Proactive Spatiotemporal Resource Allocation and Predictive Visual Analytics for Community Policing and Law Enforcement

Abstract

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.

HotSketch: Drawing Police Patrol Routes among Spatiotemporal Crime Hotspots

“…This function can be expressed as , in Equation 2, in which is the date of the incident and is the current date. This process bears a resemblance to the Seasonal Trend decomposition based on Loess (STL) approach utilized by Malik et al [10], though there are some noticeable differences. In this example, we specify the individual weights to increase the importance of an event that occurs close to the same day of the week and time of day while deemphasizing the importance of the seasonal bandwidth.…”

“…Linked displays have been employed to capture the combination of spatial and temporal data by allowing the user to select identified hotspots on a map, which populates secondary linked views with representations of the temporal characteristics of the selected region (e.g., frequency for times of day) [8] [9]. By decomposing the individual elements of an event's time and date, Malik et al were able to more accurately model the seasonal variation in critical events and make predictions for future occurrences [10].…”

Community Outreach Using Incident Records and Visual Analytics

“…VALET was originally designed and tested for internal use by a police department to facilitate interactive and visual spatiotemporal exploration of crime, traffic, and civil incident reports (Malik 2014;Wang et al 2017).…”

Novel evaluation metrics for sparse spatio-temporal point process hotspot predictions - a crime case study

“…Spatial KDEs (SKDEs) are ubiquitous in the analysis of crime data (Chainey et al 2008, Malik et al 2014 and offer a generic means of estimating an underlying spatial distribution from a data sample. In the context of crime mapping, the popularity of the SKDE is in part due to the smoothly varying heat maps that it generates (Eck et al 2005, Chainey et al 2008.…”