites. Gillis’s travel 

behavior was calibrated with different mathematical functions that were derived from the 

known travel patterns of 301 homicide cases in Baton Rouge. 

The profile was estimated using Euclidean distance and the negative exponential 

distance decay function. It predicts the actual residence of Gillis extremely accurately. 

The straight-line error distance between the predicted and the actual residence is only 

0.49 miles. The proportion of the entire study area that must be searched in order to 

successfully identify the serial offender’s residence is 0.05% (approximately 0.98 square 

miles out of a 2094.75 square miles study area). 

FEATURES 

$T Actual Residence 

%U Predicted Residence 

Search Cost 

6 0

Miles

OFFENDER RESIDENCE PREDICTION 

LOW HIGH 

NUMBER OF CRIME SCENES: 9 

6

8

#S7

#S Crime Scenes 

Interstates 

Water 

Study Area Gird 

CELL SIZE: 0.49 miles 2 

METRIC: Direct Path Euclidean Distance Metric 

MODEL: Negative Exponential Distance Decay Function 

HIT SCORE: 0.05 (99.95% accuracy) 

SEARCH COST: 0.98 miles (2 grid cells) 2 

ERROR DISTANCE: 0.49 miles (distance from predicted to actual residence) 

,-

110 . .,-

1 2

#S3

.,- #

1

8

#S$T%U 1 0 S

#S5 #S2

S#6

#S4

1 0 .,-

#S3
Predicting Serial Offender Residence by Cluster in Korea 

Kang Eun Kyoung 

Scientific Investigation Center, Korea National Police Agency 

Since April, 2009, the Korea National Police Agency have been operating a Geographic 

Profiling system called GeoPros. GeoPros automatically links three information systems: information 

the police have collected, CrimeStat, and electronic mapping. Police are able to select areas that need 

crime prevention and decide which need CCTV. Profilers also use this system to predict the residence of 

serial offenders using CrimeStat routines linked with GeoPros. Specifically, the Nnh and Jtc routines 

were useful in predicting offender residence locations. We analyzed multiple serial offenders’ data and 

found that offenders typically have 2 or 3 clusters related to their activities (home, workplace, and 

possibly evening entertainment). 

Serial rapist’s map 

1st Cluster 

home 

Another case involved unsolved arson 

cases over ten years (101 offences). There were 

two clusters that emerged. One consisted of 

activities in the morning or the evening while 

the second cluster consisted of activities in the 

afternoon. We hypothesized that the first cluster 

was related to the offender’s home while the 

For example, one offender committed 32 

crimes including rape, robbery, and theft. With the 

Nnh, we found 3 clusters. The first was associated 

with the criminal’s residence; the second was 

associated with his father’s residence, and the third 

with his lover’s house. The criminal’s residence was 

only 10 meters away from the first cluster. 

1st Cluster : Morning and Evening

2nd Cluster : afternoon

home

Workplace 

second with the offender’s workplace. It turned out that the criminal’s residence was only 60 meters 

away from the first cluster while his workplace was near the second cluster. We gave the detectives 

involved the information and within two weeks were able to catch the criminal. 

One important finding is that offender’s residence is typically not inside of the cluster areas, but 

nearby, which may relate to a buffer zone. In addition to these cases, we have predicted the residence of 

many cases of serial rape and arson by cluster analysis.