Geographic Data Science

Regionalisation
Dani Arribas-Bel

Regionalization

Unsupervised Spatial Machine Learning

Aggregating basic spatial units (areas) into larger units (regions)

Regionalization

Split a dataset into groups of observations that are similar within the group and dissimilar between groups, based on a series of attributes

…with the additional constraint observations need to be spatial neighbors

Regionalization

  • All the methods aggregate geographical areas into a predefined number of regions, while optimizing a particular aggregation criterion;
  • The areas within a region must be geographically connected (the spatial contiguity constraint);
  • The number of regions must be smaller than or equal to the number of areas;
  • Each area must be assigned to one and only one region;
  • Each region must contain at least one area.

Duque et al. (2007)

Regionalization

  • All the methods aggregate geographical areas into a predefined number of regions, while optimizing a particular aggregation criterion;
  • The areas within a region must be geographically connected (the spatial contiguity constraint);
  • The number of regions must be smaller than or equal to the number of areas;
  • Each area must be assigned to one and only one region;
  • Each region must contain at least one area.

Duque et al. (2007)

Algorithms

  • Automated Zoning Procedure (AZP)
  • Arisel
  • Max-P

See Duque et al. (2007) for an excellent, though advanced, overview

Examples

Census geographies

Choropleth

Livehoods

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A course on Geographic Data Science by Dani Arribas-Bel is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.