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Article

A Machine Learning-Based Study on the Demand for Community Elderly Care Services in Central Urban Areas of Major Chinese Cities

by
Fang Wen
1,*,
Zihao Liu
1,
Bo Zhang
1,
Yan Zhang
1,
Ziqi Zhang
2 and
Yuyang Zhang
1,*
1
Department of Architecture, School of Architecture and Art, North China University of Technology, Beijing 100144, China
2
Department of Architecture, School of Design, Shanghai Jiao Tong University, Shanghai 200240, China
*
Authors to whom correspondence should be addressed.
Appl. Sci. 2025, 15(8), 4141; https://doi.org/10.3390/app15084141
Submission received: 23 February 2025 / Revised: 31 March 2025 / Accepted: 7 April 2025 / Published: 9 April 2025
(This article belongs to the Special Issue Advances in Robotics and Autonomous Systems)
Browse Figures
Versions Notes

Abstract

:
China’s population is aging rapidly, with a large proportion of elderly individuals “aging in place”. In central areas of large cities, the amount of community and home-based elderly care services provided by the government and for-profit organizations are insufficient to meet the demands of these “aging in place” elderly. Taking the core area of Beijing as the spatial scope, this empirical study collects the demand on services of the main types of elderly residents in community and home-based dwelling through questionnaires (n = 242) and employs a mixed-methods approach for analysis. Descriptive statistics and exploratory factor analysis are used to determine the categories and levels of those demands, and machine learning methods (random forest regression model) are used to calculate the importance of various influencing factors (features of the elderly and subdistricts’ built environment) on them. It is shown that elderly residents have a higher demand for psychological and physical condition maintenance services (mean = 3.40), and a lower demand for reconciliation and rights defense services (mean = 3.08). The results also show that the built environment factors are very important for the elderly on choosing demands, especially mean distance of CECSs (community elderly care stations) to downtown landmarks and main roads in subdistricts, and characteristics of CECS. The elderly’s own features also have a relatively important impact, especially their living arrangements, caregivers, and occupations before retirement. This study applies machine learning techniques to sociological survey analysis, helping to understand the intensity of elderly people’s demand for various community and home-based elderly care services. It provides a reference for the allocation of such service resources.

1. Introduction

The increasing aging of the world’s population has become a formidable challenge [1]. Global life expectancy is expected to reach 77.2 years by 2050 [2,3], and researchers predict that by 2080 there will be more people aged 65 and over than children under 18 [4]. The health and well-being of older people has become a very important issue [5], and there is already evidence that staying in a community or home environment for as long as possible has significant advantages over entering a nursing home [6,7,8]: the former promotes health and stability both physically and psychologically [9,10,11]. Therefore, it has become important to promote both active aging and aging in place in many parts of the world [12], which are seen as the best solution to cope with the rapidly growing number of older people [13]. Research on aging in place covers a wide range of areas such as housing, community, health, and social services [14], and community and home-based elderly care services have been found to be an important determinant of active aging of older people [15].
China has the largest elderly population in the world [16]. By the end of 2024, the population aged 60 and over amounts to 310.31 million individuals, accounting for 22.0% of the total population. Specifically, the population aged 65 and over is 220.23 million individuals, representing 15.6% of the total population [17]. The Chinese government advocates that more than 97% of the elderly should “age in place” [18,19], and this is also the preferred choice of the elderly themselves in China [20]. Aging in place not only slows down the physical and mental deterioration experienced by older people but also reduces the pressure on the construction of elderly care institutions, eases the strain on the allocation of care resources, and reduces public expenditures [21,22]. The role of community and home-based care services is crucial in supporting healthy and active aging in place, and studies have shown that using such services significantly improves older people’s self-reported health [23], increases their life satisfaction [24,25], and promotes their physical and mental health [26].
However, the current elderly care services in China cannot fully meet the demands of those elderly who choose to age in place [27,28]. For example, in some communities there is a shortage of service staff, inadequate or misplaced services for different groups of older people, which leads to a decrease in satisfaction and acceptance of such services from older people [29,30,31]. In particular, for the care-dependent elderly, the low quality of services and the inability to achieve integrated care have greatly affected the sustainability of aging in place [32]. Therefore, clarification of Chinese older people’s demands for all manner of elderly care services when aging in place is a crucial first step toward solving the problem [33].
The demands of older people for aging in place cover a wide range of categories, and there is great variability in different types of elderly groups. For example, health status has an impact on the demand for elderly care services [34]. Because younger and more vigorous older people are fully capable of self-management, they tend to focus on physical exercise, and cultural and recreational activities [24]. As physical function declines, the care demands of older people increase [35], requiring more “in place” support [36], such as Community Elderly Care Facilities (CECFs) that the Chinese elderly can use on a regular basis, or the purchase of a certain amount door-to-door services [37,38]. Adequate community and home-based care services are positively associated with the health status of older adults [23]. Of course, health problems and difficulties in accessing medical care increase with age, and most disabled older people find it difficult to avoid entering a nursing home [39,40]. Studies in China have shown that the main body of elderly living in community and home environments is dominated by healthy and assisted living, including a small proportion of nursing-cared elderly and even those who are completely unable to take care of themselves [41,42]. In recent years, due to the significant population growth of older people of advanced age; however, there has been an increase in the demand for professional care resources for those who want to live in a community environment [43]. In short, the care demands for those who age in place are diverse and vary from group to group [44], and this naturally has important implications for the planning and configuration of community elderly services and facilities.
The demands of older people who age in place are related to their socio-economic characteristics and family structure [40,45,46], such as income, education, and living arrangements [34,47,48]. Taking economic factors as an example, research indicates that individuals with poor economic conditions are more likely to fall into health poverty, with economically disadvantaged elderly experiencing higher rates of morbidity and mortality compared to groups with better economic status [49,50,51]. Coupled with an inability to afford nursing home care, poor elderly people must live in community and home environments for longer periods of time [52], and they require more affordable health and care resources [53,54]. Similarly, changes in the physical condition of older people influence their service demands, as do other factors such as age and gender [55,56]. There are also differences in the demands of specific elderly groups; for example, the demands of older people with cognitive disorders and disabilities are significantly different from those of healthy people [57]. Living arrangements and availability of caregivers are also determining factors for the demand for care services [58]. In addition, daily activities are associated with successful aging [59]. Such activities reflect the vitality and socialization patterns of older people and also indirectly influencing their demand for care services [60].
Differences in the built environment of residential areas also lead to differences in the demands of older people, especially demands on exclusive community care service resources. The condition of the built environment in residential areas, such as floor area ratio, housing price, road density, and accessibility to public transport, can indicate the degree of age-friendliness of a residential environment [61] and also influence the accessibility of elderly care services [62,63]. Adequate facilities in built environments can increase the life satisfaction of older people [24], which is conducive to their stable aging in place [64]. CECFs provide services exclusively for the elderly [38], and have been found to have an important link with the quality of life of elderly who age in place [38,65]. Studies in Lanzhou have found that the coverage of elderly care facilities and grassroots cultural facilities at all levels is seriously inadequate, however [66]. The distribution of CECFs of different types is also uneven, resulting in the demands of some older people not being met [67]. Furthermore, the study found that there is a general mismatch between function and demand, and insufficient usable space in the facilities in older communities [68].
Previous studies on the demand for elderly care services have focused on the distribution and location of resources, with the elderly population and entire urban areas as main indicators, which is spatially macroscopic. Analyses of the influencing factors of older people have mostly been explored in terms of their own socio-economic characteristics, and there is a lack of research on their built environments. This study therefore focuses on the community and home-dwelling elderly who live in urban core areas specifically as the target group in order to examine their demand for elderly care services more comprehensively. We explore how the multidimensional indicators of the elderly as individuals, the characteristics of the built environment, and community elderly care facilities impact the demand for aging in place at a subdistrict level. The aim of the study is thus to evaluate the demand for elderly care services on the basis of clear influencing factors, rather than from a macro-level allocation of resources. Also, the findings are conducive to promoting the allocation of elderly care services and provide further reference for the construction of community elderly care facilities, in order to better enhance the “aging in place” environment for the elderly people.

