Optimizing the Location and Typology of Healthcare Centers through an Integrated Machine Learning and Urban Morphology Approach: A Case Study of Borujerd City
Keywords:
geographic information system (GIS), machine learning, urban morphology, advanced planning , Health centersAbstract
This study aimed to optimize the location and typology of healthcare centers in Borujerd by integrating machine learning algorithms, geographic information system analyses, and urban morphological indicators. This applied study employed a descriptive–analytical spatial design. The expert population comprised 30 specialists in urban planning, geography and geographic information systems, and healthcare services management, selected through purposive sampling. The study area was divided into 50 × 50-meter grid cells, and 2,400 balanced spatial points, including 1,200 suitable and 1,200 unsuitable points, were extracted for modeling. The variables included healthcare service shortage, population density, network travel time, street configuration, land-use mix, block size, and urban permeability. Nearest-neighbor analysis, global Moran’s I, network analysis, support vector machine, random forest, extreme gradient boosting, and a weighted ensemble model were applied. The overall nearest-neighbor ratio was 0.68, with a Z value of -5.62, indicating a significant clustered distribution (p < 0.001). Global Moran’s I was 0.39 and statistically significant (p < 0.001). The weighted ensemble model achieved the strongest performance, with a testing accuracy of 0.932, sensitivity of 0.936, specificity of 0.928, F1 score of 0.932, kappa coefficient of 0.864, and area under the curve of 0.976. Model–expert agreement was confirmed by a kappa coefficient of 0.817, while the correlation between their rankings was 0.862 (p < 0.001). Implementing the proposed scenario increased favorable healthcare coverage from 72.13% to 89.46%. Integrating machine learning with urban morphology provided an accurate, interpretable, and stable framework for identifying priority areas and assigning appropriate healthcare facility types, thereby contributing to reduced spatial inequality and improved healthcare accessibility in Borujerd.
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Copyright (c) 2025 Saeideh Ravanmard (Author); Mohammad Jalili; Farhad Karvan, Hossein Yarahmadi (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
