Course Preparation & Readings
General (All Participants)
(1) Unicorns, Show Ponies, and Gazelles [link]
What it covers: A typology of geospatial data systems—"unicorns" (large, centralized, well-funded platforms), "show ponies" (polished, user-facing tools and dashboards), and "gazelles" (smaller, agile, often mission-driven data efforts).
Why it matters: Highlights the trade-offs between scale, usability, and flexibility in data infrastructures, and shows how different models shape who can access data, how easily it can be used, and what kinds of analysis are possible.
(2) Introduction to STAC (SpatioTemporal Asset Catalog) [link]
What it covers: A standard for organizing and accessing geospatial data in cloud-based systems.
Why it matters: Provides a foundation for working with large datasets that cannot be downloaded locally.
Note: Don't worry about the technical details—we'll work through this together in class.
Pick One (In-Class Exercise Preparation)
We will work in groups on real policy problems. Please read one of the following and come prepared to discuss the data used in the study and where those data live.
(3a) Biodiversity Monitoring for a Just Planetary Future [link]
What it covers: How global biodiversity monitoring systems are built from many data sources (e.g., citizen science, institutional datasets, global repositories) and how these data reflect social and political histories.
Why it matters: Shows that biodiversity data are not neutral—data collection is uneven across regions and contexts, and these disparities can shape conservation priorities and policy decisions.
(3b) Global Air Pollution Exposure and Poverty [link]
What it covers: Links between global air pollution exposure and socioeconomic inequality using large-scale environmental and demographic data.
Why it matters: A concrete example of combining multiple large datasets (e.g., satellite and census data) to produce policy-relevant insights—and the challenges of integrating data across sources.
What to Keep in Mind
As you read, focus less on the results of each study and more on:
- What data are used
- Where those data exist (platforms, repositories, APIs)
- Whether and how you could access them yourself
This perspective will form the foundation of the in-class exercises.