Spatial Analysis: Statistics, Visualization, and Computational Methods

Spatial Analysis: Statistics, Visualization, and Computational Methods

<P>An introductory text for the next generation of geospatial analysts and data scientists, <STRONG>Spatial Analysis: Statistics, Visualization, and Computational Methods </STRONG>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. </P><br /><P></P><br /><P>The book enables readers to:</P><br /><P></P><br /><UL><br /><P><br /><LI>Identify types and characterize non-spatial and spatial data</LI><br /><LI>Demonstrate their competence to explore, visualize, summarize, analyze, optimize, and clearly present statistical data and results</LI><br /><LI>Construct testable hypotheses that require inferential statistical analysis</LI><br /><LI>Process spatial data, extract explanatory variables, conduct statistical tests, and explain results</LI><br /><LI>Understand and interpret spatial data summaries and statistical tests</LI><br /><P></P></UL><br /><P></P><B><br /><P>Spatial Analysis: Statistics, Visualization, and Computational Methods</B> 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.</P>