Carnegie Mellon University

File(s) stored somewhere else

Please note: Linked content is NOT stored on Carnegie Mellon University and we can't guarantee its availability, quality, security or accept any liability.

Application of Tracking Signals to Detect Time Series Pattern Changes in Crime Mapping Systems

journal contribution
posted on 2003-01-01, 00:00 authored by Wilpen Gorr, Shannon A. McKay
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 paper 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 work load for crime analysts, easily on the order of 1,000 time series per month for mediumsized 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.




Usage metrics


    Ref. manager