223.600.01 Application of Spatial Analysis Tools to Inform Decision-Making in LMICs
- International Health
- 3rd term
- 3 credits
- Academic Year:
- 2017 - 2018
- East Baltimore
- Class Times:
- Tuesday, 3:30 - 5:20pm
- Lab Times:
Friday, 10:30 - 11:20am
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. Some previous knowledge of mapping or GIS is highly recommended but it does not exclude a student from participating in the course. 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 and map scale, 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 QGIS, mapping, and spatial data analysis software.
- Learning Objectives:
- Map disease and mortality rates using crude and Empirical Bayes Smoothed rates
- Interpret basic spatial data analysis methods including cluster detection and small area estimation, and confounding by spatial neighborhood
- Access, download, and process environmental, demographic, and census data from global websites for linking to maps
- Apply GIS tools to specify and characterize populations and communities for global health intervention and research
- 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
- 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
- Methods of Assessment:
Class participation 10%
Final project/presentation 40%
- 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.