Recommended readings

• Anselin, L. (1988). Spatial Econometrics: Methods and Models. Studies in Operational Regional Science. Springer Netherlands, Dordrecht.

• Anselin, L. & A. Getis (1992). Spatial statistical analysis and geographic information systems. Ann. Reg. Sci. 26, 19–33.

• Anselin, L. & D. Arribas-Bel (2013). Spatial fixed effects and spatial dependence in a single cross-section. Pap. Reg. Sci. 92, 3–17.

• Anselin, L. & S.J. Rey (2014). Modern Spatial Econometrics in Practice: A Guide to GeoDa, GeoDaSpace and PySAL. GeoDa Press LLC.

• Brunsdon, C., A.S. Fotheringham & M. Charlton (1996). Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity. Geogr. Anal. 28, 281–298.

• Comber, A., C. Brunsdon et al. (2023). A Route Map for Successful Applications of Geographically Weighted Regression. Geographic Analysis, 55, 155-178.

• Fanning, A.L., D.W. O’Neill, J. Hickel, & N. Roux. (2022). The social shortfall and ecological overshoot of nations. Nat. Sustain. 5, 26–36.

• Fotheringham, A.S., C. Brunsdon, M. Charlton (2002). Geographically Weighted Regression: The Analysis of Spatially Varying Relationships. Wiley.

• Fotheringham, A.S., W. Yang & W. Kang (2017). Multiscale Geographically Weighted Regression (MGWR). Ann. Am. Assoc. Geogr. 107, 1247–1265.

• O’Neill, D.W., A.L. Fanning, W.F. Lamb, & J.K. Steinberger (2018). A good life for all within planetary boundaries. Nat. Sustain. 1, 88–95.

• Raworth, K. (2017). Doughnut Economics: Seven Ways to Think Like a 21st-Century Economist. Chelsea Green Publishing, White River Junction, Vermont.

• Rey, S.J. (2019). PySAL: the first 10 years. Spat. Econ. Anal. 14, 273–282.

• Rey, S.J., W. Kang, L.J. Wolf (2019). Regional inequality dynamics, stochastic dominance, and spatial dependence. Pap. Reg. Sci. 98, 861–881.

• Rey, S.J., D. Arribas-Bel & L.J. Wolf. (forthcoming). Geographical Data Science with Python. CRC Press.