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Application of Spatial Analysis Tools to Inform Decision-Making in LMICs


East Baltimore
3rd term
International Health
4 credits
Academic Year:
2022 - 2023
Instruction Method:
Class Times:
  • Tuesday,  3:30 - 5:20pm
Auditors Allowed:
Yes, with instructor consent
Undergrads Allowed:
Grading Restriction:
Letter Grade or Pass/Fail
Course Instructors:
  • Mohammad Ali
  • Moise C. Ngwa
Mohammad Ali

Two courses in Biostatistics (either 140.621 and 140.622 or 140.651 and 140.652), and one course in Epidemiology (either 340.601, or 340.721, or 340.751), or consent of the instructor. Working knowledge of ArcGIS. One is expected to have a working knowledge of MS Office and Windows operating systems.


A picture speaks louder than words and so is a map of your data

Are you interested in applying Exploratory Data Analysis techniques and GIS to Epidemiological research?

Spatial public health data can effectively be analyzed using spatial statistical methods and be used by health policy decision-makers

Applies spatial analysis tools relevant for policy decision-making in resource-poor settings. Analyzes the concepts and techniques of Geographic Information Systems (GIS) and Exploratory Spatial Data Analysis (ESDA) with a global health focus. Introduces both descriptive and analytical functions of GIS along with additional spatial and geographic concepts including: cartographic communication, automated mapping characteristics, map projections, geocoding, coordinate systems, the nature of spatial public health data, and spatial statistical methods. Provides students with an opportunity to gain hands-on experience in the use of ArcGIS, QGIS, Geoda, SatScan, and Geographically Weighted Regression for spatial data analysis and mapping.

Learning Objectives:

Upon successfully completing this course, students will be able to:

  1. Create, edit, and manage spatial databases
  2. Mapping GIS data using ArcGIS, QGIS, and R
  3. Appraise and evaluate concepts, technical issues, and applications appropriate for GIS technology, including linking spatial data, conducting spatial queries, and analyzing feature relationships with regard to the strengths and weaknesses of data from low- and middle- income countries
  4. Map disease and mortality rates using crude and Empirical Bayes Smoothed rates
  5. Access, download, and process environmental, demographic, and census data from global websites for linking to maps
  6. Interpret basic spatial data analysis methods including cluster detection and small area estimation, and confounding by spatial neighborhood
  7. Apply GIS tools to specify and characterize populations and communities for global health intervention and research
  8. Apply and interpret the concept of spatial autocorrelation (SA) and be able to assess SA in data with special focus to inform resource-poor setting decision-makers
  9. Use of spatial lag regression and geographically weighted regression for problem solving
Methods of Assessment:

This course is evaluated as follows:

  • 20% Quizzes
  • 45% Lab/homework
  • 14% Reading reflections
  • 21% In-class Exercises

Instructor Consent:

Consent required for some students

Consent Note:

Consent required if they don't meet the prerequisites

For consent, contact:

Special Comments:

Students must have their own laptops and download open access softwares in their laptops. A list of software required is in the CoursePlus syllabus. Course is 2 hour lecture and 1 hour lab with students' own laptops.