Educational Resource Hub
This crowd-sourced database of educational resources is meant to encompass any tools relevant to people working in the climate and health space. This might include submissions by the content authors themselves, or simply recommendations from community members for resources they have found helpful. This collection includes only links directing users to existing resources - it is not meant to house or archive content.
Keep in mind, this is a crowd-sourced database. CAFE does not verify the quality nor endorse the use of any materials included in this database. Make sure to follow the terms of use and attribution requirements specific to each resource. If you have created or used sources that would be relevant to the community of practice, please add it to the database by entering it in the submission form below.
Explore the world of spatial analysis and cartography with geographic information systems (GIS). In this class you will learn the basics of the industry’s leading software tool, ArcGIS, during four week-long modules:
Week 1: Learn how GIS grew from paper maps to the globally integrated electronic software packages of today. You will install ArcGIS on your computer and learn how to use online help to answer technical questions.
Week 2: Open up ArcGIS and explore data using ArcMap. Learn the foundational concepts of GIS, how to analyze data, and make your first map.
Week 3: Make your own maps! Symbolize data and create an eye-catching final product.
Week 4: Share your data and maps and learn to store and organize your data.
This book will introduce you to the methods required for spatial programming. We focus on building your core programming techniques while helping you: leverage spatial data from OSM and the US Census, use satellite imagery, track land-use change, and track social distance during a pandemic, amongst others. We will leverage open source Python packages such as GeoPandas, Rasterio, Sklearn, and Geowombat to better understand our world and help predict its future. Some Python programming experience is required, however the material will be presented in a student-friendly manner and will focus on real-world application.
This course challenges you to consider how one might lift societies out of poverty while also mitigating greenhouse gas emissions. We explore the inherent complexity of developing country governments wanting to grow their economies in a climate-friendly way. This course will cover topics such as facilitation process techniques, energy modeling, scenario building, innovation, and policy making. You will have the opportunity to respond to these topics with ideas and reflection from your own context. Whether you are a climate change practitioner, work in development, or are simply curious about how climate mitigation is understood, this course will give you insights into the complexity of how countries from the South pursue development goals while addressing climate mitigation.
This site hosts a compilation of tutorials and course materials covering topics including data integration, GIS and data intensive science. Explore 312 earth data science lessons that will help you learn how to work with data in the R and Python programming languages.
The book starts by providing a comprehensive overview of the types of spatial data and R packages for spatial data retrieval, manipulation, and visualization. Then, it provides a detailed explanation of the theoretical concepts of spatial statistics, along with fully reproducible examples demonstrating how to simulate, describe, and analyze areal, geostatistical, and point pattern data in various applications. The book combines theory and practice using real-world data science examples such as disease risk mapping, air pollution prediction, species distribution modeling, crime mapping, and real state analyses.
This tutorial introduces the reader to the ggeffects package and walks through an example of how to conduct a MAIHDA analysis in R to assess intersectional risk among uniquely stratified groups.
This tutorial walks the user through several applications of geospatial statistical analysis for Health in R using the Bayesian INLA package.
Crowd-Sourced Climate Change and Health Educational Resources Collection Submission Form
Do you have a resource you’d like to share with the community in this educational resource collection? Please fill out the submission form below.
Your entry will be checked to ensure the content is appropriate, but will not be assessed for accuracy or completeness, and no other quality checks will be done.
If you have a dataset you’d like to share with the community, think about posting it to the CAFE collection on Dataverse!
Please fill out the form to add a resource you think might be helpful for the climate change and health community of practice.
The type of resources that should be shared here are one of the following:
- Book or reference text (e.g. textbook or guidebook on best practices or other essential knowledge)
- Code repository (e.g. a GitHub code bank of an existing analysis)
- Online code tutorial or vignette (e.g. a walkthrough of specific code or methods with examples and explanations)
- Online course (e.g. a series of learning objectives with content and assessment)
- Video or recorded webinar (e.g. educational resources presented in video format)
