Evaluation of the Basic Public Services of City Based on National Geographic Conditions Monitoring Data Minghai Luo*, Jing Luo†, Jiansong Li‡, Guangping Chen†

* Wuhan Geomatics Institute † The College of Urban and Environmental Sciences, Central Normal University ‡ School of Remote Sensing and Information Engineering, Wuhan University

The evaluation of basic public services (BPS) generally refers to assess their overall content and equalization levels via the statistical indices per capita and accessibilities. In this study, we evaluated the BPS in Wuhan, including fundamental education, medical and health care, and welfare facilities in different scales with National Geographic Conditions Monitoring (NGCM) data and socio-economic statistical data. Firstly, the total amount of contents and service levels per thousand people were calculated globally. Secondly, the coverage and convenience of BPS were explored with spatial data to evaluate their equalization at a meso level. Thirdly, the spatial variations happening in the central urban zones, suburban districts and rural areas are analyzed. On this basis, we conducted studies on the issues in the evaluation of BPS at a finer scale by using some geographic survey and big data methods, including the differences between the registered citizens and permanent population and its impact on the evaluation of fundamental education, the divergences among intraregional service, cross-regional service and cross-city radiation of different level medical institutions, and the diversities among the reasons for aging, the composition of the elderly population, and their demand for welfare guarantee in urban and rural areas. In particular, the “hollow villages” in rural areas are recognized and their social background are concerned. On the whole, this study explored new approachs for the evaluation of BPS, which overcomes the shortcomings of traditional research from a global view, and reveals the detailed differences of BPS at a micro level. Key Words: Wuhan city, basic public services, national geographic conditions, geological survey, big data.

asic public services (BPS) in China are led by governments, which guarantee the basic needs of all citizens to survive and develop, and they are appropriate B to the level of socioeconomic development. BPS are the basic social conditions contributing to maintain economic and social stability, social justice and cohesiveness, to protect citizens’ basic rights to subsistence and development and to realize overall human development (Chen 2007; Ding et al. 2010; Zhang 2010). Current studies on the evaluation methods of BPS can be divided into two categories. The first is the comprehensive evaluation method that established an index system, which took municipal, county, subdistrict and other administrative districts as statistical units. Then, a comprehensive evaluation system that involves amount and per capita indicators was established, and the indicators were derived from the population and public service facilities statistics. On this basis, the evaluation results were summarized and compared horizontally after conducting the analytic hierarchy process (AHP) method (Wang et al. 2011; Li 2011; Yang et al. 2012). The second is the GIS-based spatial analysis method. In this method, the spatial distribution of residential areas (i.e., the service demander) and basic public service facilities (i.e., the service provider) data were collected, then the coverage and accessibility analysis will be carried out to reflect the spatial equalization and service standards of BPS after the reasonable service radius or actual travel time were defined (Tao et al. 2007; Song et al. 2009; Ma 2010; Peng et al. 2012). The former was a macrolevel study that reflected the overall level of supply and interregional differences in facilities but did not reflect differences within the region. The latter belongs to the mesoscopic level of research, which better reflected the relationship between the spatial distribution of facilities and the residential area and revealed the differences in the level of facilities’ services within the region. However, the latter still relied on static statistical data; this means we could not distinguish the population between the registered and the resident from statistical data, and we were unable to explain the different reasons for the aging phenomenon in the developed central urban areas and the less developed rural areas. Therefore, there are still many limitations in microscopic research and application. The geographical condition is the most important component of the national condition (Hubbard 1932). Geographic conditions refer to information generated through observation of the elements of national conditions in terms of nature and humanity from the perspectives of geographic space distribution rules and distribution status (Kozak 2008; Loiseau et al. 2012; Zhang et al. 2015). National Geographic Conditions Monitoring (NGCM) has surveyed the natural resources of the land, such as landscapes, forests, lakes and grasses, and the urban and rural construction of railways and roads, buildings (houses), and urban comprehensive functional units. It has collected, mapped and sorted the data involving the vector format administrative boundaries (including counties and districts, townships, communities and villages), population data (including the registered population distributed by districts and resident population distributed by communities), as well as the location and volume of public service facilities (including primary schools, middle schools, hospitals, welfare homes, etc.) (Zhang 2016). These data laid a good foundation for our research on comprehensive index evaluation at the macro level, the spatial analysis and evaluation at the meso level, and the analysis of some specific problems at the micro level. In addition, traditional geo-survey methods and emerging big data methods have also provided a new perspective for the establishment of microlevel human-land relationships and in-depth public service analysis research. Taking Wuhan as a case study, this paper used fundamental education, medical care and welfare facilities as research objects, and the statistical yearbook data, population data, as well as public service facility spatial data of 2014-2016 were employed to carry out a comprehensive evaluation at the macro level, including the total amount of guarantee and the service level of the thousand-person index. Then, at a meso level, spatial overlay analysis methods were used to evaluate the service level of public service facilities covering the population, including a coverage and convenience analysis. On this basis, with an in-depth analysis of regional land use and economic and social development, supplemented by geographic surveys and big data methods, we conducted a microlevel case study on the typical problems of fundamental education, medical and health care, and welfare facilities, The differences between the registered population and the resident population were included in the evaluation of the total amount of fundamental education and the evaluation of the spatial distribution service level, the differences in internal services, cross-regional services and cross-city radiation in different levels of health care institutions, as well as the differences in the causes of urban and rural aging, the composition of the elderly population, and the need for welfare satisfaction. Our research not only makes up for the shortcomings of macro research but also explores some new research methods and improves the research methods of analysis and evaluation of basic public service levels. Study Area and Data

