<![CDATA[The COVID-19 pandemic has highlighted the limitations of conventional case-based zoning, emphasizing the need for spatially detailed risk mapping in heterogeneous urban environments such as Medan, where density, mobility, and socioeconomic conditions vary considerably across neighborhoods. This study develops a neighborhood-level pandemic risk mapping framework for Medan City using fuzzy logic and multi-criteria analysis, examining relationships among pandemic risk, spatial planning, and city preparedness across 151 urban village in 21 sub-districts. Primary data were gathered through structured interviews with 151 urban villages (kelurahan) representatives and field observations, complemented by secondary statistical data from official government agencies. Data accuracy was verified through data triangulation across primary and secondary sources and expert consistency ratio checks prior to multi-criteria evaluation. Threat, vulnerability, capacity, spatial planning, and preparedness data were standardized using fuzzy membership functions, with risk weights determined through Principal Component Analysis and planning weights derived from the Analytical Hierarchy Process. Spatial layers were integrated through Multi-Criteria Evaluation and weighted overlay in a Geographic Information System, with inter-index relationships assessed using Ordinary Least Squares and Geographically Weighted Regression. The pandemic risk index ranged from 0.015 to 0.809, with 40.44% of urban villages classified as low risk, 46.36% as moderate risk, and 13.25% as high risk. High-risk areas concentrated in the city center and eastern zones, reflecting elevated density and mobility. While the global OLS model showed weak direct effects of risk and spatial planning on preparedness, the local GWR model revealed substantially stronger spatial relationships, with risk demonstrating a significant influence on spatial planning at an R² of 0.7182. In conclusion, this study demonstrates that combining fuzzy multi-criteria risk mapping with local spatial regression provides a robust, spatially targeted framework for urban spatial planners and policy makers to strengthen disaster resilience and prioritize pandemic interventions in highly vulnerable urban neighborhoods.Received: 2026-06-08 Revised: 2026-07-08 Accepted: 2026-09-29 Published: 2026-11-03]]>
