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Application of Tracking Signals to Detect Time Series Pattern Changes in Crime Mapping Systems
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Author(s): Wilpen L. Gorr (Carnegie Mellon University, USA)and Shannon A. McKay (Massachusetts Institute of Technology, USA)
Copyright: 2005
Pages: 12
Source title:
Geographic Information Systems and Crime Analysis
Source Author(s)/Editor(s): Fahui Wang (Northern Illinois University, USA)
DOI: 10.4018/978-1-59140-453-8.ch010
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Abstract
Tracking signals are widely used in industry to monitor inventory and sales demand. These signals automatically and quickly detect departures in product demand, such as step jumps and outliers, from “business-as-usual”. This chapter explores the application of tracking signals for use in crime mapping to automatically identify areas that are experiencing changes in crime patterns and thus may need police intervention. Detecting such changes through visual examination of time series plots, while effective, creates too large a workload for crime analysts, easily on the order of 1,000 time series per month for medium-sized cities. We demonstrate the so-called smoothed-error-term tracking signal and carry out an exploratory validation on 10 grid cells for Pittsburgh, Pennsylvania. Underlying the tracking signal is an extrapolative forecast that serves as the counterfactual basis of comparison. The approach to validation is based on the assumption that we wish tracking signal behavior to match decisions made by crime analysts on identifying crime pattern changes. We present tracking signals in the context of crime early warning systems that provide wide-area scanning for crime pattern changes and detailed drill-down maps for crime analysis. Based on preliminary results, the tracking signal is a promising tool for crime analysts.
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