Study on the Location of Private Clinics Based on K-Means Clustering Method and an Integrated Evaluation Model

• Main Authors: Xiaojia Wang, Changyan Shao, Sheng Xu, Shanshan Zhang, Weiqun Xu, Yuxiang Guan
• Format: Article
• Language: English
• Published: IEEE 2020-01-01
• Series: IEEE Access
• Subjects: Evidence theory; k-means clustering; location selection; uncertain linguistic variables; TOPSIS
• Online Access: https://ieeexplore.ieee.org/document/8963670/

Appropriate location is an important prerequisite for the long-term survival and development of private medical institutions. However, in both theory and practice, the issue of location decision-making for private clinics has not been fully studied. We therefore aimed to provide a feasible scheme for the location of new private clinics. This paper combines the k-means clustering method and an integrated 2DULVs (two-dimensional uncertain language variables)-TOPSIS (technique for order preference by similarity to ideal solution)-DSCCR (Dempster-Shafer conjunctive combination rule) model to screen and evaluate all of the areas in the target region for an Internet medical company to set up offline clinics. We first created geographic grids using GIS and collected point of interest (POI) data. We then used the k-means clustering method to obtain 10 suitable grids as alternatives. Last, we established an evaluation index system and used the proposed model to rank them. The results show that grids 178, 179 and 202 are more suitable for the company to establish offline clinics in the expansion of business. The results of this study are also consistent with those of the other three fusion methods. This paper provides a beneficial attempt for private clinics to make location decisions and can be extended to the strategic decision-making of other industries or other issues.