Study Area Wuhan is the capital of province, which is located in central China. Wuhan occupies a land area of 8576 km2, and its urban built-up area covers an area of 572.56 km2. At the end of 2016, the resident population had reached 10.76 million, and Wuhan’s total gross domestic product (GDP) grew to 1.19 trillion yuan (US$ 175 billion). Wuhan currently consists of 17 administrative districts, which comprised 183 township-level divisions, including 3144 community-level areas (i.e., Urban-rural resident autonomous units (URAUs)). According to the functional classification of the city, the districts in Wuhan can also be divided into three categories: central urban districts (CUD), development zones (DZ) and new urban districts (NUD). The CUD refer to the core area of the city, including Jiangan, Jianghan, Qiaokou, Hanyang, Wuchang, Qingshan and Hongshan districts, in which the industrial structure is dominated by the modern service industry. The DZ refer to suburban districts, which are located between the CUD and NUD, including the economic & technological development zone (ETDZ), East Lake high-tech development zone (ELHTDZ), East Lake scenic area (ELSA) and chemical industry park (CIP), in which the mainstay industry is advanced manufacturing. The NUD refer to rural areas, which include Dongxihu, Caidian, Jiangxia, Huangpi, Xinzhou and Hannan districts, and the mainstay industry is agriculture. The administrative divisions and spatial structure are shown in Figure 1.

Figure 1. Administrative divisions and spatial structure of Wuhan City Study Data and Preprocessing The study data were derived from the National Geographic Conditions Monitoring (NGCM) of Wuhan executed in 2016 and included two types: population data and basic public service facilities data. (1) Population data. In recent years, the registered population of Wuhan has been relatively stable, whereas the resident population has continued to increase. At the end of 2016, Wuhan had a registered population of 8.34 million and a resident population of 10.76 million; the net inflow of migrants had reached 2.43 million, and the net inflow rate was 22.55%, which means Wuhan is very attractive for immigrants. Figure 2(a) depicts the spatial distributions of resident population in persons per square kilometer. The registered population data was divided into ages by , which can classify the age-appropriate population into three types: kindergarten period (i.e., aged 3-7 years), primary school period (i.e., aged 6-12 years) and middle school period (i.e., aged 13-18 years). In addition, the statistical unit of resident population data is the URAU; these data merely provides the population of residents aged 0-18 years old.

Figure 2. Study data visualization; (a) the population distribution map; (b) the fundamental education institutions; (c) the medical and health care institutions; (d) the social welfare institutions. (2) Basic public service facilities data. The data are in GIS vector point layer format and extracted from the NGCM. The data can be divided into three categories: educational institutions, medical and health care institutions and social welfare institutions. Specifically, fundamental education institutions comprised 1303 kindergartens, 594 primary schools, and 367 middle schools. Medical institutions comprised 284 general hospitals, 450 community health centers, 69 township-level health centers, 17 health and epidemic prevention institutions, and 17 maternal and child health centers. Social welfare institutions comprised 85 welfare homes and 196 nursing homes for the aged. Figure 2(b), (c) and (d) demonstrate the spatial distribution of educational, medical and health care, as well as social welfare institutions in Wuhan, respectively.

Methodology

A Gridding Approach of Redistributing Population The population grid is calculated using the residential building population density model and the population spatial distribution model. Based on the residential building information, the population density of residential buildings is estimated according to the land use density method (Fu et al., 2006). The basic principle of the land use density method is: Suppose we have m administrative township-level districts in a certain area, n kinds of residential buildings, and the statistical population of each township-level district is Pm. Assuming that the population density of the same kind of residential buildings in the area is identical, the population densities of each type of residential buildings are D1, D2, …, Dn. Using the building survey data to obtain each kind of residential building’s area Smn (m=1, 2, …, m; n=1, 2, …, n), then linear equations can be established. When m > n, the population density estimation of different residential buildings with the smallest statistical error in the region can be obtained in accordance to the principle of the least squares method, and then the estimated population Pe can be calculated, and the estimated error ΔP can be analyzed. After obtaining the population density of various residential buildings, the grid population is used to rasterize the urban population, and the population distribution data is obtained. This article adopted the community-based resident population data to obtain the spatial rasterized population data in different age groups after the grid processing. In addition, the resident population of 0-18-year-olds was divided into the same proportion according to the structure of the registered population. Spatial analysis method of basic public service (1) Spatial overlay analysis Count the residential areas covered by a certain public service facility within a certain distance or time service radius and calculate the coverage of public service facilities based on the road network dataset by using the public service facilities and the thematic elements of the residential area in the survey and monitoring results of the geographic national conditions. (2) Distance analysis of grid cost The distance analysis of grid cost divides the study area into grids of a certain size. The cost value of the grid is set according to the land type or road grade of each grid location. All possible paths and cumulative costs for each raster to the source raster are calculated by iterative calculations by using the node and the connection calculation rule and the lowest cumulative cost value is assigned to the raster. Here, different travel speeds are set according to the road level, and a traffic time cost grid is constructed. The cost time distance tool of ARCGIS is used to generate the shortest time cost distance grid with the public facilities as the source. The time cost (raster value) of each community is calculated. The average time cost of each community point is counted, and the average accessibility indicator of the community is obtained, that is, the convenience. Geographic survey method Geographic survey is a research method of traditional geography. The main methods include field surveys, visit surveys, intention surveys, historical investigations and data statistics surveys. According to regional characteristics, we can set up survey routes and survey programs to understand natural conditions and humanities, as well as the relationship between the geographical environment and human economic activities, and design questionnaires for specific problems to obtain a subjective evaluation of various factors within the city. Big data method There are many types of big data, such as mobile phone signaling, Baidu thermal power, Tencent population migration, Sina Weibo, residential water and electricity, etc. They reflect an individual’s social and economic activities on a micro level. (1) Road network map API Euclidean distance cannot reflect the true accessibility under the current conditions of urban diversification, integration and rapid development based on traditional road network topology data. Utilize the Baidu map application programming interface (API) to select the public facility focus point (POI), set the starting and ending point (OD) matrix of the public facilities within a certain threshold range, return planning route distance according to the start and end points and reaching time based on real-time road conditions; they can help analyze the accessibility of public service facilities. (2) O-D analysis method based on mobile phone signaling and Baidu footprint Using mobile phone signaling and Baidu footprint data to analyze the user’s O-D matrix, which can track the source of the public service population and help analyze the service area of public service facilities.

