Assessing Service Accessibility and Optimizing the Spatial Layout of Elderly Canteens: A Case Study of Nanjing, China

Xiaoli Wei, Xu Yuan and Yong Xie ()
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Xiaoli Wei: College of Public Administration, Nanjing Agricultural University, Nanjing 210095, China
Xu Yuan: College of Public Administration, Nanjing Agricultural University, Nanjing 210095, China
Yong Xie: College of Public Administration, Nanjing Agricultural University, Nanjing 210095, China

Land, 2025, vol. 14, issue 11, 1-28

Abstract: Equitable accessibility to elderly canteens is critical for addressing the challenges of an aging population. Using Nanjing as a case study, this paper constructed an integrated framework that fuses GIS spatial analysis with interpretable machine learning to diagnose, evaluate, and optimize the service network’s spatial layout. The study found that the existing design is a direct manifestation of the conflict between “market logic” and “social demand.” First, Nanjing’s elderly canteen service suffers from a severe spatial mismatch and inequality of opportunity. Approximately 80% of the elderly population resides in areas that share less than 15% of the canteen resources. Second, a multi-modal accessibility analysis revealed the phenomenon of “false equity.” The high service coverage under the car accessibility model masks the systemic service deprivation faced by the majority of seniors who rely on walking and micromobility. Third, this study proposed and validated a data-driven “stock activation” strategy. An XGBoost model, guided by a “demand-oriented and spatially efficient” decision-making logic, identified 161 high-potential optimization sites. At the same time, the framework also diagnosed its own strategic boundaries by identifying “resource vacuums” where a lack of convertible stock necessitates alternative solutions, such as new builds.

Keywords: elderly canteen; spatial accessibility; spatial layout optimization; POI; machine learning (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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