2. Materials and Methods

2.1. Study Area

The two central urban areas in Beijing, Dongcheng District, and Xicheng District, were selected as the study area (Figure 1). The aging problem in Beijing is relatively severe: by the end of 2021, the aging rate had already reached 20.2%, especially 27.0% in Dongcheng District and 26.9% in Xicheng District [69,70], which are the top two out of sixteen districts ranked by aging. Dongcheng District covers an area of 41.84 km2, and Xicheng District covers 50.7 km2, with high population densities and relatively well-developed public services in each. Due to the large number of elderly groups, the shortage of elderly care service is relatively pronounced. Subdistricts are administrative units under district level, and each subdistrict has unified management of residential areas, business areas, and public service facilities within its boundaries. Community elderly service facilities are also built and managed on a subdistrict basis. Driven by spatial convenience and administrative affiliation requirements, residents typically prioritize the use of service facilities within their own subdistrict. Dongcheng District currently has 17 subdistricts, and Xicheng District has 15 (Figure 2).

2.2. Research Framework

In the preliminary research stage, three parts of the study were prepared in advance. First, a large literature review was conducted to understand and summarize the demands of the aging in place elderly in urban area of large Chinese cities. Second, interviews were conducted with older people in several communities of Dongcheng and Xicheng regarding their demands for community and home-based care services. By comparing the demands with the first step, the interview results further clarify the similarities and differences in service demands among the elderly residents in the core areas of Beijing. Additionally, services provided by CECFs and their utilization in the two districts were investigated and summarized via on-site survey. All the above information was then combined into one dependent variable representing demand for care services. Through these preparatory efforts, the main types of service demands among the community-dwelling elderly population in the study area were ultimately identified, forming the dependent variable of this study. Future data on dependent variable (the elderly residents’ attitudes toward these service demands) were collected via a questionnaire.
Subsequently, the independent variables of this study were determined, consisting mainly of two sets of data: the features of the elderly (including their attributes and daily activity characteristics) and the built environment of the subdistricts in which they reside. The data on the elderly residents were together collected through the same questionnaire used for the dependent variable. The built environment data were calculated using ArcGIS v.10.2 (Geographic Information System), encompassing general environmental spatial data and data on CECFs.
Prior to conducting the random forest regression analysis, basic analyses of the questionnaire data were performed using statistical methods. These included exploratory factor analysis to identify the main categories of various elderly care service demands and descriptive statistics to analyze the intensity of them. Thereafter, the focus was shifted to the analysis of machine learning results, the significant impacts of the elderly residents’ personal attributes, and built environment characteristics factors (independent variables) on the elderly choosing various service demands (Figure 3).

