Locations of Motor Vehicle Theft and Recovery

Geetha Suresh & Richard Tewksbury

Received: 29 September 2011 /Accepted: 22 February 2012 /Published online: 4 April 2012# Southern Criminal Justice Association 2012

Abstract In a community-level analysis, this study examines risky locations for motorvehicle theft in Louisville, Kentucky from 2004 to 2007. Maps will display clusteringpatterns, density, displacement of motor vehicle thefts and relationships with spatialattributes and neighborhood characteristics. Clustering indicates heavy concentration ofmotor vehicle theft and recovery in the neighborhoods characterized by indicators ofsocial disorganization (poverty, unemployment, vacant houses). Parking lots belongingto churches in a socially disorganized neighborhood are also an auto crime attractor.

Keywords Auto theft . Social disorganization . Parking lots . Clustering patterns .

Auto crime attractor

Introduction

Motor vehicle theft is one of the most common criminal offenses in American society,but one of the least studied forms of crime. According to the Uniform Crime Reports,in 2009 a total of 794,616 reports of motor vehicle theft were received by Americanlaw enforcement agencies (Federal Bureau of Investigation, 2010). Accordingly,there were 258 motor vehicle thefts reported in the United States for every 100,000residents. Motor vehicle theft ranks as the fifth most commonly reported indexoffense, and fully 95 % of such thefts are reported to law enforcement (Bureau ofJustice Statistics, 2011). Although the number and rate of motor vehicle thefts havebeen decreasing in the last two decades, it remains a common and serious criminaloffense. And, motor vehicle theft has the lowest clearance rate of all index offenses(Federal Bureau of Investigation, 2010).

The stolen vehicle is not recovered in a majority of incidents; and according to theBureau of Justice Statistics (2011) only 42.6 % of stolen motor vehicles in 2008 wererecovered. Victims of motor vehicle theft report a mean loss of $7,821 (Bureau of Justice

Am J Crim Just (2013) 38:200–215DOI 10.1007/s12103-012-9161-7

G. Suresh : R. Tewksbury (*)Department of Justice Administration, University of Louisville, Louisville, KY, USAe-mail: ratewk01@exchange.louisville.edu

G. Sureshe-mail: g0sure01@exchange.louisville.edu

Statistics, 2011). According to the Federal Bureau of Investigation in 2010 the value ofstolenmotor vehicleswas $4.5 billion.The average value of amotor vehicle reported stolenin 2010 was $6,152. Clearly, motor vehicle theft is a serious problem in American society.

A careful study of the locations where motor vehicle theft is most likely to occurcan assist in constructing strategies and policies to address this significant problem.Knowing who is likely to commit motor vehicle theft, offenders’ motivations andmethods and places where it is most likely to occur can provide important informa-tion to law enforcement officials, as well as the general public, in seeking to deter andsolve this offense. Towards this goal, this paper presents mapping and identification of atrisk locations of stolen and recoveredmotor vehicles and assessment of characteristics ofcommunities as they relate to rates of motor vehicle thefts and recoveries.

Literature on Motor Vehicle Theft

Scholarly research examining motor vehicle theft1 is one of the least developedbodies of academic literature on types of crime (Clarke & Harris, 1992; Herzog,2002; Maxfield, 2004; Rice & Smith, 2002; Walsh & Taylor, 2007a). What is knownis primarily focused on offenders’ perspectives, experiences and motivation(Cherbonneau & Copes, 2006; Copes & Tewksbury, 2011; Light, Nee & Ingham,1993; O’Connor & Kelly, 2006; Spencer, 1992), types of locations where vehiclethefts are most likely (Hollinger & Dabney, 1999; Lu, 2006; Weisel, Smith, Garson,Pavlichev & Wattrell, 2006) and demographics of known vehicle thieves (ArizonaCriminal Justice Commission, 2004).

It is widely recognized that motor vehicle thefts are of three basic varieties andmotivations. Theft for profit (from sale of the vehicle or parts), theft so as to securetransportation and recreational theft (“joy riding”). Characteristics of offenders large-ly reflect differences in motivation. For example, recreational theft is most oftencommitted by adolescents or young adults (Arizona Criminal Justice Commission,2004). Thefts for profit tend to be by older, and more criminally involved, offenders.Across all motor vehicle thefts, however males are by far the most common offenders.

