School of Law, Faculty of Social Sciences
Advancing Analytics for Police Resource Deployment
LIDA Data Scientist Internship Programme
To date, predictive policing analytics have concentrated on identifying locations at an increased risk of future crime events. The implicit assumption of these techniques is that all crimes are created equal in terms of prospective resource allocation. However, the practical allocation of crime reduction resources is a considerably more nuanced enterprise which requires practitioners to balance multiple constraints including crime risk, seriousness/harm, and resourcing impact of response.
Building on the ongoing N8 PRP Project – Analytics for Resource Deployment, this project aims to develop new predictive policing analytics capable of incorporating event-weighting schema into spatio-temporal forecasts of crime. These techniques will allow forecasted crime risks to be weighted based on a range of crime and harm reduction priorities. For example, serious offences against the person may be weighted more strongly than property crimes. This approach will also produce multiple forecasts; the second half of the internship will explore how forecasts might be combined using ensemble approaches to solve multiple constraints problems. These efforts will support the design and evaluation of new decision support systems that better reflect the diverse demands of police and their partners.
- Develop methods for weighting prospective mapping algorithms based on various metrics (e.g. seriousness, estimated harm, resource availability);
- Incorporate these extensions into existing predictive policing analytics developed through the N8 PRP Analytics for Resource Deployment project.
- Explore the application of ensemble prediction approaches that combine and where appropriate select outputs from multiple forecasts of risk, seriousness, resourcing etc.
- Explore how analytics can be best delivered for operational evaluation in real world policing environments (via dashboards etc.)