This study establishes a research framework for analyzing the spatial distribution of older adult(s) care facilities in Liaoning Province and their multi-scale influencing factors (Figure 1). The framework divides the research process into three steps: Step 1 focuses on acquiring and processing point data on older adult(s) care facilities; Step 2 describes the spatial distribution characteristics of these facilities; Step 3 analyzes the factors influencing their spatial distribution.

Liaoning Province is located in Northeast China, covering an area of 148,000 square kilometers. The province has abundant older adult(s) care resources and is nationally renowned for its hot spring–based, pristine natural environment–based, and coastal older adult(s) care services. Liaoning ranks first nationwide in terms of population aging and has a large number of older adult(s) care facilities; therefore, it was selected as the study area. This study employed web crawling technology in combination with the China Elderly Care Information Service Platform¹, utilizing defined POI queries. Using “older adult(s) services” as the primary keyword, “nursing homes” as the secondary keyword, and “senior apartments,” “older adult(s) care,” and “senior activity centers” as tertiary keywords, POI data on older adult(s) care facilities in Liaoning’s 14 prefecture-level cities in 2024 were extracted from Baidu Maps, yielding 4,100 sample records. During the sample verification stage, the operational status of these facilities was cross-checked using the China Elderly Care Institutions Database of the Ministry of Civil Affairs². Telephone interviews were conducted to confirm operational status and address accuracy. As a result, 913 facilities were removed due to duplication, closure, or abnormal operation, leaving 3,187 older adult(s) care institutions in Liaoning Province. This dataset forms the basis for analyzing the spatial distribution characteristics of older adult(s) care facilities.

Using map coordinate pickers, geographic coordinates were obtained based on the location information of older adult(s) care facilities. At the provincial scale, the data were treated as point features. ArcGIS software was used to perform coordinate and projection transformations, generating a spatial distribution map of older adult(s) care facilities in Liaoning Province (Figure 2). At the same time, based on the number of older adult(s) care facilities in each of Liaoning’s 14 prefecture-level cities, the point data were aggregated into polygon data to produce a map showing the distribution of older adult(s) care facilities by city (Figure 3).

To analyze the spatial distribution characteristics and influencing factors of older adult(s) care facilities across Liaoning cities, six categories of influencing factors were selected from economic, social, and environmental perspectives. These include population size, economic development level, tourism resources, healthcare standards, transportation accessibility, and environmental quality, represented by nine indicators: total population (X1), older adult(s) population (X2), urban GDP (X3), number of tourist attractions (X4), number of medical facilities (X5), transportation accessibility (X6), average January temperature (X7), average August temperature (X8), and air quality (X9).

The influencing factors are explained as follows (Table 1). ① Population size includes total population and older adult(s) population. A larger older adult(s) population increases demand for older adult(s) care facilities, while population base and demographic structure determine aging trends and future demand; therefore, these indicators are expected to be strongly correlated with facility distribution. ② Economic development level, measured by urban GDP, reflects a city’s capacity to supply older adult(s) care services. Higher GDP indicates stronger investment capability and is expected to correlate with the quantity and spatial distribution of older adult(s) care facilities. ③ Tourism resources are represented by the number of tourist attractions. Web crawling was used to collect POI data on A-level scenic sites, parks, museums, and recreational plazas. Rich tourism resources indicate a high-quality living environment and are expected to attract older adult(s) residents and stimulate demand for older adult(s) care services. ④ Medical standards are measured by the number of medical facilities, including general hospitals, specialized hospitals, and community clinics. Convenient access to healthcare is essential for older adult(s) daily life, and cities with higher medical standards are expected to host more older adult(s) care facilities. ⑤ Transportation convenience is measured by road network density in urban built-up areas. Cities with better transportation accessibility are expected to support a wider distribution of older adult(s) care facilities. ⑥ Environmental quality is reflected by average January temperature, average August temperature, and air quality. Elderly populations generally prefer locations with mild winters, cool summers, and good air quality; thus, environmental quality is an important indicator of urban livability and is expected to influence the spatial distribution of older adult(s) care facilities.