2.3. Dependent Variables

Many previous studies have classified the demand for elderly care services into four main categories, though not always the same four. Some break down the demand into life care services (LCS), medical care services (MCS), spiritual and cultural services (SCS), and reconciliation and legal services (RLS), and some [24], while others break the demand into LCS, MCS, RLS, and spiritual and comfort services [34], and still others into life care, medical care, spiritual comfort, and economic assistance [71]. Under each of these categories, there are also more specific subcategories of services that cover various aspects such as older people’s demand for diverse care services [72], their perceptions of the importance of door-to-door services [73], the demand for mobility assistance and travel companionship services [74], and the demand for leisure activities such as physical exercise [75]. The demand for spiritual care, health care knowledge, and neighborhood dispute resolution have also been studied [76].
Community elderly care station (CECS) is the collective term for CECFs in Beijing, and the six types of services required by the government in order to be a CECS, namely day care, emergency call service, dining, health guidance, psychological comfort, and cultural and recreational activities, illustrate the basic demand of community and home-dwelling older people. In the pre-survey stage of this study, we recorded and summarized the different types of services provided by CECSs of 11 randomly selected subdistricts and ultimately found that there are more services offered in CECSs beyond the required ones, such as education and legal assistance, which suggests that these services are also important to the community and home-dwelling elderly population.
In addition, semi-structured interviews were randomly conducted with elderly people in the communities of the study area to collect their views on their own demands. These collected demands were summarized and collated into categories, and the final summary is given in Table 1.
The above 22 categories were each taken to be dependent variables and were obtained from a questionnaire in the form of a 5-point Likert scale. A score of 1 represented “no need” and a score of 5 represented “extremely need”.

2.4. Independent Variables

2.4.1. Older People’s Own Attributes and Data

(1)
Indicators of attributes
One set of independent variables is the socio-economic characteristics of the elderly, such as age, gender, living arrangements, monthly disposable income, etc., and the other set is the characteristics of their daily activities, including the type of daily activities and places (Table 2).
(2)
Questionnaire design and data collection
Data for the above was obtained through a questionnaire and combined with the assessment of the needs in Table 1 (dependent variable) into one questionnaire. These were distributed during a two-week period in June 2023 in 24 subdistricts randomly selected from Dongcheng and Xicheng. Two periods, 8:00–10:00 a.m. and 4:00–5:30 p.m. in good weather were selected, when the temperature was favorable, and the elderly were presumed to be most active. The questionnaires were distributed near CECSs in each subdistrict, and the elderly who filled out the questionnaire have all been informed and have consented to the anonymous collection of their information. A total of 300 questionnaires were distributed, and the final number of valid questionnaires recovered was 242, covering 14 subdistricts in Dongcheng District and 10 subdistricts in Xicheng District (Figure 4). Because the study questionnaire does not collect sensitive personal information, does not involve commercial interests, and also does not cause harm to human subjects, ethics approval is not required for this type of study.

2.4.2. Built Environment Data

The built environment data also included two parts. One is the basic spatial indicators, such as road density and floor area ratio within each subdistrict, and the other is the characteristics of the CECSs such as their area, location, and accessibility. Therefore, ArcGIS 10.2 was used to prepare the built environment data on 24 subdistricts [77]. These were combined appropriately, and the built environment indicators were obtained, as shown in Table 3.
As mentioned above, in Beijing, CECS must have six types of service functions. They are typically well-managed and standardized so that CECFs [78] basically cover most of the needs of home-bound elderly people. Strictly speaking, CECFs are a class of public service facility, but because of the important role of community elderly service facilities in home care services for the elderly, the number and distribution characteristics of such facilities must be studied with separate indicators. In our study area, the spatial characteristics of CECS s in 24 subdistricts were collected (Table 4).

2.4.3. Calculation Process

Exploratory factor analysis was used to examine the relationships between all of the above data points after calculating a KMO greater than 0.6, and p < 0.05 for Bartlett’s sphericity test. A table of explained variance explained was produced, which was rotated using the maximum variance rotation method (varimax) to obtain a table of rotated factor loading coefficients that grouped the 22 categories of needs into five main areas.
In addition, machine learning techniques were also used to analyze the data [79,80,81]. First, the questionnaire data, the subdistrict variable data, and the POI data were red using the Python 3.12 Pandas library and merged based on the subdistrict name where the respondents reside. Next, feature columns were selected and StandardScaler was employed to standardize these features. Following preprocessing, the data were divided into training and test sets in an 8:2 ratio using the sklearn library. A random forest classification model was then trained with parameters n_estimators = 50 and random_state = 42. Upon completion of the model training, its predictive accuracy on the test set was calculated. Finally, the SHAP package was employed to generate SHAP bar plots, which elucidate the significance each variable within the random forest model.

3. Results

3.1. The Intensity of Elderly People’s Demand for Various Services and Analysis of Differences

The 242 questionnaires were tested for reliability and validity, and yielded Cronbach’s alpha = 0.915 and KMO = 0.905, indicating that the reliability and validity of the questionnaires were both good. Then, the 22 categories of demand were condensed into five major aspects by using exploratory factor analysis: living services, nursing care services, health care services, psychological and physical condition maintenance, and reconciliation and rights defense services (Table 5).
An analysis of the frequency means of the options of the five main types of demands showed that the most frequently chosen option was “3”, followed by “4”, “2”, and “5” points were chosen more or less the same quantity, and “1” (no need) was chosen by the fewest people. This suggests that there is a relatively high level of demand for elderly services, with only a minority of cases indicating no demand (Figure 5). Among the five main demands, the highest demand was for psychological and physical condition maintenance (mean = 3.40) and the lowest demand was for reconciliation and rights defense services (mean = 3.08). Among the 22 subcategories, the ones with the three highest levels of demand were cultural and recreational activities, repair and troubleshooting, and emergency call service, which belong to the three categories of psychological and physical condition maintenance, living services and nursing care services, respectively. These popular services were followed by physical exercise, companionship and comfort, community pharmacy services, education, health guidance, rehabilitation and physiotherapy, and routine care (mean > 3.3), which belong to the three categories of psychological and physical condition maintenance, nursing care service and health care service, respectively. Overall, older people had a low demand for living services, although the lowest demand was for legal assistance and dispute resolution, the only two types in the category of reconciliation and rights defense services.

