Enhancing the Spatial Policy Framework with Ecological Analysis

Abstract

The spatial distribution and redistribution of population and economic activities is critically important to a nation’s life and future. Geographical separation and interpenetration both constrain and facilitate social interaction, economic organization, institutional roles and capacities, and political life. Whether concern is with distribution among states or broad geographic regions, metropolitan or nonmetropolitan areas, central cities or suburbs within metropolises, where people live or work, or where social and economic activities are located, spatial distribution affects virtually every aspect of social, economic, and political life. Accordingly, nations have a legitimate interest in creating policies to influence their spatial organization. The President’s Commission on Population Growth and the American Future pointed out over two decades ago, however, that public policy responses to spatial distributional issues in the United States have been fragmented and problem-oriented. They have not embodied a comprehensive set of objectives that would contribute to overall national goals of economic efficiency, socioeconomic equity, community stability, environmental protection, and freedom of locational choice (Mazie 1972). As Alonso (1972) stressed at that time, one of the reasons we lack a “national strategy of urbanization” is because we lack an adequate understanding of spatial distribution as a system. Moreover, he observed that this lack of systemic understanding has contributed to ineffective distribution policies as well as to nondistribution policies that have strong, unanticipated geographic results. We have learned much about the dynamics of spatial distribution since the commission submitted its report to President Nixon in 1972, but, for the most part, Alonso’s critique is still valid today.KeywordsHuman EcologyPublic InterventionMedian Family IncomeSpatial InequalityCumulative ExpansionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.