An introductory text for the next generation of geospatial analysts and data scientists, Spatial Analysis: Statistics, Visualization, and Computational Methods focuses on the fundamentals of spatial analysis using traditional, contemporary, and computational methods. Outlining both non-spatial and spatial statistical concepts, the authors present practical applications of geospatial data tools, techniques, and strategies in geographic studies. They offer a problem-based learning (PBL) approach to spatial analysis-containing hands-on problem-sets that can be worked out in MS Excel or ArcGIS-as well as detailed illustrations and numerous case studies.





The book enables readers to:











Identify types and characterize non-spatial and spatial data
Demonstrate their competence to explore, visualize, summarize, analyze, optimize, and clearly present statistical data and results
Construct testable hypotheses that require inferential statistical analysis
Process spatial data, extract explanatory variables, conduct statistical tests, and explain results
Understand and interpret spatial data summaries and statistical tests








Spatial Analysis: Statistics, Visualization, and Computational Methods incorporates traditional statistical methods, spatial statistics, visualization, and computational methods and algorithms to provide a concept-based problem-solving learning approach to mastering practical spatial analysis. Topics covered include: spatial descriptive methods, hypothesis testing, spatial regression, hot spot analysis, geostatistics, spatial modeling, and data science.