3.2. Factors That Affect the Demands for 22 Subcategories

Using the random forest regression model, the importance of all the indicators that influence the elderly demand for services were analyzed and the top 10 indicators were ranked (Figure 6). The highest R2 in the model was 0.75 for travel companionship, followed by home cleaning at 0.64, rehabilitation and physiotherapy at 0.65, and psychological counseling at 0.6, all of which are above 0.6. Those with R2 below 0.5 included dining for the elderly at 0.46, errands and payment service at 0.46 as well, cultural and recreational activities at 0.47, and dispute resolution at 0.49, indicating that the fit was relatively poor. Nevertheless, certain patterns were still discernible. In general, among all the influencing factors with the highest importance in the 22 subcategories of demand, ‘mean distance from CECS to downtown landmark’ occurred with the highest frequency, followed by “living arrangements” and “area ratio of all CECSs to subdistrict”, belonging to characteristics of built environment and elderly themselves, with the former being more important. The second most important factors are “occupation before retirement”, “living arrangements”, “caregiver”, “age”, “gender”, “monthly disposable income”, “mean distance from CECS to downtown landmark”, and “housing prices”, which shows that the characteristics of the elderly themselves are more important than any others. The third most important factors were “educational background” and “mean distance from CECS to nearest subway stations”. The first three positions of importance show that characteristics of the built environment, such as the accessibility of CECS, the locational advantage of the subdistricts where the elderly reside, and economic level affect their demand for elderly care services the most. Also, the elderly’s attributes, such as age, living arrangements and economic status are also important factors.
Statistics on the frequency of the top three, top five, and top ten factors (Figure 7) show that among the top three, the most frequent were “mean distance from CECS to downtown landmark”, “living arrangements”, and “mean distance from CECS to nearest main roads”, followed by “gender”, “floor area ratio”, “occupation before retirement”, and “caregiver”; in the top five, the most important ones were the same as in the top 3 group, with the addition of “educational background”; and in the top 10, the most important one was still “mean distance from CECS to downtown landmark”, followed by “living arrangements”, “caregiver”, “mean distance from CECS to nearest main roads”, and “educational background”. “Mean distance from CECS to downtown landmark” was the most important factor we found, which reflects the fact that the location of a CECS as well as its transportation accessibility are both keystones for the acquisition of elderly services in city centers.
Overall, the importance of activity characteristics among the attributes of the elderly was relatively low. Combining importance ranking with frequency of ranking, the relatively most important types of activities were “singing”, “walking”, and “Tai Chi”, and the preferred place for activities was “outdoor space in the community”. However, their importance was much lower than that of the elderly’s own socio-economic attributes, and even less than built environment factors as well.
In the top three subcategories with the highest level of demand, cultural and recreational activities, repair and troubleshooting, and emergency call service, the most important influencing factors were, respectively, “housing prices”, “mean distance from CECS to downtown landmark”, and “occupation before retirement”. In the subcategories with a higher level of demand, such as physical exercise and companionship and comfort, the most important factors were mainly the characteristics of CECS (6 in 7), except for “community pharmacy services”, which corresponded to “living arrangements”.
The top 10 important influencing factors in each of the 22 subcategories of demand all covered two types of characteristics, the built environment and the elderly themselves, but from the cross-sectional comparison of these subcategories, we find that with demand for travel companionship, eight factors stemmed from the built environment and only two factors from elderly people’s own attributes, which may be due to the fact that the accessibility of the built environment is highly correlated with the ease of travel for older people. On the contrary, in the five subcategories of repair and troubleshooting, community pharmacy services, rehabilitation and physiotherapy, family doctor, and educational, factors from elderly’s own attributes accounted for 7 out of 10. In addition, in 15 out of the 22 subcategories, the proportion of factors of the characteristics of CECS to the built environment was more than half, which shows again the importance of exclusive elderly resources such as in the built environment. Especially for psychological counseling, the proportions of characteristic of CECS in built environment features were 80%, and 75%, respectively, for both home care and legal assistance. These services are provided in CECSs, which suggests that elderly people who demand them should pay attention to the layout of CECSs in their areas.

3.3. Factors That Influence Five Categories of Demand

In the top three factors for the categories of living and health care service, the factors with the highest frequency were both characteristics of CECSs and living arrangements, but in nursing care service the highest frequency was for attributes of the elderly. In psychological and physical condition maintenance both highest-frequency factors were characteristics of the built environment. There was a single factor with highest frequency among the top five factors that was highly consistent in five categories, and it again was a built environment attribute. Finally, in the top 10, the factor with highest frequency in living service was a built environment attribute as well but in nursing care service was an elderly people attribute. The three remaining categories had the most important factors from both the built environment and the elderly people themselves (Table 6).
Demand for living services and reconciliation and rights defense service were highly consistent among the top 3, 5, and 10 most frequent factors, with mean distance from CECS to downtown landmark and living arrangements the most important. Psychological and physical condition maintenance, and health care services, were highly consistent as well, with the most important factors being accessibility of CECSs, followed by having a caregiver, and educational background. In this result, apart from the characteristics of CECS, no other built environment features appeared, nor did activity characteristics from older people’s own attributes.

