Start Date: Fall 2022
Geospatial computation is the study of general computational methods that are applied to spatial and spatiotemporal data for exploratory, confirmatory, descriptive or predictive analysis. This course introduces foundational concepts in geospatial computation and its applications in spatial data science within the context of geographic information systems.
This course uses the current version of ESRI’s ArcGIS Pro, as well as, Python, Rstudio, and R-Bridge for ArcGIS Pro.
Computational approaches in spatial simulation, exploratory data analysis, predictive analysis and geospatial data visualization will be elaborated. Implementations of data-science centric geospatial approaches in R and ArcGIS will be covered in the context of spatial problem solving in a string of applied computational project assignments. No prior knowledge of R programming is required.
By the end of this course, the student will be able to:
- Identify feasible geocomputational approaches in spatial data science for a wide variety of spatial problems
- Gain knowledge of spatial pattern mining, spatial regression and exploratory data analysis
- Recognize stages of spatial analysis that rely on geospatial computation and form repeatable analysis workflows leveraging ArcGIS and R
- Obtain working knowledge of R language within the context of geospatial computation and its integration to a ArcGIS