GEO-INTELLIGENT GOVERNANCE FRAMEWORK FOR PREDICTING CITIZEN MOBILITY PATTERNS TO SUPPORT SMART URBAN ADMINISTRATION

Abstract

This study proposes a geo-intelligent governance framework that combines the Multi-Context Integrated Deep Neural Network (MCI-DNN) and Geo-Spatial Transformer Networks to assist municipalities in understanding and managing the dynamics of spatial movement within urban jurisdictions. In contrast to conventional location-based social network (LBSN) models that are solely focused on commercial or social prediction tasks this framework is recontextualized for public administration enabling data-driven resource allocation traffic regulation and urban planning. By using anonymized citizen mobility datasets the proposed MCI-DNN model is able to identify new movement patterns and service needs by capturing temporal behavioral and semantic mobility contexts. Simulating the interdependencies between administrative zones the Geo-Spatial Transformer component makes it easier to identify high-density movement corridors that affect infrastructure strain and service delivery. By fusing spatial and temporal intelligence the model improves local governments capacity to practice participatory urban management and proactive governance. Empirical validation using real mobility data from urban areas showed that the integrated approach performed better in terms of predictive accuracy than traditional models. By providing useful insights for e-governance systems sustainable transportation policy and interdepartmental coordination the findings demonstrate how artificial intelligence can support local administrative efficiency and citizen-centric service delivery.