Spatial (map) is considered as a core infrastructure of modern IT world, which is substantiated by business transactions of major IT companies such as Apple, Google, Microsoft, Amazon, Intel, and Uber, and even motor companies such as Audi, BMW, and Mercedes. Consequently, they are bound to hire more and more spatial data scientists. Based on such business trend, this course is designed to present a firm understanding of spatial data science to the learners, who would have a basic knowledge of data science and data analysis, and eventually to make their expertise differentiated from other nominal data scientists and data analysts. Additionally, this course could make learners realize the value of spatial big data and the power of open source software's to deal with spatial data science problems.
This course will start with defining spatial data science and answering why spatial is special from three different perspectives - business, technology, and data in the first week. In the second week, four disciplines related to spatial data science - GIS, DBMS, Data Analytics, and Big Data Systems, and the related open source software's - QGIS, PostgreSQL, PostGIS, R, and Hadoop tools are introduced together. During the third, fourth, and fifth weeks, you will learn the four disciplines one by one from the principle to applications. In the final week, five real world problems and the corresponding solutions are presented with step-by-step procedures in environment of open source software's.

From the lesson

Spatial Data Analytics

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.