4. Discussion

4.1. Valuing and Using the Different Levels of Demand for Services

Descriptive statistics results for the 22 categories are consistent with some of the findings in previous studies, such as that healthy older people have a high demand for physical exercise and cultural and recreational activities [82,83,84], and disabled older people have a significant demand for routine care and emergency call service [85,86]. However, what was previously thought to be a high level of demand for living services among older people is contradicted by the results of the present study: with the exception of repair and troubleshooting, the level of demand for other types of living services was relatively low, especially for meal preparation and delivery, shopping and grocery buying, and travel companionship (mean < 3.20). This low level of demand for living services may be due to financial cost concerns [87], or it may be related to the fact that older people who are aging in place are predominantly active ones [41,42].
Situations in which older people need repair and troubleshooting are not common, but this work is both necessary and highly technical and difficult for older people to manage independently [88]. In addition, due to the current imperfect allocation of service resources and a lack of sufficient personnel, community pharmacy services in China cannot meet all the door-to-door medication needs of all elderly residents, and there is a severe shortage of both professional personnel and volunteers for companionship and comfort services [89,90].
In terms of the five main categories, older people’s high demand for psychological and physical condition maintenance is consistent with previous research [91]. Studies have examined the varying frequency of use of social services by older people at risk of hospitalization who are aging in place and have found that social and cultural activities and catering services are frequently in demand [92]. Another study of the same type of older people found that personal care, social and cultural activities, and catering services were frequently in demand as well [93], demonstrating the importance of social and cultural activities. Therefore, CECFs should emphasize psychological and physical condition maintenance. Specifically, planning should be based on their subcategories, such as organizing health-related educational activities, festivals, and physical activities and providing psychological counseling or companionship in order to alleviate loneliness of the elderly. The demand for reconciliation and rights defense service is the lowest. Compared to other services, the frequency and quantity of demand for these issues was not high, and older people can obtain this sort of help through neighborhood committees, specialized law firms, or professional institutions [94,95]. Therefore, it is not necessary for CECF to allocate professional personnel and dedicated spaces for these services.
Nonetheless, a significant proportion of the demand for the 22 subcategories of services can be met through allocations in CECFs [96]. A first step should thus be to continue leveraging the service carrier function of CECFs, enhance their “in station” service capabilities and improve their door-to-door services. In particular, services with high demand levels identified in this study should be strengthened in terms of personnel and space to ensure adequate provision and avoid shortages [38]. For services with weak or no demand, however, further research should be conducted to understand the underlying reasons and appropriately reduce the supply for these services [97]. Furthermore, dynamic surveys should also be conducted to avoid fluctuations in future demand. In summary, elderly care services should be allocated based on elderly’s demand for them [98].

4.2. Characteristics of the Built Environment Are Very Important in Influencing the Demands for Elderly Services

The important factors in the built environment dimension of affecting the demand for elderly services are the characteristics of the CECF, fully demonstrated that exclusive elderly care resources are very important in the built environment of “ageing in place”, which has not been found in previous studies. Studies that focused on the role of CECFs specifically have found that due to poor planning in core areas of large cities these facilities cannot meet the demands for services [61,99]. For instance, irrational spatial distribution is the main reason for the imbalance in supply and demand for services in CECSs in Beijing, where it affects over 80% of CECSs [100]. The distribution of different types of CECFs is also unbalanced in the city, which has led to a deviation in the service capacity of facilities and resulted in service needs of the elderly remaining unmet [67]. Furthermore, there is a wide variation in the utilization of CECFs in most cities [101], as well as a general mismatch between supply and demand in terms of usage area [68]. These issues reflect a lack of adequate understanding of the demand of different types of older people during the layout process of CECFs.
The importance of accessibility is more prominent among the characteristic factors of CECFs, such as mean distance from CECS to downtown landmark and nearest roads in this study, which is consistent with the results of previous studies [61,102]. Moreover, the number of CECSs, the total area of CECSs, and area ratio of all CECSs to subdistrict, all reflect the coverage of CECSs, which itself has a certain impact on the fulfillment of demand for elderly services [100]. These results suggest that when planning CECFs, the accessibility of facilities should be considered first, followed by the setting of the number and size of CECFs in terms of subdistricts.
In addition to exclusive elderly care resources, the locational characteristics of the built environment, such as the level of development, accessibility, and amenity completeness, also have an important impact on the demand for elderly care services [103]. In this study, we found that floor area ratio, housing prices, and POI density are the most important in terms of the built environment. The former two reflect population density and urban location in a certain area [104]. For example, housing prices reflect the economic characteristics and the locational value of a subdistrict, as well residents’ purchasing power and economic “capacity” [105], and POI density characterizes the level of public service facilities, which indirectly reflects the vitality of the urban built environment [106]. Other indicators of the built environment are also influential, such as green space ratio, which reflects the level of ecological friendliness within each subdistrict and is an important part of the livability of the built environment [107], and road density and density of bus stops both characterize the accessibility of the location [108]. The influence of these indicators suggests that although the built environment is difficult to change easily, it is still possible to optimize some built environment elements during the construction process of amenities for the elderly. Thus, favorable characteristics of the built environment in an area, such as good accessibility and economic development, should be considered when planning for the provision of elderly services [109].

