This class is a survey of methods, concepts, and technologies used by planners to model urban and environmental systems in order to support decision-making and design. The student will learn how to use spatial data and computational models to analyze patterns, identify trends, and visualize alternate futures. The course includes three modules. Module one deals with urban-natural interfaces and includes site suitability analysis; landscape fragmentation analysis, hydrological modeling, and spatial interpolation. Module two introduces agent-based simulation of urban and environmental systems. The final module focuses on land-use applications including handling of remotely sensed data, and urban growth modeling. Students will learn basics of geo-spatial machine learning using the statistical software language R. No experience with R is required, however, basic familiarity with ArcGIS is required.