Abstract

This paper introduces SpatialCluster, a Python library developed for clustering urban areas using geolocated data. The library integrates a range of methods for urban clustering, including Deep Modularity Networks, Gaussian Mixtures, K -Nearest Neighbours, Self Organized Maps, and Information -Theoretic Clustering, providing a comprehensive framework. These methods are evaluated using indices such as the Adjusted Rand Index and Adjusted Mutual Information, and the library includes features for detailed map visualization. SpatialCluster's online documentation offers examples, making the library accessible to researchers and urban planners. The library aims to facilitate urban data analysis and contribute to the field of urban studies.