Exploring Urban Land Use Patterns by Pattern Mining and Unsupervised Learning
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arXiv
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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866908964778672128 |
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| author | Dobesova, Zdena Dinh, Tai Novak, Pavel |
| author_facet | Dobesova, Zdena Dinh, Tai Novak, Pavel |
| contents | Urban areas are intricate systems shaped by socioeconomic, environmental, and infrastructural factors, with land use patterns serving as aspects of urban morphology. This paper proposes a novel methodology leveraging frequent item set mining and unsupervised learning techniques to identify similar cities based on co-occurring land use patterns. The Copernicus program's Urban Atlas data are used as source data. The methodology involves data preprocessing, pattern mining using the negFIN algorithm, postprocessing, and knowledge extraction and visualization. The preprocessing of spatial datasets results in a publicly available transaction dataset. The framework is scalable and the source code is made publicly available. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_13050 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Exploring Urban Land Use Patterns by Pattern Mining and Unsupervised Learning Dobesova, Zdena Dinh, Tai Novak, Pavel Databases Machine Learning Urban areas are intricate systems shaped by socioeconomic, environmental, and infrastructural factors, with land use patterns serving as aspects of urban morphology. This paper proposes a novel methodology leveraging frequent item set mining and unsupervised learning techniques to identify similar cities based on co-occurring land use patterns. The Copernicus program's Urban Atlas data are used as source data. The methodology involves data preprocessing, pattern mining using the negFIN algorithm, postprocessing, and knowledge extraction and visualization. The preprocessing of spatial datasets results in a publicly available transaction dataset. The framework is scalable and the source code is made publicly available. |
| title | Exploring Urban Land Use Patterns by Pattern Mining and Unsupervised Learning |
| topic | Databases Machine Learning |
| url | https://arxiv.org/abs/2604.13050 |