Exploring Urban Land Use Patterns by Pattern Mining and Unsupervised Learning
This work addresses urban planning and analysis by providing a tool for comparing cities, but it is incremental as it applies existing techniques to a specific domain.
The paper tackles the problem of identifying similar cities based on land use patterns by proposing a methodology that uses frequent item set mining and unsupervised learning on Urban Atlas data, resulting in a scalable framework with publicly available data and code.
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.