Motor vehicle theft is most likely to occur in urban areas, as rates are more thandouble in urban areas than in cities outside metropolitan areas and rural areas (FederalBureau of Investigation, 2010). And, within urban areas there are clear patterns inwhere motor vehicle theft is most likely to occur. Copes (1999) showed that theft ismore likely along longer roads and in neighborhoods with greater density of streets/roads. Lu (2006), focusing on 3,179 auto thefts reported to the Buffalo police in 1998showed that hot spots for such thefts can be distinguished between those related to agreater concentration of available targets and those related to enhanced opportunities.Furthermore, these factors may be a consequence of the socioeconomic activities ofstreets and neighborhoods; as particular types of activities—most notably multi-

1 An important definitional issue is seen in the common references in both public discourse and scholarlyliterature to “auto theft” or “stolen cars”. As Weisel et al. (2006) point out though, vehicle theft involves alltypes of motor vehicles, not just cars. In fact, only about three-quarters of stolen vehicles are cars (FederalBureau of Investigation, 2010). In addition to cars, motor vehicle theft also includes trucks and “other” (e.g.motorcycles, scooter, all-terrain vehicles, etc.) types of vehicles.

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family residences and commercial activities with parking lots—are more commonmotor vehicle theft rates increase (Lu, 2006). As summarized by Lu (2006: 157–8),

The location choice of an auto theft should be more related to the characteristicsof the offence location ad its immediate surroundings. It has been tested andconfirmed by many studies that the presence of certain kinds of urban facilitiesor land use has a significant impact on the spatial distribution of crime (Engstad,1980; Kennedy, 1980; Roncek & Bell, 1981; Roncek & Faggiani, 1985;Roncek & Maier, 1991). It is not that the facility itself is related to crime, butthat the social and economic activities in and around the facility create a placewith better and more offence opportunities for potential offenders.

Additionally, motor vehicle thieves frequently commit their offenses in closeproximity to their homes (Potchak, McGloin & Zgoba, 2002; however, also seeWiles & Costello, 2000). This is not unexpected, especially for recreational theftsand offenders who are seeking transportation (Copes, 2003; O’Connor & Kelly,2006); consequently, it should be expected that communities with higher rates ofoffenders, offenses and structural aspects conducive to crime would exhibit higherrates of motor vehicle theft.

According to Lu (2006) the study of motor vehicle theft has been primarily drivenby two theoretical perspectives, routine activity theory and rational choice theory.However, as seen in the literature on the topic, there are also clear indications thatcommunity structural factors may also be related to the occurrence of motor vehicletheft. In settings with less monitoring of vehicles and more diverse, unsupervised,economically disadvantaged and likely criminally involved persons present, there arelikely to be more motor vehicle thefts occurring. Such settings are those commonlyreferred to as socially disorganized locations. Building on Lu’s (2006: 144) call forexpanding theoretical perspectives applied to the understanding of this offense—“Thereis a great need for empirical studies of auto theft that apply different crime theories forthe purpose of explaining, predicting, and eventually preventing auto theft”—thepresent study breaks from this focus on routine activities and rational choice theoryand approaches motor vehicle theft from a social disorganization theory perspective.

Social Disorganization

Social disorganization theory focuses on identifying issues of community dep-rivation (economic and social) that are correlated with the presence of undesiredevents, such as criminal behavior. More specifically, social disorganizationtheory suggests that crime flourishes in communities that are less organizedbecause residents have fewer resources, less contact, and less stake in themaintenance of the community. Socially disorganized communities lack ineconomic and social capital, social cohesion and stability, are typically moreracially/ethnically heterogeneous, and home to residents who are less willing orable to enact a social control monitor role regarding others in the community.In communities that have a high level of social organization residents possessboth financial and social capital to effectively regulate and socially control (orlobby for outside regulation of) their communities and informal networks of

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interaction. Because socially disorganized communities lack this social control,they are likely to be home to both victims and offenders.

Social disorganization theory enjoys empirical support, beginning with the factthat crime is not uniformly distributed across communities (Block & Block, 1995;Eck & Weisburd, 1995), but instead is more commonly found clustered in and aroundeconomically and socially disorganized neighborhoods. Social disorganization refersto the failure of residents, social institutions or social organizations (e.g., schools,businesses, and housing) in certain communities to prevent or resist infiltration byothers seeking to engage in deviance and crime.

