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's I, 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.
"Applying Spatial Randomness To Community Inclusion,"
Journal of Modern Applied Statistical Methods:
1, Article 14.
Available at: http://digitalcommons.wayne.edu/jmasm/vol1/iss1/14