Results and Analysis

Fundamental Education Macro Level Analysis of Guarantee Degree An evaluation of the guarantee degree and the service level of a thousand people at the macro level was carried out in district units based on the segment data of household registration, population, and age, as well as elementary education facility data. (1) Guarantee degree At the end of 2016, there were 1303 kindergartens, 594 primary schools and 367 general middle schools in Wuhan. The central urban area has obvious advantages in terms of general middle schools, as it accounts for 45.78% and has well-equipped facilities, while new urban areas have relative advantages in kindergartens and primary schools, accounting for 45.20% and 53.37%, respectively. However, the development zone, which was newly built with a small population, still does not have sound facilities. The fundamental education guarantee degree is shown in Table 1. Table 1. Statistics of educational institutions between different district-level regions Kindergarten Primary school Middle school Districts Proportions Proportions Proportions Number Number Number (%) (%) (%) CUD 550 42.21% 224 37.71% 168 45.78% DZ 164 12.59% 53 8.92% 48 13.08% NUD 589 45.20% 317 53.37% 151 41.14% Total 1303 100.00% 594 100.00% 367 100.00% (2) Per thousand people index A statistical graph of primary and secondary school size in each district and the number of full-time teachers per thousand students is shown in Table 2. The CUD have a large number of primary and secondary schools, as well as full-time teachers. However, due to its large student base, the number of full-time teachers per thousand students is lower than the city’s average level. Among the DZs, the ELHTDZ has a relatively small number of schools and teachers, lower than the city’s average level as well. For the other three districts, they have well-equipped education facilities and a small population base, so the number of full-time teachers per thousand students is near or above the city’s average level. The NUD, with numerous primary and secondary schools and a relatively small population, are generally above the city’s average level in terms of the number of full-time teachers per thousand students. Compared with 2014 and 2015, the overall quality of city education facilities improved significantly in 2016. Table 2. Number of full-time teachers per thousand primary school students and middle school students between different districts from 2014 to 2016. Primary school Middle school Region District 2014 2015 2016 2014 2015 2016 Jiang’an 66.74 66.74 65.75 92.92 92.92 119.58 Jianghan 64.09 64.09 63.62 100.44 100.44 126.01 Qiaokou 62.29 62.29 59.63 89.40 89.40 111.33 Hanyang 51.27 51.27 52.40 78.01 78.01 96.39 CUD Wuchang 68.84 68.84 68.14 103.43 103.43 125.14 Hongshan 53.91 53.91 52.11 112.38 112.38 101.86 Qingshan 55.27 55.27 55.10 85.44 85.44 126.66 Average 61.47 61.47 60.70 94.55 94.55 116.15 Economic & technological 100.39 100.39 68.91 114.24 114.24 149.59 development zone - Hannan (ETDZ-HN) East Lake high-tech development zone 50.29 50.29 47.32 96.55 96.55 130.14 DZ (ELHTDZ) East Lake scenic area 84.68 84.68 64.06 110.34 110.34 198.30 (ELSA) Chemical industry park 81.28 81.28 90.32 171.10 171.10 193.13 (CIP) Average 65.72 65.72 55.62 104.23 104.23 140.91 NUD Dongxihu 48.31 48.31 47.25 133.67 133.67 125.79 Caidian 73.22 73.22 64.62 120.26 120.26 150.47 Jiangxia 65.14 65.14 67.06 121.29 121.29 155.61 Huangpi 65.69 65.69 60.07 108.95 108.95 130.74 Xinzhou 77.76 77.76 71.39 227.49 227.49 162.60 Average 66.86 66.86 62.85 147.03 147.03 144.50 Overall average 63.88 63.88 60.87 115.45 115.45 129.70 Meso Level Analysis of Fundamental Education An evaluation of the coverage and accessibility was conducted at the middle level based on the rasterized resident population data and fundamental education facilities data in the community unit. (1) Coverage A cost grid of walking time is constructed with a walking speed of 5 km/h, based on the road network data of Wuhan, and a distance grid with smallest time cost with primary and middle schools as the grid center is established using cost distance tools. The service radius is 500 meters and 1000 meters for primary and middle schools, respectively, and their service zone can be calculated through the grid. The coverage of population of the right age in the service zone can be calculated through the resident population grid data, as shown in Figure 3. The CUD have a relatively high coverage of primary and middle schools. In and , the coverage has reached more than 40%, while the coverage in the DZ was relatively mediocre. For the NUD, though they have a large number of schools, the coverage is still at a low level under 20% due to their large area and dispersed population.

