By

This report provides the results from the second of two grants funded by the National Institute of Justice for research on the new field of crime forecasting. This second grant replicates results from the first grant using new data, including crime data from a second city, and develops and evaluates advanced crime forecasting models. Our test bed for comparing and evaluating forecast methods and models now includes 6 million offense incident reports and CAD calls from Pittsburgh, Pennsylvania and Rochester, New York, which we have processed into monthly time series data over the period 1990 through 2001 and five geographies (census tracts, 4,000 foot grid cells, car beats, an aggregation of car beats we call car beats plus, and precincts) for 24 crime types. We expanded our crime forecasting methods and models from the our original set of so-called naïve methods, univariate methods, and a single-lag, leading indicator model estimated via linear regression and non-linear neural network to include 1) a multivariate model for estimating crime seasonality based on demographic and land use demographics and 2) leading indicator models with 4 and 12 time lags. We also introduce an application of tracking signals as a supporting crime analysis tool to automatically detect crime serie