METHODS FOR ANALYZING SOCIO-ECONOMIC DATA OF TERRITORIAL COMMUNITIES FOR ADAPTIVE RESOURCE MANAGEMENT

Abstract

The socio-economic development of territorial communities in modern conditions requires adaptive approaches to resource management based on the intelligent analysis of large volumes of data of various types. Effective decision-making depends on the ability to integrate structured, semi-structured, and unstructured data, enabling the prediction of dynamic processes, identification of cluster groups of objects, and evaluation of key development indicators. The proposed information technology integrates modern methods of machine learning, natural language processing, and computer vision for socio-economic data analysis, ensuring accuracy, speed, and flexibility in decision-making.
Based on the proposed approach, methods for cluster analysis, forecasting, and hybrid analysis have been improved, allowing consideration of the specifics of territorial communities and adaptation to crisis conditions. The obtained results lay the foundation for creating an innovative decision-support system that promotes sustainable community development, efficient resource management, and improved quality of life for the population.