Spatial Effects and Unobserved Heterogeneity in Modeling Urban Growth

fronted with a complex set of considerations when evaluating alternative regulatory, infrastructural, and fiscal interventions and the urban shapes to which these give rise. In this regard, a critically important question—one generally overlooked in the debates on sprawl— concerns the spatial distribution of urban growth, and in particular, how existing landscape patterns determine future trajectories of change. Although a road built in one location, for example, may offset development that would have otherwise occurred in another (3), the resulting environmental and socioeconomic impacts are likely to be highly location specific, irrespective of the total amount of development that transpires. Recognition of the need for spatially explicit approaches to the study of urbanization has led to an increasing number of studies that combine principles from landscape ecology with econometric methods to account for how human decision making, ecosystem function, and their interaction effect landscape changes across different spatial scales. Models that are fine scale are particularly meaningful because ecologists and allied disciplines perceive an intimate connection between the provision of habitat and other services by ecosystems and the pattern of the landscape mosaic in which the ecosystems function. Unlike area-based approaches, which estimate the determinants of land-use shares within aggregate geographic areas such as counties or parcels (4, 5), much of the recent literature on urban growth draws on disaggregate point data derived from remotely sensed sources or ground surveys to estimate spatially explicit models of land use. An early example of such work in the U.S. context is the multinomial logit analysis of Turner et al. that used a time series of satellite imagery to study the effect of socioeconomic, ecological, and locational factors on landscape changes in North Carolina and Washington (6). Other issues explored in this literature include the role of spatial pattern metrics created by geographic information systems (GIS) as determinants of property values (7), the joint influence of urban population growth and urban proximity on land-use change (8), and the causes of fragmented development patterns among residential land parcels on the rural–urban interface (9). The present study extends this line of inquiry by developing a spatial-temporal model of the determinants of built-up area across a 25,900-km swath in central North Carolina, an area that has undergone extensive conversion of forest and agricultural land in the past few decades. Between 1976 and 2001, the area covered by impervious surface in the region more than doubled, from 625 to 1,471 km, with the majority of the increase occurring in the metropolitan regions of Greensboro and Raleigh. In Raleigh, for example, the population increased by 32% between 1990 and 1996, when its urbanized land area increased nearly twofold (10). These landscape dynamics are modeled by exploiting a spatial database that links five satellite Cities and Satellites Spatial Effects and Unobserved Heterogeneity in Modeling Urban Growth