Noah Goldstein

The spatiotemporal form of urban growth: measurement, analysis and modeling

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 models 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.

The Fire Information Engine

ABSTRACT The recently established Center for Fire Research and Outreach at the University of California, Berkeley has developed a web-based toolkit, the Fire Information Engine Toolkit (FIET), for wildfire-related needs. The FIET is intended to meet the needs of diverse user groups (homeowners, decision-makers, including fire operations, and researchers) at a variety of scales (local, community, and regional levels) before, during, and after wildfires. During the initial phase of the FIET, we have focused on developing pre-fire tools for homeowners and decision-makers at the local and community levels. For example, we have developed a science-based, parcel-level structure vulnerability assessment and ranking approach that will help homeowners and communities to identify hazards and prioritize planning and mitigation activities. A version of this assessment can be completed anonymously by homeowners interested in decreasing their vulnerability to wildfire, and a similar version can be downloaded and used by decision-makers on a community-wide level. In addition, we have developed a number of innovative web-based Geographic Information System (webGIS) applications including an interface to display results of the structure vulnerability assessment at a community level, as well as search-by-address maps of Californias wildland-urban interface (WUI), fire threat, fire recurrence, and upcoming changes to building codes. In collaboration with researchers at Lawrence Livermore National Laboratory, the next phases of the FIET development will concentrate on enhancing the available tools, including incorporating real-time weather into fire behavior modeling and tools for evacuation modeling of natural disasters.

The Challenges to Coupling Dynamic Geospatial Models

ABSTRACT Many applications of modeling spatial dynamic systems focus on a single system and a single process, ignoring the geographic and systemic context of the processes being modeled. A solution to this problem is the coupled modeling of spatial dynamic systems. Coupled modeling is challenging for technical reasons, as well as conceptual reasons. This paper explores the benefits and challenges to coupling or linking spatial dynamic models, from loose coupling, where information transfer between models is done by hand, to tight coupling, where two (or more) models are merged as one. To illustrate the challenges, a coupled model of Urbanization and Wildfire Risk is presented. This model, called Vesta, was applied to the Santa Barbara, California region (using real geospatial data), where Urbanization and Wildfires occur and recur, respectively. The preliminary results of the model coupling illustrate that coupled modeling can lead to insight into the consequences of processes acting on their own.

Lessons and challenges in land change modeling derived from synthesis of cross-case comparisons

This chapter presents the lessons and challenges in land change modeling that emerged from years of reflection and numerous panel discussions at scientific conferences concerning a collaborative cross-case comparison in which the authors have participated. We summarize the lessons as nine challenges grouped under three themes: mapping, modeling, and learning. The mapping challenges are: to prepare data appropriately, to select relevant resolutions, and to differentiate types of land change. The modeling challenges are: to separate calibration from validation, to predict small amounts of change, and to interpret the influence of quantity error. The learning challenges are: to use appropriate map comparison measurements, to learn about land change processes, and to collaborate openly. To quantify the pattern validation of predictions of change, we recommend that modelers report as a percentage of the spatial extent the following measurements: misses, hits, wrong hits and false alarms. The chapter explains why the lessons and challenges are essential for the future research agenda concerning land change modeling.