Interested in causal inference, machine learning, or spatial analysis - and how they all fit together? New paper in @JGeoSys from @lehnert_r and I is for you: tinyurl.com/muan53be. Or if you prefer your research in 280 chr. chunks (as I do), I’ll summarise here 👾 1/ In causal inference we’re interested in finding out the direct impact that some intervention has on an outcome we’re interested in (Y), rather than a simple correlation (although correlations can still be useful in helping us to understand the world) 2/

Playing around with applying our method from doi.org/10.1177/239980… for a new project; we can use it to map, e.g., (2016) walking commutes by electoral division in #Dublin using flowmapper.org We can also combine this with our cost-distance method (self-promotion! sciencedirect.com/science/articl…) to find the cost of traveling a given distance (based on opportunity cost of travel time, buying walking shoes, etc.) to see where walking infrastructure/service is relatively poor

For all interested at the development of spatially-explicit machine learning models, new paper in @GeogAnalysis develops a straightforward approach: insert spatial lags into a random forest model and test performance against spatial econometric models onlinelibrary.wiley.com/doi/10.1111/ge…
1/8 Random forest outperforms SE models across the board, and the "spatial Durbin" RF model performs best of the lot, though not by much: 84.61% accuracy vs. 84.37% non-spatial RF accuracy vs. 81.88% spatial Durbin accuracy
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