Chapter 32: 

Case Studies in 

Crime Travel Demand Modeling II: 

Application of Travel Demand Behavior Model to 

Crime Data from Las Vegas, Nevada 

Dan Helms 

Scytale Consulting 

Reston, VA 

32.1 
 

 

 

 

 

 

 

 

 

 

Table of Contents 

Introduction 32.1 

The Las Vegas Metropolitan Area 32.2 

Source Data Provenance and Organization 32.3

 Data Screening 32.5 

 Reference Data 32.9 

 Assignment of Crime Trips 32.9 

Trip Generation 32.14 

Trip Distribution 32.20 

Mode Split 32.27 

Network Assignment 32.27 

Modeling Different Crime Types 32.28

 Auto Theft Site to Recovery Site 32.28 

 Residential Burglaries 32.28 

 Sexual Assaults 32.32 

Robberies 32.32 

Conclusions 32.32 

References 32.38 

32.2 
 

Chapter 32: 

Case Studies in 

Crime Travel Demand Modeling: 

II - Application of Travel Demand Behavior Model on 

Crime Data from Las Vegas, Nevada 

In this chapter, a case study of crime travel demand in Las Vegas, NV is discussed. 

Originally written in 2004, it is presented in order to illustrate how crime travel demand 

modeling can be applied to a primarily auto-oriented city. 

Introduction 

Strategic crime forecasting has for many years relied on a limited and simplistic suite of 

methods to predict approximately where future events may occur in broad strokes. Extrapolation 

of percentile change is probably the most commonly used means of forecasting future crime 

frequencies, based on the notion fundamental to all predictions, that the future will resemble the 

past. Unfortunately, this method is completely unable to cope with changes in the demographics, 

population, and social makeup of a jurisdiction. 

For a number of years, innovative crime analysts and criminologists have looked to other 

disciplines outside the study of criminal behavior for methods of predicting how the future will 

unfold. Economics, epidemiology, meteorology, and biology have all offered significant 

contributions as their more sophisticated and creative methods for foretelling future frequencies 

have been adapted to criminology with varying degrees of success. 

Transportation modeling is the most recent external science to suggest potential means of 

predicting criminal behavior. The success of travel demand modeling in the civilian world of 

transportation behavior has presented us with another possible technique which could be adapted 

to forecasting crime. Travel demand modeling offers a set of algorithms for estimating not only 

how much activity will occur in a given region, but also how offenders will travel across the 

jurisdiction to commit their crimes. This model has been implemented in the CrimeStat software 

application for use against crime data. 

In this study, we will review the application of this model against data from the 

metropolitan Las Vegas area over a period of three years. 

32.1 
The Las Vegas Metropolitan Area 

The Las Vegas metropolitan area is comprised of Clark County, Nevada, and several 

independent municipalities within it. The Las Vegas Metropolitan Police Department (LVMPD) 

serves Clark County (in the capacity of a Sheriff's Office) as well as the City of Las Vegas (in 

the capacity of a municipal police department). Although the vast majority of the land area, 

population, and businesses within this area are policed by the LVMPD, there are three other 

significant jurisdictions - the City of North Las Vegas, the City of Henderson, and the City of 

Boulder City, each having their own police department. 

In addition to these important sibling agencies, several other law enforcement agencies 

have overlapping jurisdiction within areas principally policed by the LVMPD - the Paiute Tribal 

Police, the Southern Pacific Railway Police, the Nevada Highway Patrol, U. S. Air Force 

Security Police, U. S. Air Force Office of Special Investigations, Federal Bureau of 

Investigation, Veteran's Administration Police, and others. Although these agencies perform 

valuable police functions, the LVMPD unquestionably deals with the vast majority of crime in 

the area, making it an attractive candidate for offender travel research. 

In many ways, Las Vegas resembles an island. Surrounded by barren desert, with very 

few roads entering or leaving the city, it is an urban oasis in a sparsely populated desert 

wilderness consisting of largely impassable terrain. This geographic position and isolation make 

Las Vegas highly interesting from the perspective of a transportation (or crime trip movement) 

modeler. 

Another unique feature of the Las Vegas area is the highly transient nature of the 

population, which falls into three discrete categories: 

1. First, the resident population consists of some one million persons, approximately 

880,000 of which live in the jurisdiction of the LVMPD (the remainder being 

served primarily by Henderson and North Las Vegas). These permanent residents 

are the mainstay of the community and the source for demographic data used by 

the census bureau and planning agencies. 