4.3. Older People’s Own Characteristics Have an Important Influence on Their Demand for Services

Living arrangements was the most important factor for three subcategories of demand (shopping and grocery buying, community pharmacy services, and legal assistance), and occupation before retirement was the most important for two (home care, emergency call service). Moreover, according to the frequency statistics in the top three, top five, and top ten importance ranking of influencing factors, it is clear that the frequency of the living arrangements was the highest, second only to the first characteristic element of CECSs. The next most important factors were caregiver, occupation before retirement, and educational background.
The important effect of living arrangements on the demand for elderly care services has already been studied before [48]. Firstly, the two types of elderly people, living alone and not living alone, have a significant effect on the demands on services [110]; and secondly, whether they live with children and grandchildren also affects their well-being. However, a very small number of elderly respondents living with nonrelatives nevertheless have high well-being and low levels of depression [111,112]. This evidence suggests that living arrangements determine who provides the care for elderly and consequently the extent to which the elderly require care from public resources [113], a result that is in line with those of this study.
The influence of having caregivers on the demand for elderly care service in this study was reflected in meal preparation and delivery, home cleaning, home care, and having a family doctor, which fully illustrates that the presence of a caregiver and the caregiver’s attributes have a significant impact on older people’s daily care, health maintenance, and nursing. Previous studies have found that the presence or absence of a caregiver can affect the services provided to older people as their health declines [114] and that differences in the type of caregiver, such as spouse, relative, or nursing staff, also affect quality of daily care and health care services [115,116].
Previous studies have also shown that occupation before retirement also has a significant impact on older people’s choice of elderly care services, as it not only influences the level of pension but also affects older people’s perceptions and acceptance of different types of services [117,118]. In addition, educational background also affects their demand for services primarily stemming from cognitive differences among the elderly that in turn lead to different lifestyles and affect the acceptance of certain services or the channels through which services are accessed [87].
The impact of activity characteristics on older people’s demand for elderly care services has yet to be observed in previous studies. However, we discovered that among the attributes of the elderly, the importance of activity characteristics factors was relatively low, although specific activity types, such as singing, chatting, and Tai Chi, were found to exert significant influence on the demand for various elderly services. Those activities are reported to be the favorite and most frequently performed outdoor activities among urban Chinese older people [119,120]. Previous studies have also found that daily outdoor activities can promote the health and well-being of the elderly [121], and among activity places, outdoor spaces in the community are important for engaging in leisure activities, socializing, and exercising [122]. In general, active older people have a high demand for many different services, and this group also constitutes the main body of the aging-in-place population.
Therefore, given the important influence of the elderly’s own attributes on their demand for elderly services, policy makers should focus on the demand of these different groups in order to better allocate service resources.

4.4. Limitations

There are several limitations to this study. First, due to the limited size of the research staff, only 242 valid questionnaires were collected, which is somewhat small. Second, this study did not stratify the elderly into different groups and set corresponding proportions of samples. Finally, although the data covered 24 subdistricts, full coverage of the core area of Beijing would have required 32. Therefore, follow-up studies need to expand the scope of sample collection, increase the sample size for the proportion of each type of elderly group, and maintain a balanced sample size in different subdistricts.

5. Conclusions

This study focused on the problem in which community and home-based elderly care services in the center area of China’s large cities cannot meet the demand for elder services of elderly residents who are aging in place, especially in terms of insufficient variety of available services. Focusing on Beijing, the study explored the demand of elderly residents provided at the subdistrict level and analyzed the significance of multi-dimensional factors influencing them. It is found that elderly residents have the highest demand for psychological and physical condition maintenance, including cultural and recreational activities, physical exercise, and companionship and comfort, and the lowest demand for reconciliation and rights defense service, with legal assistance and dispute resolution. The demand for repair and troubleshooting in the living services category, as well as emergency call service in the nursing care services category, were also very strong. In regard to the influencing factors on the elderly choosing demand for elderly care services, built environment factors were found to be very important, especially the characteristics of CECFs, such as mean distance from CECSs to the downtown landmark and to main roads. The elderly’s own attributes were also of some importance, daily life characteristics such as the living arrangements, having a caregiver, and occupation before retirement being the most important ones.
This study clarifies the differences in the demand for various types of elderly care services and identifies the significant factors influencing elderly’s choice on service demands, thereby providing a reference for adjusting the types and quantities of elderly care services at the subdistrict level, and we hope it can help to promote the efficient allocation of community elderly care resources. In addition, the results highlight the importance of CECFs in meeting the demand of older people who are aging in place. Furthermore, the findings of this study may also provide a reference for future understanding of the needs of the elderly in urban core areas. By analyzing the basic characteristics of older residents within subdistricts, we may be able to deduce the intensity of their demand for different services, thereby allowing us to help improve service provision and facilities planning in order to create a more suitable environment for aging in place.

Author Contributions

Conceptualization, F.W.; methodology, F.W. and Y.Z. (Yuyang Zhang); software, Z.L. and Y.Z. (Yan Zhang); validation, Z.Z.; data acquisition, curation, and analysis, F.W., Z.L., Z.Z. and Y.Z. (Yan Zhang); visualization, Z.L.; writing—original draft, F.W. and Z.L.; writing—review and editing, B.Z. and Z.Z.; project administration, B.Z. and Y.Z. (Yuyang Zhang). All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Beijing Municipal Social Science Foundation, grant number 21SRC024.

Institutional Review Board Statement

The ethical approval of this study is not required according to the Chinese “Regulations on Ethical Review of Life Sciences and Medical Research Involving Humans”.

Informed Consent Statement

Older adults who filled out the questionnaire were all informed and consented to the anonymous collection of their information.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors upon request.

Conflicts of Interest

The authors declare no conflicts of interest.