Numerous types of crime have been shown to be more common in socially disorga-nized communities. These include offenses ranging from violent crimes generally andhomicide (Bursik & Grasmick, 1993; Sampson & Groves, 1989; Kubrin & Weitzer,2003; Morenoff, Sampson & Raudenbush, 2001; Petee, Kowlaski & Duffied, 1994;Pratt & Cullen, 2005; Strom & Donald, 2007; Warner, 1999; Warner & Rountree,1997), robbery (Bernasco & Block, 2009), sexual offenses (Smith & Bennett, 1985;Tewksbury, Mustaine & Covington, 2010), violence against women (Benson, Fox &Demaris, 2003) and a variety of property offenses (Cancino, Varano & Schafer, 2007;Hannon, 2002; Reisig & Cancino, 2004). Socially disorganized communities have alsobeen shown to be associated with a greater number of offenders in residence in thecommunity (Mustaine & Tewksbury, 2011; Mustaine, Tewksbury & Stengel, 2006;Tewksbury & Mustaine, 2006). And, social disorganization, although originally con-ceived for explaining urban crime, can also be successfully used to explain crime innonmetropolitan communities (Barnet &Menckin, 2002; Osgood & Chambers, 2000).

Social Disorganization and Motor Vehicle Theft

Motor vehicle theft is an offense for which both official statistics (Federal Bureau ofInvestigation, 2010) and scholars (Lu, 2006; Rice & Smith, 2002) have demonstratedclear clustering in urban communities. In urban communities, such offenses may befacilitated by social disorganization factors, especially economic deprivation. A linkbetween low economic status of a community and high rates of motor vehicle thefthas been previously demonstrated (Copes, 1999; Hope & Hough, 1988; Miethe &McCorkle, 2001; Walsh & Taylor, 2007b). Not only are local economic conditionsrelated to motor vehicle theft, but so too are measures of individuals’ perceptions ofpersonal economic status, such as the Index of Consumer Sentiment (Rosenfeld &Fornango, 2007) and community structural issues.

Community structural issues that are important indicators of social disorganizationinclude residential stability, racial heterogeneity and the presence of quality of lifeoffenses. Community instability has been shown to be related to increased rates ofmotor vehicle theft (Bellair, 1997; Rice & Smith, 2002; however, also see Walsh &Taylor, 2007a for contradictory results). Weisel, Smith, Garson, Pavlichev andWattrell (2006) show that while vehicle thefts are widely dispersed, there is asignificantly higher risk of victimization in areas with higher concentration of rentalhousing and in areas with industrial land use.

The issue of community heterogeneity/diversity has been shown by someresearchers (Clarke & Harris, 1992; Walsh & Taylor, 2007a, 2007b) to be related to

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increased rates of motor vehicle theft, while others (Akins, 2003; Davison, 1995; Rice& Smith, 2002) have shown the opposite relationship. The broader social disorgani-zation literature on crime rates in general has primarily shown a link between racialheterogeneity and increased crime rates (Bursik & Grasmick, 1993; Sampson &Groves, 1989). Linking both stability and heterogeneity, Walsh and Taylor (2007a)show that when examined longitudinally, motor vehicle theft rates increased incommunities with decreasing stability (measured as percentage of housing unitsoccupied by renters, percentage of housing units occupied by one person, percentageof multiple housing units, and percentage of people age 5 and older not residing in thesame house 5 years prior), increasing racial heterogeneity, and in communities locatedcontiguous to other communities that also had increased motor vehicle theft rates.

As an indicator of quality of life offenses in a community, Corman and Mocan(2005) show that in New York misdemeanor arrest rates in a neighborhood arepredictors of motor vehicle theft rates. As arrests for minor criminal offenses increase,so too do rates of motor vehicle theft.

Specific locations where motor vehicle theft is most likely to occur have tradi-tionally been identified as places with easy access and opportunities for quicklymoving a vehicle from the area, and where numerous vehicles and persons are presentand often moving. Not surprisingly, major roads and areas immediately adjacent tomajor roads are commonly targeted by motor vehicle thieves (Lu, 2006); this is atleast in part due to concerns of motor vehicle thieves about avoiding detection andquickly and easily getting away from the theft location (Copes & Tewksbury, 2011).

