The spatiotemporal form of urban growth: measurement, analysis and modeling Martin Herold * , Noah C. Goldstein, Keith C. Clarke Department of Geography, University of California Santa Barbara, Ellison Hall, Santa Barbara, CA 93117, USA Received 25 March 2002; received in revised form 24 August 2002; accepted 28 December 2002 Abstract This study explores the combined application of remote sensing, spatial metrics and spatial modeling to the analysis and modeling of urban growth in Santa Barbara, California. The investigation is based on a 72-year time series data set compiled from interpreted historical aerial photography and from IKONOS satellite imagery. Spatial metrics were used both specifically to assess the impact of urban development in four administrative districts, and generally to analyze the spatial and temporal dynamics of urban growth. The metrics quantify the temporal and spatial properties of urban development, and show definitively the impacts of growth constraints imposed on expansion by topography and by local planning efforts. The SLEUTH urban growth and land use change model was calibrated using the multi-temporal data sets for the entire study region. The calibrated model allowed us to fill gaps in the discontinuous historical time series of urban spatial extent, since maps and images were available only for selected years between 1930 and 2001. The model also allowed a spatial forecast of urban growth to the year 2030. The spatial metrics provided a detailed description of the accuracy of the model’s historical simulations that applied also to forecasts of future development. The results illustrate the utility of modeling in explaining the amount and spatial pattern of urban growth. Even using modeling, however, the forecasting of urban development remains problematic and could benefit from further research on spatial metrics and their incorporation into the model calibration process. The combined approach using remote sensing, spatial metrics and urban modeling is powerful, and may prove a productive new direction for the improved understanding, representation and modeling of the spatiotemporal forms due to the process of urbanization. D 2003 Elsevier Inc. All rights reserved. Keywords: Urban growth; Spatial metrics; Modeling; Aerial photography; IKONOS 1. Introduction New approaches to the planning and management of urban regions, such as sustainable development and smart growth, will depend upon improvements in our knowledge of the causes, chronology, and impacts of the process of urbanization and its driving forces (Klostermann, 1999; Longley & Mesev, 2000). Given the long research tradition in the fields of urban geography and urban modeling (Batty, 1989; Knox, 1994), new sources of spatial data and inno- vative techniques offer the potential to significantly improve the analysis, understanding, representation and modeling of urban dynamics. The combination of new data and methods will be able to support far more informed decision-making for city planners, economists, ecologists and resource man- agers. Dynamic spatial urban models provide an improved ability to assess future growth and to create planning scenarios, allowing us to explore the impacts of decisions that follow different urban planning and management pol- icies (Kaiser, Godschalk, & Chapin, 1995; Klostermann, 1999). After a checkered past, there is currently a resurgence in urban modeling, primarily because the new methods and data have made computer-based models functional and useful tools for urban planning. This growth has been driven by two major factors: improved representation and modeling of urban dynamics; and increased richness of information in the form of multiple spatial data sets and tools for their processing (e.g. Geographic Information Systems, Clarke, Parks, & Crane, 2002). Yet, the application and performance of the models is still limited by the quality and scope of the data needed for their parameterization, calibration and validation. Urban modeling also still suffers from a lack of knowledge and understanding of the physical and socio- 0034-4257/03/$ - see front matter D 2003 Elsevier Inc. All rights reserved. doi:10.1016/S0034-4257(03)00075-0 * Corresponding author. Tel.: +1-805-893-4196; fax: +1-805-893- 3703. E-mail address: martin@geog.ucsb.edu (M. Herold). www.elsevier.com/locate/rse Remote Sensing of Environment 86 (2003) 286 – 302