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Predictive spatial modeling of narcotic crop growth patterns

Open-File Report 86-7
By:  and 
https://doi.org/10.3133/ofr867

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Abstract

Spatial models for predicting the geographic distribution of marijuana crops have been developed and are being evaluated for use in law enforcement programs. The models are based on growing condition preferences and on psychological inferences regarding grower behavior. Experiences of local law officials were used to derive the initial model, which was updated and improved as data from crop finds were archived and statistically analyzed. The predictive models are changed as crop locations are moved in response to the pressures of law enforcement.

The models use spatial data in a raster geographic information system. The spatial data are derived from the U.S. Geological Survey's US GeoData, standard 7.5-minute topographic quadrangle maps, interpretations of aerial photographs, and thematic maps. Updating of cultural patterns, canopy closure, and other dynamic features is conducted through interpretation of aerial photographs registered to the 7.5-minute quadrangle base. The model is used to numerically weight various data layers that have been processed using spread functions, edge definition, and categorization.

The building of the spatial data base, model development, model application, product generation, and use are collectively referred to as the Area Reduction Program (ARP). The goal of ARP is to provide law enforcement officials with tactical maps that show the most likely locations for narcotic crops.

Publication type Report
Publication Subtype USGS Numbered Series
Title Predictive spatial modeling of narcotic crop growth patterns
Series title Open-File Report
Series number 86-7
DOI 10.3133/ofr867
Year Published 1986
Language English
Publisher U.S. Geological Survey
Contributing office(s) Earth Resources Observation and Science (EROS) Center
Description 5 p.
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