Just over one-half (58.8 %) of all motor vehicle thefts in the United States in 2006occurred at or near the victim’s home (Bureau of Justice Statistics, 2010). Whenlooking at only those vehicles stole from a victim’s home, the rate of victimization forvictims residing in multi-family dwellings is approximately 50 % higher than for ratesat single family dwellings (Copes, 1999). The remaining more than 40 % of motorvehicle thefts tend to occur in locations with high concentration of vehicles. Weisel etal. (2006) identify especially high risk locations for motor vehicle theft as thoselocations with large numbers of vehicles present, including auto dealers and repairshops. Similarly, Hollinger and Dabney (1999), Lu (2006) & Plouffe and Sampson(2004) identify shopping malls as high risk locations for motor vehicle theft and theArizona Criminal Justice Commission (2004) shows any large parking lots are likelylocations for such thefts.

Mapping and Environmental Criminology

The main theoretical area that underpins crime mapping is referred to as environmentalcriminology, which is the study of criminal activity and victimization and how factors ofspace influence offenders and victims (Bottoms & Wiles, 2002). Mapping can be usedto capture and organize crime problems. In this sense mapping crime data is ascientific enterprise that highlights the interplay among method, data and theory.

Environmental criminology calls on crime prevention efforts based on character-istics and opportunities attached to physical space. The attributes of place wereviewed as key factors in explaining criminal events in a community and variationsin crime within communities. These spatial patterns of crime change over time when

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the relationship pattern between individual-place differs. However, the cumulativeand aggregate effects of place-individual attributes on a given space, explainingmacro-behavior of crime, remain relatively unexplored.

Based on the concept of spatial patterns and distribution of crime, offenderdecision making should be structured by the opportunities for crime shaped by thephysical environment (Cohen & Felson, 1979; Felson, 1987; Roncek, Bell & Francik,1981) and time. The present study explores the interaction of crime, place and time.The relationship between crime and place is neither uniform nor static (Block &Block, 1995). The major elements of this interaction are offenders, targets and place(Cornish & Clarke, 1986). Cornish and Clarke referred to these three elements ofcrime as three sides of a triangle. These three elements interacting with crime andtime explain the non-random behavior of crime distribution. Consistency of the non-randomness or the clustering in a given space with time generating a spatial pattern inmotor vehicle theft is explored in this study.

Data and Methods

All data is from Louisville, Kentucky, a city of 740,000 population located on the banks ofthe Ohio River in north-central Kentucky. Both data on official reports of motor vehicletheft and census data for the city are used in mapping the distribution of motor vehicle theftoffenses in Louisville. Our analyses draw on ARCGIS Version 9 and include standarddeviationmaps,Moran’s I, LISAmaps and spatial regression analysis done usingGEODA.

Police call data containing address level information for stolen motor vehicles andrecovery of stolen motor vehicles in the city of Louisville, Kentucky from January 2004to December 2007 were collected from the Louisville Metro Police Department. This isconsidered a very reliable source of data in that estimates suggest that at least 90% of allmotor vehicle thefts are officially reported (Harlow, 1988; Bureau of Justice Statistics,2011) due to insurance requirements and victims’ efforts to recover damages.

Louisville census block group data were provided by Louisville/Jefferson CountyInformation Consortium (LOJIC). Two types of community-level data were used inthe study: 1) Louisville block group polygon or lattice data and 2) street centerlinedata. Census block group boundaries are the approximate conversion of censusbureau polygon data. Street centerline data were also provided by LOJIC. Streetcenter line is described as ‘arc’ data. It provides all streets and intersections (whichcan be overlaid) on the census block group data. Street center line data was clippedwith Louisville block group data to create a base map of Louisville, with streets,highways and block group variables.

The variables that affect social disorganization and motor vehicle theft includepopulation characteristics, unemployment rates more specifically the unemploymentstatus, income levels, number of parking lots, housing availability and cost.

Findings

Table 1 shows the number of stolen and recovered motor vehicles along with the percentof recovered stolen motor vehicles in the city of Louisville for the years 2004 to 2007.

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As shown, nearly 30 % of the motor vehicles stolen were recovered in the sameyear. 2

Identification of At-Risk Locations of Stolen and Recovered Motor Vehicles

The point pattern with density of the distribution of motor vehicle theft for the year2004 indicates the presence of hotspots throughout the community (see Fig. 1).

