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Spatial Data Analytics

Course video 22 of 33

The fifth module is entitled to "Spatial Data Analytics", which is one of the four disciplines related to spatial data science. Spatial Data Analytics could cover a wide spectrum of spatial analysis methods, however, in this module, only some portion of spatial data analysis methods will be covered. The first lecture is an introduction, in which an overview of Spatial Data Analytics and a list of six topics are given and discussed. The second lecture "Proximity and Accessibility" will make learners realize how spatial data science can be used for business applications, while trade area analysis, supply to demand ratio, Floating Catchment Analysis (FCA), and Gravity-based index of accessibility are introduced and applied to real world problems. The third lecture "Spatial Autocorrelation" will give an instruction on how to measure spatial autocorrelation and to apply hypothesis test with Moran's I. The fourth lecture "Spatial Interpolation" will introduce trend surface analysis, inverse distance weighting, and Kriging. Particularly, in-depth explanations regarding Kriging, a de facto standard of spatial interpolation will be presented. The fifth lecture "Spatial Categorization" will make learners understand classification algorithms such as Minimum Distance to Mean (MDM) and Decision Tree (DT), clustering algorithms such as K-Means and DBSCAN with real-world examples. The sixth lecture "Hotspot Analysis" will introduce hotspot analysis and Getis-Ord GI* as the most popular method. The seventh lecture "Network Analysis" will make learners explore the algorithms of geocoding, map matching, and shortest path finding, of which importance is increasing in spatial big data analysis.

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