The role of spatial analysis in local, state and regional public health has steadily increased over the last decade with the infusion of GIS software such as ESRI ArcGIS.  This CSTE workshop provides an introduction to tools which focus on applications to public health and an opportunity for comfortability while conducting spatial analysis with health disparities. This course will also provide the ability to communicate results through producing high-quality digital maps which support public health decision making in an applied learning environment.  Advanced GIS for Public Health workshop is designed to provide an introduction to GIS users who have an interest in spatial data analysis and the opportunity to explore more advanced topics in GIS. 

Course Goals 

After completing this course, you will be able to: 

• Map geographic data related to public health 
• Download and Census data to calculate disparities and measures of structural racism
• Create vector datasets from public and private databases 
• Geocoding and Masking Coordinates for Privacy 
  
• Conduct Moran’s I tests for spatial autocorrelation
• Use model builder to create data workflows
• Basics of outputting Arcpro to automate spatial data processing
• Create hotspot maps to identify clusters 
• Create time-series plots and space-time cube clusters for public health monitoring 
• Apply GIS to several public health disciplines 

For successful participation in the workshop it is expected that each attendee has a basic understanding of the ArcPro software and can conduct basic geoprocessing steps such as attribute and spatial joins and queries. A video tutorial on the basics of ArcPro will be made available to attendees two weeks before the session to allow to practice before the workshop begins. Each session will be recorded over zoom and made available to the attendees. All datasets being used will come from publicly available resources such as the U.S. Census, CDC, NASA as well as open source geospatial datasets at academic universities. Attendees will be taught how to download, process and identify the pros and cons of these data. 

The speaker for this course is Kevin J. Lane, Ph.D., MA, assistant professor in the Department of Environmental Health at Boston University School of Public Health. 

Lesson 1: Introduction to Advanced GIS and Intro GIS Refresher

By the end of the lesson, participants will be able to:

1. Use healthcare maps and buffers to overlay with census population data to identify demographic characteristics of an at-risk populations. 
2. Create a buffer to identify Euclidian distance from hospitals.
3. Create a network distance driving and walking buffer.
4. Use spatial selections and joins to identify at risk population without access to emergency resources.
5. Create and export maps.

Lesson 2: Using Census and American Community Survey to Study Social Determinates of Health and Disparities: Data, Processing Methods and Analysis Applications
By the end of the lesson, participants will be able to:

1. Complete a Census API data retrieval. 
2. Clean the API data.
3. Integrate into a SAS or R system for data processing and variable derivation.
4. Calculate measures of the index of concentrations for extremes as well as dissimilarity.
5. Calculate and map the coefficient of variant (CV).
6. Interpret the CV and assess acceptable levels.
7. Identify potential sources of measurement error from spatial aggregation.

Lesson 3: Spatial Autocorrelation and Cluster Mapping: Analyzing the Relationship Between COVID-19 and Social Vulnerability Index 

By the end of the lesson, participants will be able to:

1. Create hotspot maps.
2. Create cluster maps.
3. Assess variables for spatial auto correlation.
4. Download and map social determinates of health metrics from ATSDR's Social Vulnerability Index.

Lesson 4: Space-time Cluster Analysis: Analyzing the Relationship Between COVID-19 and Social Determinates of Health 

By the end of the lesson, participants will be able to:

1. Create a space-time cube netcdf dataset. 
2. Use the emerging hotspot analysis.
3. Create a hillshade map to display spatial-temporal clusters.

This training series was funded by CDC Cooperative Agreement No: 6 NU38OT000297-02-08. The contents of this training are solely the responsibility of the authors and do not necessarily represent the official views of CDC.

Competencies:

• 1.2 – Data Analytics and Assessment Skills – Describes factors that affect the health of a community
• 1.3 – Data Analytics and Assessment Skills – Designs surveillance systems using the principles of ethics, DEIA, and justice
• 1.4 – Data Analytics and Assessment Skills – Conducts surveillance activities
• 1.6 – Data Analytics and Assessment Skills – Manages data
• 1.7 – Data Analytics and Assessment Skills – Analyzes data
• 1.8 – Data Analytics and Assessment Skills – Interprets results from data analysis
• 2.4 – Public Health Sciences Skills – Manages information systems to promote effectiveness and security of data collection, processing, and analysis
• 3.1 – Communication Skills – Determines communication strategies
• 4.1 – Community Partnership Skills – Describes epidemiologic conditions, systems, programs, and policies affecting community health and resilience