The point pattern with density of the distribution of motor vehicle theft indicatesthe presence of three hotspots around city of Louisville. They are:

1. Central Business District, Smoketown, Shelby Park and California towards thenorthwest of the city

2. Russell, Shawnee and Portland towards the west of the city3. Taylor Berry, Wyandotte and Park Hill towards the south of the city of Louisville

The standard deviational map3 of the point pattern of motor vehicle theft in theyear 2004 also indicates the same hotspots as the point pattern of the crime distribu-tion which is shown in Fig. 2.

The standard deviational maps indicate that there are four spatial outlier areas(central business district, Smoke town, Shelby Park and California neighborhoods)where especially high numbers of reported motor vehicle theft clustering occurs.Those areas (7 census block groups) had more than 91.86 motor vehicle thefts (spatialoutliers) in 2004 which is three standard deviations above the mean. These areascorrespond to the hot spots for recovery which is shown in Fig. 3. When locations ofthe recovered stolen motor vehicles are mapped, results suggest same hotspot loca-tions as where thefts occurred. Mapping patterns indicate that stolen locationscoincide with recovery locations, so an analysis of stolen with the recovery locations

2 One limitation of this study is that our data is restricted to examination of recovered motor vehicles thatare recovered in the same year that they are stolen; we are not able to consider lagged recovery insubsequent years.3 Standard Deviational Map: A standard deviational map groups observations according to where theirvalue fall on the standardized range, expressed as standard deviational units away from the mean. Astandardized variable has a mean of zero and a standard deviation of 1, by construction. Hence astandardized value can be interpreted as multiples of standard deviational units (Anselin, 2003). Numberof data in each category depends on the distribution of the data. Areas with points more than 2 standarddeviations are spatial outliers

Table 1 Motor vehicles stolen and recovered with percent recovery to stolen

Stolen (#) Recovery (#) Percent of recovery to stolen in the same year

2004 5299 1354 25.6 %

2005 5129 1567 30.6 %

2006 6636 1985 29.9 %

2007 5822 1860 31.9 %

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is an integral part of the analysis of this study. An analysis based on aggregate dataprovides an insight into the trends and hotspots in thefts and recoveries of autos in agiven year.

Figure 3, showing the hotspot analysis of stolen motor vehicle recoveries indicatesthat these are the same geographic areas (primarily in and around the central businessdistrict) as are the hotspots for motor vehicle thefts.

Analysis of point patterns and hotsots of motor vehicle theft for 2007, however,shows a different pattern. The concentration of locations for motor vehicle theft wasdisplaced to different neighborhoods, as shown in Fig. 4.

The recovery hotspots and the standard deviational map for the year 2007indicates that in the 3 year period there was a displacement of motor vehicletheft to new neighborhoods in the city, specifically locations to the immediate

Fig. 1 The point pattern of the distribution of motor vehicle theft for the year 2004

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west and east of downtown and to the southern regions of the city (includingthe locations of the Fair and Exposition Center, the International Airport andmajor industrial locations).

Fig. 2 Standard deviational map of the point pattern of motor vehicle thefts in the year 2004

Fig. 3 Recovery hotspots for 2004

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Space Time Correlation of Motor Vehicle Theft

Analysis of the point pattern of motor vehicle thefts and recoveries indicates thatthere is a strong autocorrelation of motor vehicle theft in one location time after time,even with considerable displacement to neighboring areas. Tables 2, 3 and 4 reportthe Moran’s I4 statistics for motor vehicle thefts and recoveries. The Moran’s Istatistics confirm the spatial dependency of motor vehicle theft and recovery hotspots.The spatial auto correlation of recovered vehicle data has a slightly higher depen-dency on space across time compared to theft data. This indicates that recoveryhotspots are the same locations as theft hotspots and suggest that typically stolenvehicles were not driven far from the locations from which they were stolen to wherethey were recovered.

