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Measuring Crime Concentration Across Cities of Varying Sizes: Complications Based on the Spatial and Temporal Scale Employed

An Erratum to this article was published on 04 April 2017

This article has been updated

Abstract

Objectives

We argue that assessing the level of crime concentration across cities has four challenges: (1) how much variability should we expect to observe; (2) whether concentration should be measured across different types of macro units of different sizes; (3) a statistical challenge for measuring crime concentration; (4) the temporal assumption employed when measuring high crime locations.

Methods

We use data for 42 cities in southern California with at least 40,000 population to assess the level of crime concentration in them for five different Part 1 crimes and total Part 1 crimes over 2005–2012. We demonstrate that the traditional measure of crime concentration is confounded by crimes that may simply spatially locate due to random chance. We also use two measures employing different temporal assumptions: a historically adjusted crime concentration measure, and a temporally adjusted crime concentration measure (a novel approximate solution that is simple for researchers to implement).

Results

There is much variability in crime concentration over cities in the top 5 % of street segments. The standard deviation across cities over years for the temporally adjusted crime concentration measure is between 10 and 20 % across crime types (with the average range typically being about 15–90 %). The historically adjusted concentration has similar variability and typically ranges from about 35 to 100 %.

Conclusions

The study provides evidence of variability in the level of crime concentration across cities, but also raises important questions about the temporal scale when measuring this concentration. The results open an exciting new area of research exploring why levels of crime concentration may vary over cities? Either micro- or macro- theories may help researchers in exploring this new direction.

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Fig. 1

Change history

  • 04 April 2017

    An erratum to this article has been published.

Notes

  1. 1.

    This spatial randomness assumption is used to construct an appropriate baseline measure; for a discussion of proper baselines, see (Hipp et al. 2011). And this is consonant with the approach in the literature of assessing the level of crime concentration in cities regardless of the characteristics of smaller geographic units.

  2. 2.

    Given that random chance is driving those in the first category of segments—those that have an unexpectedly high number of crimes in a particular year—it is extremely unlikely that there would be something systematically driving this. If there is something systematic about them, this would imply that they instead belong in the second category, as they were legitimately high crime segments in one year but then changed the following year.

  3. 3.

    If one wished to construct a theory at the level of the micro-units, one would need to posit at least two classes of high crime street segments: (1) consistently high crime segments; (2) variable high crime segments. And this would require an explanation for the existence of these latter segments that vary between high and low crime levels from year to year. Whereas most existing micro-geography theories explain why some locations have more or less crime, this would instead require a theory of some characteristic(s) that cause certain locations to be locked in an equilibrium in which they fluctuate between high and low crime levels from year to year. We are aware of no such theory.

  4. 4.

    In city A, 1 segment has 10 crime incidents and the other 19 have 1, thus: (1*10) + (19*1) = 29 crime incidents. The one segment (the top 5 %) has 10/29 of the crime incidents, or 34.5 %. In city B, 6 segments have 10 incidents and the other 14 have 1, thus: (6*10) + (14*1) = 74 crime incidents. The top segment (the top 5 %), has 10/74 of the crime incidents, or 13.5 %.

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Acknowledgments

This research is supported in part by NIJ Grant 2012-R2-CX-0010.

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Corresponding author

Correspondence to John R. Hipp.

Additional information

An erratum to this article is available at https://doi.org/10.1007/s10940-017-9349-6.

Appendix

Appendix

See Tables 7, 8, 9, 10.

Table 7 Summary statistics for the cities in the analyses (based on various population thresholds)
Table 8 Crime counts for cities in analyses
Table 9 Crime clustering for cities, by five types of crime
Table 10 The 42 cities with at least 40,000 population

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Hipp, J.R., Kim, YA. Measuring Crime Concentration Across Cities of Varying Sizes: Complications Based on the Spatial and Temporal Scale Employed. J Quant Criminol 33, 595–632 (2017). https://doi.org/10.1007/s10940-016-9328-3

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Keywords

  • Neighborhoods
  • Crime
  • Aggregation
  • Imputation