Measuring community inclusion of people with developmental disabilities in a spatial context
The shift of human services from a centralized environment has created the need for strategies focusing on how individuals with disabilities interact with their environment. As community inclusion for people with developmental disabilities increases, the utility of alternative methods for assessing the individual's interactions with their physical environment increases. Using a variety of spatial analytic approaches centering on point pattern analysis, the physical inclusion of people in their communities was assessed. Independent variables included the degree to which people with disabilities had involvement of family, friends and allies in planning their futures; number of people with disabilities residing in their home; and the level of individual earned income. Monte Carlo procedures using Moran's I spatial autocorrelation statistics for both distance band and nearest neighbor did not reveal significant clustering for any of the independent variables. Regression procedures, with individual measures of spatial autocorrelation as the dependent variable, identified proportion of earned income was the independent variable that most explained the degree to which people were physically included in their communities. Logistic regression with presence of earned income as the dependent variable resulted in two significant independent variables, not needing assistance with transportation and number of people in home. People living in smaller homes benefited from residing in the more populated areas of the region. While this reduced their geographic dispersion it resulted in a more random dispersion pattern. Spatial analysis appears promising for measuring the degree that physical inclusion of people with developmental disabilities occurs in their communities. The issues discussed do not purport to assess the degree that people interact with others, only the degree to which people were dispersed geographically throughout their community. Further application of these spatial methods should focus on issues prevalent in the current body of literature, including: the modifiable areal unit problem, sensitivity issues when measuring households versus individuals, integration of spatial statistics with geographical information systems, and improved identification of inclusion through implementing a network analysis approach.
|Year of publication:||
|Authors:||Wolf-Branigin, Michael Emery|
Wayne State University
|Type of publication:||Other|
ETD Collection for Wayne State University
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