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#REDIRECT [[Spatial analysis#Geospatial and Hydrospatial 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)]'', Natureserve, Arlington, Virginia, June 1, 2006, last referenced December 29, 2009</ref>
<ref>*Donald Brown, Jason Dalton, and Heidi Hoyle. ''[http://www.springerlink.com/content/9k5tqr6xtb1br393/ 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 ==
There are two broad types of geospatial predictive models: deductive and inductive.
[[Image:Signature_Analyst_Assessment_of_DC.jpg|thumb|right|250px|Crime Forecast of Washington DC. Red and orange colors indicate areas of high risk.]] The risk assessment was generated using an inductive predictive modeling tool.
"You're gay"
=== Inductive method ===
The inductive method relies on the empirically-calculated spatial relationship between
historical or known event occurrence locations and factors that make up the environment
(infrastructure, socio-culture, topographic, etc.). Each event occurrence is plotted in
geographic space and a quantitative relationship is defined between the event occurrence
and the factors that make up the environment. The advantage of this method is that software
can be developed to empirically discover – harnessing the speed of computers, which is
crucial when hundreds of factors are involved – both known and unknown correlations
between factors and events. Those quantitative relationship values are then processed by a
statistical function to find spatial patterns that define high and low suitability areas for event
occurrence.
==See also==
* [[Predictive Analysis]]
* [[Predictive modelling]]
* [[Suitability analysis]]
==References==
{{reflist}}
[[Category:Geographic information systems]]
[[Category:Spatial data analysis]]
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