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Chen, H, Gao, X, Li, H and Yang, Z (2023) A framework for the optimal deployment of police drones based on street-level crime risk. Applied Geography, 162. ISSN 0143-6228
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A framework for the optimal deployment of police drones based on street-level crime risk.pdf - Published Version Available under License Creative Commons Attribution. Download (9MB) | Preview |
Abstract
Drones are increasingly adopted for policing in many countries, as they can aid police officers to detect hazards and respond to incidents with timely and low-cost services. However, the planning and deployment of police drones are subject to several challenges, including the proper distance metric for drone flying and the risk-based location optimisation of drone base stations. This study proposes a new framework that enables the optimal deployment of police drones to address crime risk issues on urban street networks. This risk-based decision framework takes into account three potential distance metrics that regulate and shape the flying routes of drones, which in turn affects the optimal location of drone base stations. In addition, this framework takes into account the major risk constraints of flying drones in urban areas, including domestic privacy and elevation. The proposed risk-based decision framework is validated using the real case study of Liverpool with historical crime data and street network layouts. The findings contribute to the operations and management of police drones in urban areas and shift the paradigm of policing drones towards a risk-based regime.
| Item Type: | Article |
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| Uncontrolled Keywords: | 0406 Physical Geography and Environmental Geoscience; 1604 Human Geography; Geography |
| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) H Social Sciences > HV Social pathology. Social and public welfare. Criminology > HV7231 Criminal Justice Administrations > HV7551 Police. Detectives. Constabulary |
| Divisions: | Engineering |
| Publisher: | Elsevier |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 10 Jul 2024 15:53 |
| Last Modified: | 10 Jul 2024 16:00 |
| DOI or ID number: | 10.1016/j.apgeog.2023.103178 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/23739 |
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