Private Journal of Sustainable Urban & Regional Development Studies (JSURDS) 2783-0764 Articles in Press 2025 11 22 Evaluation and Ranking of Neighborhoods in Tehran's District 6 Based on the Development of Smart City Indicators and Factors Influencing Their Enhancement Evaluation and Ranking of Neighborhoods in Tehran's District 6 Based on the Development of Smart City Indicators and Factors Influencing Their Enhancement 223731 FA Hosein Nazmfar Professor, Department of Geography & Rural Planning, Faculty of Planning and Environmental Sciences, University of Tabriz, Tabriz, Iran. 0000-0001-8088-6910 Yaser Afshon Ph.d student. Department of Geography and Urban Planning, Mohaghegh Ardabili University, Ardabil, Iran. Mohamad Mahdi Mahabadi Pour Ph.d student. Department of Geography and Urban Planning, Mohaghegh Ardabili University, Ardabil, Iran. 0009-0001-5746-2252 Journal Article 2025 04 08 Background and Objective: The smart city, as a modern approach to urban management, aims to enhance quality of life, service efficiency, and sustainable development through advanced information technologies, citizen participation, and data-driven decision-making. This study evaluates and ranks the neighborhoods of Tehran's District 6 based on the development level of smart city indicators. <br />Methodology: The research is applied in nature and employs a descriptive-analytical method. Initially, smart city indicators were identified through a review of theoretical sources, followed by the development of a specialized questionnaire based on the COCOSO model. Field data were collected from 30 urban planning experts, including university professors and municipal officials, using snowball sampling. The Shannon entropy method was used to determine the weight of each indicator, and the COCOSO decision-making model was applied to rank the neighborhoods.<br />Results and Findings: Results indicate that Neighborhood 4, with a score of 5.442, ranks first, followed by Neighborhood 5 (5.411) and Neighborhood 6 (5.118) in second and third places, respectively. Neighborhoods 3, 1, and 2 rank fourth to sixth with scores of 4.099, 3.322, and 1.447, respectively. The findings suggest that balanced smart city development in this district requires targeted policymaking, enhanced digital infrastructure, and leveraging local capacities in each neighborhood.<br />Conclusion: These results highlight that utilizing local advantages, particularly in lower-performing neighborhoods, can significantly contribute to improving smart city indicators and reducing spatial inequalities. Background and Objective: The smart city, as a modern approach to urban management, aims to enhance quality of life, service efficiency, and sustainable development through advanced information technologies, citizen participation, and data-driven decision-making. This study evaluates and ranks the neighborhoods of Tehran's District 6 based on the development level of smart city indicators. <br />Methodology: The research is applied in nature and employs a descriptive-analytical method. Initially, smart city indicators were identified through a review of theoretical sources, followed by the development of a specialized questionnaire based on the COCOSO model. Field data were collected from 30 urban planning experts, including university professors and municipal officials, using snowball sampling. The Shannon entropy method was used to determine the weight of each indicator, and the COCOSO decision-making model was applied to rank the neighborhoods.<br />Results and Findings: Results indicate that Neighborhood 4, with a score of 5.442, ranks first, followed by Neighborhood 5 (5.411) and Neighborhood 6 (5.118) in second and third places, respectively. Neighborhoods 3, 1, and 2 rank fourth to sixth with scores of 4.099, 3.322, and 1.447, respectively. The findings suggest that balanced smart city development in this district requires targeted policymaking, enhanced digital infrastructure, and leveraging local capacities in each neighborhood.<br />Conclusion: These results highlight that utilizing local advantages, particularly in lower-performing neighborhoods, can significantly contribute to improving smart city indicators and reducing spatial inequalities.