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Figure 1. Dongcheng and Xicheng Districts in Beijing.
Figure 1. Dongcheng and Xicheng Districts in Beijing.
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Figure 2. All subdistricts belonging to Dongcheng District and Xicheng District.
Figure 2. All subdistricts belonging to Dongcheng District and Xicheng District.
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Figure 3. Flowchart of the machine learning part in the study.
Figure 3. Flowchart of the machine learning part in the study.
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Figure 4. Twenty-four subdistricts covered by the questionnaire.
Figure 4. Twenty-four subdistricts covered by the questionnaire.
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Figure 5. (a) Questionnaire responses for the 5 main categories; (b) the degree of demand for the 22 subcategories.
Figure 5. (a) Questionnaire responses for the 5 main categories; (b) the degree of demand for the 22 subcategories.
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Figure 6. (a) Meal preparation and delivery; (b) home cooking services; (c) dining spot for the elderly; (d) repair and troubleshooting; (e) home cleaning; (f) shopping and grocery buying; (g) errands and payment service; (h) travel companionship; (i) routine care; (j) home care; (k) emergency call service; (l) community pharmacy services; (m) rehabilitation and physiotherapy; (n) family doctor; (o) health guidance; (p) cultural and recreational activities; (q) education; (r) physical exercise; (s) companionship and comfort; (t) psychological counseling; (u) legal assistance; (v) dispute resolution.
Figure 6. (a) Meal preparation and delivery; (b) home cooking services; (c) dining spot for the elderly; (d) repair and troubleshooting; (e) home cleaning; (f) shopping and grocery buying; (g) errands and payment service; (h) travel companionship; (i) routine care; (j) home care; (k) emergency call service; (l) community pharmacy services; (m) rehabilitation and physiotherapy; (n) family doctor; (o) health guidance; (p) cultural and recreational activities; (q) education; (r) physical exercise; (s) companionship and comfort; (t) psychological counseling; (u) legal assistance; (v) dispute resolution.
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Figure 7. Frequency statistics of the top three, top five, and top ten most important influencing factors.
Figure 7. Frequency statistics of the top three, top five, and top ten most important influencing factors.
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Table 1. Summary of 22 categories of the demands of older people.
Table 1. Summary of 22 categories of the demands of older people.
NameContents
Meal preparation and deliverya service that prepares meals and delivers them to a person’s doorstep
Home cooking servicesprovides services for cooking up to three meals per day as needed
Dining spot for the elderlydining spaces within walking distance in the community
Repairs and troubleshootingregular or on-demand services to inspect and repair household appliances and check for safety hazards
Home cleaningservices that include cleaning the home, doing laundry, washing dishes, etc.
Shopping and grocery buyingservices for shopping and purchasing groceries
Errands and payment servicesincludes picking up packages, handling various business payments, and calling for transportation services, etc.
Travel companionshipaccompaniment to hospital, shopping, or other outings, and full-time companionship for outdoor activities
Routine carehaircuts, nail trimming, and assisted bathing provided at home
Home carefull-time or part-time home care services offering complete nursing care during the daytime
Emergency call serviceeffective calling for help to responsible departments or other emergency contacts
Community pharmacy servicesdispensing medications at community health stations or hospitals
Rehabilitation and physiotherapyvarious rehabilitation services, such as massage, strength training, traditional Chinese health care, etc.
Family doctorproviding regular consultations or follow-up services, including home visits for blood pressure checks, blood sugar testing, medical guidance, etc.
Health guidanceoffering advice on senior nutrition, health care, common disease prevention, etc.
Cultural and recreational activitiesengaging in cultural and entertainment activities, such as playing chess, singing, dancing, painting, etc.
Education interest classes in calligraphy, painting, etc., as well as educational lectures
Physical exerciseconducting sports activities indoors or outdoors
Companionship and comfortcompanionship, chatting, and emotional comfort services
Psychological counselingregular or as-needed psychological consultations, emotional help, and interventional therapy
Legal assistanceproviding legal advice or assistance as needed
Dispute resolutionassisting in resolving internal family disputes and neighborhood disputes
Table 2. Indicators of the characteristics of the elderly themselves.
Table 2. Indicators of the characteristics of the elderly themselves.
IndicatorsMeasurement Items
GenderMale/Female
Age50–59/60–69/70–79/80 and above
Educational backgroundjunior high school or below/senior high school (including technical secondary school)/junior college/university
Occupation before retirementagricultural production/government departments or public institutions/enterprise organization/self-employed business
Monthly disposable income<3000/3000–5000/5000–8000/8000–10,000/≥10,000 CNY (RMB)
Living arrangementsalone/only with spouse/only with children/with spouse and children/only with grandchildren/with spouses and grandchildren/three generations/with other relatives/with a nanny or caregiver
Physical conditionindependent/assisted/nursing care
Caregiverspouse/children/other relatives/friends/hourly worker/live-in nanny or caregiver/self-care/none of the above
Daily activityreading/walking/chatting/playing chess/singing/dancing/ball sports/Tai Chi/watching TV/playing cards/sunbathing/other
Activity placesoutdoor spaces in community/parks or plazas nearby/senior activity rooms/community activity centers/other
Table 3. Data indicators and sources for subdistricts.
Table 3. Data indicators and sources for subdistricts.
IndicatorsCalculation MethodRepresentationData Sources
