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#REDIRECT [[Geospatial analysis]]
{{morefootnotes|date=June 2009}}
'''Geospatial predictive modeling''' is conceptually rooted in the principle that the occurrences of
events being modeled are limited in distribution. Occurrences of events are neither uniform
nor random in distribution – there are spatial environment factors (infrastructure, sociocultural,
topographic, etc.) that constrain and influence where the locations of events occur.
Geospatial predictive modeling attempts to describe those constraints and influences by
spatially correlating occurrences of historical geospatial locations with environmental factors
that represent those constraints and influences. Geospatial predictive modeling is a process
for analyzing events through a geographic filter in order to make statements of likelihood for
event occurrence or emergence.<ref>*Gary P. Beauvais, Douglas A. Keinath, Pilar Hernandez, Larry Master, Rob Thurston. ''[http://www.natureserve.org/prodServices/pdf/EDM_white_paper_2.0.pdf Element Distribution Modeling: A Primer (Version 2)] {{Webarchive|url=https://web.archive.org/web/20160303185712/http://www.natureserve.org/prodServices/pdf/EDM_white_paper_2.0.pdf |date=2016-03-03 }}'', Natureserve, Arlington, Virginia, June 1, 2006, last referenced December 29, 2009</ref>
<ref>*Donald Brown, Jason Dalton, and Heidi Hoyle. ''[https://doi.org/10.1007%2F978-3-540-25952-7_33 Spatial forecast methods for terrorist events in urban environments]'', In Proceedings of the Second NSF/NIJ Symposium on Intelligence and Security Informatics, Lecture Notes in Computer Science, pages 426–435, Tucson, Arizona, Springer-Verlag Heidelberg, June 2004.</ref>
== Predictive models ==
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