Análise vocacional das áreas de transbordamento da mancha urbana de Natal

Abstract

This study's primary objective is to explore the potential of geospatial analysis in studying population dynamics and spatial distribution in the city of Natal, aiming to comprehend the current urban structure and provide technical and theoretical support for housing provision. To achieve this goal, the Self-Organizing Maps (SOM) method, an unsupervised learning algorithm of artificial neural networks, was utilized, enabling the visualization and analysis of high-dimensional data. The study is based on data from the 2000 and 2010 demographic censuses (IBGE), WorldPop, and other sources. It investigated the urbanization process and the unequal spatial organization of the city. The results reveal that the Natal/Parnamirim area exhibits higher economic potential, whereas the Natal/São Gonçalo do Amarante area has lower potential, attributed to lower average family income. The application of geoprocessing, combined with the SOM method, proves to be an effective tool for urban planning, the real estate market, and the development of public policies, enabling the identification of opportunities and data-driven decision-making based on the socio-economic characteristics of the population. In summary, this study details the use of geospatial analyses and the SOM method in the analysis of population dynamics and spatial organization in Natal, providing valuable insights for urban planning, public policies, and the real estate market.