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Spatial Data Science deals with the processing, manipulation, analysis and visualization of spatial data in a variety of forms. Spatial data are those which contain geographical coordinates enabling them to be used for spatial analysis and mapping and include, for example, images from remote sensing, coordinates collected using navigation technologies, or census information by area, among many others. Spatial data are fundamental to many geographical analyses and spatial data science draws strongly from key geographical concepts - such as Tobler’s classic 1970 law: "everything is related to everything else, but near things are more related than distant things".
This course provides a practical introduction to concepts and methods in data science for the analysis of spatial data. By completing the course, you will gain an understanding of the key concepts in spatial data and their collection, how to represent the environment and the world in spatial data, and the ability to apply basic spatial analysis techniques to geographic data using open source platforms such as R, QGIS, and Python. You will develop skills such as importing, manipulating, analyzing, and visualizing spatial data particularly using algorithms in R and Python. You will also develop an awareness of the current limitations and implications of geographic technology, its future development and data stewardship (particularly bi-cultural aspects of stewardship).