Exploring Urban Land Use Patterns by Pattern Mining and Unsupervised Learning
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 ...
| Authors | Zdena Dobesova, Tai Dinh, Pavel Novak |
|---|---|
| Primary category | cs.DB |
| Published | 17 March 2026 |
| Last updated at source | 10 September 2026 |
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