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Locations of Motor Vehicle Theft and Recovery

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Abstract

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

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Notes

  1. An important definitional issue is seen in the common references in both public discourse and scholarly literature to “auto theft” or “stolen cars”. As Weisel et al. (2006) point out though, vehicle theft involves all types of motor vehicles, not just cars. In fact, only about three-quarters of stolen vehicles are cars (Federal Bureau 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.

  2. One limitation of this study is that our data is restricted to examination of recovered motor vehicles that are recovered in the same year that they are stolen; we are not able to consider lagged recovery in subsequent years.

  3. Standard Deviational Map: A standard deviational map groups observations according to where their value fall on the standardized range, expressed as standard deviational units away from the mean. A standardized variable has a mean of zero and a standard deviation of 1, by construction. Hence a standardized value can be interpreted as multiples of standard deviational units (Anselin, 2003). Number of data in each category depends on the distribution of the data. Areas with points more than 2 standard deviations are spatial outliers

  4. Moran’s I statistics: Moran’s I statistics indicates spatial autocorrelation and clustering across time. Spatial autocorrelation is the similarity between two observations of a measured variable based upon their spatial location (Griffith, 1992; Legendre, 1993; Lennon, 2000; Fortin et al., 2002) across time. Moran’s I is a conventional measure of auto correlation, values ranging from −1 to 1 depending on the degree and direction of autocorrelation. (+1 indicates strong positive spatial autocorrelation, 0 indicates random spatial ordering and −1 indicates strong negative spatial autocorrelation). The interpretation of Moran’s I is similar to the nonspatial correlation coefficient.

  5. LISA Maps: LISA (Local Indicators of Spatial Association) could be useful in assessing significant local spatial clustering around an individual location (spatial heterogeneity) and the indication of pockets of spatial non-stationarity (Anselin, 1995). In the map the high-high (in red) shows neighbors with high values of same attribute. Positive clustering of similar values of high (red) or low (blue) indicates spatial heterogeneity and the negative values (spatial clustering of dissimilar values) are indicated as low—high or high—low. For example a location with high values surrounded by neighbors with low value is represented 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.

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Authors and Affiliations

  1. Department of Justice Administration, University of Louisville, Louisville, KY, USA

    Geetha Suresh & Richard Tewksbury

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  1. Geetha Suresh
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  2. Richard Tewksbury
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Correspondence to Richard Tewksbury.

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Suresh, G., Tewksbury, R. Locations of Motor Vehicle Theft and Recovery. Am J Crim Just 38, 200–215 (2013). https://doi.org/10.1007/s12103-012-9161-7

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