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Partnering to Protect You from Peril

Examine the network of connections among local health departments in the United States.

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  • 10 tasks
  • 433 participants
  • 1,500 XP

Project Description

Have you ever wondered who keeps an eye on your favorite restaurants to make sure your food is safe? Or removes old tires from vacant lots before they fill with standing water that could attract mosquitos that spread disease? These tasks are among the services provided by local health departments in the United States. These health departments partner with other health departments to share information and coordinate services, which is especially crucial during public health emergencies. In this project, you will explore the 2016 national network of local health departments and use centrality measures and visualization to identify key health departments nationally, regionally, and locally. Which health departments are most connected? Where are there gaps? What are the characteristics of central health departments?

The project uses igraph and tidyverse commands (from readr and dplyr) to import and examine a network made up of an edgelist and an attribute file. Most commands are covered in the DataCamp courses: Network Analysis in R and Network Analysis in the Tidyverse.

The data in this project was taken from a survey by the National Association of County and City Health Officials (NACCHO), which you can read about here.

Project Tasks

  • 1Ebola, hurricanes, and forest fires, oh my
  • 2Cleaning up the network object
  • 3Getting to know the network
  • 4Connections facilitating coordination nationwide
  • 5Connections for regional coordination
  • 6Which health departments are central in Texas and Louisiana?
  • 7Visualizing the central health departments
  • 8What about state-level networks during emergencies?
  • 9Are central health departments urban?
  • 10Which health departments have high betweenness?
Jenine Harris

Associate Professor at Washington University in St. Louis

Jenine teaches biostatistics in the public health program at Washington University in St. Louis and is the author of the Sage book on statistical models for social networks, "An Introduction to Exponential Random Graph Modeling." She is an enthusiastic R user who co-leads the R-Ladies chapter in St. Louis and has an interest in reproducible research practices.

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  • Topics

    Data ManipulationData VisualizationProbability & StatisticsImporting & Cleaning Data