Spatial data (and) science
Fundamentals, applications, and frontiers in env. and resource economics
Ed Rubin
University of Oregon (USA)
Spatial data science
Why?
In environmental/resource/energy economics, space really matters.
At the heart of a lot of our empirical work: Who is exposed to what?
Who?
- ind/HH decisions/outcomes
- schools (test scores)
- power plants
- governments
- crops (in a plot)
- ecosystems, trees, animals
What?
- (dis)amenities
e.g., PM2.5, PFAS, lead, fecal col.
- weather, climate, disasters
- govt policy (nat, state, local)
- information, prices, taxes
- transportation network
Spatial data science
Why?
Spatial data science underpins how we connect
- outcomes (individuals) to
- treatments (exposures/instruments).
The process typically involves (1) spatial data and (2) assumptions.
Example Estimate the PM2.5 damage function for perinatal health.
- Birth data Identified at residence… hospital… city… state?
- PM2.5 Sparse monitors, coarse AOD satellites, limited emissions data
- More People move; indoor AQ \(\neq\) outdoor AQ; non-clas. meas. err.
So what do people (we) do?
- match births to nearest regulatory PM2.5 monitor (or IDW?);
- use satellite-derived PM2.5 predictions (now with ML!);
- run a particle transport model for upwind PM2.5 sources (+ ML?);
… future: proxy daily movements with phone-based movement data …
… or just hope for the best?
Focusing on (regulatory) monitors…
