Empirical Urban Growth Dynamics (2000–2025) and A Conceptual Architectural Framework for Spatial Digital Twins in Secondary African Cities: A Case Study of Owerri, Nigeria
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Abstract
Rapid urban expansion in secondary cities of Sub-Saharan Africa has intensified pressure on land resources and infrastructure, exposing the limitations of conventional planning systems reliant on periodic assessments. While Geographic Information Systems (GIS), Remote Sensing (RS), and Artificial Intelligence (AI) have improved monitoring, their integration into continuous Spatial Digital Twin (SDT) environments remain limited in Nigeria. To address this gap, this study presents an empirical baseline analysis and formulates a conceptual Spatial Digital Twin framework for sustainable urban growth monitoring in Owerri Metropolis, Nigeria. Historical land use and land cover dynamics between 2000 and 2025 were first quantified using multi-temporal Landsat imagery in Google Earth Engine via Random Forest classification to establish the empirical foundation. Based on observed growth patterns, the study identified key spatial, environmental, and administrative data requirements and formulated a five-layer system architecture. Comprising Physical Environment, Data Acquisition, Spatial Integration, Analytical Intelligence, and Application layers, the framework establishes an architectural roadmap for integrating multi-source spatial data, scenario simulations, and predictive analysis into local planning. The practical utility of the framework is demonstrated through targeted applications in urban expansion tracking, flood hazard evaluation, and Sustainable Development Goal (SDG) 11 monitoring. This study provides a scalable, transferable blueprint to guide municipal authorities rather than claiming an active, fully deployed live software or sensor environment..
Keywords
- Spatial Digital Twin
- Sustainable Urban Development
- Urban Growth Monitoring
- Artificial Intelligence
- Internet of Things
- SDG 11
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