A Comparative Study of Topic Modelling Approaches for User-generated Point of Interest Data - Ravi Satyappa Dabbanavar; Arindam Biswas (Proceedings of the International Conference of Contemporary Affairs in Architecture and Urbanism-ICCAUA)
This study aims to enhance urban planning and management by harnessing the power of machine learning (ML) and big data. We focus on Urban Functional Zones (UFZs), the fundamental units for human socio-economic activities. Our methodology involves compiling Point of Interest (POI) data from various sources for comprehensive analysis. We employ various topic modeling approaches such as Latent Dirichlet Allocation (LDA), Latent Semantic Index (LSI), Hierarchical Dirichlet Process (HDP), and Top2Vec. Our principal results reveal significant differences in the performance and coherence of these models on short text documents. Consequently, our major conclusion is identifying the better-performing topic model for classifying UFZs from POI data. We also explore four text preprocessing steps to optimize the performance of the topic models. This study contributes to the field by providing a nuanced understanding of UFZs, paving the way for future data-driven urban planning and management.