Analysing the global and local spatial associations of medical resources across Wuhan city using POI data

Abstract

Background There is a sharp contradiction between the supply and demand of medical resources in the provincial capitals of China. Understanding the spatial patterns of medical resources and identifying their spatial association and heterogeneity is a prerequisite to ensuring that limited resources are allocated fairly and optimally, which, along with improvements to urban residents’ quality of life, is a key aim of healthy city planning. However, the existing studies on medical resources pattern mainly focus on their spatial distribution and evolution characteristics, and lack the analyses of the spatial co-location between medical resources from the global and local perspectives. It is worth noting that the research on the spatial relationship between medical resources is an important way to realize the spatial equity and operation efficiency of urban medical resources. Methods Localized colocation quotient (LCLQ) analysis has been used successfully to measure directional spatial associations and heterogeneity between categorical point data. Using point of interest (POI) data and the LCLQ method, this paper presents the first analysis of spatial patterns and directional spatial associations between six medical resources across Wuhan city. Results (1) Pharmacies, clinics and community hospitals show “multicentre + multicircle”, “centre + axis + dot” and “banded” distribution characteristics, respectively, but specialized hospitals and general hospitals present “single core” and “double core” modes. (2) Overall, medical resources show agglomeration characteristics. The degrees of spatial agglomeration of the five medical resources, are ranked from high to low as follows: pharmacy, clinic, community hospital, special hospital, general hospital and 3A hospital. (3) Although pharmacies, clinics, and community hospitals of basic medical resources are interdependent, specialized hospitals, general hospitals and 3A hospitals of professional medical resources are also interdependent; furthermore, basic medical resources and professional medical resources are mutually exclusive. Conclusions Government and urban planners should pay great attention to the spatial distribution characteristics and association intensity of medical resources when formulating relevant policies. The findings of this study contribute to health equity and health policy discussions around basic medical services and professional medical services.

A Community-Oriented Accessibility Index of Public Health Service Facilities: A Case Study of Wuchang District, Wuhan, China

“…The old city area is where most of the city's population is concentrated, the problems with the configuration of medical facilities are more prominent, and the planning is more complicated. When studying all levels of the medical system together [45], it is easy to ignore the differences in the supply of public medical service facilities. This paper focuses on public health facilities and proposes some spatial optimization layout strategies.…”

Mining significant local spatial association rules for multi-category point data

“…Wang et al developed a simulation-based statistical test for the local indicator of the colocation quotient (LCLQ) [41] . Chen et al used the LCLQ method with POI data to identify the spatial associations and heterogeneity among six medical resources in Wuhan, China [42] . Several scholars have proposed an algorithm combining light GBM and Apriori to establish a mining model of strong association rules [43] .…”

“… CLQ Wang et al (2017) [41] Develop a simulation-based statistical test for the local indicator of colocation quotient (LCLQ). LCLQ Chen et al (2023) [42] Present the first analysis of spatial patterns and directional spatial associations between six medical resources across Wuhan city by POI data and LCLQ method. LCLQ Local Moran's I Zhang et al (2022) [44] Propose a method for association rule mining based on spatial autocorrelation clustering events and apply it to polymetallic ore deposits.…”

Analisis Spasial Aksesibilitas Fasilitas Kesehatan di Kabupaten Jember

“…Buffer dapat diatur dengan radius tertentu, sesuai dengan kebutuhan penelitian, seperti 1 kilometer atau 5 kilometer. Sementara itu, analisis Nearest Neighbour digunakan untuk menentukan fasilitas kesehatan terdekat dari setiap titik dalam populasi (Chen et al, 2023;Lovett et al, 2014;Mishra et al, 2019;Pei et al, 2009). Ini memberikan informasi tentang jarak fisik antara individu atau populasi dengan fasilitas kesehatan terdekat.…”

“…Di sisi lain, terdapat pola persebaran yang cenderung berkumpul untuk 2 Rumah Sakit Khusus (RSK) di Kabupaten Jember. Visualisasi persebaran menggunakan pendekatan Nearest Neighbour Analysis (Chen et al, 2023;Lovett et al, 2014;Mishra et al, 2019;Pei et al, 2009) dapat dilihat pada Gambar 1. Dalam penelitian ini, wilayah pelayanan rumah sakit dan puskesmas dibagi ke dalam empat kategori jarak, yaitu 1 kilometer (sangat dekat), 3 kilometer (dekat), 5 kilometer (sedang), dan 7 kilometer (jauh).…”