“Hot street” of crime detection in London borough and lockdown impacts

ABSTRACT

In recent years, the police intervention strategy “Hot spots policing” has been effective in combating crimes. However, as cities are under the intense pressure of increasing crime and scarce police resources, police patrols are expected to target more accurately at finer geographic units rather than ballpark “hot spot” areas. This study aims to develop an algorithm using geographic information to detect crime patterns at street level, the so-called “hot street”, to further assist the Criminal Investigation Department (CID) in capturing crime change and transitive moments efficiently. The algorithm applies Kernel Density Estimation (KDE) technique onto street networks, rather than traditional areal units, in one case study borough in London; it then maps the detected crime “hot streets” by crime type. It was found that the algorithm could successfully generate “hot street” maps for Law Enforcement Agencies (LEAs), enabling more effective allocation of police patrolling; and bear enough resilience itself for the Strategic Crime Analysis (SCA) team’s sustainable utilization, by either updating the inputs with latest data or modifying the model parameters (i.e. the kernel function, and the range of spillover). Moreover, this study explores contextual characteristics of crime “hot streets” by applying various regression models, in recognition of the best fitted Geographically Weighted Regression (GWR) model, encompassing eight significant contextual factors with their varied effects on crimes at different streets. Having discussed the impact of lockdown on crime rates, it was apparent that the land-use driven mobility change during lockdown was a fundamental reason for changes in crime. Overall, these research findings have provided evidence and practical suggestions for crime prevention to local governors and policy practitioners, through more optimal urban planning (e.g. Low Traffic Neighborhoods), proactive policing (e.g. in the listed top 10 “Hot Streets” of crime), publicizing of laws and regulations, and installations of security infrastructures (e.g. CCTV cameras and traffic signals).

KEYWORDS:

• Hot street
• crime
• kernel density estimation
• spatial regression
• lockdown
• mobility change

Acknowledgements

The authors would like to thank Mr Broca Sandeep, the Intelligence Analysis Manager from the Haringey local authority, for providing valuable suggestions in this research.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data Availability Statement

Data available on request from the authors. The data that support the findings of this study are available from the corresponding author, upon reasonable request.

Additional information

Funding

This work is partly supported by King’s Global Engagement Partnership Fund [2020-2021 #PF2021_Mar_005].

Notes on contributors

Yuying Wu

Yuying Wu received her Master’s degree on Urban Informatics from CUSP London, Dept. of Informatics, King’s College London.

Yijing Li

Yijing Li is an Assistant Professor in CUSP London, Dept. of Informatics, King’s College London. She received the PhD degree from University of Cambridge. Her research interests are Geography of Crime, Spatial Analysis & Visualisation and Urban Data Mining.