The confluence of factors driving urban growth is highly complex, resulting from a combination of ecological and social determinants that co-evolved over time and space. Identifying these factors and quantifying their impact requires models that capture both why urbanization happens and where and when it happens. A database that links five satellite images spanning 1976 to 2001 to a suite of socioeconomic, ecological, and geographic information system–created explanatory variables was used to develop a spatial–temporal model of the determinants of built-up area across a 25,900-km2 swath across central North Carolina. Extensive conversion of forest and agricultural land in the past decades is modeled by using the complementary log-log derivation of the proportional hazards model and thereby affords a means for modeling continuous–time landscape change by using discrete–time satellite data. To control for unobserved heterogeneity, the model specification includes an error component that is gamma distributed. Results confirm the hypothesis that the landscape pattern surrounding a pixel has a major influence on the likelihood of its conversion and, moreover, that the omission of external spatial effects can lead to biased inferences about the influence of other covariates, such as proximity to road. Cartographic and nonparametric validation exercises illustrate the utility of the model for policy simulation.