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Applying Spatial Randomness To Community Inclusion

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dc.contributor.author Wolf-Branigin, Michael
dc.date.accessioned 2009-02-11T18:59:27Z
dc.date.available 2009-02-11T18:59:27Z
dc.date.issued 2002 en_US
dc.identifier.uri https://hdl.handle.net/1920/3445
dc.description http://tbf.coe.wayne.edu/jmasm/ en_US
dc.description.abstract A spatial analytic methodology incorporating true locations is demonstrated using Monte Carlo simulations as a complement to current psychometric and quality of life indices for measuring community inclusion. Moran 'sl,a measure of spatial autocorrelation, is used to determine spatial dependencies in housing patterns for multiple variables, including family/friends involvement in future planning, home size, and earned income. Simulations revealed no significant spatial autocorrelation, which is a socially desirable result for housing locations for people with disabilities. Assessing the absence of clustering provides a promising methodology for measuring community inclusion.
dc.language.iso en_US en_US
dc.subject Spatial analysis en_US
dc.subject Monte Carlo methods en_US
dc.subject community inclusion en_US
dc.subject Spatial randomness en_US
dc.title Applying Spatial Randomness To Community Inclusion en_US
dc.type Article en_US


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