This study sets the range of 0.9–1.1 as the discriminant threshold for “resource-matching type.” This threshold setting is based on the practical consideration that theoretical equilibrium (CRP = 1) is difficult to achieve in actual resource configuration. Referencing the commonly used ±10% fluctuation range in supply–demand matching research, this reflects the reasonable elasticity range allowed between the supply and demand of older adult(s) care services. To verify the robustness of this threshold, this study conducted a sensitivity analysis, reclassifying urban types using 0.85–1.15 (lenient threshold) and 0.95–1.05 (strict threshold) respectively.

The results of the sensitivity analysis are shown in Table 3: compared to the baseline threshold (0.9–1.1), the relaxed threshold only shifted one city from the matching type to the resource-advanced type. Strict thresholds shift two cities from the matching type to resource-lagging cities, with overall classification consistency rates of 92.9 and 85.7%, respectively. The results indicate that the threshold selected in this paper exhibits good robustness within a reasonable fluctuation range.

Based on the number of older adult(s) care facilities and the older adult(s) population in each city (Equation 4), the consistency coefficient between older adult(s) care facilities and the older adult(s) population at the municipal scale was calculated to analyze the matching status of older adult(s) care facilities across Liaoning Province (Figure 5). To clearly classify matching types, cities with a higher concentration of older adult(s) care facilities than older adult(s) population (CRP > 1.1) are defined as resource-advanced; cities with roughly equivalent concentrations (0.9 ≤ CRP ≤ 1.1) are defined as resource-matching; and cities with a lower concentration of older adult(s) care facilities than older adult(s) population (CRP < 0.9) are defined as resource-lagging, indicating insufficient provision and a need for increased investment.

According to the matching situation of older adult(s) care facilities and older adult(s) population in cities of Liaoning Province (Table 4), there are 6 cities in Liaoning Province where the construction of older adult(s) care facilities and older adult(s) population distribution show a “resource-advanced type,” namely Benxi, Jinzhou, Dandong, Panjin, Fushun and Fuxin, accounting for 42.86% of the province. The economic development levels of these cities are all in the middle or lower ranks in the province, with smaller populations. Except for Jinzhou and Dandong with populations exceeding 2 million, the resident populations of the other 4 cities are all below 2 million. Consequently, the overall development level of older adult(s) care facilities in resource-advanced cities is not high; rather, their classification as resource-advanced mainly results from smaller total and older adult(s) populations, leading to a relatively high per capita allocation of facilities.

Four cities—Yingkou, Anshan, Dalian, and Liaoyang—belong to the resource-matching category, accounting for 28.57% of the total. Liaoyang, Anshan, and Yingkou are all located in central–southern Liaoning (Figure 5), where close socioeconomic linkages have formed a pattern of regional resource sharing and functional complementarity. As traditional industrial bases, Anshan and Liaoyang, together with Yingkou as a port city, have long-standing interactions in industrial development and population mobility, providing a foundation for coordinated allocation of older adult(s) care resources. In addition, high transportation accessibility among these cities facilitates cross-city use of older adult(s) care services and encourages institutions to consider regional service coverage when planning facility layouts. Dalian benefits from unique natural endowments, including a favorable climate, an extensive coastline, and abundant leisure and older adult(s)-friendly spaces such as tourist attractions, parks, and green areas. It also possesses strong comprehensive economic strength, complete infrastructure, and a relatively mature social security system. As a result, despite having a large older adult(s) population, Dalian achieves a basic balance between older adult(s) care facilities and demand. Moreover, through a combined “siphon–radiation” effect, Dalian attracts older adult(s) residents from surrounding areas seeking a higher quality of life, while its transportation advantages reduce both psychological and physical barriers to cross-regional older adult(s) care.