LISA5 (Local Indicators of Spatial Association) maps indicate the same area in andaround the Central Business district having strong spatial correlation and outliers ofmotor vehicle theft. Standard deviational maps, local spatial autocorrelation (LISA)

4 Moran’s I statistics: Moran’s I statistics indicates spatial autocorrelation and clustering across time. Spatialautocorrelation is the similarity between two observations of a measured variable based upon their spatiallocation (Griffith, 1992; Legendre, 1993; Lennon, 2000; Fortin et al., 2002) across time. Moran’s I is aconventional measure of auto correlation, values ranging from −1 to 1 depending on the degree anddirection of autocorrelation. (+1 indicates strong positive spatial autocorrelation, 0 indicates random spatialordering and −1 indicates strong negative spatial autocorrelation). The interpretation of Moran’s I is similarto the nonspatial correlation coefficient.5 LISAMaps: LISA (Local Indicators of Spatial Association) could be useful in assessing significant localspatial clustering around an individual location (spatial heterogeneity) and the indication of pockets ofspatial non-stationarity (Anselin, 1995). In the map the high-high (in red) shows neighbors with high valuesof same attribute. Positive clustering of similar values of high (red) or low (blue) indicates spatialheterogeneity and the negative values (spatial clustering of dissimilar values) are indicated as low—highor high—low. For example a location with high values surrounded by neighbors with low value isrepresented as high-low. LISA (Local Indicators of Spatial Association) maps for stolen (2004 and 2007)and recovered (2004 and 2007) can be obtained from authors on request.

Fig. 4 Standard deviational map of motor vehicle theft for the year 2007

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maps and calculation of Moran’s I also are all supportive of the existence ofsignificant spatial outliers.

Association of Motor Vehicle Theft and Recovery with Spatial Characteristics

To assess the characteristics of communities that are identified as hotspots for motorvehicle theft and recovery analysis examines six community characteristics (at thelevel of block group level) and their relationship to the numbers of annual motorvehicle thefts and recoveries. Included in the analysis are number parking lotsbelonging to churches, median household income, percent of population living belowpoverty, vacant dwelling units, median housing value and number of people betweenthe ages of 15 and 29. These six variables were selected after analyzing multi-collinearity statistics of the variables available through census reports to geocode usingARCGIS All six factors are shown to be significantly related to the number of motorvehicle thefts and recoveries. Table 4 presents the regression results of motor vehiclethefts and recoveries as dependent variables with these six independent variables.

The spatial Poisson regression using GeoDA was done since the dependentvariable is a count variable. Poisson regression is used to model with non negativecount dependent variable. Only the statistically significant variables for each year arereported in Table 4. Un-standardized b is reported in Table 4.

As shown, the regression analysis using these six independent variables explains alarge portion of the variation in the numbers of motor vehicle thefts and recoveries;Across all 4 years between 61 % and 70 % of the variation in number of stolen motorvehicles and 71 and 79 % of the variation in the number of recovered stolen motorvehicles is explained by these models.

The existence of large number of parking lots belonging to churches in the blockgroup is shown to be the strongest predictor of both number of stolen motor vehiclesand recovered stolen vehicles. Higher counts of church parking lots located in a lowincome location are shown to have a positive influence on motor vehicle theft andrecoveries. Parking lots with low or no guardianship often encourages motivatedoffenders to easily connect with potential targets (motor vehicles) especially in a

Table 2 Moran’s I statistics formotor vehicle theft data

2004 2005 2006 2007

2004 – 0.53 0.50 0.53

2005 – – 0.45 0.47

2006 – – – 0.45

Table 3 Moran’s I statistics formotor vehicle recoveries data

2004 2005 2006 2007

2004 – 0.58 0.59 0.61

2005 – – 0.56 0.58

2006 – – – 0.58

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disorganized neighborhood with vacant dwellings associated with higher unemploy-ment. This, however, may be a confounding factor in this study. Similarly, morevacant sites are associated with regions with greater degrees of social disorganization.A larger number of vacant sites are associated with a higher count of motor vehiclethefts and recoveries for all the years in this study. Correspondingly, the calculated Bstatistics for median income is negative indicating that as median income increases,motor vehicle theft and recovery counts decreases is supportive of the existingliterature on social disorganization and crime. And, as the population of young adultsbetween the ages of 15 to 29 increases, the number of recovered stolen motor vehiclesdecreases (however, size of the young adult population is not related to number ofstolen motor vehicles).

The presence of spatial association between stolen and recovered areas is becausethe average distance travelled in miles between the stolen and recovered locations forall the years is less than 3 miles with a standard deviation of 3 miles.