Figure 3. Coverage of primary schools (a) and middle schools (b). (2) Accessibility Based on the above time cost grid, an accessibility analysis was conducted by using primary and middle schools as the grid center. As is shown in Figure 4(a), the accessibility of primary schools in the CUD was the highest. Among them, the accessibility of primary schools in Jianghan, Qiaokou and Jiangan districts has reached the national standard (i.e., 500 meters). DZ and NUD were still far from the national standards in this regard, especially Jiangxia, Huangpi, CIP, and ELHTDZ. For middle schools, as shown in Figure 4(b), the accessibility was generally higher, as most of the residents of the CUD can get to schools within 1000 meters. However, the accessibility of the DZ and NUD were at a relatively low level, especially in Jiangxia, Huangpi, Xinzhou, CIP, and ETDZ. In terms of high-quality education resources, there are 34 model middle schools at the provincial level and 29 model middle schools at the municipal level with relatively high accessibility, mainly distributed in the CUD, including Wuchang, Jiangan and Jianghan districts. However, high quality education resources are scarce and relatively inconvenient for the NUD.

Figure 4. Accessibility of primary schools (a) and middle schools (b) Micro case study: The actual differences of fundamental education services in ELHTDZ The data used above was registered population data, which takes the district as the statistical unit and divides by several age groups. In the study of spatial distribution, the community-based resident population was rasterized. However, the district-based registered population has two defects: One is that it does not reflect the actual resident population, and the other is that it does not reflect the difference in spatial distribution of the population in townships and communities. The community- based resident population does not subdivide the internal composition of the population aged 0-18 years; it can only be split in the same proportion according to the demographic structure of the registered population, but that ignores the difference in the age structure of the resident population and the registered population. Therefore, the above evaluation conclusions have some limitations. It is necessary to study the characteristics of land use structure and population distribution within the region from the micro level and make necessary amendments. The following takes ELHTDZ as an example for a specific analysis. The ELHTDZ is one of the three national development zones in Wuhan. It has formed mainstay industries, including optoelectronics, medicine and biology and is an important economic growth area and employment center of the city. The ELHTDZ has a great advantage in the number of colleges and universities, as well as the scales of science and technology. The population has two characteristics, the first is younger and more knowledgeable, and the second is that the proportion of migrant population is relatively high compared to the other districts. At the end of 2016, the resident population and registered population has reached 0.52 million and 0.36 million respectively; the former is 0.16 million higher than the latter, and nearly one-third of the total population was inflow population. Current fundamental education institutions in the ELHTDZ comprised 98 kindergartens, 24 primary schools, and 24 middle schools. Geographic surveys show that the ELHTDZ has expanded rapidly from the west to the east and has extended to the Biological City and Future City near the Ring Expressway. The Metro Line 11th has been connected to Zuoling Avenue, and the Future City in the east has more than 100 thousand inhabitants. However, the construction of kindergartens, primary schools, etc., is seriously lagging behind. Judging from the overall guarantee degree, the number of fundamental education indicators should decrease by one-third approximately if taking the resident population as the service demander. The gap is too large such that the ELHTDZ ranked the last in all districts. In terms of the distribution of residential areas and fundamental education institutions, Figure 5 shows that the newly developed areas in the east have a large number of inhabitants, but kindergartens and primary schools are mainly concentrated in the west, and the eastern part is extremely scarce, which means the spatial distribution of limited basic educational resources was very uneven. In addition, the accessibility in the east was completely at a low level.

