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The Influence of Stadia and the Built Environment on the Spatial Distribution of Crime

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Abstract

Objectives

The aim of this study was to explore the influence of “micro-” (e.g., pubs and fast-food restaurants) and “super-facilities” on area level counts of crime. Soccer stadia were selected as an example of a super-facility as their episodic use provides conditions not unlike a natural experiment. Of particular interest was whether the presence of such facilities, and their influence on the flow of people through neighborhoods on match days affects crime. Consideration was also given to how the social composition of a neighborhood might influence crime.

Methods

Crime, street network, and points of interest data were obtained for the areas around five UK soccer stadia. Counts of crime were computed for small areal units and the spatial distribution of crime examined for match and non-match days. Variables derived from graph theory were generated to estimate how micro-facilities might influence the movement flows of people on match days. Spatial econometric analyses were used to test hypotheses.

Results

Mixed support was found for the influence of neighborhood social composition on crime for both match and non-match days. Considering the influence of facilities, a selective pattern emerged with crime being elevated in those neighborhoods closest to stadia on match but not non-match days. Micro-facilities too were found to influence crime levels. Particularly clear was the finding that the influence of pubs and fast-food restaurants on estimated movement flows to and from stadia on match (but not non-match) days was associated with area level crime.

Conclusions

Our findings provide further support for ecological theories of crime and how factors that influence the likely convergence of people in urban spaces affect levels of crime.

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Notes

  1. For example, fan surveys for the 2005/2006 through the 2009/2010 seasons of professional soccer in the UK indicate that approximately one-quarter to one-third of all fans used public transport to attend matches during this period. Moreover, roughly one-half of all supporters go to a pub, and one-quarter of all supporters eat outside the stadium and get fast food prior to match kickoffs.

  2. Discussions with ACPO lead for soccer in England and Wales, along with UK Football Policing Unit Director, and local match day police commanders for each respective area helped ensure that there had been no variation in policing tactics or crime recording policies across the different areas during the study period.

  3. To avoid any potential confounders for the clubs located in Sheffield (these clubs were 4.98 km apart) we eliminated potential comparison days that were match days at the other stadium as well as eliminating any other dates where alternative types of events took place at any of the relevant stadia in the study.

  4. Stadium-specific crime count totals are available in “Appendix 1” section.

  5. Stadium-specific and facility-specific “spillover” effects are available in “Appendix 2” section.

  6. Summary statistics from each OA to each specific stadium are available in “Appendix 3” section.

  7. Golledge (1995) conducted experimental research on path selection and route preference in human navigation. He found that compared to a variety of contending alternatives, including the simplest route or following the longest line, the shortest path is most influential in route choice selection between activity nodes (see also, Conroy-Dalton and Bafna 2003).

  8. Descriptive statistics for each respective stadium are provided in “Appendix 3” section.

  9. The ethnic groups used were based on the ONS classification system as follows: White British, White Irish, other White, White and Black Caribbean, White and Black African, other mixed, Indian, Pakistani, Bangladeshi, other Asian, Caribbean, African, other Black, Chinese, and other ethnic background.

  10. Statistics for social disorganization variables for each respective area are provided in “Appendix 4” section.

  11. Models for each respective stadium are provided in “Appendix 5” section.

  12. Results from these diagnostic tests (and diagnostic plots) confirmed that negative binomial models were more appropriate than Poisson equivalents (Long and Freese 2006) and that there was general consensus across each of the stadium areas in terms of direction of effect, relatively small size of the standard errors, and overall in(equality) of coefficients between match and comparison days.

  13. One concern of the approach adopted herein is the issue of multiplicity (Type I statistical error) that occurs when making multiple comparisons. However, the stage 1 model results were largely consistent (and in line with expectation) and this regularity in itself represents a test of whether the findings are likely to reflect Type I error. For this reason, we felt that using a Bonferroni adjustment would be unnecessarily conservative (see Rothman 1990; Savitz and Olshan 1995; Perneger 1998).

  14. VIFs can be estimated using a standard or generalized method. The MASS package in R (used here) selects the relevant VIF, given the data analyzed.

  15. The differences observed for OAs with more than four pubs are not significant as the 95% confidence interval extends below zero. Tables that include the AMEs for the full distribution of pubs, and other variables of interest included in our model are available upon request.

  16. This result is consistent with Kurland et al. (2018) in which a custom-made non-parametric permutation approach was used to quantify the spatial extent of differences in the count of crime events across both match and non-match days.

  17. We did not test theories of social disorganization here, but the results suggest that crime was lower in areas with community characteristics that are associated with social control. As such, in locating facilities, account might be taken of the social fabric of communities as well as their physical characteristics.

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Authors and Affiliations

  1. National Center for Spectator Sports Safety and Security, The University of Southern Mississippi, 118 College Drive #5193, Hattiesburg, MS, 39406, USA

    Justin Kurland

  2. Department of Security and Crime Science, University College London, London, UK

    Shane D. Johnson

Authors
  1. Justin Kurland
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Correspondence to Justin Kurland.

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Appendices

Appendix 1

See Table 5.

Table 5 Total crime counts for each stadium on match and comparison days, percentage of OAs that experienced one more crimes, and of variation in crime counts across the OAs (2005–2010)
Full size table

Appendix 2

See Table 6.

Table 6 The percentage of OAs with facility of each type and variation in the number of facilities in each OA, variation in the exposure of an OA to facilities in contiguous OAs
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Appendix 3

See Table 7.

Table 7 Descriptive statistics for the shortest network distance (SND) each OA was from the relevant stadium, estimates of movement potential for OAs in each of the five study areas
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Appendix 4

See Table 8.

Table 8 Social disorganization independent variables used to characterize the OAs for the five study areas
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Appendix 5

See Table 9.

Table 9 Negative binomial regression of crime counts per OA around the five stadia (coefficients are Incidence Rate Ratios)
Full size table

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Kurland, J., Johnson, S.D. The Influence of Stadia and the Built Environment on the Spatial Distribution of Crime. J Quant Criminol 37, 573–604 (2021). https://doi.org/10.1007/s10940-019-09440-x

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