2. Second, we must consider the visitor population, consisting of some 

35,000,000 - 40,000,000 persons per year. On any given day, between 100,000 

and 500,000 visitors will be staying in the Las Vegas area, a critical factor in 

transportation, demography, and crime! These tourists sometimes act as crime 

importers (e.g., criminal street gangs from neighboring Californian cities often 

visit Las Vegas for weekend mayhem or more professional criminal purposes); in 

most instances, however, they serve as a pool of prey for local criminals. 

32.2 
3. Third, and finally, there is a substantial homeless population in Las Vegas, drawn 

by the seasonally warm climate and the ease with which this city can be reached 

as a destination. Although not famous for a "friendly" attitude toward the 

homeless, these persons are protected by law enforcement in Las Vegas and are 

well served by many charitable social institutions and services. Because Las 

Vegas is also an easy place to sin, homeless individuals with drug, alcohol, and 

gambling addictions often gravitate there; the possibility of "winning big" and 

instantly reversing a life of misfortune also weighs in the consideration of many 

homeless who choose to make their base in Las Vegas. However, due to the 

inability to accurately measure a "home" location for these persons when they do 

commit crimes, few of these have been represented in this study. 

This study will focus on the criminal movement behavior of the resident population of the 

greater Las Vegas metropolitan area. 

Source Data Provenance and Organization 

Data concerning the Las Vegas metropolitan area was provided by the Las Vegas 

Metropolitan Police Department's Investigative Division. Often, researchers underestimate the 

severe difficulties and chronic shortcomings of law enforcement data. Thanks to a first-rate 

records management system (RMS) and a voluminous tactical database repository, the Las 

Vegas Metropolitan Police Department's data presented relatively few problems; however, 

geocoding accuracy issues, missing data fields from modus operandi tables, and erroneous 

arrestee home locations result in sources of error that can contaminate analysis. These had to be 

overcome before any analysis or testing of new methods was possible. 

Crime report data for the LVMPD is maintained in an SQL-Server 7.0 database 

constructed by the Printrak (now owned by Motorola) company, makers of the Law RMS 

(LRMS) police records management system used by Las Vegas, among others. This repository 

currently houses many hundreds of thousands of crime reports, field interviews, and other critical 

police data in a well-organized, relational database. 

Crime reports are filled out by either sworn officers (when taken in the field) or by station 

personnel (when reported in person at an LVMPD substation or city hall). These paper reports 

include ample MO detail and descriptive information in compartmentalized, "force-choice" 

fields, as well as substantial expository narratives. "Forced-choice" fields are also typically 

supplemented by "Other" options which can then be individually explained, to deal with very 

unusual crime behaviors, descriptions, or details. 

32.3 
At the end of each shift, officers submit their reports to their sergeant for review. After a 

quick check to ensure the most basic levels of data quality and integrity, the reports are then 

placed in a mailbox for pickup, which occurs several times each day and night. Reports are 

transferred by intradepartmental couriers to city hall where they are collected by the Records 

Section. Professional data entry specialists then meticulously type each report into the LRMS 

database. 

The data entry process includes several validation and error trapping elements. These 

usually greatly enhance the completeness and accuracy of each report, but are sometimes 

bypassed by busy clerks. Perhaps the most significant validity check which can be bypassed is 

the address verification system which performs a ‘brute force’ match against a geofile of known, 

valid locations. When a matching address is entered into the system, geographic coordinates and 

other useful data is automatically propagated into the file. Because many crimes do not occur at 

valid, documented physical street addresses (crimes in remote or desert areas or in new 

construction zones or on buses or in taxi cabs, for example), however, data entry clerks have 

grown accustomed to overriding the address verification module. This is also sometimes done in 

the interests of speed and expediency, even when a valid, matchable address is provided in the 

crime report. When this happens, the resulting address must be cleaned using a data cleaning 

application prior to successfully matching in a geocoding operation. Once entered into the LRMS 

database, crime report information may be extracted through a variety of standard methods. 

The LVMPD routinely downloads crime reports on a daily basis into an ATAC analytical 

database where crime analysts and investigators can examine and study the data without creating 

any drag on the primary server. The ATAC database is streamlined for analysis, and is much 

easier to query and analyze than the LRMS repository itself. The ATAC databases are 

Microsoft-compliant relational databases very similar to the MS Access database. 

Data used for the Next-Generation Offender Crime Travel Model project were derived 

from records stored in several ATAC analytical databases created and maintained by the 

LVMPD Crime Analysis Section. These databases are archived by calendar year and by crime 

category. The archive dates for calendar year are assigned based on the year of occurrence. 

Crime categories are: auto crimes (including motor vehicle thefts, burglaries from motor 

vehicles, and criminal damage to automobiles); burglaries (including all burglary statutes); 

Larcenies (including all Larceny/Theft statutes); and personal crimes (including all sexual 

offenses, assaults and aggravated assaults, robberies and home invasions, kidnappings, and 

homicides). 