Figure 5. Land use (a) and distribution of fundamental education institutions of ELHTDZ (b) Medical and Health Care Service Macro Level Analysis of the Guarantee Degree Based on the resident population data and medical and health care institutions data, an evaluation of the guarantee degree and the services of a thousand people at a macro level was carried out in the district unit. (1) Guarantee degree At the end of 2016, there were 837 medical and health care institutions, including 284 general hospitals, 450 community health centers, 69 township-level health centers, 17 health and epidemic prevention institutions, and 17 maternal & child health centers. The number of community health centers was the largest, accounting for 53.76% of the total number. The next largest was the hospitals, accounting for 33.93%. The smallest number was the maternal & child health centers and health & epidemic prevention institutions, which accounted for only 2.03%. The overall allocation of medical and health care services in Wuhan has increased steadily. As shown in Table 3, the CUD have an absolute advantage in the main indicators of total medical and health care medical and health care services, such as the number of hospital beds, certified physicians and registered nurses. The ratio of physicians to nurses has reached the national standard except in the . However, medical and health care resources in DZ and NUD were less allocated, only three regions’ (i.e., ETDZ-HN, ELHTDZ and ) ratios have reached the national standard. Table 3. Main indicators of medical and health care services between different districts in 2016 Number of Number of Number of The ratio of Region District hospital certified registered physicians beds physicians nurses to nurses Jiang’an 8424 3899 4865 1:1.25 Jianghan 13141 4629 7290 1:1.57 Qiaokou 14043 4432 7854 1:1.77 CUD Hanyang 3943 1959 2216 1:1.13 Wuchang 6332 446 565 1:1.27 Hongshan 497 427 859 1:2.01 Qingshan 8879 4124 6191 1:1.50 Economic & technological development zone - 805 431 607 1:1.41 Hannan (ETDZ-HN) East Lake high-tech development zone 1749 737 1404 1:1.91 DZ (ELHTDZ) East Lake scenic area 80 70 79 1:1.13 (ELSA) Chemical industry park 20 0 0 0 (CIP) Dongxihu 3597 1370 1951 1:1.42 Caidian 1955 1271 1013 1:0.80 NUD Jiangxia 1806 1239 1378 1:1.11 Huangpi 3422 2165 2335 1:1.08 Xinzhou 3060 1338 1449 1:1.08 Overall average 71753 28537 40056 1:1.40 (2) Per thousand people index Per thousand people index of the medical and health care services was calculated using statistics on the scale of medical and health care institutions in each district. As is shown in Table 4, the overall level of the institution’s allocation in Wuhan was relatively good. The numbers of certified physicians, registered nurses and hospital beds were 2.69, 3.78 and 6.76 per thousand people, respectively, which were higher than the national planning standard, and CUD in particular have significant advantages. Table 4. Main indicators of per capita medical and health care between different districts in 2016 Certified Registered Hospital beds physicians nurses per Region District per thousand per thousand thousand people people people Jiang’an 4.09 5.10 8.83 CUD Jianghan 6.37 10.04 18.09 Qiaokou 5.12 9.08 16.24 Hanyang 3.06 3.47 6.17 Wuchang 0.36 0.45 5.06 Hongshan 0.82 1.64 0.95 Qingshan 3.61 5.42 7.78 Economic & technological development zone - 1.12 1.58 2.10 Hannan (ETDZ-HN) East Lake high-tech development zone 1.48 2.82 3.52 DZ (ELHTDZ) East Lake scenic area 0.86 0.97 0.98 (ELSA) Chemical industry park 0.00 0.00 0.45 (CIP) Dongxihu 2.60 3.70 6.83 Caidian 2.82 2.25 4.33 NUD Jiangxia 1.78 1.98 2.60 Huangpi 2.29 2.47 3.62 Xinzhou 1.52 1.64 3.47 Overall average 2.69 3.78 6.76 National standard 2.5 3.14 6.00 Meso Level Analysis of Medical and Health Care Services An evaluation of the coverage and accessibility was conducted at a meso level based on the rasterized resident population data and medical and health care institutions data in the community unit. (1) Coverage As is shown in Figure 6, the large-scale medical and health care institutions in Wuhan have good population coverage in 30 minutes, and the population coverage in the CUD was above 80%, but the coverage in the DZ and NUD was below 80%. The small-scale medical and health care institutions in Wuhan have a good population coverage in 15 minutes, and the coverage in Jiangan, Jianghan, Qiaokou, Wuchang, Qingshan districts exceed 95%.

Figure 6. Coverage of large-scale (a) and small-scale medical and health care institutions (b) (2) Accessibility The accessibility of medical and health care institutions refers to the convenience of transportation from residential areas to medical facilities, and the travel time cost was usually used to measure the accessibility in previous studies. Generally, large- scale medical and health care institutions comprised the general hospital, maternal & child health center and health & epidemic prevention institution. Assuming that walking is the only means of transportation for residents in Wuhan to obtain medical and health care services, then the travel time was calculated by the O-D time cost matrix based on the road network analysis in ArcMap 10.3. Specifically, the accessibility of the three regions basically satisfy the relationship: CUD > DZ > NUD. Most of the residents in Jiangan, Jianghan, Qiaokou, Hanyang and Wuchang can walk to the large-scale medical and health care institutions within 30 minutes. Meanwhile, small-scale medical and health care institutions comprised community health centers and township-level health centers. Similarly, the walking travel time from residential areas to small-scale medical and health care institutions was calculated by the method mentioned above. The accessibility of the CUD was very good because walking travel time was less than 15 minutes. The accessibility of the DZ has significant differences through internal comparison, because ELHTDZ and ELSA were at a medium level but ETDZ-HN and CIP were at a low level. However, the cost of travel time in the NUD was more than 30 minutes. Figure 7(a) and 7(b) demonstrates the accessibility of large-scale and small-scale medical and health care institutions based on the walking travel time, respectively.