There are four cities in which the construction of older adult(s) care facilities and the distribution of the older adult(s) population exhibit a resource-lagging pattern: Chaoyang, Tieling, Shenyang, and Huludao. The lag in older adult(s) care resources in these areas results from multiple factors, including economic structure, population mobility, fiscal capacity, and geographical location. First, cities such as Chaoyang and Tieling have experienced slowing economic growth due to resource depletion and industrial decline, leading to insufficient financial support for older adult(s) care resources. As an old industrial base, Huludao has a relatively single industrial structure, and limited local fiscal revenue has constrained investment in public services such as older adult(s) care. Although Shenyang, as the provincial capital, leads the province in the total number of older adult(s) care facilities, it faces a pronounced “center-periphery” imbalance: high-quality older adult(s) care resources are concentrated in central urban areas, while suburban areas and older communities lack adequate provision. In addition, as the provincial capital, Shenyang broader development responsibilities, with a large share of investment directed toward economic sectors. As a result, despite some financial input into older adult(s) care, per capita resource coverage remains insufficient. Second, the aging rates in cities such as Chaoyang and Tieling have long ranked among the highest in Liaoning Province, causing older adult(s) care demand to exceed local resource carrying capacity. Shenyang also has a large older adult(s) population base; in 2024, its registered older adult(s) population exceeded two million, and the growth of older adult(s) care demand has outpaced the expansion of facilities. Finally, the lack of economic vitality in cities such as Chaoyang and Tieling has led to significant outmigration of young and middle-aged labor to Shenyang, Dalian, and other regions, weakening local purchasing power for older adult(s) care services and reducing the labor supply for the older adult(s) care industry.

In this study, the number of older adult(s) care facilities in each city of Liaoning Province is taken as the dependent variable, while total population (X1), older adult(s) population (X2), urban GDP (X3), number of tourist attractions (X4), number of healthcare facilities (X5), transportation accessibility (X6), average January temperature (X7), average August temperature (X8), and air quality (X9) are used as independent variables. City-level explanatory variables were input into the geographical detector model, and R-language programs were used to select the optimal discretization method (Equation 5). The resulting q-values represent the influence of each indicator on the spatial distribution of older adult(s) care facilities across the 14 cities of Liaoning Province (Figure 6).

To enhance the model’s interpretability and the robustness of the results, before conducting formal analysis, this study systematically optimized the discretization processing of continuous independent variables using R language programs. The specific optimization process is as follows: the system compares four common discretization methods: natural breakpoint method, quantile method, geometric interval method, and standard deviation method, and sets each method with 3 to 10 categories for parameter debugging. The discretization scheme that maximizes the q-value of the geographical detector through cross-validation is selected as the final parameter for each variable. This optimization process aims to preserve the core features of the original data structure and maximize the explanatory power of each variable for the spatial distribution phenomenon of older adult(s) care facilities. The optimal discretization method for each variable and the final number of classifications are detailed in Table 5.

The q-value indicates the explanatory power of an independent variable X_i on the dependent variable Y, while the p-value reflects the statistical significance of the results. All p-values are less than 0.01, indicating that the results pass the significance test. The influence of factors, ranked by q-value, is as follows: older adult(s) population > total population > urban GDP > number of healthcare facilities > average January temperature > transportation accessibility > number of tourist attractions > air quality index > average August temperature. Among these, older adult(s) population, total population, urban GDP, and number of healthcare facilities all have q-values greater than 0.8, indicating a very strong influence on the spatial distribution of urban older adult(s) care facilities.

Based on the ranking of influencing factors and the spatial distribution characteristics of older adult(s) care facilities in Liaoning Province (Figure 7), The analysis of influencing factor indicators adopts the natural breaks method to uniformly classify all factors into five levels, which can determine the optimal classification breakpoints based on the distribution characteristics of the data. A detailed analysis of each indicator was conducted. Among all factors, the older adult(s) population exerts the greatest influence on the spatial distribution of older adult(s) care facilities. As a type of public resource, older adult(s) care facilities should adhere to a “demand-oriented” principle. The older adult(s) population constitutes the direct service group, and the number of older adult(s) residents in a region forms the fundamental basis for planning service staff, bed capacity, and capital investment (49).

Total population plays a foundational role in the allocation of older adult(s) care resources. While the older adult(s) population determines direct market demand, the total population serves as the starting point for overall infrastructure planning and directly affects infrastructure provision. It also reflects a city’s development potential and the economic feasibility of facility construction, thereby influencing the spatial distribution of older adult(s) care facilities (50).

