Geospatial Modeling and Analysis


Course description

The course explains digital representation and analysis of geospatial phenomena and provides foundations in methods and algorithms used in GIS analysis. Special focus is on terrain modeling, geomorphometry, watershed analysis and introductory GIS-based modeling of landscape processes (water, sediment). The course includes analysis from lidar data, coastal change assessment and 3D visualization.

See slides and video which introduce the course in more depth.


Corey White, see C.T. White's CGA page Google Scholar
Office hours: by appointment
Appointment: please always make an appointment by email


Knowledge of GIS principles at introductory level or strong computational background; GIS280 Introduction to GIS, GIS510 Fundamentals of GIST, GIS520 Spatial Problem Solving or GIS530 Spatial Data Foundations are recommended.

Educational approach

This course will consist of lectures, readings, hand-on exercises, homework assignments, and a major project. All the work will be collected within an electronic portfolio that will systematically include the work that you will do for this and other GIS courses. Extra credits will be given for innovative solutions, creativity in problem solving and extensions to given tasks.

Class materials and schedule

See the course Introduction


No required textbook, on-line material is used. You may find the following titles helpful for some topics:


GRASS GIS, ArcGIS Pro. To download, follow the links at the Course logistics web page.

Grading policy

40% homeworks, 20% midterm, 40% project (10% proposal + 5% progress report and title submission on wiki + 10% presentation + 15% paper), 100% is the maximum number of points (total + extra credits) achieved in class. Points are taken off for late submissions.
GradeCourse and each HW


  • Geospatial data acquisition
    • mapping natural phenomena, concept of continuous fields and discrete sampling
    • units, projections, coordinate transformations, georeferencing
    • geospatial formats, conversions, geospatial data abstraction library
    • raster and vector representation, raster-vector conversions and resampling
  • Data display and visualization
    • display of continuous and discrete data, use of color, shading, symbols, to extract the spatial pattern and relationships
    • 3D visualization: multiple surfaces and volumes, 3D vector objects
    • visualization for data analysis (lighting, zscaling, transparency, cutting planes, animations)
  • Geospatial Analysis
    • foundations for analysis of continuous and discrete phenomena
    • neighborhood operations and buffers,
    • analysis and modeling with map algebra,
    • cost surfaces and least cost path,
    • spatial interpolation and approximation (gridding)
  • Terrain Modeling and Analysis (Geomorphometry I-III)
    • terrain and bathymetry mapping
    • mathematical and digital representations (point clouds, contour, raster, TIN)
    • DEM, DTM and DSM, working with multiple return lidar data
    • spatial interpolation of elevation data and topographic analysis
    • line of sight, viewshed analysis
    • solar irradiation, photovoltaic energy potential
    • time series of elevation data, analysis of coastal change
  • Flow tracing, Watershed Analysis and Landforms I-II
    • methods for flow routing and flowaccumulation
    • extraction of stream networks
    • extraction of watershed boundaries
    • feature extraction, landforms
  • Introduction to Modeling of Geospatial Processes
    • model formulation, input data processing
    • introduction to GIS-based hydrologic and erosion modeling
  • Project

Academic integrity
Overview, Code of Student Conduct
Attendance policy
in on-campus section, attendance is recommended.
Accommodation of students with disabilities
Disability Services Office
Large Language Model (LLM) (i.e., ChatGPT, Bard, etc..) policy
The use of LLMs in this course is permitted for the purposes of completing assignments and projects. However, the use of LLMs for the purposes of completing exams is strictly prohibited. Students are also required to cite the use of LLMs in their assignments and projects. Failure to do so will result in a violation of the courses academic integrity policy. Students are also to be aware that the use of LLMs in this course is not a substitute for learning the material. Assignments and projects must be properly cited and contain no plagerized materials. (The policy was created with the aid GitHub CoPilot)

For non-NCSU visitors

This course material is open and is often used by people outside NCSU. Note that although we are trying to have maximum of our resources open, some linked material like online library resources and virtual computing lab are accessible only to people at NCSU. However, GRASS GIS and the dataset used in the assignments are available to anybody under and open license. The most useful page for an outside visitor is the list of GRASS GIS assignments.