Figure 7. Accessibility of large-scale medical institutions (a) and small-scale medical institutions (b) Micro case study: Cross-regional radiation of high-quality hospitals The above medical and health care service research distinguished between large- scale and small-scale medical institutions and set service radius of 15 minutes and 30 minutes, respectively, but it still has great limitations. Small-scale medical and health care institutions include community health centers and township-level health centers, whose service scope is generally in the township-level area and does not cover other townships. Large-scale medical and health care institutions generally include many hospitals with excellent specialties, which are not restricted by the region, and the phenomenon of cross-regional treatment is very common. For example, the radiation range of three famous top hospitals, such as Wuhan Tongji Hospital and Union Hospital, has far exceeded the scope of Wuhan’s region. Therefore, the characteristic of medical and health care institution’s service is on-demand visits, which are very different from those of primary and middle schools in round-trip commuting throughout the year. The research results of mobile phone signaling data (derived from China Unicom) and footprint O-D big data (derived from Baidu) show that several grade-A general hospitals with specialties in Wuhan (as shown in Figure 8(a)), such as No. 1 Hospital, No. 3 Hospital, No. 8 Hospital, Women and Children’s Hospital, Chinese Medicine Hospital, etc., have service scopes that have surpassed the restrictions of administrative districts. For example, the dermatology department of No. 1 Hospital, the burn department of No. 3 Hospital, the anorectal department of No. 8 Hospital, as well as the massage department of Wuhan Chinese Medicine Hospital have patients who are evenly distributed in all districts of Wuhan city. In addition, the radiation range of top hospitals such as Wuhan Tongji Hospital and Union Hospital has far exceeded the city’s area. A 2-hour car travel radius by expressway has totally covered the Wuhan City Circle. As shown in Figure 8(b), Wuhan is one of many major cities and important transportation hubs in central China, and it has great advantages in medical and health care resources. With the development of high-speed rail and urban railways, the radiation range will continue to expand, especially to the west of Hubei province.

Figure 8. Spatial distribution of grade-A tertiary hospitals in Wuhan (a) and the service radiation range of Wuhan Tongji Hospital (b) Social Welfare Macro Level Analysis of Guarantee Degree Based on the population aged ≥60 years old in the resident data and social welfare institutions data, an evaluation of the guarantee degree and the services of a thousand people at a macro level was carried out by district unit. (1) Guarantee degree The number of welfare institutions and beds between different districts from 2014 to 2016 were sorted in Table 5. In terms of the two indicators, we found that the CUD have obvious advantages over the DZ and NUD. Table 5. Statistics of welfare institutions between different districts from 2014 to 2016. Number of welfare Number of welfare Region District institutions institutions’ beds 2014 2015 2016 2014 2015 2016 Jiang’an 22 22 22 1762 1762 1762 Jianghan 18 18 20 2182 2182 2182 Qiaokou 24 24 26 1002 1002 1002 Hanyang 22 21 18 2759 2759 2759 CUD Wuchang 37 37 39 4558 4558 4558 Hongshan 16 16 17 1724 1724 1724 Qingshan 9 9 11 1283 1283 1283 Total 148 147 153 15270 15270 15270 Economic & technological 9 9 8 1818 1818 1818 development zone - Hannan (ETDZ-HN) East Lake high-tech development zone 5 5 6 1210 1210 1210 DZ (ELHTDZ) East Lake scenic area 3 3 3 544 544 544 (ELSA) Chemical industry park 1 1 1 252 252 252 (CIP) Total 18 18 18 3824 3824 3824 Dongxihu 15 15 16 2046 2046 2046 Caidian 20 20 19 1811 1811 1811 NUD Jiangxia 14 14 15 1646 1646 1646 Huangpi 37 37 30 5422 5422 5422 Xinzhou 29 29 31 5419 5419 5419 Total 115 115 111 16344 16344 16344 Cumulative value 281 280 282 35438 35438 35438 (2) Per thousand people index As shown in Table 6, the two main indicators were calculated after counting the number of social welfare institutions, resident population and welfare institutions’ beds. In 2016, the number of welfare institutions and beds per thousand people of Wuhan city were 0.03 and 3.29, respectively. The NUD have the highest level of social welfare service. Except for Jiangxia, the number of people per thousand index in other districts exceeds Wuhan’s overall average value. The CUD have the largest number of welfare institutions and beds, but the number of people per thousand index was lower than the city’s average level due to the large population. In addition, the service in the DZ was at a low level. Table 6. Statistics of welfare institutions’ per capita indicators between different districts from 2014 to 2016. Number of welfare Number of welfare institutions per thousand institutions’ beds per Region District people thousand people 2014 2015 2016 2014 2015 2016 Jiang’an 0.02 0.02 0.02 1.88 1.85 1.83 Jianghan 0.03 0.02 0.03 3.03 3.00 2.99 Qiaokou 0.03 0.03 0.03 1.17 1.16 1.16 Hanyang 0.04 0.03 0.03 4.42 4.32 4.25 CUD Wuchang 0.03 0.03 0.03 3.68 3.64 3.63 Hongshan 0.03 0.03 0.03 3.33 3.29 3.27 Qingshan 0.01 0.01 0.01 1.17 1.12 1.10 Average 0.02 0.02 0.02 1.88 1.85 1.83 Economic & technological development zone - 0.02 0.02 0.02 4.97 4.74 4.56 Hannan (ETDZ-HN) East Lake high-tech development zone 0.01 0.01 0.01 2.63 2.43 2.32 DZ (ELHTDZ) East Lake scenic area 0.04 0.04 0.04 6.82 6.69 6.49 (ELSA) Chemical industry park 0.02 0.02 0.02 5.77 5.66 5.48 (CIP) Average 0.02 0.02 0.02 4.03 3.80 3.64 Dongxihu 0.03 0.03 0.03 4.03 3.88 3.78 Caidian 0.05 0.04 0.04 4.13 4.02 4.00 Jiangxia 0.02 0.02 0.02 2.43 2.37 2.34 NUD Huangpi 0.04 0.04 0.03 5.95 5.74 5.61 Xinzhou 0.03 0.03 0.03 6.30 6.14 6.06 Average 0.03 0.03 0.03 4.81 4.67 4.59 Overall average value 0.03 0.03 0.03 3.43 3.34 3.29 Meso Level Analysis of Social Welfare An evaluation of the coverage and accessibility was conducted at a meso level based on the rasterized resident aging population data and social welfare institutions data in the community unit. (1) Coverage Based on the minimum travel time cost measurement method and population grid data, the population coverage within the 30-minute service area of the social welfare institutions in each district was calculated using network analysis tools. As shown in Figure 9(a), we found that each CUD has high-level coverage, and more than 80% of the communities can reach the institutions within 30 minutes, while the DZ have low- level coverage. In addition, the total number of institutions in the NUD is small, but the area is large, and residential areas are dispersed; hence, the coverage is low. (2) Accessibility Similarly, the accessibility of social welfare institutions was calculated using the minimum travel time cost measurement method. As shown in Figure 9(b), we found that the city's social welfare institutions are more convenient and can cover a large area of the city within a 30-minute walk. The 1-hour walking range can cover more than 80% of the area.

