Exploring Urban Land Use Patterns by Pattern Mining and Unsupervised Learning

Abstract:Comparative planning needs reproducible methods for identifying recurring land-use configurations across cities. Using Urban Atlas 2018 data for 100 European urban areas, we construct 290,396 focal-neighborhood transactions and 1,543 frequent-itemset support features at 10\% minimum support. Ward clustering is applied in the original normalized feature space, with UMAP used only for visualization. A seven-cluster descriptive solution is retained through multi-criterion evaluation and 500 paired feature-subsampling repetitions. Sensitivity analyses show stable city-similarity geometry across 5--15\% thresholds but greater membership sensitivity to representation choices and non-artificial land-use content. The framework supports peer-city comparison while making scale and boundary limitations explicit.

Databases (cs.DB); Machine Learning (cs.LG)

arXiv:2604.13050 [cs.DB]

(or arXiv:2604.13050v2 [cs.DB] for this version)

https://doi.org/10.48550/arXiv.2604.13050 Focus to learn more arXiv-issued DOI via DataCite

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