Abstract
The accessibility of hospital facilities is of great importance not only for maintaining social stability, but also for protecting the basic human right to health care. Traditional accessibility research often lacks consideration of the dynamic changes in transport costs and does not reflect the actual travel time of urban residents, which is critical to time-sensitive hospital services. To avoid these defects, this study considered the city of Kaifeng, China, as an empirical case, and directly acquired travel time data for two travel modes to the hospital in different time periods through web mapping API (Application Program Interface). Further, based on travel time calculations, we compared five baseline indicators. For the last indicator, we used the optimal weighted accessibility model to measure hospital accessibility for each residential area. The study discovered significant differences in the frequency and spatial distribution of hospital accessibility using public transit and self-driving modes of transportation. In addition, there is an imbalance between accessibility travel times in the study area and the number of arrivals at hospitals. In particular, different modes of transportation and different travel periods also have a certain impact on accessibility of medical treatment. The research results shed new light on the accessibility of urban public facilities and provide a scientific basis with which local governments can optimize the spatial structure of hospital resources.
Highlights
Health is fundamental to human survival and development and is related to the quality of life of individuals, and to national security and social stability [1]
The technical process is as follows: (a) The starting point is defined by writing the code for the API of Gaode Map, building the web application program, parsing a certain range of building contour information, and extracting the coordinates of the unit’s centroid; (b) the destination point is acquired by requesting the hospital POI in batches from Gaode Map and converting the data returned into coordinates; and (c) traffic lines and time consumption are available in corresponding OD (Origin–Destination) matrices that are ready to be imported into ArcGIS by setting the travel mode, route selection, and travel time consumed to get to the starting point in batches
FFiigguurree66. .AAcccecsessibsiibliitlyitlyevleelvsepl astpiaaltdiailstrdiibsutrtiibount.i(oan).O(faf)-pOeafkf-ppeearikodpsepriuobdlsicptruabnlsipc otrrta;n(bsp) oPreta;k(bp)erPioedask ppuebriloicdtsrapnusbploicrt;tr(ac)nOspfof-rpt;ea(ck)pOefrfi-opdesaskelpf-edrrioivdisngsetlrfa-dnsripvoinrtg;anPsepakorpt;er(dio)dPsesaeklf-pderrivioindgs tsrealnf-sdproivrtin. g Atrlatnhsopuogrth. the overall spatial pattern of accessibility of the same transportation mode during peak and non-peak periods does not change significantly in different travel periods, when we microcosmic each residential area, we find that a different travel time has a significant impact on the spatial pattern of hospital assistance accessibility, and the most significant impact is on the residential areas within the boundaries of the old town areas
Summary
Health is fundamental to human survival and development and is related to the quality of life of individuals, and to national security and social stability [1]. The distribution of hospital facilities has long been an important socio-spatial issue at the regional and national levels [6]. The Chinese hospital system has a strict, complex hierarchical structure, with the government playing a leading role in the spatial allocation of resources, urban planning, and construction. This impedes free flow in the spatial distribution of hospital facilities. Researchers are paying increasing attention to the accessibility of hospital facilities and the socio-spatial problems caused by it [17,18]
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