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Risky Facilities: Crime Radiators or Crime Absorbers? A Comparison of Internal and External Levels of Theft

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Abstract

Objectives

To undertake the first exploration of the nature of the relationship between internal crime (those that happen within facilities) and external crime (those occurring outside but in the nearby locale of facilities). The following questions are addressed. Do those localities that suffer high volumes of crime internally within their facilities also suffer high levels of crime in their immediate external environment? How is this influenced by the distribution of internal theft across facilities? What are the likely mechanisms for any relationship found?

Methods

Spatial regression is used to explore these relationships using data for 30,144 incidents of theft from a Metropolitan area of the UK arranged into small 50 × 50 m grid squares. Variables used in the analysis include counts of external and internal theft, counts of victimized and ‘risky’ facilities, indicators of land-use and a proxy for the on-street population.

Results

There is found to be a strong positive relationship between internal and external theft that appears to be strengthened by the existence of facilities suffering particularly high crime volumes. Results suggest that internal theft problems precede external ones and that the physical concentration of chronically risky facilities is a particularly strong predictor of external theft problems.

Conclusions

An argument is made that risky facilities act as crime ‘radiators’, causing crime in the immediate environment as well as internally. This has implications for crime prevention policy in terms of facility placement and management.

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Notes

  1. In this research, land-use is used in a broad sense and it is taken to mean the defined function of a piece of land or a place.

  2. There are also crime ‘enablers’ that occur when there is a lack of regulation or enforcement of rules (Clarke and Eck 2005).

  3. Note for example that this sample is 40 % of the size of the robbery data set used by Bernasco and Block (2011) in their large scale analysis of 25,000 census blocks in Chicago.

  4. It is possible that a number of uncategorised ‘facilities’ were in fact private residential properties. However, for a large number of these, this seems unlikely for two reasons. First, the theft types involved: snatches, pick pocketing and other theft are by definition supposed to occur outside the home, so any of these crimes would be a misclassification. Second, the study area was located in a central business district, and hence did not contain many residential properties.

  5. Here, for convenience ‘victimized facilities’ are those properties that had one or more recorded thefts in the data set time scale.

  6. As an additional precaution, the test regression was also re-run for two transformed versions of the dependent variable (the square root of the count of external crime and the logarithm of 0.1+ the external crime count). This was to check that the use of raw crime count data didn’t affect the results of analysis. Similar results, with the same independent variables demonstrating significance in the same direction, were found using these alternatives.

  7. Note there is fairly large attrition in the number of facilities that can be included in the analyses as risker facilities are chosen.

  8. These were used rather than the cumulative categories in Table 4 so that the variables would be independent of each other.

  9. The cut-off point for identifying the most risky grid squares as those in the top 20th percentile for external theft was chosen because it sits well with the 80-20 rule which is often applied when discussing risky facilities

  10. The ability of a risky facility to radiate risk is presumably unaffected by whether it is a crime attractor or a crime generator.

  11. Ekblom’s commentary on this can be found in ‘Crime Risk Assessment/Crime Impact Assessment’ available at: http://www.designagainstcrime.com/files/crimeframeworks/Crime_risk_asssement_EKBLOM.pdf. Last accessed on 19 oct 2011.

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Acknowledgments

This work was supported by AHRC Research Grant, Ref No: 120340. Thanks goes to the UK Police Force that provided data for the research. Thanks to all those that have made suggestions or given advice including John Eck, Shane Johnson, Ken Pease, Aiden Sidebottom and Lusine Tarhanyan.

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Correspondence to Kate Bowers.

Appendices

Appendix 1

See Table 11.

Table 11 Relationship between external theft, internal theft and risky facilities: 100 × 100 m grid squares (n = 49)

Appendix 2

See Table 12.

Table 12 Relationship between external theft, internal theft and risky facilities: negative binomial regression (n = 210)

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Bowers, K. Risky Facilities: Crime Radiators or Crime Absorbers? A Comparison of Internal and External Levels of Theft. J Quant Criminol 30, 389–414 (2014). https://doi.org/10.1007/s10940-013-9208-z

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