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Spatio-Temporal Interaction of Urban Crime

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Abstract

Over the past decade, a renewed interest in the analysis of crime hot-spots has emerged in the social and behavioral sciences. Spurred by improvements in computing power, data visualization and geographic information systems, numerous innovative approaches have materialized for identifying areas of elevated crime in urban environments. Unfortunately, many hot-spot analysis techniques treat the spatial and temporal aspects of crime as distinct entities, thus ignoring the necessary interaction of space and time to produce criminal opportunities. The purpose of this paper is to explore the utility of statistical measures for identifying and comparing the spatio-temporal footprints of robbery, burglary and assault. Functional and visual comparisons for spatio-temporal clusters are analyzed across a range of space–time values using a comprehensive database of crime events for Cincinnati, Ohio. Empirical results suggest that robbery, burglary and assault have dramatically different spatio-temporal signatures.

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Notes

  1. The Mantel test is widely used in epidemiology and has been applied to space–time analyses of burglary (Johnson and Bowers 2004b), but will not be used in this analysis. Although the Mantel test does not require the specification of a critical space and time distance, it does require the subjective selection of a constant to use in the Mantel recommended transformation (Jacquez 1996). An argument in favor of the Mantel test could be made because of its insensitivity to edge effects (Johnson and Bowers 2004b) however, a Monte Carlo approach to the Knox test (which is used in this study) has been shown to minimize edge effects (Johnson et al. 2007).

  2. Critical distances represent a subjective threshold value that dictates the spatial and temporal distances where events are defined as “close” in space and time.

  3. The subjectivity issue is not specific to the Knox test, but is also a problem for other methods used in crime analysis. A good example is the selection of the appropriate number of groups in hierarchical or non-hierarchical cluster analysis.

  4. 2,810 assaults includes UCR codes 400, 401, 432, and 810. 1,268 robberies includes UCR codes 301, 303, and 304. 1,489 burglaries includes UCR code 551.

  5. Crime events are assigned a date only. Time of day for these crimes was not available.

  6. All geographic data are projected to the Ohio State Plane coordinate system using North American Datum 1983 and are measured in meters.

  7. It is possible for there to be more nearest neighbors than the total number of crime events. Theoretically, each crime event can have n−1 nearest neighbors in space and n−1 nearest neighbors in time. If all of the potential events are nearest neighbors in both space and time, n−1 = 1,267 in the case of robbery, there are a theoretical (n−1)(n−1) or 1,605,289 space–time k-nearest neighbors.

  8. Results for burglary and assault are not shown due to space limitations.

  9. The critical distances used in this study are in meters for comparative purposes with other studies (i.e. Johnson and Bowers 2004b). Theses critical distances correspond to various subdivisions of a mile (161 m = 1/10 mile, 201 m = 1/8 mile, 268 m = 1/6 mile, 402 m = 1/4 mile, 804 m = 1/2 mile, 1,207 m = 3/4 mile, 1,609 m = 1 mile).

  10. As a neighborhood unit, Over-the Rhine is extremely impoverished, with 95% of its residents living below the federal poverty guidelines. Recent estimates suggest the presence of 500 vacant buildings, 700 vacant lots and 1,667 vacant housing units (5,261 total) in the neighborhood (3CDC 2006). Although the data utilized for this analysis are from 2003, as recently as 2006, neighborhood residents requested that Cincinnati City Council declare a state of emergency in OTR in an effort to qualify for additional state and federal funds to fight crime (Osborne 2006).

  11. See Fig. 2

  12. Readers should carefully note the values on each y-axis.

  13. This can also apply to patrons of a business, particularly if there have been robberies occurring during the closing time of a bar or tavern.

  14. For a more thorough review of crime seasonality, see Landau and Fridman (1993).

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  1. Department of Geography, Indiana University, 702 E. Kirkwood Ave, Student Building 120, Bloomington, IN, 47405-7100, USA

    Tony H. Grubesic & Elizabeth A. Mack

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  1. Tony H. Grubesic
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Correspondence to Tony H. Grubesic.

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Grubesic, T.H., Mack, E.A. Spatio-Temporal Interaction of Urban Crime. J Quant Criminol 24, 285–306 (2008). https://doi.org/10.1007/s10940-008-9047-5

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