1. Introduction
Public service facilities include various types of facilities that provide necessary resources and services for residents’ lives [
1]. The rational layout of public service facilities is related to the fair and efficient allocation of government resources, the quantity and quality of services enjoyed by the public, and the equality of access to such services [
2]. Efficient public service provision is a common problem in urban governance, affecting residents’ perceptions of life and the formulation of urban development strategies. The accessibility of public facilities reflects whether the layout of public facilities is reasonable and whether public resources are equitable; assessing such accessibility can help overcome inequalities and alleviate poverty [
3,
4]. With rapid urbanization, there is increasing demand for culture, education, healthcare, and transportation facilities, among others. Meanwhile, the differentiated needs of different groups place increased pressure on the supply of public service facilities, thus highlighting the importance of investigating public service facility accessibility.
Accessibility research has a long history. In the late 1950s, Hansen et al. [
5] used various methods and mathematical models to introduce the accessibility theory into the layout and planning of public service facilities. Wachs et al. [
6] characterized accessibility as the average likelihood that people will participate in an activity, given factors such as travel time, distance, or cost. Dalvi et al. [
7] suggested that accessibility is an inherent feature or advantage of a place in overcoming spatial operation friction—that is, the convenience of reaching a place from a particular spatial location using the transportation system. Accessibility is widely used to define reachability in terms of the travel time required to reach the nearest target facility and construct global spatial friction [
8]. Since the development of the Geographic Information System (GIS), many studies have used it to measure transportation accessibility, incorporating land use, population, and employment characteristics into the assessment [
9]. Kalogirou et al. [
10] analyzed healthcare accessibility in Ireland using a GIS-based weighted accessibility model, overlaying population, hospital, and road networks. Others, meanwhile, have investigated the fairness of different groups’ access to public service facilities [
11]. Taking Detroit as an example, Zenk et al. [
12] used GIS and spatial regression modeling to explore neighborhoods’ accessibility to supermarket chains in terms of racial composition and income. Other studies have shown that public service facility accessibility is affected by the proportion of mobile population [
13], the regional economy [
14], individuals’ socioeconomic and activity characteristics [
15,
16], and environmental attributes [
17].
Many of the UN’s Sustainable Development Goals (SDGs) mention public service accessibility. Relatedly, the EO4SDG initiative of the Group on Earth Observations proposes using remote sensing for SDG monitoring [
18]. SDG 11.2 states, “By 2030, provide safe, affordable, accessible and sustainable transport systems for all and improve road safety, in particular by expanding public transport, paying special attention to the needs of people in vulnerable situations, women, children, persons with disabilities and older persons”. The proposed calculation of the “proportion of the population with easy access to public transport” as an indicator aligns with the idea of calculating accessibility and emphasizes the need to focus on different population groups. Health, education, and sanitation are in line with SDGs 3, 4, and 6, which also refer to differences in the attributes of the study population.
Hospitals are important public service facilities that affect people’s lives, and unequal access to healthcare is one of the main obstacles to achieving SDG 3—“Ensure healthy lifestyles and promote well-being for people of all ages” [
19]. Measuring the accessibility of healthcare facilities can support equitable access to healthcare [
20,
21] and contribute to SDG achievement [
22]. SDG 3.8 also notes that essential health services should be calculated as an indicator by stating, “Achieve universal health coverage, including protection from economic risks, access to quality essential healthcare services for all, and access to safe, effective, good quality and affordable essential medicines and vaccines for all”. Many studies have investigated healthcare facility accessibility. Alegana et al. [
23] modeled the probability of public health facility involvement in children’s fever treatment in Namibia based on travel time, predicting the number of people who would use a public health facility. Weiss et al. [
24,
25] improved on Nelson’s spatial friction and mapped the travel time cost of cities and health facilities globally using the least cost distance algorithm.
Education is another important aspect of development. SDG 4 states, “Ensure inclusive and equitable quality education and lifelong learning opportunities for all”. Yet, there is still widespread inequality in terms of the accessibility and quality of schools [
26]. SDG 4.5, which suggests using the Education Parity Index as a measurement indicator, states, “By 2030, eliminate gender disparities in education and ensure equal access to all levels of education and vocational training for disadvantaged groups, such as persons with disabilities, indigenous peoples and children in vulnerable situations”. Studies have found that different groups have different accessibility to schools based on income and location [
27], highlighting the need to study the different attributes of inhabitants. It has been shown that accessibility and equality are significantly better in eastern and central China than in western China [
28]. It is important, therefore, to study the western inland areas with poor accessibility to further promote sustainable development.
Public sanitation is a basic guarantee for hygienic cities and public health. SDG 6 proposes that “water and sanitation should be made available to all and managed sustainably”. The criteria reflecting public sanitation’s level of implementation and progress include availability, quality, physical accessibility, affordability, and acceptability [
29]. Improved sanitation reduces the risk of diarrheal infections in children [
30,
31] and is an effective intervention for most water-related diseases [
32]. Therefore, improving sanitation accessibility is essential for a healthy, sustainable future in developing countries [
33] and contributes to achieving SDG 6. SDG 6.2 more specifically states that “by 2030, everyone will have access to adequate and equitable sanitation and hygiene, eliminating open defecation, with particular attention to meeting the needs of women, girls and vulnerable groups”.
