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Application of Tracking Signals to Detect Time Series Pattern Changes in Crime Mapping Systems
Any type of content formally published in an academic journal, usually following a peer-review process.
posted on 01.01.2003by 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.