Using geographical information systems to organize police patrol routes effectively by grouping hotspots of crash and crime data
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Abstract
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DOI: 10.1016/j.jtrangeo.2013.04.006
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References listed on IDEAS
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Cited by:
- Sukanya Samanta & Goutam Sen & Soumya Kanti Ghosh, 2022. "A literature review on police patrolling problems," Annals of Operations Research, Springer, vol. 316(2), pages 1063-1106, September.
- Shewkar Ibrahim & Tarek Sayed, 2021. "Using Bayesian Tobit Models to Understand the Impact of Mobile Automated Enforcement on Collision and Crime Rates," Sustainability, MDPI, vol. 13(11), pages 1-9, June.
- Castillo-Manzano, José I. & Castro-Nuño, Mercedes & Lopez-Valpuesta, Lourdes, 2024. "Planning traffic surveillance in Spain: How to optimize the management of police resources to reduce road fatalities," Evaluation and Program Planning, Elsevier, vol. 102(C).
- Wu, Xiaoyun & Lum, Cynthia & Koper, Christopher, 2021. "Do everyday proactive policing activities reduce vehicle crashes? Examining a commonly held law enforcement belief using a novel method," Journal of Criminal Justice, Elsevier, vol. 76(C).
- Michela Bonera & Benedetto Barabino & Giulio Maternini, 2022. "A Straightforward Framework for Road Network Screening to Lombardy Region (Italy)," Sustainability, MDPI, vol. 14(19), pages 1-26, September.
- Juan Andrés Cabral, 2021. "Understanding unsolved crimes hotspots: a spatial approach," Asociación Argentina de Economía Política: Working Papers 4445, Asociación Argentina de Economía Política.
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More about this item
Keywords
Kernel Density; DDACTS; Hotspots; Cluster pattern; Crash and crime;All these keywords.
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