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.
journal contributionposted on 1999-01-01, 00:00 authored by Wilpen Gorr, Andreas M. Olligschlaeger, Janusz Szczypula, Yvonne Thompson
Organizations in the private sector must do strategic planning over long-term horizons to locate new facilities, plan new products, develop competitive advantages, and so forth. Consequently, long-term forecasts of demand, costs of raw materials, etc. are important in the private sector. There is no such strategic counterpart to police work; consequently, long-term forecasts are of little value to police. Police primarily need short-term forecasts; for example, crime levels one week or one month ahead. Currently, police mostly respond to new crime patterns as they occur. Client-server computing for realtime access to police records and computerized crime mapping have made it possible for police to keep abreast with crime. With short-term forecasting police may be able to get one step ahead of criminals by anticipating and preventing crime. The organization of this paper proceeds first with a description of short-term forecasting models, to provide basic terms and concepts. Next is a discussion of unique features of crime space-time series data, and the need for data pooling to handle small-area model estimation problems. Lastly are a discussion of particular forecasting requirements of police and a summary.