Anticipating the Next Half‐Century of Geographical Analysis

Prompted by a series of panel sessions at a recent American Association of Geographers annual meeting entitled “A Globe‐Shaped Crystal Ball: The Next Fifty Years of Geographical Analysis,” participants were asked to speculate on the future of the journal, which of course has broader implications for spatial and geographic analytics. In what follows, I provide my thoughts on the journal as a reader, contributor, referee, and former editor of Geographical Analysis. The major points touched upon include the following. First, application to address substantive concerns will come to dominate the field. Second, the spatial data deluge will continue unabated, but will lead to important advances because of better detail and less abstraction of reality. Third, analytical methods will evolve specifically for big data. Fourth, the point‐and‐click revolution will result in ever more use of spatial analytics, but also will lend itself to greater and more widespread abuse of these methods. Fifth, addressing assumptions and theoretical foundations of long utilized approaches will revolutionize a new generation of spatial analytics. Sixth, geographic uncertainty and bias will be more than an afterthought, and methods will emerge to support meaningful analysis. Finally, spatial optimization will have increased prominence in fundamental analysis, particularly associated with establishing and evaluating significance.     

Contextualizing Welfare's Work Disincentive: The Case of Female-headed Family Poverty

The conventional model of the relationship between welfare incentives and poverty rates holds that welfare payments produce an income-enhancement effect that removes families from poverty until some threshold. Beyond this point increased payments engender a work-disincentive effect resulting in increased rates of poverty. We challenge the model's assumption that poverty levels are a simple and spatially invariant response to welfare incentives, contending instead that local employment conditions may substantially alter the relation. Our reformulation of the conventional modeling approach is based on the expansion method. Specifically, we extend the model to include the effects of local labor-market conditions on the response of poverty levels to welfare incentives. In contesting the invariance assumption, the expansion method allows us to determine where and in what contexts welfare is “work discouraging.” The empirical analysis, which is undertaken at the county level, indicates that welfare payments vary in their influence on poverty rates across different employment contexts. A national map portraying this parameter instability demonstrates that female-family poverty rates are most responsive to welfare assistance in the rural South and least responsive in the metropolitan Northeast. Finally, we examine two sharply contrasting locales to illustrate how poverty is governed by specific employment and welfare characteristics.