An open-data, agent-based model of alcohol related crime

Redfern, Joseph, Sidorov, Kirill ORCID: https://orcid.org/0000-0001-7935-4132, Rosin, Paul L. ORCID: https://orcid.org/0000-0002-4965-3884, Moore, Simon C. ORCID: https://orcid.org/0000-0001-5495-4705, Corcoran, Padraig ORCID: https://orcid.org/0000-0001-9731-3385 and Marshall, David 2017. An open-data, agent-based model of alcohol related crime. Presented at: 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), Lecce, 29 August - 1 September 2017. Advanced Video and Signal Based Surveillance (AVSS), 2017 14th IEEE International Conference. IEEE Xplore, 10.1109/AVSS.2017.8078513

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Abstract

The allocation of resources to challenge city centre violent crime traditionally relies on historical data to identify hot-spots. The usefulness of such data-driven approaches is limited when historical data is scarce or unavailable (e.g. planning of a new city) or insufficiently representative (e.g. does not account for novel events, such as Olympic Games). In some cities, crime data is not systematically accumulated at all. We present a graph-constrained agent based simulation model of alcohol-related violent crime that is capable of predicting areas of likely violent crime without requiring any historical data. The only inputs to our simulation are publicly available geographical data, which makes our method immediately applicable to a wide range of tasks, such as optimal city planning, police patrol optimisation, devising alcohol licensing policies. In experiments, we evaluate our model and demonstrate agreement of our model's predictions on where and when violence will occur with real-world violent crime data. Analyses indicate that our agent based model may be able to make a significant contribution to attempts to prevent violence through deterrence or by design.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics Dentistry
Publisher:	IEEE Xplore
ISBN:	978-1-5386-2939-0
Date of First Compliant Deposit:	31 October 2017
Date of Acceptance:	23 October 2017
Last Modified:	05 Jan 2024 02:06
URI:	https://orca.cardiff.ac.uk/id/eprint/106075

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