These databases contain MO, Persons, and Vehicles tables related by event number. The 

MO table contains all information pertinent to the location, timing, category, and methods of 

each crime event; the Persons table all information on personal identification, description, and 

32.4 
 

 

histories, not only for suspect and arrestees, but also victims, witnesses, reporting parties, etc.; 

the Vehicle table all information concerning any vehicles which may be involved in the offense, 

including descriptive and identification information, whether the vehicle relates to the criminals, 

victims, or has some other relationship to the crime. 

For purposes of this project, the LVMPD authorized access and transmission of the 

contents of the complete ATAC database inventory for the Crime Analysis Section. Of the 

fifty-odd databases provided, the Personal crimes databases for the years 1996 - 2002 were 

initially selected. 

Data Screening 

Three broad categories were selected from the complete data inventory provided: 

1. Confrontational 

2. Burglary, and 

3. Vehicular crimes. 

These intentionally disparate data were selected in the interests of increasing the latitude 

of the study. It was hypothesized that travel behavior would vary between these categories of 

events. Confrontational crimes included sexual assaults, robberies, kidnappings, and murders. 

These crimes were included in a single group as part of this initial appraisal of the effectiveness 

of travel demand modeling on criminal behavior even though it is obvious that the behaviors 

exhibited by offenders across these crime types are likely to vary. These crimes were grouped in 

spite of these likely differences because similarities in targeting behavior across these crimes 

might make them amenable to collective analysis; a hypothesis which can be tested using the 

techniques built into the travel demand module. 

Burglaries used in this analysis included both residential and commercial burglaries, but 

not burglaries from motor vehicles. Only crimes in which a building or property was illegally 

entered for the purpose of theft were included in this study, thereby eliminating the prolific 

larceny category. 

Vehicular crimes included both auto thefts and burglaries from motor vehicles. 

"Carjackings" were not specifically included, but some auto thefts in which the modus operandi 

followed the confrontational "carjacking" pattern may have been included when specifically 

statutory designations were missing to differentiate these from more typical auto thefts. 

32.5 
Some operational definitions of these crimes are in order: 

1. Sexual assaults used in this analysis included forcible rapes with victims of either 

sex, as well as any other physical, sexual abuse of another person of either 

sex - such as digital or objective penetration, fondling, etc. - and also open and 

gross lewdness (e.g., "flashing"). Statutory sexual seduction ("statutory rape") was 

excluded. 

2. Robberies used in this analysis included all robbery-related statutes in the Nevada 

Revised Statutes as of 2002 including home invasions (see State of Nevada, 

2012). 

3. Kidnappings were included in confrontational crimes, but the application of 

kidnapping as a statutory offense by law enforcement in Las Vegas (and 

elsewhere) may be counter-intuitive to some readers. Kidnapping is often attached 

as an additional offense to other crimes, such as robberies or sexual assaults, in 

any case in which the victim is forcibly moved from one location to another. This 

practice is used primarily as an adjunct to prosecution because kidnapping (unlike 

either robbery or sexual assault) is a federal crime and, in some cases, may be 

easier to prove in court. 

4. Homicides used in this analysis included all murder statutes, as well as all 

manslaughter statutes. No justified homicides were included. 

Once the target crime categories had been defined, separate databases for each of the 

three categories were compiled. Although data for several years was made available, all but three 

years of data were excluded from the study. Data prior to 1997 was often relatively poorly 

maintained and prepared and sometimes contained serious omissions which made it unreliable. 

Data for the year 2002 was incomplete when this study was commissioned. Although crime data 

for the years 1997 and 1998 was functionally reliable, socio-economic and transportation data for 

these years was not readily obtainable at the time this study commenced; since these data were 

necessary for implementation of this model, these years, too, were excluded from analysis. 

Therefore, only the years 1999, 2000, and 2001 were included in this study. 

Because this study focuses on spatial relationships between crime event locations and 

criminal home locations, only solved crimes could be used. Crimes were included as "Solved" 

when an arrest was made - unfortunately, difficulties in obtaining data from the justice system 

and the long delays inevitable in the prosecutorial process made it impossible to identify crimes 

in which a conviction had been obtained; an arrest was the closest approximation to a reliable 

solution possible for this research. 

32.6 
 

 

 

 

 

Of those "solved" crimes in which an arrest was made, only those in which the offender's 

home address and the precise location of the crime itself were both known could be used. Even 

when crimes were closed by arrest and adequate data was available to geographically plot and 

analyze the case, some have still been excluded. Instances in which the offender and victim both 

live at the scene of the crime have been excluded from these analyses since no travel was 

involved. However, instances in which either party lived at the scene of the crime but the other 

did not have been retained. The reasoning behind this decision is that the decision to commit a 

crime at a given place does include the decision to commit a crime in one's own home. 

