Tutorial III: Special Topics

Module 3.1 Proximity Based Analyses

Goals: The goal of this exercise is to compare the results of two proximity-based analyses to evaluate geographic access to Federally Qualified Health Care Centers (FQHCs) in and around Connecticut. First, you will calculate a 1-mile Euclidean distance from each FQHC using the buffer tool. Next, you will calculate a 1-mile Network based distance service area for each FQHC. With these measures, you will estimate geographic access for the state of Connecticut using US Census Tract level population aggregated to the population weighted centroid.

Skills: After completing this exercise, you should have a basic familiarity with both Euclidean and Network based proximity analyses, generation of population weighted centroids, and understand the implications of population aggregation.

Module 3.2 Web Apps and Web Maps

Goals: The goal of this exercise is to create a data dashboard that quickly presents information on current COVID-19 testing locations.

Skills: After completing this exercise, you should have a basic familiarity with creating ArcGIS dashboards. You will be able to add items and configure interactive actions.

Module 3.3 Rate Stabilizing Tool Training

Goal: Use the Rate Stabilizing Tool (RST) to produce easily mapped age-standardized, smoothed sub-county estimates.


    1. Gain experience installing the tool and managing the user interface;
    2. Develop an understanding of the required data inputs; and
    3. Interpret and map the output

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About the Authors

This GIS training curriculum was developed by the Children’s Environmental Health Initiative in partnership with the U.S. Centers for Disease Control and Prevention, Division for Heart Disease and Stroke Prevention.

Children's Environmental Health Initiative (CEHI)

The Children’s Environmental Health Initiative (CEHI) is a research, education, and outreach program committed to fostering environments where all people can prosper. CEHI has developed, maintains, and extends an extensive fully spatially referenced data architecture on children’s environmental health. This makes it possible to jointly consider diverse variables collected by different disciplines, creating the opportunity to explore the complex and dynamic relationships among the components of health.