External authors:

• Axel Reyes ( Universidad Tecnica Federico Santa Maria , Millennium Inst Fdn Res Data IMFD )
• Camila Vera ( Pontificia Universidad Catolica de Chile , Millennium Inst Fdn Res Data IMFD , Natl Ctr Artificial Intelligence CENIA )
• Francesca Lucchini ( Pontificia Universidad Catolica de Chile , Natl Ctr Artificial Intelligence CENIA )
• Jan Dimter ( Pontificia Universidad Catolica de Chile , Millennium Inst Fdn Res Data IMFD )
• Felipe Gutierrez ( Pontificia Universidad Catolica de Chile , Natl Ctr Artificial Intelligence CENIA )
• Naim Bro ( Universidad Adolfo Ibanez , Millennium Inst Fdn Res Data IMFD )
• Ariel Reyes ( Pontificia Universidad Catolica de Chile , Natl Ctr Artificial Intelligence CENIA )

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.