Therefore, the spatial travel (none) component of this decision should still be reflected in the 

model if we hope to eventually derive a valid statistical representation of offender travel 

behavior. 

Also, crime in which the offender lived outside the study area (Clark County, Nevada) 

have been excluded in most cases, but not all. In some cases, "tourist" offenders may have been 

included when their temporary "base of operations" (i.e., local lodgings) had been recorded. In 

these instances, the hotel, motel, resort, or private dwelling they lived in has been used as a 

"home" location for purposes of originating a crime trip. 

The number of cases usable for each category of crime varied significantly from year to 

year (Table 32.1). 

Table 32.1: 

Confrontational Crimes Available for Analysis 

Year Total Offenses Usable Offenses 

1999 5,272 1,080 

2000 7,560 1,643 

2001 3,588 991 

The large increase in number of offenses from 1999 to 2000 is difficult to explain; the 

following substantial drop from 2000 to 2001 (52%!) is even more troubling. A similar, but 

inverted, discrepancy emerges in the frequency of burglaries reported during those years (Table 

32.2). 

32.7 
 

 

 

 

Year Total Offenses Usable Offenses 

1999 6,871 646 

2000 15,025 1,219 

2001 8,349 894 

Table 32.2: 

Burglary Crimes Available for Analysis 

Year Total Offenses Usable Offenses

 1999 17,234 2,520 

2000 12,899 2,040 

2001 16,403 2,733 

A final enigma, most significant of all, is obvious when we look at the frequency of auto 

crimes over the same three-year period (Table 32.3). 

Table 32.3: 

Vehicular Crimes Available for Analysis 

These disparities are hard to account for. On the whole, 1999 had a middling number of 

auto thefts and confrontations but a high number of burglaries; in 2000, on the other hand, the 

confrontations and auto thefts radically increased (auto crimes more than doubled!), but 

burglaries dropped notably. Finally, in 2001, confrontational crimes dropped to the lowest levels 

as did auto crimes while burglaries leaped to nearly 1999 levels! 

How can we explain these strange fluctuations? Given the large percentages involved, it's 

tempting to imagine some change in counting or reporting procedures in 2000; however, a 

scrutiny of the policies and procedures for the LVMPD does not seem to bear this out. Previous 

years (1996 - 1999) did not evince a similar wide degree of variation. The reason for these crime 

reporting changes remains unknown. 

Is there reason, therefore, to distrust these data? For purposes of this study, the answer 

appears to be, "No." That is, the data used for these analyses should, even allowing for as 

yet-unexplained vagaries in reporting, comprise a representative sample of the reported crime 

activity in Las Vegas over these years. 

Since forecasting the frequency of crime is a relatively minor component of the travel 

demand model, these numeric variations should not cause too much concern. Instead, since the 

focus of this model is the effective explanation and representation of the distribution of crime 

32.8 
trip generators and crime trip destinations (and, as a function thereof, of the crime trip paths 

between them), the frequencies themselves should matter little. 

Reference Data 

The Traffic Analysis Zone (TAZ) file for Las Vegas was selected as the optimum 

polygonal reference theme for this study (Figure 32.1). This file was provided by the 

Metropolitan Planning Office for Las Vegas, the Regional Transportation Commission. The data 

provided included historical data for 1999, 2000, and 2001 enabling more accurate modeling of 

the importance of various factors longitudinally across time. The TAZ dataset was provided in 

ESRI shapefile format, which is intrinsically legible to the CrimeStat application on which the 

model is to be built. 

The TAZ shapefile includes information on housing, employment, income, population, 

road mileage, and a variety of subset data specific to particular types of employment (e.g., 

"Strip" jobs, Nellis Air Force Base employment, entertainment-related jobs, vacant properties, 

number of pawn shops, etc.). 

An additional reference theme was needed to apply the final step in the travel demand 

model, the network assignment method. The Major Street Centerline file (LVMAJSCL.shp) in 

ESRI shapefile format was selected (Figure 32.2). Although only including arterial streets, 

freeways, and major thoroughfares, this transportation network layer is all that is needed to 

describe the vast majority of trips (of any sort) in Las Vegas. The addition of bus route 

information may prove a useful supplementary network to future analyses using this model. 

Assignment of Crime Trips 

Data from each year, by category, was assigned to a simple tabular database consisting of 

an identifying variable (Event Number as primary key), origination coordinates (coordinates of 

the offender's home address, or local base of operations in the case of external offenders), and 

destination coordinates (coordinates of the crime scene). These data were then combined into an 

MS Access 977 database for analysis using CrimeStat. Figures 32.3 and 32.4 show the assigned 

origins and destinations. 

