We develop a leading indicator model for forecasting serious property and violent crimes based on the crime attractor and displacement theories of environmental criminology. The model, intended for support of tactical deployment of police resources, is at the microlevel scale; namely, 1-month-ahead forecasts over a grid system of 141 square grid cells 4000 feet on a side (with approximately 100 blocks per grid cell). The leading indicators are selected lesser crimes and incivilities entering the model in two ways: (1) as time lags within grid cells and (2) time and space lags averaged over grid cells contiguous to observation grid cells. Our validation case study uses 1.3 million police records from Pittsburgh, Pennsylvania, aggregated over the grid system for a 96-month period ending in December 1998. The study uses the rollinghorizon forecast experimental design with forecasts made over the 36-month period ending in December 1998, yielding 5076 forecast errors per model. We estimated the leading indicator model using a robust linear regression model, a neural network, and a proven univariate, extrapolative forecast method for use as a benchmark in Granger causality testing. We find evidence of both the crime attractor and displacement theories. The results of comparative forecast experiments are that the leading indicator models provide acceptable forecasts that are significantly better than the extrapolative method in three out of four cases, and for the fourth there is a tie but poor forecast performance. The leading indicators find 41-53% of large crime volume changes in the three successful cases. The corresponding workload for police is quite acceptable, with on the average 5.2 potential large change cases per month to investigate and with 31% of such cases being positives.
The Informedia Digital Video Library system extracts information from digitized video sources and allows full content search and retrieval over all extracted data. This extracted 'metadata' enables users to rapidly find interesting news stories and to quickly identify whether a retrieved TV news story is indeed relevant to their query. This article highlights two unique features: named faces and location analysis. Named faces automatically associate a name with a face, while location analysis allows the user to visually follow the action in the news story on a map and also allows queries for news stories by graphically selecting a region on the map. The Informedia Digital Video Library ProjectThe Informedia Digital Video Library project [1], initiated in 1994, uniquely utilizes integrated speech, image and natural language understanding to process broadcast video. The project's goal is to allow search and retrieval in the video medium, similar to what is available today for text only.To enable this access to video, fast, high-accuracy automatic transcriptions of broadcast news stories are generated through Carnegie Mellon's Sphinx speech recognition system and closed captions are incorporated where available. Image processing determines scene boundaries, recognizes faces and allows for image similarity comparisons. Text visible on the screen is recognized through video OCR and can be searched. Everything is indexed into a searchable digital video library [2], where users can ask queries and retrieve relevant news stories as results. TheNews-on-Demand collection in the Informedia Digital Library serves as a testbed for automatic library creation techniques of continuously captured television and radio news content from multiple countries in a variety of languages. As of October 1999, the Informedia project had about 1.5 terabytes of news video indexed and accessible online, with over 1600 news broadcasts containing about 40,000 news stories dating back to 1996.The Informedia system allows information retrieval in both spoken language and video or image domains. Queries for relevant news stories may be made with words, images or maps. Faces are detected in the video and can be searched. Information summaries can be displayed at varying detail, both visually and textually. Text summaries are displayed for each news story through topics and titles. Visual summaries are given through thumbnail images, filmstrips and dynamic video skims. Every location referenced in the news stories is labeled for geographic display on a map and the corresponding news item can be retrieved through a map area selection. The system also provides for extraction and reuse of video documents encoded in MPEG-1 format for web-based access and presentation.A multi-lingual component, currently implemented for Spanish and Serb/Croatian corpora, translates English language queries for text search into the target language. English language topics are also assigned to news stories. A user can add spoken or typed annotations to any news sto...
This paper introduces a pattern recognizer, similar to weighting schemes used in combining time series forecasts, for use in spatial adaptive filtering applied to estimating multivariate cross-sectional models. The pattern recognizer enhances the ability to automatically detect and estimate parameters with discontinuous or sharp gradient changes over geographic contexts. Results from Monte Carlo studies suggest that the weighted spatial adaptive filter is at least as accurate as the unweighted filter for cases having smoothly changing parameters, but superior for cases having discontinuous, step-jump parameters. A case study on illicit drugmarket activities using census tract-level data from Pittsburgh, Pennsylvania, further illustrates the advantages of the weighted filter.
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