A Data Envelopment Analysis-Based Methodology for Ranking Cities and Prioritizing Urban Criteria
Document Type
Article
Keywords
multiple-criteria decision making; data development analysis; fixed-effects model; random-effects model; quality of life; city rankings
Publisher
Springer Nature Link
Publication Source
Information Technology and Management
Rights Management
© 2025 Springer Nature
Abstract
This paper proposes a methodology for ranking cities and prioritizing the criteria that influence these rankings within a multi-criteria decision-making (MCDM) framework. A composite Data Envelopment Analysis (DEA) model is developed to generate a fully ranked list of cities, ensuring robustness through data-driven and non-arbitrary weight assignments. Additionally, fixed-effects and random-effects models are employed to identify the criteria significantly impacting city rankings over time. The performance of these models is compared using the Hausman, Dickey-Fuller, and Breusch-Pagan tests. By analyzing large U.S. cities at five-year intervals from 2000 to 2020, this study determines city rankings and evaluates the influence of various quality-of-life criteria. The results show that cost of living, education, and income have the most substantial impacts on city rankings. These insights offer valuable guidance for policymakers aiming to improve urban socio-economic and environmental attributes. Furthermore, they provide businesses with critical information for strategic planning, market analysis, human resource management, and sustainable development.