Each origin-destination pair is termed a "Crime Trip." Following the reasoning of 

transportation modelers, it is understood that offenders to not leave their homes, travel directly to 

a crime scene to commit an attack, and then return home. Instead, each "sortie" is likely to 

consist of several stages. 

32.9 
Figure 32.1: 

Traffic Analysis Zones in Las Vegas 
Figure 32.2: 

Las Vegas Major Street Centerline Network Las Vegas Major Street Centerline Network 
Figure 32.3: 

Trip Origins: Origins: All Confrontational Confrontational Crimes , 1999-2

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Trip Destinations: Destinations: All Confrontational Confrontational Crimes, 199

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For example, a sexually predatory offender may get up in the morning, leave home, drive 

to work (stopping for coffee along the way), then go out to lunch before returning to the office, 

then on his way home depart from his usual route to drive through a residential neighborhood, 

looking for targets for potential victims. If a promising target is observed, he may then commit 

an attack, then drive back toward his home area, stopping off for gas or at a drive-through 

restaurant on the way, before parking at his house. Although this round-trip from home to home 

consists of multiple destinations, some of which are repeated throughout the day, the whole 

journey is considered to be a single "Crime Trip@. 

In some cases, a single offender was responsible for many crimes. When this happens, 

the single origin is paired with multiple destinations, resulting in separate Crime Trips. In other 

cases, one crime may be perpetrated by multiple offenders. When this happens, each offender's 

origin is paired with the single destination, again resulting in separate Crime Trips. 

While it is possible to distinctly model each Crime Trip based on precise spatial 

locations, the type of model used is an aggregate one. Thus, both origins and destinations of 

each crime trip were aggregated to the centroid of each Traffic Analysis Zone. This enables the 

spatial assignment of TAZ variables such as income and population to the aggregate frequencies 

of both origins and destinations. 

This assignment is performed in CrimeStat by centroid allocation - the nearest TAZ 

centroid is used to assign the TAZ data to each origin and destination. This method is faster and 

simpler than "point-in-polygon" spatial aggregation and assignment, but should result in 

comparatively few mistaken assignments due to unusual TAZ polygon shape or distribution. 

Since crime trip data is aggregated to the zonal level, therefore, the resulting analyses and 

forecasts are only applicable to this level and cannot meaningfully disaggregated to a more 

refined resolution. 

The accepted travel demand model framework contains a built-in "error factor" for 

external trips - that is, crime trips originating outside the study area but having internal 

destinations. These "external trips" were culled from the crime database during the data 

screening process; therefore, "External Zone" data is inapplicable to the trip generation stage of 

the analysis. 

Trip Generation 

Each origin/destination pair having been aggregated to the TAZ polygon layer, it is now 

possible to evaluate the relationship between socio-economic variables available in the TAZ 

database with the frequency of crime origins and destinations. This is accomplished through 

32.14 
 

 

regression modeling and may prove one of the most useful single features in the new modeling 

capabilities of the CrimeStat application. 

There are two main regression options available in the software at present: Ordinary 

Least Squares (OLS) and Poisson.1 The Poisson estimation also includes a separate option which 

allows backward elimination of variables. This option, Poisson Regression with backward 

elimination, was the most effective of the techniques evaluated resulting in consistently better 

visual fits to the data and lower residuals. This very useful step examines each variable element 

suggested by the analyst for its predictive value as a coefficient in estimating the frequency of 

either origins or destinations by TAZ. 

In every case, three variables within the TAZ database for Las Vegas proved consistently 

useful as predictive measures: 

1. Income, 

2. Population, and 

3. Total Employment. 

The measurable successfulness of these variables to account for the predictable 

distribution of both origins and destinations was somewhat counter-intuitive. It was suspected 

prior to the application of this model that other variables would be critical predictors of crime, in 

particular the number of pawn shops, the number of Strip employment opportunities, and the 

number of Nellis AFB employment opportunities. In fact, however, all of these variables 

demonstrated strong multicolinearity with the three primary variables listed above. When these 

other, extraneous factors were excluded from the regression process, the effectiveness of the 

model's predictive capabilities was substantially improved. 

A suggested and accepted travel demand modeling techniques widely implemented by 

transportation planners is the adoption of Aspecial generator@ variables to explain unusual or 

unique factors implicit in some areas. It was expected that Nellis AFB, the Las Vegas Strip itself, 

and some other seemingly significant factors would likely fill the role of "special generator;" 

however, results indicated that none of these were as effective in a predictive or explanatory role 

as income, population, and total employment. 

