Сборник текстов для перевода. Борисова Л.А. - 31 стр.

duced on a daily basis by 911 and police record management systems, police hot
line tips and citizen complaints for signs of impending flare-ups, geographic
displacement or other unusual criminal activity. In other words, proactive law
enforcement needs tools that can anticipate or provide early warning of criminal
patterns so that they may be prevented.
       This dissertation makes a contribution in this area by providing the model
specification and framework, a GIS-based data collection system, and a new
spatio-temporal forecasting method – chaotic cellular forecasting (CCF) – for
use by an early warning system for emerging drug markets.
       The second chapter focuses on the development of a geographic informa-
tion system that provides the underlying data for the dissertation. This practical
application of GIS to narcotics enforcement arose out of the Drug Market
Analysis Program (DMAP) funded by the National Institute of Justice (NIJ). A
by-product of the DMAP program was a very accurate data set consisting of
point (i.e., address) level data on illicit drug market activity and related crimes.
        Chapter 3 is a study employing multiple regression techniques to analyze
the effects of both traditional and ecological variables on illicit drug markets.
The study was in part made possible due to the fact that DMAP includes high
quality location data on ecological variables such as land use and the built envi-
ronment.
        Chapter 4 is an empirical study introducing weighted spatial adaptive fil-
tering which provides evidence that spatial interaction, local context and spa-
tially varying model parameters are important indicators of street level drug
dealing.
        The fifth chapter introduces chaotic cellular forecasting. CCF employs
the findings of the previous chapters and combines chaos theory, artificial neural
networks (ANN's) and grid cell aggregated GIS-based data to produce one-step-
ahead forecasts of street level drug market activity. One of the underlying as-
sumptions of CCF is that spatio-temporal patterns of criminal activity can be
modeled as a chaotic system. Artificial neural networks, more specifically feed-
forward networks with backpropagation, are then used to estimate the forecast-
ing model. Backpropagation models are uniquely qualified for this purpose be-
cause they are self adapting and are universal approximators (Hornik et al.,
1989). Two versions of CCF, one using spatially constant weights (analogous to
spatial regression using spatially constant parameters) and the other a hybrid
model of spatially varying input to hidden unit weights and constant hidden to
output units weights are tested. The results are compared to both a simple and a
state-of-the art spatial regression model using spatially lagged variables and
tested for forecast accuracy on a holdout data sample.
       The sixth and final chapter provides a summary and outlines future work.




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