Cities with higher levels of economic development generally have larger older adult(s) populations and more diversified demands. In such regions, both market forces and government capacity support increased supply and higher-quality facilities. In contrast, cities with lower economic development levels often face problems such as insufficient numbers of older adult(s) care facilities, low facility density, and inadequate service quality (50).

Elderly people are the group that uses medical services most frequently. Many older adult(s) individuals suffer from one or more chronic diseases, such as hypertension, diabetes, and cardiovascular or cerebrovascular conditions, which creates a strong dependence on medical resources. Therefore, the availability of healthcare facilities is a crucial consideration when selecting locations for high-quality older adult(s) care facilities.

Transportation accessibility affects not only the time cost for older adult(s) people to access other social services, but also the cost for family members and service providers to reach older adult(s) care facilities. Accessibility depends not only on physical distance, but also on transportation modes, travel time, economic cost, and physical and psychological burden. On the one hand, older adult(s) people prefer facilities that are convenient for family visits; on the other hand, very old, disabled, or non-driving older adult(s) individuals rely heavily on public transportation. Good transportation accessibility is therefore a key factor in ensuring equitable access to older adult(s) care resources, promoting social participation, and reducing social isolation among the older adult(s) (51).

Environmental factors exert influence by affecting the physiological health, behavioral patterns, and living costs of the older adult(s). Older adults have poor tolerance to high and low temperatures, and many chronic conditions are sensitive to changes in temperature and humidity. Therefore, environmental factors have a significant impact on the site selection of older adult(s) care facilities. Winters in Northeast China are relatively cold, so the average temperature in January has a greater influence on the site selection of older adult(s) care facilities than in August. Healthy and active seniors with choices tend to prefer older adult(s) care facilities with good air quality. Good air quality directly manifests as better living comfort and happiness. The number of tourist attractions is closely related to quality of life and spiritual needs, as abundant tourism resources provide older adult(s) people with more leisure, social, sports, and cultural activities.

The spatial distribution characteristics of older adult(s) care facilities in Liaoning Province result from the combined effects of multiple factors, and their spatial patterns should therefore be examined comprehensively from multiple perspectives (Figure 8).

According to the interaction detection results of the geographical detector (Table 6), the explanatory power of all nine influencing factors is significantly enhanced after pairwise interaction, and all interactions belong to the bifactor enhancement type. This indicates that the spatial distribution of older adult(s) care facilities in Liaoning Province is shaped by the joint effects of multiple factors rather than by any single factor alone.

The interaction between older adult(s) population and urban GDP is most significant (0.9297), indicating that economically developed regions with larger older adult(s) populations can better promote the construction of older adult(s) care facilities. After the superposition of older adult(s) population and healthcare facilities, the q value reaches 0.9185, suggesting that areas with dense older adult(s) populations and well-developed medical facilities are more likely to form older adult(s) care facility clusters. The interaction between older adult(s) population and tourist attractions (0.9057), between tourist attractions and healthcare facilities (0.8347), and between transportation accessibility and healthcare facilities (0.8157) are also prominent. Therefore, the interaction effects of factors such as older adult(s) population and healthcare facilities are all greater than their single-factor influence, indicating that these factors have synergistic or mediating effects when affecting older adult(s) care resources. That is, under the superposition of factors such as healthcare facilities, tourist attractions, and transportation accessibility in a region, the older adult(s) population will more significantly influence the formation, development, and distribution characteristics of older adult(s) care facilities in that area. Regions with suitable climates, larger older adult(s) populations, developed economies, and more medical facilities are more conducive to the clustering of older adult(s) care facilities.

This study utilized POI data from 3,187 older adult(s) care facilities across 14 prefecture-level cities in Liaoning Province as sample points. Web crawling technology was used to collect the data, which were then analyzed using ArcGIS software. Research methods including the nearest neighbor index, kernel density analysis, consistency coefficient, and geographic detector were applied to examine the spatial distribution characteristics and influencing factors of older adult(s) care facilities in Liaoning Province. The conclusions are as follows:

Liaoning Province is among the earliest regions in China to enter a stage of deep aging. The construction and layout of older adult(s) care facilities, as well as the rational allocation of older adult(s) care resources, are crucial for achieving high-quality economic and social development. Based on the findings of this study, the following recommendations are proposed: