Spatial regionalization based on optimal information compression

نویسندگان

چکیده

Abstract 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 number regions or similarity measure between regions, which does not allow extraction natural defined solely by itself. Here we view problem one compression develop an efficient, parameter-free algorithm based on minimum description length principle. We demonstrate that our method is capable recovering planted spatial clusters noisy synthetic data, it can meaningfully coarse-grain real demographic data. Using formulation, find ethnoracial U.S. metropolitan has become less compressible over period from 1980 2010, reflecting rising complexity urban segregation patterns these metros.

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ژورنال

عنوان ژورنال: Communications physics

سال: 2022

ISSN: ['2399-3650']

DOI: https://doi.org/10.1038/s42005-022-01029-4