Shaanxi Province is the most developed province in northwest China, the area was 205,800 square kilometers with a total of 38,350 thousand populations in 2017 . Geographically, the central, south, and north regions of Shaanxi Province differ significantly. The central part is a plain and the wealthiest area in Shaanxi Province, the south part is the Qinling Mountains, the north part is the Loess Plateau. The economy is less developed with a relatively small population density in the south and north part. This study divides Shaanxi Province into three regions based on the condition of geography and economy.
Data Collection Scheme
In order to measure spatial accessibility of health resources, three aspects of data were basically needed: geographical distribution of population, the geographical location of hospitals, and time and distance between residents and hospitals. Therefore, we collected data in three steps.
Firstly, considering the uneven distribution of population, we used the geographical location of the villages and neighborhoods to identify the population distribution. Two strategies were adopted in our study:
i) for villages and neighborhoods with village clinic: we selected the coordinates of the village clinics to represent the population distribution since the village clinics should be in a relatively concentrated area of the village population to cover the population of the village to the greatest extent when they were set.
ii) for the villages and neighborhoods whose village clinic cannot be acquired or have multiple village clinics, we selected the default coordinates provided by web map navigation service This coordinate usually defaults to the location of the villages and neighborhoods’ office that is usually located in a populated area.
Secondly, we collected the name of county hospitals from the Health Commission of Shaanxi Province, and then we directly used the name of hospitals to get the geographical location from the web map.
Thirdly, the time and distance between each village and neighborhood to the county hospitals were collected from the results of navigation of the web map navigation service. Chose the fastest but not the highway route (because China's highway import and export are usually set around the county) to get the time and distance from villages and neighborhoods to the local county hospital by using the real-time navigation data of AutoNavi map under the driving mode. The reason why only the local county hospitals were selected is that the Chinese new rural cooperative medical insurance implemented in rural areas is at the county level. In this study, we assumed that due to the medical insurance reimbursement strategy, residents were less likely to visit a doctor in another county-level hospital outside the county .
Data Collection Method
Firstly, we obtained the names of village clinics and county hospitals in overall Shaanxi Province from the Shaanxi Provincial Health Statistics Annual Report in 2017 that was provided by the Health Commission of Shaanxi Province. In addition, we also obtained the name of village and neighborhood committees in overall Shaanxi Province from the website of the National Bureau of Statistics .
Secondly, we used the geocoding interface of AutoNavi map to collect the coordinates of villages and neighborhoods and county hospitals. The requests for the API of geocoding of AutoNavi map were conducted by using a web crawler in the Python 3.6 program . The URL of this geocoding interface can be found here. AutoNavi map knows as Gaode in Chinese is founded in 2011 and is one of the largest web mapping, navigation, and location-based services providers in China. It offers map services at Amap.com and a mobile app.
Thirdly, navigation data, including driving time and distance, were collected by using the path planning interface by setting the coordinates of the villages and neighborhoods as the starting point and the coordinates of a county hospital in the district as the endpoint. The URL of path planning interface can be found here. Considering the influence of traffic conditions at different times, this study was performed four times randomly: the morning (10:00 to 11:00) and afternoon (14:00 to 15:00) on November 23, 2018 (Friday) and November 27, 2018 (Tuesday). During the time period, 4 times crawling requests by Python were made to the AutoNavi map, and took the average value of 4 times. Finally, data on 10,350 villages and neighborhoods (total 13,074 villages and neighborhoods) from 73 counties of Shaanxi Province were obtained in our study (Fig. 1).
The travel impedance to a nearest provider (TINP) was used to evaluate the spatial accessibility in this study. TINP measured the spatial accessibility by using indicators such as the distance, time, or cost from the place of residence to the nearest medical institution, it was expressed in terms of straight Euclidean distance (straight line) . Distance and time are indicators that directly reflect spatial accessibility, the closer the distance is, the shorter the time and the higher the accessibility. Although TINP ignores the supply of health resources, this method is applicable to the situations that the choice of seeking healthcare service is relatively simple in rural areas. In addition, we used more precise traffic distance downloads from a web map instead of Euclidean distance in this study.
We calculated Getis-Ord Gi* statistics for the spatial association of each county to explore the disparity of spatial accessibility [27, 28]. The Gi* statistic returned for each county is a z-score . A high positive z-score and small p-value for a county represent a spatial clustering of high values (hot spot). A low negative z-score and small p-value represent a spatial clustering of low values (cold spot). The higher or lower the z-score, the more intense the clustering. A z-score close to zero means no significant spatial clustering. Getis-Ord Gi* statistics were calculated by ‘spdep’ package of R language . The spatial relationships of counties were defined as Queen's Case. The distance and time of counties were the averages of distance and time of villages and neighborhoods.