Figure 9. Coverage (a) and accessibility (b) of social welfare institution Micro case study: Population aging the difference between urban and rural area The above research is based on the aging population of each district but ignores the differences in urban and rural economic development levels, the reasons for aging, the composition of the elderly population, and the need for welfare satisfaction. Therefore, the evaluation results have great limitations; hence, an in-depth analysis of urban and rural economic and social development levels and the characteristics of aging from a microscopic perspective is necessary to determine different welfare satisfaction solutions. A detailed comparison analysis of the most developed area of Jianghan district in the CUD and the less developed area of in the NUD follows. Jianghan district is the earliest developed central urban area of Wuhan city and occupies a land area of 28.55 km2. At the end of 2016, Jianghan’s total GDP grew to 103.46 billion yuan, with per capita GDP of 141.8 thousand yuan, and ranked 1st among the districts in Wuhan. The total registered population was 0.49 million, and the population density was 17037 persons per km2, while the total resident population was 0.73 million, and the population density was 25551 persons per km2. By contrast, the resident population was 0.24 million more than the registered, and the inflow population accounted for 50%. Among the registered population, there were 0.13 million elderly people (aged ≥60 years) and the aging rate was 25.84%, which was higher than the city's average level (i.e., 20.72%). In addition, the social welfare institutions, including 2 welfare homes and 18 nursing homes for the aged, were distributed evenly. Huangpi district is the largest NUD in Wuhan, with an area of 2250.42 km2. It is a less developed district where many mountains and hills are located, and the mainstay industry is agriculture. At the end of 2016, Huangpi’s total GDP was 63.49 billion yuan, with per capita GDP of 65.70 thousand yuan, ranked in the last position. The total registered population was 1.13 million, and the population density was 503 persons per km2, while the total resident population was 0.97 million, and the population density was 430 persons per km2. By contrast, the resident population was approximately 0.17 million less than the registered population, and the outflow population accounted for 14.63%. Among the registered population, there were 0.22 million elderly people (aged ≥60 years) and the aging rate was 19.76%, which was slightly lower than the city's average level. The social welfare institutions, including 17 welfare homes and 13 nursing homes for the aged, ranked 2nd among the NUD in terms of total amount and had a high level of configuration. Figure 10 and Figure 11 demonstrate the economic development and population aging conditions, respectively, between different districts.

Figure 10. Comparison of economic conditions between different districts

Figure 11. Comparison of population aging between different districts From the perspective of economic development, Jianghan was the most developed district, while Huangpi was the least developed, and the former was 2.16 times as large as the latter in terms of per capita GDP. In terms of population density, Jianghan was 59.42 times as large as Huangpi. Moreover, from the perspective of population aging, the aging rate in Jianghan was 25.84% and that in Huangpi was 19.76%, both of which were far exceeded the international standard (10%), which showed that they have entered into a severely aging society. Figure 12(a) and (b) show the distribution of population aging at a community level in Jianghan district and Huangpi district, respectively.

