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The local neighborhood forms an integral part of our lives. It provides the context through which social networks are nurtured and the foundation from which a sense of attachment and cohesion with fellow residents can be established. Whereas much of the previous research has examined the role of social and demographic characteristic in relation to the level of neighboring and cohesion, this paper explores whether particular environmental features in the neighborhood affect social porosity. We define social porosity as the degree to which social ties flow over the surface of a neighborhood. The focus of our paper is to examine the extent to which a neighborhood's environmental features impede the level of social porosity present among residents. To do this, we integrate data from the census, topographic databases and a 2010 survey of 4,351 residents from 146 neighborhoods in Australia. The study introduces the concepts of wedges and social holes. The presence of two sources of wedges is measured: rivers and highways. The presence of two sources of social holes is measured: parks and industrial areas. Borrowing from the geography literature, several measures are constructed to capture how these features collectively carve up the physical environment of neighborhoods. We then consider how this influences residents' neighboring behavior, their level of attachment to the neighborhood and their sense of neighborhood cohesion. We find that the distance of a neighborhood to one form of social hole–industrial areas–has a particularly strong negative effect on all three dependent variables. The presence of the other form of social hole–parks–has a weaker negative effect. Neighborhood wedges also impact social interaction. Both the length of a river and the number of highway fragments in a neighborhood has a consistent negative effect on neighboring, attachment and cohesion.

In this paper we present a new methodology by which regional employment forecasts can be spatially disaggregated to smaller administrative units. We develop a statistical model for disaggregating spatial data based upon related employment determinants (for example, the proximity of an area to a shopping centre), demonstrating there is a degree of spatial dependence and spatial heterogeneity in relationships. Applying an advanced statistical procedure, Geographically Weighted Regression (GWR), to account for these spatial effects this method utilises the locally fitted relationships to estimate employment numbers at the smaller geography whilst being constrained by the regional forecast. Results demonstrate that our GWR method generates superior estimates over a global regression model for spatially disaggregating regional employment forecasts.

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