Current research on public service accessibility is dominated by large-scale data, mostly corresponding to a resolution of 1 km. As a result, the data are not precise enough to guide practice. Moreover, it makes research unfavorable for further analysis combined with other factors. At the same time, the differentiated needs of different types of inhabitants and groups have been neglected, resulting in mismatches between the supply of public service facilities and residents’ needs. Therefore, evaluating accessibility using multisource data with higher accuracy can make research more scientific and can overcome the data shortfall problem of previous studies [
34,
35,
36]. Meanwhile, studies have shown that socioeconomic and demographic factors play a key role in accessibility [
37]. Thus, it is important to use more clear, scientific classification methods to study the relationship between accessibility and inhabitants’ social attributes, which can improve public service facility accessibility research and guide policy. It is also necessary, then, to study differences in inhabitant and resident attributes to allocate public service facilities in precise differentiated ways. Relatedly, remote sensing technology has been increasingly used for mapping in socioeconomic research [
38,
39,
40]. You et al. [
41], for example, used remote sensing to study the effectiveness of shelters. Wang et al. [
42] measured the urban poverty space based on remote sensing, while Cui et al. [
43] used it to measure the service capacity of primary schools. Thus, by using remote sensing to measure service accessibility, we can better analyze the equity and convenience of residents’ use of public service facilities based on high-precision data.
China was the first country to achieve the UN’s Millennium Development Goal of halving the number of people living in poverty [
44]. By the end of 2020, China had eliminated absolute rural poverty under the current standard and entered a phase of relative poverty characterized by regional and urban–rural income disparities, unequal access to public services, and multidimensional poverty [
45]. With rapid economic development, problems of social inequality have gradually emerged in China, including unequal access to public services. The government has noted such inequalities, projecting in the Fourteenth Five-Year Plan for Public Services that basic public services will be equalized by 2035.
Assessing public service facility accessibility in underdeveloped regions can help alleviate poverty, equalize public services, and promote sustainable development. This study takes Lincang, a typical underdeveloped region in Yunnan Province, as the study area and uses a method based on multisource remote sensing data to measure and assess the accessibility of three types of infrastructure: healthcare, education, and sanitation. We further explore the matching relationship between the accessibility of these three infrastructures and the social attributes of the inhabitants and different poverty groups. The findings can help guide policy and support the planning and optimization of the layout of public facilities.
This study’s marginal contributions are threefold. (1) We use remote sensing to measure the accessibility of service facilities and synthesize multisource data to construct Points-of-Interest (POI) data, which can improve measurement accuracy. (2) The 30 m accuracy of the remote sensing data used in this study is relatively high, which overcomes the problem of the low resolution of Open Street Map (OSM) road network data and compensates for the data shortcomings of previous studies. (3) We use a clear, scientific classification method to study the relationship between public service facility accessibility and inhabitants’ social attributes and different poverty groups, which has implications for both theoretical research and policy practice.
4. Discussion
First, in terms of healthcare facility accessibility, the distribution of general hospitals in each county and district of Lincang is relatively balanced. The accessibility of health centers is much better than that of general hospitals, which confirms that the higher the level of public health infrastructure, the lower the balance of its spatial layout; meanwhile, the lower the level of health infrastructure, the more balanced the spatial layout [
56]. The accessibility of educational facilities in Lincang is significantly better than that of medical facilities. Unlike previous studies that took primary or middle schools alone as the study subject, we compared the accessibility of primary and middle schools and concluded that the accessibility of primary schools is significantly better than that of middle schools in all counties and districts. This could be related to the current primary school zoning enrollment policy, wherein parents who buy housing tend to choose locations that are more accessible to their children’s primary school. Regarding sanitation facilities, the accessibility of public washbasins in Lincang’s counties and districts is slightly better than that of public toilets, and the overall distribution is characterized by multipoint diffusion. This also suggests that the “Patriotic Hygiene Special Action” to implement washbasins has helped improve accessibility.
Second, the public infrastructure accessibility to different inhabitant groups with different economic and social characteristics differ obviously. There is a significant negative correlation between population density, aging level, and income level and the least costly distance to public facilities. Transportation-based accessibility has been shown to be unevenly distributed across populations, with the most underserved group being low-wage earners, followed by seniors and less educated people [
57]. Currently, there are more studies on income as a factor influencing accessibility, with many confirming that low-income households are more likely to be located in low-accessibility areas, while middle- and high-income households are more likely to be located in high-accessibility areas [
58,
59]. In terms of educational facility accessibility, inhabitant clusters with high population density and high income have better access. The larger population density promotes the construction of educational facilities in the region, and good educational facilities attract more people, thus forming a virtuous circle. Proximity to good schools often means higher housing prices, which means that families capitalize on excluded public goods. As a result, the spatial differentiation of socioeconomic groups further reduces educational access for low-income people, which has also been shown in previous studies [
60]. The improper allocation of educational resources restricts social mobility and further exacerbates differences in urban social spaces [
61], which is detrimental to overall city development. Regarding healthcare access, aging, low-income, low-population-density inhabitant clusters have less access. Studies have shown that distance from medical and health facilities has a significant effect on people’s use of medical care [
62,
63]. The low affordability of healthcare itself reduces low-income people’s use of medical services. Moreover, low-income people often live in places with poor accessibility, further reducing their access.
