Cartographic modeling of demographic processes using remote sensing data
Abstract
This study explores the intricate interactions between demographic processes and spatial variables through the lens of cartographic modeling, leveraging remote sensing data for enhanced precision. Land cover classifications reveal the dominance of urban and agricultural landscapes, setting the stage for a nuanced examination of demographic dynamics. Spatial correlations highlight the interdependencies between demographic variables, while regression coefficients provide insights into their impacts on the overall cartographic model. Predictive accuracy assessments validate the model's robustness, and spatial autocorrelation analyses unveil geographic clustering of demographic patterns. The integration of remote sensing data proves instrumental in enhancing the granularity of our understanding, offering valuable insights for sustainable urban planning and resource allocation. While acknowledging limitations, this study contributes to the broader discourse on urban development, offering a comprehensive framework for policymakers and researchers to make informed decisions in the context of evolving demographic and spatial dynamics.