Latitudinal forecasting of crime trip origins and destinations performed fairly well; 

comparison of expected versus observed trip numbers did not match particularly well but the 

relative distribution by TAZ was a very close match (Figures 32.5 through 32.8). 

Since this was first written, the regression capabilities have been expanded to include a variety of 

Poisson-type models including Poisson-Gamma, Poisson-lognormal, and Conway-Maxwell Poisson all 

with a spatial component. See Chapter 20. 

32.15 

1 
Figure 32.5: 

Relative Relative Distribution Distribution ofObserved Crime Trip Or

Observed Crime Trip Or
Figure 32.6: 

Relative Relative Distribution Distribution ofObserved Crime Trip Desti

Observed Crime Trip Desti
Figure 32.7: 

Relative Distribution of Predicted Crime Trip Or

Predicted Crime Trip Or
Figure 32.8: 

Relative Distribution of Predicted Crime Trip Desti

Predicted Crime Trip Desti
Longitudinal forecasting of crime trip frequency by data from one year to the next year 

performed very poorly; this is probably an artifact of the still-unexplained drastic variation in 

frequency between the three years considered in this study. Results from other years may 

exemplify very different findings. 

Side-by-side comparisons of observed and predicted crime trip origins reveal some 

persuasive similarities, but significant discrepancies, also (Figure 32.9 and Figure 32.10). In 

general, relative proportions are very accurately described, but smaller producing zones are 

somewhat underestimated (the model seems to perform better on zones with higher productions). 

A side-by-side comparison of observed and predicted crime trip destinations suggests 

that, proportionally, the model again performs very well, particularly on zones with higher 

production scores. Zones with very weak crime trip destination productions (of one or two 

crimes) are not as accurately depicted. 

Trip Distribution 

Assignment of trip links between TAZ polygons performed very well (Figure 32.11). 

Originally, some concern was felt that the assignment of crime events to TAZ centroids (rather 

than using the actual crime scene and home address coordinates) might result in significant 

distortion; however, this does not appear to have occurred. Compare the raw (actual) crime trip 

lines with the centroid-corrected trip lines to see how neatly they match (Figure 32.12). The 

resulting distance decay and impedance functions perform perfectly well. There are almost no 

discrepancies visible to the naked eye. 

Various impedance function calculations were attempted in the course of this study. 

Eventually, an adaptive (100-bin) normal interpolation with 100 minimum samples was selected 

as the best fit. However, a negative exponential impedance function also fit well, similar to the 

Baltimore County and Chicago models. 

Intra-zonal crime trips - those having both origin and destination within the same TAZ, 

cannot be displayed as lines since they have no length. Instead, they can be represented by points 

(Figure 32.13). Inter-zonal crime trips, on the other hand, are better displayed by lines (Figure 

32.14). 

Overall, intra-zonal crime trips accounted for 42% of all crime trips but only 12% of 

robberies, indicating a much longer "hunting range" for robbers; this may be in keeping with the 

hypothesis that the tourist corridors draw robbery crime trips as destinations which originate in 

other neighborhoods. More than 50% of sexual assaults were intra-zonal, indicating a 

32.20 
Figure 32.9: 

Comparison of Observed and Predicted Crime Trip Comparison of Observed and Predicted Crime Trip 

Observed Predicted 
Figure 32.10: 

Comparison of Observed and Predicted Crime Trip DComparison of Observed and Predicted Crime Trip D

Observed Predicted 
Figure 32.11: 

Observed Crime Trips Observed Crime Trips 
Figure 32.12: 

Comparison of Observed and Predicted Crime TriComparison of Observed and Predicted Crime Tri

Observed Predicted 
Figure 32.13: 

Predicted Intra Predicted Intra -zonal Crime Trips zonal Crime Trips 

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Figure 32.14: 

Top 100 Predicted Inter Top 100 Predicted Inter-zonal Crime Trips zonal Crime Trips 

Allocated to All Streets 
shorter-than-usual hunting range for sexual attackers, who seem to prefer striking in their home 

neighborhoods. 

Mode Split 

Unfortunately, the mode split portion of the travel demand model is the weakest element 

for the Las Vegas data. Transportation modes across metropolitan Las Vegas are varied. Typical 

of a western city, the overwhelming majority of residents rely on private automobiles for 

transportation as do many tourist visitors. However, this mainstay is supplemented by a robust 

bus system as well as alternate personal transportation for short trips (i.e., walking, bicycling, or 

scooters). The picture of automobile transportation is somewhat muddled by the higher than 

usual dependency on taxi-cabs and limousines for transportation by out-of-state visitors. 

