Despite growing research
for residential crowding effects on housing market and public health
perspectives, relatively little attention has been paid to explore and model
spatial patterns of residential crowding over space. This paper focuses upon
analyzing the spatial relationships between residential crowding and
socio-demographic variables in Alexandria neighborhoods, Egypt. Global and
local geo-statistical techniques were employed within GIS-based platform to
identify spatial？variations of
residential crowding determinates. The global ordinary least squares (OLS)
model？assumes homogeneity of
relationships between response variable and explanatory variables？across the study area. Consequently,
it fails to account for heterogeneity of spatial relationships. Local model
known as a geographically weighted regression (GWR) was also employed using the
same？response variable and
explanatory variables to capture spatial non-stationary of residential？crowding. A comparison of the outputs
of both models indicated that OLS explained 74 percent of？residential crowding variations while
GWR model explained 79 percent. The GWR improvedstrength of the model and
provided a better goodness of fit than OLS. In addition, the findings of this
analysis revealed that residential crowding was significantly associated with
different structural measures particularly social characteristics of household
such as higher education and illiteracy. Similarly, population size of
neighborhood and number of dwelling rooms were found to have direct impacts on
residential crowding rate. The spatial relationship of these measures
distinctly varies over the study area.
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