Finally, unlike studies that measure poverty only in terms of income and do not categorize poverty groups, we categorized poverty groups into different types with corresponding salient features. Infrastructure accessibility is essential for poverty alleviation and protection against the risk of returning to poverty. Our findings show that the least-cost distances to healthcare, education, and sanitation infrastructure are positively correlated with different degrees of poverty. Echoing our findings, a study in Chicago found that accessibility was lower in areas with high populations of minorities, poor people, and less educated people [
57]. Moreover, a study in Perth similarly revealed poor accessibility for elderly and low-income households [
64]. In terms of the weighted-average distance to public facilities, poor group 4 had the least access among the four poverty groups. Most of this group are elderly people left behind in the countryside, for whom infrastructure development is needed to lift them out of poverty. As an underdeveloped multiethnic frontier region, Lincang’s poor group 1 is the main type of poverty-stricken population. The poor accessibility to health and sanitation infrastructure for poor group 1 is attributable to the fact that this group is dominated by ethnic minorities who mostly live in remote mountainous areas, where the lack of infrastructure curtails their survival and development. When developing public facilities, consideration can be given to prioritizing allocation to disadvantaged groups. While this could lead to an unequal spatial distribution of public infrastructure, it could help equalize opportunities and outcomes. This is in line with Rawls’ view that “public facilities should be allocated in favor of the more disadvantaged” [
65].
The findings of this paper are validated by the fact that less developed regions around the world are alleviating poverty by enhancing infrastructure accessibility. Akbar et al. suggested that improving female educational infrastructure in rural areas and reformulating health policies should be prioritized for poverty alleviation in Pakistan, with a special focus on middle and high-level poverty groups [
66]. In their study, Fereira and Cateia showed that investment in infrastructure has a significant impact on structural transformation and poverty reduction in Guinea, while the economic and social characteristics of the different groups should be emphasized in the process of poverty reduction [
67]. Xiao et al. empirically estimated the impact of infrastructure development on the income level and distribution of poor households, and showed that fine-tuned investment in infrastructure in poor rural areas is conducive to sustainable development for the poor [
68]. Infrastructure accessibility is a key national development priority and an important condition for achieving sustainable development. Especially in poor and underdeveloped areas, enhancing the precise allocation of infrastructure is conducive to upgrading local economic development and people’s living standards, thus promoting local sustainable development.
5. Conclusions
Taking Lincang in Yunnan Province, a typical underdeveloped region in China, as the study area, this study used multisource remote sensing data and POI data to assess the accessibility of healthcare, education, and sanitation facilities. Moreover, we explored the relationship between public service facility accessibility and different inhabitant attributes and heterogeneous poverty groups. The results of this study reveal large disparities in the accessibility of different types of infrastructure in Lincang. We also found that public service facility accessibility varies significantly among inhabitants with different socioeconomic attributes and among different groups of poor people. Inhabitant clusters with high population density, high levels of aging, and high income have better access to public facilities. The accessibility of healthcare, education, and sanitation infrastructure is negatively correlated with the poverty level of poor groups to varying degrees.
Combined with the findings of the study, we propose the following three policy recommendations. First of all, it is necessary to provide precise differentiated configurations of public service facilities based on inhabitant characteristics to improve the service level and efficiency of public facilities. Promoting the accessibility of public facilities should be prioritized in areas with less developed economies, lower residential income levels, and serious aging. Secondly, precise development policies should be formulated for poor districts to improve the accessibility of infrastructure, such as education and health care. And upgrading the infrastructure configuration according to poverty-causing characteristics among different groups will enhance efficiency. Finally, less developed regions need to strengthen the universality and equity of public services as a means of preventing the risk of returning to poverty and promoting their sustainable development.
We explored the main infrastructure accessibility through multisource data fusion and accuracy improvement, expanding the application of remote sensing technology and better utilizing the social and economic benefits of remote sensing applications. The assessment methodology based on multisource geospatial data is equally applicable to other less developed regions for infrastructure accessibility measurement. Our findings provide a reference for the same type of underdeveloped areas, which is conducive to promoting their sustainable development through the planning and optimal layout creation of public facilities. However, this study still has some limitations. We measured infrastructure accessibility based on a spatial dimension perspective and did not account for nonspatial factors such as residents’ self-selection, economic affordability, and institutional barriers, especially regarding the inequality of such factors among disadvantaged groups. Thus, future studies can consider integrating route determination and transportation decision-making more closely to enrich the results. Similarly, infrastructure accessibility depends not only on facility allocation but also on intermodal transportation networks. Some studies have shown that transportation-related factors contribute to almost half of the spatial inequalities in healthcare accessibility [
69]. Thus, more transportation factors could be incorporated into future research.