Аңдатпа
The presence and equal distribution of social infrastructure, such as healthcare facilities, educational institutions, and public transportation systems, is critical to promoting long-term urban development and improving community life quality. This study looks into how data mining and machine learning approaches might be used to assess, predict, and eventually improve access to these critical services. We create prediction models that deliver actionable insights to urban planners and policymakers by leveraging large-scale datasets that include demographic profiles, geospatial information, and historical usage trends. The combination of Geographic Information Systems (GIS) and unsupervised learning techniques, such as clustering approaches, enables us to examine spatial distribution trends and identify underserved locations. This study focuses on Almaty, Kazakhstan, and uses techniques such as population density mapping, online scraping for up-to-date facility data, and algorithms such as k-nearest neighbors (k-NN) to discover the best locations for new infrastructure. The data-driven technique demonstrates that strategic resource allocation guided by predictive analytics can result in more fair and successful urban planning outcomes. Nonetheless, the study acknowledges some limitations, such as the need for more comprehensive socioeconomic statistics, the integration of dynamic (real-time) data streams, and the consideration of urban people' behavioral patterns. Future research should look into the use of advanced models like ensemble learning and deep learning methodologies to improve forecast accuracy and policy responsiveness. This paper contributes to the growing field of smart urban planning by emphasizing the necessity of intelligent, data-driven approaches to creating more inclusive, responsive, and resilient communities