Data provided by the LVMPD included a field called "Method of Departure" which was 

intended to contain information about how the offender departed the scene of the crime which, in 

turn, would have been an effective way of calculating probable mode split for crime trips 

sampled. Unfortunately, this data field was blank in the overwhelming majority of cases 

(approximately 4% contained entries, and only 75% of these - 3% overall - contained apparently 

valid data). 

Therefore, any empirical estimation of mode split for these data requires inference from 

other data. For example, auto theft crimes may safely be assumed to use a car to provide 

transportation for at least some portion of the crime trip. In other cases, the plain-text narrative 

includes vehicle descriptions or statements about how the offender moved that were not distilled 

into the correct field. Unfortunately, the large volume of cases makes recovering information 

from these free narratives impractical for the small number of cases in which mode split 

information can beneficially be derived. 

Due to this lack of reliable data, only two mode split options were included in this 

analysis: Walking and Driving. Default impedance functions proved very acceptable for both 

modes: Inverse Exponential for walking trips and Lognormal for driving. 

Network Assignment 

The complete street centerline (SCL) file for the metropolitan Las Vegas area was 

available in a routable format (topologically rectified ESRI Shapefile); however, this file proved 

prohibitively large and unwieldy for the A* shortest-path/least-cost algorithm implemented in 

CrimeStat. Instead of the complete SCL data layer, a layer consisting only of arterial streets 

and freeways was used instead. This major roads file proved adequate to neatly explaining the 

32.27 
probable transportation path choices made by the top 100 and top 300 inter-zonal crime trips 

(Figures 32.15 and 32.16). 

In general, the visual goodness-of-fit for predicted crime trips improved as the category 

of crime was narrowed. Predictions from one year to the next remained weak, probably as a 

result of the as-yet-unexplained radical variance in crime frequencies across all study categories. 

However, within discrete crime categories predictive capabilities were sometimes visually 

impressive. 

Modeling Different Crime Types 

Auto Theft Site to Recovery Site 

In the case of auto thefts, an attempt was made to isolate the movement from vehicle theft 

site to vehicle recovery site rather than use the theft site and offender home location as the 

destination and origin, respectively, of the crime trip. It was hoped that this variation of the travel 

demand model for crime trip analysis might prove more useful for this type of data than 

home-based crime trips partly because more accurate location information was available for 

recovery sites than for home locations. Also, it was hypothesized that the theft/recovery "trip" 

segment might prove more representative than the home/theft trip. 

Results for auto thefts appeared weak with predicted crime trips much longer than the 

observed (Figure 32.17). While the observed trips focused tightly on the central core areas and 

densely-populated residential zones, the predicted trips seemed to skirt the edges of the 

metropolitan area. This is possibly due to an implied overemphasis on freeway travel which may 

be correctible with better network allocation parameters. The median distance for observed crime 

trips was 2.3 miles. 

Residential Burglaries 

Differentiation of residential from commercial or auto burglaries was accomplished by 

three filtering criteria: Statute, Premise, and Zoning. Some specific Nevada Revised Statutes 

have been reserved for residential burglaries; burglaries in which these statues were cited were 

therefore accepted as residential in nature. Categorical Premise type data was provided in the 

MO data for each crime; when this data explicitly noted a residential site, these cases were also 

accepted as residential. 

Some burglaries did not specifically include a residential statue or explicitly residential 

premise code; but were spatially located in areas of the jurisdiction reserved for residential rather 

32.28 
Figure 32.15: 

Top 100 Predicted Inter Top 100 Predicted Inter-zonal Crime Trips zonal Crime Trips 

Allocated to Freeways and Major Streets 
Figure 32.16: 

Top 300 Predicted Inter Top 300 Predicted Inter- and Intra and Intra-zonal Crime 

zonal Crime 

Allocated to Freeways and Major Streets 

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Figure 32.17: 

Top 100 Observed & Predicted Auto Theft Crime Top 100 Observed & Predicted Auto Theft Crime 

Allocated to Freeways and Major Streets 

Observed Predicted 
than commercial, industrial, or other zoning purposes. These cases were therefore also accepted 

as residential in character. 

Results for analysis of residential burglaries was more promising than for auto thefts, or 

for burglaries overall (Figure 32.18). While, again, observed crime trips focused on the most 

densely-populated residential neighborhoods and predicted crime trips were much longer and 

spread more far afield, this spread was much smaller than that seen in auto thefts and more 

closely conformed to the observed distribution. The median distance for residential burglary 

crime trips was 1.1 miles. 

