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Spatial regionalization based on optimal information compression
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DOI：10.1038/s42005-022-01029-4.png)
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Abstract
En 中文
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. Aggregating fine-grained data allows for the reduction of the impact of noise and outliers when analysing real-world systems. Here the author proposes a principled and efficient method based on information theory to compress spatial systems into macroscopic regions, capturing spatial clusters able to reveal the complex spatial organization of socioeconomic systems and demographic data.
Keywords:
RACIAL DIVERSITY
UNITED-STATES
CITIES
SEGREGATION
PATTERNS
SCALE
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