Hospital distribution in a metropolitan city: assessment by a geographical information system grid modelling approach

Abstract

Abstract. Grid models were used to assess urban hospital distribution in Seoul, the capital of South Korea. A geographical information system (GIS) based analytical model was developed and applied to assess the situation in a metropolitan area with a population exceeding 10 million. Secondary data for this analysis were obtained from multiple sources: the Korean Statistical Information Service, the Korean Hospital Association and the Statistical Geographical Information System. A grid of cells measuring 1 × 1 km was superimposed on the city map and a set of variables related to population, economy, mobility and housing were identified and measured for each cell. Socio-demographic variables were included to reflect the characteristics of each area. Analytical models were then developed using GIS software with the number of hospitals as the dependent variable. Applying multiple linear regression and geographically weighted regression models, three factors (highway and major arterial road areas; number of subway entrances; and row house areas) were statistically significant in explaining the variance of hospital distribution for each cell. The overall results show that GIS is a useful tool for analysing and understanding location strategies. This approach appears a useful source of information for decision-makers concerned with the distribution of hospitals and other health care centres in a city.

Inequality in geographical distribution of hospitals and hospital beds in densely populated metropolitan cities of Iran

“…A study in Japan identified scarcity of healthcare resources in low socioeconomic population groups, an imbalance in healthcare services provision, and a positive correlation between hospital bed density and inpatient flow ratio [54]. An association between highway and major arterial roads, number of subway entrances, and row house areas with hospital distributions in a metropolitan city was also reported [55]. The different analytical approaches applied in our study clearly demonstrated the inequality in hospital and hospital beds concentrations.…”

Making regional facility location decisions: the example of Hospital do Oeste Norte, Portugal

“…For this reason, the location of public facilities becomes a central factor in the quality of life of populations and in guaranteeing an appropriate redistribution of public funds (Witter, 2012). The problem is thus generally centred around reducing spatial friction (Hyndman and Holman, 2001;Lee and Moon, 2014), but this concern is gradually replaced by others as one comes closer to defining the specific location area. The reason is that one has to consider the compatibility of the facility with the characteristics of a location option (urban dynamics, noise, pollution, congestion, availability of space, environmental sensitivity, etc.)…”