GSOC brainstorming

raster awareness for pysal [Dani, Serge, Levi, …]

  • weights & API compatibility to treat rasters as first class citizens

  • optimization of the raster case, in that there’s a simpler graph required for the regular graph

  • fragstats equivalent for ESDA?

Panel Data Spatial Econometrics

With the exception of seemingly unrelated regressions (SUR), the models covered in pysal.spreg only deal with cross-sectional data. There is a lack of support to deal with common spatial panel model settings, i.e., situations with observations in both the spatial and time domain.

The goal of this project is to extend the functionality in pysal.spreg with data handling, estimation methods and specification tests for both static and dynamic spatial panel models. This will cover fixed effects as well as random effects specifications. The initial focus will be on models where the cross-sectional dimension dominates (N >> T), and include estimation methods and specification tests for spatial lag, spatial error and spatial Durbin specifications. The ultimate goal is to also include functionality to deal with more general spatial effects in models with both large N and large T.

Skills

  • Familiarity with pysal.spreg, Scipy sparse matrices (scipy.sparse) and Numpy
  • Solid understanding of panel data econometrics and fundamentals of spatial econometrics
  • Anselin, Luc, Julie Le Gallo and Hubert Jayet (2008). Spatial panel econometrics. In L. Matyas and P. Sevestre (Eds.), The Econometrics of Panel Data, Fundamentals and Recent Developments in Theory and Practice (3rd Edition), pp. 627-662. Berlin: Springer-Verlag.
  • Lee, Lung-Fei and Jihay Yu (2011). Estimation of spatial panels. Foundations and Trends in Econometrics 4, 1-164.
  • Elhorst, J. Paul (2014). Spatial Econometrics, From Cross-Sectional Data to Spatial Panels. Berlin: Springer-Verlag.

Difficulty Level: intermediate

Mentors: Pedro Amaral, Luc Anselin, Sergio Rey

spatial gaussian process models [Levi]

  • specifically, on top of sklearn, gpflow, or gpy

ESDA enhancement (esda#61)

  • local join counts

  • multinomial join counts (i.e. multiclass/color)

  • multivariate join count statistics (i.e. more than one binary variate)

  • cage statistic criterion for aggregation error

  • local gamma

  • multivariate geary (d) ljwolf/multi_c.py

  • local indicator of spatial heterogeneity (LOSH, getis & ord?)

spopt models [Serge, Levi, …]

What Stef’s got planned

  • extending pointpats
    • allowing comparisons of patterns
    • visualization
    • co-location?
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