20: Mapping Multidimensional Urban Prosperity Through Grid-Based Partitions Across Temporal Scenarios: A Natural Communication Modeling Approach Using Gis and Machine Learning

Understanding and fostering urban prosperity is essential for developing sustainable and inclusive built environments. This paper introduces an adaptable framework for mapping integrated multidimensional prosperity across adjustable intra-regional grid-based partitions and temporal scenarios, spanning historical analysis (2000, 2010, 2020) to future forecasting (2030). Leveraging Geographic Information Systems (GIS), Machine Learning (ML), and Open Data, the framework employs a novel Natural Communication Modeling (NCM) approach to synthesize diverse indicators into unified prosperity metrics. By unlocking latent dimensions beyond direct human perception, NCM enhances comprehension of complex urban phenomena. Demonstrated through a case study in São Paulo, Brazil—a complex, multifaceted metropolis exemplifying diverse urban dynamics found worldwide—this methodology delivers integrated insights to support plural decision-making, thereby contributing to more prosperous and resilient built environments globally.