Sexual Assaults 

The spatial distribution of sexual assault crime trips in many ways seemed to invert the 

problems seen in the predicted crime trips for auto thefts and residential burglaries. In the 

previous examples, an observed tendency toward centrality seemed to be confused with a 

predicted tendency toward dispersion toward outlying areas. In this case, however, a very 

nebulous, outlying distribution of observed crime trips (centering in three faint clusters around 

the perimeter of the central metropolitan region) was observed. The predicted crime trip 

distribution mistakenly emphasized central areas, and seemed to completely fail to predict the 

southeastern-most "cluster" of crime trips (Figure 32.19). 

The large median crime trip length for sexual assaults - 3.2 miles, may help explain the 

relatively poor predictions of these results. Different impedance functions will probably help 

improve the reliability of this model against these types of crimes. 

Robberies 

Robbery crime trips in Las Vegas appear to closely parallel the major gaming and 

transportation corridors running north to south through the center of the metropolitan area 

(Figure 32.20). The visual fit of predicted against observed crime trips was most impressive 

against these cases. Although the predicted crime trip distribution appears more compact and 

centralized than the observed, the directionality and polarity of the two parallel nicely, and make 

a striking visual match. The median crime trip distance for robberies was 2.3 miles. 

Conclusions 

Overall, the model appeared to perform well for some crime types but weaker for others. 

One of the most troubling problems facing the evaluation of the network assignment stage of the 

model is the lack of any good final metric other than visual approximation for determining the 

32.32 
Figure 32.18: 

Top 100 Observed & Predicted Residential Burglary CTop 100 Observed & Predicted Residential Burglary C

Allocated to Freeways and Major Streets 

Observed Predicted 
Figure 32.19: 

Top 100 Observed & Predicted Sexual Assault CrimTop 100 Observed & Predicted Sexual Assault Crim

Allocated to Freeways and Major Streets 

Observed Predicted 
Figure 32.20: 

Top 100 Observed & Predicted Robbery Crime Top 100 Observed & Predicted Robbery Crime 

Allocated to Freeways and Major Streets 

Observed Predicted 
 

value of the resulting prediction. Some measurement of congruence is needed to make the 

determination of usefulness reliable and valid. 

The first stage of the model - crime trip generation, is arguably the most useful to law 

enforcement. This elegantly simple model can readily be adapted to different types of data, and 

with the inclusion of additional regression methods (specifically the negative binomial 

distribution model) to supplement the existing ordinary least squares (OLS) and Poisson variants, 

this feature is likely to remain useful for the foreseeable future.2 

The second stage of the model - crime trip distribution, is also potentially highly useful. 

The analysis not merely of where offenders live or where crimes are committed but of the travel 

and transportation decisions linking the two locations, may have significant repercussions for 

crime analysts. This type of analysis will be particularly useful for strategic and administrative 

analysts when recommending manpower allocation, beat boundaries and precinct/district 

configuration schemes, and assessing the impact of major developments such as transportation 

corridors, shopping malls, or sports complexes on the distribution of crime. 

The mode split stage of the model was difficult to apply meaningfully to the Las Vegas 

data in this study because of deficiencies in the data itself. Either transportation choice values 

were not recorded, or were recorded in irretrievable formats, making an empirical evaluation of 

offenders' transportation choice proclivities impractical. Failing the availability of empirical data, 

falling back on overall trends in public transportation choice are all that is possible for the 

analyst. Since it is possible that crime trips may be qualitatively different than other types of trips 

on which these statistical models have been based, further research is required to assess whether 

or not these standards will be applicable to criminal behavior. 

The final stage of the model - network assignment, functioned mechanically as expected, 

but did result in some potentially weak results (such as overemphasis on the speed of freeways 

apparent in some results) which may be overcome with better mode split and network choice 

parameters. 

One aspect of the model that caused for initial concern, the aggregation of crimes to the 

Traffic Analysis Zone polygon level, proved to have no significant impact on the resulting 

analysis. The TAZ structure seems admirably suited to analysis of this sort of movement - as 

indeed one might expect from its provenance. 

The most successful predictive variables for estimating crime trip production, whether of 

origins or destinations, were infallibly total population, total employment, and income. Inclusion 

This has been implemented since this chapter was initially written. 

32.36 

2 
 

of additional variables distorted rather than improved the predictive value of the model, most of 

the time with measurable multicolinearity which was not always apparent a priori. 

With the mechanical aspects of the model - as implemented in the latest version of 

CrimeStat, complete and functioning correctly, it remains to be learned how to better calibrate 

and implement the model to make it an effective tool for law enforcement analysis and planning. 

32.37 
References 

State of Nevada (2012). Laws of the State of Nevada. Nevada Law Library: Reno. 

http://www.leg.state.nv.us/law1.cfm. 

32.38