Exploring Urban Land Use Patterns by Pattern Mining and Unsupervised Learning
cs.DB, cs.LG
Submitted: 2026-03-17
Updated: 2026-09-10
License: http://creativecommons.org/licenses/by/4.0/
The gist: Comparative planning needs reproducible methods for identifying recurring land-use configurations across cities.
Terminology
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.
Sources
- A typology of street patterns
- Adaptive wireless power transfer in mobile Ad Hoc networks
- Mining GIS Data to Predict Urban Sprawl
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