GEOG3531: Numerical Spatial Analysis

"Everything is related to everything else, but near things are more related than distant things." The First Law of Geography proposed by Waldo Tobler implies the complex yet fascinating nature of the geospatial world. Spatial analysis in order to understand geographic numbers is becoming increasingly necessary to support knowledge discovery and decision-making. The objective of this course is to teach the fundamental theory and quantitative methods within the scope of geospatial analysis. The course starts with basic statistics, matrix, the background of spatial analysis, and exploratory spatial data analysis. Then, we will dive into the special nature of our spatial world, with fundamental geographic ideas and theories being introduced. The focus will be on numerical methods and models including descriptive statistics, pattern analysis, interpolation, and regression models. Finally, some advanced topics regarding spatial complexities and spatial networks will be introduced to arouse further interest in this realm. To sum, this is an introductory course that makes use of quantitative analytics such as linear algebra, statistics, and econometrics for spatial data analysis. By taking this course you will: -quantitatively understand critical concepts behind geospatial processes, such as scale, spatial weights, spatial autocorrelation, spatial dependence, spatial pattern. -learn key methods of analyzing spatial data: e.g., point pattern analysis, spatial autocorrelation statistics, spatial prediction, and spatial regression. -examine the lectured methods/models with data from geographic scenarios using Python and related programming packages. prereq: high-school algebra; Basic stats and linear algebra recommended

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