Educating students about crucial environmental and environmental justice ideas and how to leverage data in their own research or projects is a critical mission for the EIDC. We created resources for both educators and students who are new to the environmental data space to provide both an introduction to this work, as well as ways to discover approaches to research. Reach out if you’d like to collaborate on a new course, understand any topic in more depth, or need more data for a research project.
EIDC in the Classroom: Teaching with the Environmental Impact Data Collaborative
On July 13th, the Environmental Impact Data Collaborative hosted a virtual informational session about how professors, educators and researchers can unleash the power of EIDC in their classroom.
The EIDC platform is a perfect tool for student groups, or instructors and students to collaborate. The platform provides an opportunity to share and work on code in real time, and for an instructor or TA to check progress, provide feedback, and publish the results.
To help initial classroom integration, we created two sets of assignments, both containing introductory materials to data science and statistical analysis through the lens of environmental justice.
The EIDC is partnering with Dr. Amy Yeboah Quarkume of Howard University to develop a set of problem sets for an upcoming undergraduate course on environmental justice. The course will leverage the EIDC’s datasets and low- to no-code wrangling and analysis tools to introduce students to environmental justice concepts and work with environmental data.
Getting Started with EIDC — Get acquainted with the EIDC platform. Learning objectives include becoming familiar with available datasets, the elements of the dataset landing page and the project map, and creating an original project
Data Types — Introduce students to categorical and numeric data types as well as interpreting summary statistics and performing basic transforms like filtering data.
Data Wrangling — Expand students’ knowledge of data wrangling techniques including filtering, selecting, aggregating, and merging data.
Data Visualization — Introduce students to data visualization concepts, including different types of visualizations (e.g. scatter plots, line charts, bar charts, and maps) and when each type is appropriate, leveraging the EIDC’s low-code visualization dashboards.1
Putting It All Together — Combine the lessons of the previous problem sets to have students conduct a start-to-finish data analysis, including interpreting summary statistics, data wrangling and visualization, and interpreting results.
EIDC has developed problem sets with the aim of facilitating educators introducing concepts of statistical analysis of environmental justice to students, while also realizing the full potential of the platform, resources and tools provided. While the problem sets are designed to encourage use of the Redivis platform, allowing for low-to-no code wrangling and analysis of datasets, there are also R-based versions available.
Problem Set 1: Ordinary Least Squares Regression: Investigating Air Quality In Public Schools: Using a merged dataset of education and air quality, we will investigate how PM2.5 levels vary across racial, ethnic, economic and urban/rural divides for the state of California. Concepts included in this problem set are data preparation, visualizations, ordinary least squares regression and interaction variables.
The team has developed guides and tutorial videos on how to start using the data collaborative and how to perform research in specific areas. In addition to these instructional tools, we also share to the public some of our platform members’ projects, as well as a Knowledge Hub that comprehensively compiles and stores research materials on essential environmental domains:
We want to hear from you If you have any questions, need some help, have feedback or requests for us, or would like our help setting up EIDC for your classroom, don’t hesitate to reach out to firstname.lastname@example.org!