Table 4 Regression results: motor vehicle theft and recovery counts as dependent variable with significantindependent variables

Year Variables Stolen motor vehicle count Recovered motor vehicle count

B t R2 B T R2

2004 Church Parking lots 1.24 3.15*** 0.70 0.70 8.9*** 0.79

Median Household Income −0.0001 −5.31*** −0.0001 −2.43***Percent Poverty 0.68 2.41** 0.29 3.98***

Vacant Sites 0.11 4.72*** 0.03 4.24***

Median House Value 0.0001 3.67*** N.S

2005 Church Parking lots 1.41 3.35*** 0.64 1.05 8.4*** 0.76

Median Household Income −0.0001 −4.66*** N.S

2006 Percent Poverty 0.68 2.24* 0.39 3.43***

Vacant Sites 0.11 4.8*** 0.02 3.22***

Median House Value 0.0001 3.29*** N.S

Auto shops 1.10 2.58** N.S

2006 Church Parking lots 2.03 3.32*** 0.61 1.34 7.96*** 0.75

Median Household Income −0.0001 −5.66 −0.001 −2.75**Percent Poverty N.S 0.28 2.29*

Vacant Sites 0.16 4.46*** 0.04 4.89***

Median House Value 0.0001 4.63*** N.S

Persons between the ages (15 to 29) N.S −0.001 −2.51**2007 Church Parking lots 2.63 5.33*** 0.64 1.28 7.83*** 0.71

Median Household Income −0.0001 −4.52*** 0.001 −2.16*Percent Poverty 0.84 2.38** 0.26 2.20*

Vacant Sites 0.11 4.16*** 0.03 3.18***

Median House Value 0.0001 2.65*** N.S

Persons age 15–29 N.S −0.001 −2.17*

*** 0 p≤ .001, ** 0 p≤ .01, * 0 p≤ .05N.S = not significant

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Discussion and Conclusion

The overall pattern ofmotor vehicle thefts is shown to be highly clustered in certain partsof the city. These areas with high levels of motor vehicle theft are also areas of thecommunity displaying significantly higher rates of indicators of social disorganization.Standard deviational maps and local spatial autocorrelation (LISA) and calculation ofMoran’s I indicate hotspots of stolen and recovered motor vehicles with significantspatial outliers. The results indicate that recovery locations’ spatial dependency isslightly higher than the spatial dependency for stolen motor vehicle locations.

Perhaps the most interesting finding here is that neighborhoods with a greaternumber of church parking lots present are those with higher rates of motor vehicletheft. As previously shown by others (Arizona Criminal Justice Commission, 2004;Hollinger & Dabney, 1999; Plouffe & Sampson, 2004; Weisel et al., 2006) motorvehicle theft is most common in areas with large numbers of vehicles in congregatelocations. Similarly, Lu (2006) shows that concentrations of potential targets—atproperties such as multi-family residences and commercial parking lots—are hotspotsfor thefts. Interestingly, however, Lu (2006) did not include in her analyses land usefor community services (including churches). As such, the present finding that a highconcentration of church parking lots in socially disorganized neighborhoods appear tofunction as what P. Brantingham and P. Brantingham (1995) refer to as “crimeattractors” is not necessarily at odds with her findings. In actuality, such locationsshare the critical characteristics of low or no observation/guardianship and presenceof large numbers of vehicles that may serve to attract thieves. It may be that elevatedrates of motor vehicle theft in neighborhoods with higher number church parking lots,and vacant dwellings occur because on certain times and days such places attractnumerous vehicles that remain collectively unguarded for significant periods of time.Parking lots of churches are common property which provides opportunities for manyto be present and unnoticed on certain days and certain times. As already mentionedparking lots of churches located in and around a socially disorganized neighborhoodcreate a confounding factor to attract auto thieves. Community variations in socialdisorganization transmit much of the effect of community structural characteristics(Sampson & Groves, 1989) on elevated auto theft. Community structural issues inwest Louisville, the Center Business District and the locations of the Fair & ExpoCenter includes residential instability, racial heterogeneity, higher concentrations ofrental and scattered housing, with economically challenged populations which mayexplain the reason why motor vehicle theft and recovery is elevated in these areas. Assummarized by Walsh and Taylor (2007a, p. 65), what is especially lacking areexaminations “addressing the relationship between (motor vehicle theft) and com-munity structure.” The present study is a step forward in filling this gap.

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Geetha Suresh is an Assistant Professor in the Department of Justice Administration at the University ofLouisville. Her research centers on the relationship of place and crime and economics of crime.

Richard Tewksbury is Professor of Justice Administration at the University of Louisville. His researchfocuses on issues of community structure and crime, formal and informal responses to labeled offendersand issues of identity development.

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