This study examines the determinants of urbanized area across a 10,000-mile square swath in central North Carolina, an area undergoing extensive conversion of forest and agricultural land. We model the temporal and spatial dimensions of these landscape changes using a database that links five satellite images spanning 1976–2001 to a suite of socioeconomic, ecological and GIS-created explanatory variables. By specifying the complementary log-log derivation of the proportional hazards model, we employ a methodology for modeling a continuous time process—the conversion of land to impervious surface—using discrete-time satellite data. Spatial hypotheses are tested using several variables derived from the imagery that measure the landscape configuration surrounding a pixel. Empirical results confirm the significance of several determinants of urbanization identified elsewhere in the literature, including proximity to roads and population density, but also suggest that the parameterization of these variables is biased when the influence of landscape configuration is unaccounted for. We conclude that the inclusion of spatial pattern metrics significantly improves both the explanatory and predictive power of the estimated model of urbanization.