Proactive spatiotemporal resource allocation and predictive visual analytics for community policing and law enforcement

Abish Malik, Ross Maciejewski, Sherry Towers, Sean McCullough, David S. Ebert

Research output: Contribution to journalArticlepeer-review

56 Scopus citations

Abstract

In this paper, we present a visual analytics approach that provides decision makers with a proactive and predictive environment in order to assist them in making effective resource allocation and deployment decisions. The challenges involved with such predictive analytics processes include end-users' understanding, and the application of the underlying statistical algorithms at the right spatiotemporal granularity levels so that good prediction estimates can be established. In our approach, we provide analysts with a suite of natural scale templates and methods that enable them to focus and drill down to appropriate geospatial and temporal resolution levels. Our forecasting technique is based on the Seasonal Trend decomposition based on Loess (STL) method, which we apply in a spatiotemporal visual analytics context to provide analysts with predicted levels of future activity. We also present a novel kernel density estimation technique we have developed, in which the prediction process is influenced by the spatial correlation of recent incidents at nearby locations. We demonstrate our techniques by applying our methodology to Criminal, Traffic and Civil (CTC) incident datasets.

Original languageEnglish (US)
Article number6875970
Pages (from-to)1863-1872
Number of pages10
JournalIEEE Transactions on Visualization and Computer Graphics
Volume20
Issue number12
DOIs
StatePublished - Dec 31 2014

Keywords

  • Law enforcement
  • Natural scales
  • Seasonal Trend decomposition based on Loess (STL)
  • Visual analytics

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Computer Graphics and Computer-Aided Design

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