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How Concentrated Are Police on Crime? a Spatiotemporal Analysis of the Concentration of Police Presence and Crime

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Abstract

Research Question

What were the spatiotemporal patterns of police patrol in a major European city across the pre-COVID year of 2019, how did these patterns change over time, and to what extent did the concentrations of patrol correspond to concentrations of crimes?

Data

We analyzed more than 77 million GPS signals from 130 police patrol cars showing where and when police patrols were present in police districts and street segments. We also plotted location, time and days of the week of the locations, and times of more than 50,000 recorded crimes.

Methods

We calculated concentration ratios within both crimes and patrols relative to their distributions in time and space. We then compared the concentration ratios for crime to the concentration ratios for patrols. We concluded the analysis by comparing the extent to which concentrations of crime and patrol locations and times were overlapping.

Findings

We found that police patrols, much like crime, were concentrated on a small proportion of street segments. Yet spatiotemporal police presence is unrelated to local levels of crime and crime concentration. Relative to temporal crime concentrations, police patrols were substantially under-concentrated from 1500 to 0100 h, all day on Fridays, and the entire months of June, July, August, and December. There was very little overlap in patrol concentrations with crime concentrations.

Conclusions

After three decades of research showing crime prevention benefits of patrol concentrations on micro-level crime concentrations, police in one European city concentrate patrol presence at locations, times, days, and months where crime is not concentrated. Whether this conclusion can be reached in other cities will depend on replications of this study, both in Europe and other continents.

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Availability of Data and Materials

The research data (consisting of police recorded crime data and patrol car GPS data) and code cannot be publicly shared.

Notes

  1. “Fixed” reporting points were distributed across patrol beats for officers to report back to patrol sergeants and to receive intel on their assigned patrol beat. This system offered additional security for officers but came with a certain level of supervision (Wain & Ariel, 2014).

  2. GPS pings describe the frequency with which GPS signals are sent to the receiving unit. Pings vary due to technology and patrol types. Foot patrols are tracked through body worn radios and send signals every 30 s to 5 min. Motor patrol can carry more powerful AVLs, which often have GPS pings of under 10 s (Hutt et al., 2018, p. 343).

  3. Oatley et al. (2019) have not reported the GPS ping of the tracking app used to collect the officer GPS data.

  4. The 130 analyzed patrol cars constitute 38% of the APD vehicle fleet, including unmarked and service cars.

  5. Given that 10% of all streets experience 25% of all crime at time x and that 5% of all streets experience 25% of all crime at time y, it follows that 5% is more concentrated as a lower number of streets experience 25% of crime.

  6. Open access data retrieved from Flemish Roads Register (https://www.vlaanderen.be/)

  7. The percentage levels describe that, for example, 10% of street segments receive 25% of all recorded crime. The levels for police and crime are fixed, as we are interested in the proportion of street segments that receive these levels of police and crime and, thus, examining their concentration.

  8. As there are 21 zones, each zone presents 4.76% of all meso-level places.

  9. Non-zero street segments include all segments that received crime or police presence at least once during the study period.

  10. Higher levels of police are negatively associated with the proportion of segments that hold a certain percentage of police presence, thus, expressing a higher concentration.

  11. The overlap describes the number of segments that were included in the subsets for the whole year. It shows whether one street segment that ranked at least once in h100 or h10 for police presence is within the set of ranked segments for crime (and crime types). The spatiotemporal exact overlap expresses that one segments ranked the same during the same week for both police presence and crime.

  12. Felson questioned the ability of police officers to act as a guardian due to the unlikeliness of their presence as crimes occur infrequently and police beats are hard to cover in their entirety (Felson, 2002)

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Acknowledgements

We would like to thank the Antwerp Police Department (APD) for providing us with the anonymized datasets.

Funding

This work was supported in part by the Ghent University Research Council (UGent-BOF) Interdisciplinary Research Project funding scheme [BOF18/IOP/001 to C.V., T.V.B., F.W.]. Frank Witlox’s contribution was supported by the Estonian Research Council [PUT PRG306 501 to F.W.].

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Authors and Affiliations

  1. Department of Criminology, Criminal Law and Social Law, Ghent University, Universiteitstraat 4, 9000, Ghent, Belgium

    Philipp M. Dau, Tom Vander Beken & Christophe Vandeviver

  2. Department of Geography, Ghent University, Krijgslaan 281 S8, 9000, Ghent, Belgium

    Maite Dewinter & Frank Witlox

  3. Department of Geography, University of Tartu, Vanemuise 46, 51014, Tartu, Estonia

    Frank Witlox

  4. College of Civil Aviation, Nanjing University of Aeronautics and Astronautics, Nanjing, 210016, China

    Frank Witlox

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  1. Philipp M. Dau
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Correspondence to Christophe Vandeviver.

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Appendix

Appendix

Fig. 8
figure 8

Concentration of police presence and crime across police zones

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Fig. 9
figure 9

Concentration of police presence and crime across street segments (a) and non-zero street segments (b)

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Dau, P.M., Dewinter, M., Witlox, F. et al. How Concentrated Are Police on Crime? a Spatiotemporal Analysis of the Concentration of Police Presence and Crime. Camb J Evid Based Polic 6, 109–133 (2022). https://doi.org/10.1007/s41887-022-00079-6

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