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GEOG 884 - Spatial Data Science and Intelligence Analysis

This is a sample syllabus.

This sample syllabus is a representative example of the information and materials included in this course. Information about course assignments, materials, and dates listed here is subject to change at any time. Definitive course details and materials will be available in the official course syllabus, in Canvas, when the course begins.


GEOG 884 focuses on spatial data science as a framework supporting intelligence analysis. The integration of spatial thinking and spatial data science in a professional work environment defines the geospatial intelligence tradecraft. Here spatial data science makes analysts more effective by distilling geographical knowledge from data and providing a basis for challenging biases. Importantly, a key objective of this course is to prepare you with the agility to quickly and smoothly adapt and integrate newer, different, disruptive, expansive, or convergent technologies.

Geospatial Intelligence (GEOINT) leverages geographic information science and technology (including cartography, geographic information systems, remote sensing, and global positioning systems) with intelligence tradecraft to develop intelligence products that support national security, disaster response, and international relief efforts.

Students who complete GEOG 884 will have a basic understanding of spatial data science and intelligence analysis. These data products will be used in a variety of application scenarios, using commercially available and open-source software tools. Finally, each student will complete a capstone project demonstrating their mastery of the basic concepts acquired in the course.


Students who excel in this course are able to:

  • Critique empirical and theoretical scholarship related to intelligence analysis;
  • Assess statistical and cognitive methodologies to identify an optimal approach;
  • Evaluate data representing complex spatial phenomena;
  • Judge a geospatial intelligence product relative to the nature of current and potential future threats using data science, cognitive analytic techniques, and theoretical concepts of geography to inform decision-making processes;
  • Create written work that clearly and succinctly synthesizes and communicates existing information, insights into complex phenomena, and recommendations for action

Required Materials

The materials listed here represent those that may be included in this course. Students will find a definitive list in the course syllabus, in Canvas, when the course begins.

Paul Bolstad, 2019, GIS Fundamentals: A First Text on Geographic Information Systems, 6th edition, 764 pages, ISBN: 978-1-59399-552-2


GEOG 882: Geographic Foundations of Geospatial Intelligence


We have worked hard to make this the most effective and convenient educational experience possible. How much and how well you learn is dependent on your attitude, diligence, and willingness to ask for clarifications or help when you need them. We are here to help you succeed. Please keep up with the class schedule and take advantage of opportunities to communicate with us and with your fellow students. You can expect to spend an average of 12 – 15 hours per week on class work.

Major Assignments

Students earn grades that reflect the extent to which they achieve the course learning objectives. Opportunities to demonstrate learning include quizzes, labs, reflection papers, and a course project. 

  • 9 Lesson Quizzes: 18%
  • 8 Labs: 40%
  • 9 Lesson Discussions: 18%
  • 2 Papers: 6%
  • CAPSt: 18%

Course Schedule

Course Schedule
1Spatial Data Science and Intelligence Analysis    
  • Quiz
  • Discussion
  • Lab: Criminal Investigation
2Geodesy for GEOINTERs    
  • Lesson Quiz
  • Discussion
  • Lab: Butte Fire
3Data Quality and Standards    
  • Lesson Quiz
  • Discussion
  • Lab: Dire, Mali Location and Attribute Accuracy
4Vector Data & Analytics    
  • Lesson Quiz
  • Discussion
  • Lab: Dire, Mali Location and Attribute Accuracy
5Vector Analysis    
  • Lesson Quiz
  • Discussion
  • Lab: Pinellas, Florida Disaster Vulnerability
6Advanced Spatial Analysis    
  • Lesson Quiz
  • Discussion
  • Lab: Pinellas County, Florida (Advanced Spatial Analysis)
  • Reflection Paper
7Raster Data & Analytics    
  • Lesson Quiz
  • Discussion
  • Lab: Laredo, Texas Border Crossing
8Raster Analysis    
  • Lesson Quiz
  • Discussion
  • Lab: Rio Grande Viewshed Analysis
9Introduction to the Capstone Analysis & Recent Developments in the Discipline    
  • Lesson Quiz
  • Discussion
  • Capstone Analysis Problem Capstone Analysis Problem : 
10Capstone Analysis Project    
  • Capstone Analysis Report
  • Video Presentation