Figure 12. Population aging distribution map of Jianghan district (a) and Huangpi district (b) However, their aging characteristics are different. First, the elderly people of Jianghan were distributed evenly in each community, while the elderly people of Huangpi were mainly distributed in the south. Second, the reason why Jianghan has a high aging rate is that citizens there have a high living standard and can obtain high quality medical and health care services. However, the main reason for the high aging rate in Huangpi is that a large number of young and middle-aged laborers have left their hometown to work outside and caused a "hollow village" phenomenon. Specifically, we took Liqiao village as an example. Liqiao is located in the south of Yanjiawan Township, Huangpi, and it is 5 kilometers away from Tianhe airport. Liqiao has 372 households with 1178 people. It has only 102.67 hectares of prime cropland and 0.08 hectares of farmland per capita. According to the geographical survey, 80% of the young adults in the village left their hometown and worked in cities throughout the year. Therefore, most of those staying in the village are older adults and left-behind children. What is worse is that large amounts of farmland are covered with withered weeds, and more than 66.67 hectares farmland has been abandoned. We also found that there are only 3 cattle in the village, and the people who planted the land are mainly older adults. In 2000, two rows of new buildings were built outside the village, close to Xiaotian highway. Since 366 of 372 households have moved into the new community, the original homesteads were abandoned and then became a “hollow village”. Although the two districts have similar aging rates, they have distinct economic and social backgrounds and have different requirements for social welfare services. Jianghan district has a wealthy community’s aging problem, which should not only build more welfare homes and nursing homes for the aged but also promote community pension experiences and explore new approaches for housing pensions. However, Huangpi district has the less developed village’s aging problem, and obviously, a social welfare guarantee is more difficult. To break the predicament of economic and social development in rural areas, the fundamental solution is that governments or policymakers should make targeted measures to help people lift themselves out of poverty and adhere to the implementation of the rural revitalization strategy.

Conclusions

This article aimed to assess Wuhan’s BPS in three aspects (i.e., fundamental education, medical and health care and social welfare) and to analyze in three dimensions (i.e., macro, meso and micro levels). We found that the basic public service level in Wuhan was generally good, but the difference between urban and rural areas was obvious. There existed a significant hierarchical distribution characteristics between the CUD, DZ and NUD. (1) In terms of fundamental education, the CUD have a large number of schools and high-quality educational resources, hence it has a high guarantee degree of fundamental education. Because of the high density of the residential population and educational institutions, the coverage and accessibility were at a relatively high level. However, per thousand people indices were at a low level because the CUD have a large quantity of the population. The number of schools in the NUD was basically matched with the population, and their guarantee degree and per thousand people index were better. However, due to the large area and scattered residents, the coverage and accessibility of the NUD were at a low level. The distribution of the educational institutions in the DZ was imperfect due to the short construction time, and each indicator was lower than the city’s average. The emerging high-tech park, represented by Wuhan’s ELHTDZ, has attracted a large number of excellent talent. The ELHTDZ’s migrant population accounted for a large proportion, which has aggravated the contradiction of insufficient educational services. (2) In terms of medical and health care service, the overall allocation level of the city is relatively high, and per thousand people index was higher than the national standard. The CUD have a great advantage of high quality medical resources, not only was the coverage and accessibility higher than other districts but also the radiation range of many grade-A general hospitals covered the whole city and nearby cities. (3) In terms of social welfare, the CUD have a high level of guarantee degree, for the coverage as well as the accessibility, but a low per thousand people index. The NUD have a high guarantee degree and per thousand people index, but the coverage and accessibility were at a low level because of the large area and scattered residential areas. The indicators of the DZ were at a relatively low level because of the short construction time, low aging rate and small number of welfare institutions. Actually, the CUD and NUD were in a stage of deep aging, but the levels of economic development were very different. NUD belongs to the aging society of less developed rural areas where the “hollow village” phenomenon was very common. The fundamental solution is that the policymaker should implement the rural revitalization strategy and improve the level of economic and social development in all rural areas. It can be seen from this study that traditional research on BPS is still effective, such as the research on the guarantee degree and per thousand people index based on the statistics of population and public service facilities at the macro level. Based on the coverage and convenience evaluation of population distribution, public service facilities and road networks, the basic public service level was evaluated from the perspectives of the total guarantee degree and spatial allocation. However, the data used in these studies were based on static statistical data; hence, it is impossible to distinguish the difference between the registered population and the resident population. The cross-regional service scope of high-quality resources is unable to be dynamically identified, and it is impossible to explain the different essences of the aging phenomenon in developed central urban areas and backward rural areas. Therefore, there existed great limitations in microscopic research. Based on the completion of the macro and mesoscopic evaluations, this study employed the geographic survey and big data methods to conduct a microlevel case study on the typical problems of fundamental education, medical and health care, as well as the social welfare, which have improved the evaluation method of the BPS. However, this study has some limitations in big data acquisition and research conditions, and only a small number of case studies have been carried out. To comprehensively identify the resident population’s distributions and scales, the quality of education resources, the cross-regional service scope of medical and health care resources, and the distribution of “hollow villages” in rural areas will further improve the evaluation of BPS. Obtaining more comprehensive data, such as mobile phone signaling data, Baidu footprint data, residential water and electricity consumption data, etc., will be important work for our future study.

Acknowledgements The authors would like to thank all the colleagues from Wuhan Geomatics Institute (WGI) for completing the wrok of the geographic conditions monitoring of Wuhan city and contirbutions during data collection. We thank Wuhan Municipal Bureau of Statistics (WMBS) for providing the data from statistical yearbook. Especially, we are deeply grateful to teachers and students from Wuhan University for their participations in completing the research work of basic public services evaluation.

Funding This research was supported by the National Natural Science Foundation of China (Key Program) (Grant 41331175), the National Key Technology R&D Program (Grant 2012BAJ15B04) and the Open Foundation of Key Laboratory for National Geographic Census and Monitoring, National Administration of Surveying, Mapping and Geoinformation (Grant 2016NGCM10).

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