Geospatial Data Analytics


Credit Hours:


Learning Objective:

The course will introduce fundamental theories, analytical methods and programing skills, all of which are needed to work with geospatial data. Students will learn the techniques to access, visualize, analyze and model various geospatial data through programming practice and/or using open source GIS software. Besides, the course will provide each student an opportunity to apply the course content to a topic area of their own interest. By successfully completing the course, students should be able to
  1. Command the theories and methods
  2. Implement and practice typical geospatial methods
  3. analyze and visualize these implementations
  4. be familiar with open source GIS tools and data


Fall 2019 Syllabus

Topics Covered:

  1. Introduction
    • Geospatial data
    • R basics
  2. Data visualization and mapping with R
    • Reading and writing of geospatial data
    • Mapping geospatial data
    • Create descriptive statistics
    • Access to open source geospatial data
  3. Geospatial analysis (GIS) with R
    • Geometric calculation
    • Topologic analysis
    • Object and layer operations
    • Raster/image analysis
  4. Geospatial regression
    • Autocorrelation
    • Geographically weighted regression
  5. Point pattern analysis with R
    • Basic statistics and metrics
    • Kernel density methods
    • Hot and cold spots
  6. Surface modeling
    • De-noising and filtering
    • Triangulation and mesh
    • Spatial interpolation
  7. Object modeling
    • Model recognition
    • Boundary detection and regularization
    • Model based data fitting


  1. Graduate students with an introductory GIS course, preferably at graduate level
  2. having experience in computer programming
  3. or under consent of the instructor

Applied / Theory:


Official textbook information is now listed in the Schedule of Classes. NOTE: Textbook information is subject to be changed at any time at the discretion of the faculty member. If you have questions or concerns please contact the academic department.

Computer Requirements:

Other Requirements:

R and Python (some samples provided) Open source GIS packages/QGIS

ProEd Minimum Requirements:


Tuition & Fees: