Applying Spatial Randomness To Community Inclusion

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 ’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.