# Spatial regionalization based on optimal information compression

arXiv: https://arxiv.org/abs/2111.01813 · HTML: https://mlsanity.com/paper/2111.01813/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Nov 2021.

## Built on by (most cited first, 4 of 4)

- [Estimation of partial rankings from sparse, noisy comparisons](https://mlsanity.com/paper/2501.02505/) (2025)
- [Extending regionalization algorithms to explore spatial process heterogeneity](https://mlsanity.com/paper/2206.09429/) (2022)
- [Mutual information and the encoding of contingency tables](https://mlsanity.com/paper/2405.05393/) (2024)
- [Scalable inference of spatial regions and temporal signatures from time series](https://mlsanity.com/paper/2605.05008/) (2026)

## Abstract (arXiv)

Regionalization, spatially contiguous clustering, provides a means to reduce the effect of noise in sampled data and identify homogeneous areas for policy development among many other applications. Existing regionalization methods require user input such as the number of regions or a similarity measure between regions, which does not allow for the extraction of the natural regions defined solely by the data itself. Here we view the problem of regionalization as one of data compression and develop an efficient, parameter-free regionalization algorithm based on the minimum description length principle. We demonstrate that our method is capable of recovering planted spatial clusters in noisy synthetic data, and that it can meaningfully coarse-grain real demographic data. Using our description length formulation, we find that spatial ethnoracial data in U.S. metropolitan areas has become less compressible over the period from 1980 to 2010, reflecting the rising complexity of urban segregation patterns in these metros.