For this activity, students will investigate patterns of and relationships between historic redlining data and modern statistics of human health. Students will work in small groups to research a city of their choice using maps found University of Richmond's site "Not Even Past: Social Vulnerability and the Legacy of Redlining" . The "Not Even Past" site enable students to explore the Home Owners Loan Corporation (HOLC) redlining maps in conjunction with modern data from the Center for Disease Control and Prevention (CDC) and demographic data from the Census Bureau. Like many data science projects, the project has impressive graphics or, using the data science lingo, “visualizations”. As students explore patterns of redlining and public health statistics, they are encouraged to reflect on how modern technology allows opportunities to explore data patterns in new ways.
This project is composed of a simple worksheet containing an overview and a set of questions that will help introduce students to redlining and how historic racism may have impacted modern patterns of health. This activity is very simple, and the only tools used in this activity are several internet sites. Students do not need to be familiar with any specialized computer programs to complete this activity. Although it is a very simple activity, students with extensive data science backgrounds may still find this exploration interesting.
Credit to Tamara Basham and Pat Marsteller for leading the Social Justice and Community Change Faculty Mentoring Network within the Quantitative Undergraduate Biology Education and Synthesis (QUBES) network, where this activity was developed.
Content learning objective(s)
- Use maps to explore public health issues by location.
Social justice and/or diversity/equity/inclusion learning objective(s)
- Describe redlining as an example of systemic racism.
- Describe how redlining could be related to current patterns of community health.
Quantitative learning objective(s)
- Describe how biologists answer research questions using databases and data science tools.
Prerequisite skills or knowledge
An understanding of percentages and proportions.
This activity requires no special knowledge of any particular software.
Level: lower or upper level
Course type: lecture
Delivery mode: online or in-person, requires availability of computers
Students: biology majors or nonmajors
Number of students: variable