Area of subdistrictTotal area of each subdistrictSize of subdistrictAmap
Road densitySum of the lengths of all roads within each subdistrict and divided by the total subdistrict areaAccessibilityAmap, Map World
Floor area ratioSum of all building areas within each subdistrict and divided by the total subdistrict areaDegree of urbanizationAmap, OpenStreetMap
Number of bus stopsSum of all bus stops within each subdistrictAccessibilityAmap
Density of bus stopsDivide the number of bus stations by the total subdistrict areaAccessibilityAmap, OpenStreetMap
Housing priceMean of all housing prices in all residential neighborhoods within the subdistrictCity location advantageAmap
Green space ratioSum of all green areas and divide by the total subdistrict areaResidential environmental livabilityAnjuke, Baidu
POI (Point of Interest) densityNumber of different POIs (eating, shopping, science, education and culture, scenic spots, transport services, amenities, finance and insurance, sports and leisure, health care, etc.) divided by total road area.Capacity of public services facilitiesAmap
Table 4. Characteristic indicators of elderly service stations.
Table 4. Characteristic indicators of elderly service stations.
IndicatorsCalculation MethodRepresentationData Sources
Number of CECSsSum of all CECSs in each subdistrictSize of CECSsAmap, On-site survey
Total area of CECSsSum of the areas of all CECSs within each subdistrictSize of CECSsOn-site survey
Area ratio of all CECSs to subdistrictTotal area of all CECSs divided by area of the corresponding subdistrictCoverage of CECSsOn-site survey, Amap
Mean distance from CECS to nearest main roadsWithin each subdistrict, sum the distances from all CECSs to the nearest main roads, then divide by the number of CECSsAccessibilityAmap, OpenStreetMap
Mean distance from CECS to nearest subway stationsWithin each subdistrict, sum the distance from all CECSs to the nearest subway station, then divide by the number of CECSsAccessibilityAmap, OpenStreetMap
Mean distance from CECS to downtown landmarkWithin each subdistrict, sum the distances from all CECSs to Tiananmen Square, then divide by the number of CECSsAccessibilityAmap, OpenStreetMap
Table 5. Factor loading coefficients after rotation.
Table 5. Factor loading coefficients after rotation.
Summary of Demands in Five Main AreasName of DemandsFactor LoadingCommunality
Factor 1Factor 2Factor 3Factor 4Factor 5
Living services Meal preparation and delivery 0.7860.2120.1550.2690.0970.768
Home cooking services 0.7650.0120.1050.1500.1770.650
Dining spot for the elderly 0.7150.232−0.0100.136−0.1060.595
Repair and troubleshooting 0.6820.1760.2090.1230.0580.558
Home cleaning 0.7210.1230.1690.1820.0270.597
Shopping and grocery buying 0.6560.1130.1600.1020.1730.509
Errands and payment service 0.6690.1560.2010.1410.1250.547
Travel companionship 0.7420.1070.232−0.0290.1180.630
Nursing care services Routine care 0.1800.1100.1090.8370.0870.765
Home care 0.2090.1140.1390.7740.1300.692
Emergency call service 0.2550.1660.2490.6170.2000.575
Health care services Community pharmacy services 0.1820.2050.7320.1530.0330.634
Rehabilitation and physiotherapy 0.1810.0900.7790.0800.0800.661
Family doctor 0.2160.1000.7020.1000.1400.579
Health guidance 0.1810.2230.7480.1590.0540.670
Psychological and physical condition maintenance Cultural and recreational activities 0.1330.7910.1250.1470.0080.681
Education 0.1700.7200.2060.057-0.0080.593
Physical exercise 0.2210.6950.1770.0460.0990.576
Companionship and comfort 0.0910.7200.0130.0820.3180.634
Psychological counseling 0.2840.5080.3040.2780.1570.533
Reconciliation and rights defense services Legal assistance 0.1440.1800.1700.1260.8300.786
Dispute resolution 0.1790.1280.0810.2130.8070.751
Rotation method: varimax maximum variance.
Table 6. Highest frequencies in the top three, five, and ten rankings of the factors in the five categories of demand (including ties).
Table 6. Highest frequencies in the top three, five, and ten rankings of the factors in the five categories of demand (including ties).
Demand on Five Main AreasFactors with the Highest
Frequency in Top 3		Factors with the Highest Frequency in Top 5Factors with the Highest
Frequency in Top 10
Living servicesMean distance from CECS to downtown landmark/Living arrangementsMean distance from CECS to downtown landmarkMean distance from CECS to downtown landmark
Nursing care serviceOccupation before retirementMean distance from CECS to downtown landmarkMean distance from CECS to downtown landmark/Caregiver/Occupation before retirement
Health care serviceMean distance from CECS to nearest main roads/Living arrangementsMean distance from CECS to nearest main roadsEducational background/Caregiver
Psychological and physical condition maintenanceMean distance from CECS to downtown landmark/Mean distance from CECS to nearest main roadsMean distance from CECS to downtown landmarkMean distance from CECS to downtown landmark/Educational background/Caregiver
Reconciliation and rights defense service/Mean distance from CECS to downtown landmarkMean distance from CECS to downtown landmark/Living arrangements
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Wen, F.; Liu, Z.; Zhang, B.; Zhang, Y.; Zhang, Z.; Zhang, Y. A Machine Learning-Based Study on the Demand for Community Elderly Care Services in Central Urban Areas of Major Chinese Cities. Appl. Sci. 2025, 15, 4141. https://doi.org/10.3390/app15084141

AMA Style

Wen F, Liu Z, Zhang B, Zhang Y, Zhang Z, Zhang Y. A Machine Learning-Based Study on the Demand for Community Elderly Care Services in Central Urban Areas of Major Chinese Cities. Applied Sciences. 2025; 15(8):4141. https://doi.org/10.3390/app15084141

Chicago/Turabian Style

Wen, Fang, Zihao Liu, Bo Zhang, Yan Zhang, Ziqi Zhang, and Yuyang Zhang. 2025. "A Machine Learning-Based Study on the Demand for Community Elderly Care Services in Central Urban Areas of Major Chinese Cities" Applied Sciences 15, no. 8: 4141. https://doi.org/10.3390/app15084141

APA Style

Wen, F., Liu, Z., Zhang, B., Zhang, Y., Zhang, Z., & Zhang, Y. (2025). A Machine Learning-Based Study on the Demand for Community Elderly Care Services in Central Urban Areas of Major Chinese Cities. Applied Sciences, 15(8), 4141. https://doi.org/10.3390/app15084141

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