Spatiotemporal Evolution of the Institutional Eldercare in the Yangtze River Delta Urban Agglomeration

Spatiotemporal Evolution of the Institutional Eldercare in the Yangtze River Delta Urban Agglomeration | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatiotemporal Evolution of the Institutional Eldercare in the Yangtze River Delta Urban Agglomeration Rong Zhou, Jinghang Cui, K. Jason Crandall This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3849846/v2 This work is licensed under a CC BY 4.0 License Status: Posted Version 2 posted You are reading this latest preprint version Show more versions Abstract Background: The objective of this study is to examine the spatiotemporal evolution mechanism of the institutional eldercare in the Yangtze River Delta Urban Agglomeration (IE-YRDUA). It aims to analyze the developmental patterns and spatial correlations of these institutions over a twenty-year period, in order to shed light on the increasing demand for eldercare services in this economically significant region. Methods: This study utilizes spatial analysis and factor detection methods within an economic geography framework to analyze data from 2001 to 2020. The focus of the analysis is on understanding spatial correlations and identifying factors that influence the evolution of eldercare services. Results: The findings indicate a significant growth and distribution of eldercare institutions, with notable spatial correlations suggesting a trend towards regional agglomeration. The study also reveals imbalances in the spatial development of eldercare, with a concentration of facilities in central urban areas and a decline on the periphery. Additionally, factors such as economic level and capacity to consume have a significant impact on the spatial evolution of eldercare services. Conclusions: This study emphasizes the dynamic nature of institutional eldercare in the Yangtze River Delta, highlighting the necessity for strategic planning and resource allocation to address spatial imbalances in eldercare provision. The insights gained from this study are crucial for policymakers and stakeholders in optimizing eldercare infrastructure and meeting the growing demands of the population. Health Economics & Outcomes Research Health Policy institutional eldercare economic geography geographical nature the Yangtze River Delta urban agglomeration Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction The high-quality development of eldercare services is an important goal for China as it actively addresses the challenges posed by an aging population. Institutional eldercare, with its strong industry relevance, high degree of service integration, and comprehensive benefits, has become an important lever. The “14th Five-Year Plan” has raised the prominence of enhancing inclusive eldercare services and advancing the reform of eldercare institutions to the status of long-term objectives at the national level. According to the data from the 7th National Population Census, the proportion of senior citizens in the Yangtze River Delta Urban Agglomeration, YRDUA, with household registration has exceeded 20%, indicating that it has entered a moderately aged society, making it the region with the most severe aging issue in China. However, due to the long-term influence of various factors, the YRDUA still faces development problems such as imbalances and mismatches in basic public services (Wu et al., 2023). With the severe trend of population aging, this widespread gap may gradually widen. To alleviate the social pressure of eldercare, the YRDUA has taken the lead in conducting regional coordinated eldercare practices. With a policy orientation towards regional cooperation in eldercare resources, it aims to develop a relatively mature and stable model to provide experience and reference for the spatial allocation of integrated regional eldercare resources in China. Current relevant research primarily focuses on three key areas. Firstly, there is a strong emphasis on accessibility analysis. For instance, Shah et al. utilized the GIS-based accessibility approach and 3SFCA method to investigate the accessibility of eldercare services in Canada. Their findings revealed disparities in the allocation of basic healthcare services concerning the percentage of individuals aged 65 and above, especially within rural and isolated communities (Shah et al., 2017). Ding et al. (2016; Wang at al., 2021; Gao et al., 2018) employed the potential function, Lorenz curve, and spatial distance method respectively. Their research highlighted significant variations in the distribution level of eldercare facilities in major Chinese cities, with spatial accessibility exhibiting a decreasing distribution pattern from the central urban area towards the periphery. Secondly, the research delves into the analysis of resource utilization differences. Joseph et al. (1996) discovered a correlation between eldercare institutions in New Zealand and the geographic evolution of the elderly population, that is, urban centers benefited from an expansion of long-term care driven by private-sector initiatives, while rural communities suffered a broad-based depletion of services. Stubbs et al. (1992) attributed this phenomenon to the exacerbation of social inequality resulting from the privatization of social welfare, leading to a spatial imbalance in resource utilization. Additionally, Liu et al. (2021) verified through their survey in China that both the quality and utilization of institutional eldercare services among urban residents are significantly higher compared to those in rural areas, both in terms of tangibility and effectiveness. The third area of focus is the analysis of influencing factors. Eldercare institutions, as public facilities, have their spatial distribution rooted in political-economic contexts and social processes. They are highly correlated with regional population density and socioeconomic factors (Dear, 1978; Bi et al., 2020). Niefeld (2005) and Ryvicker at al., (2012) further propose that barriers to the use of eldercare resources are more attributable to geographic constraints and information access barriers rather than economic restrictions. Factors such as spatial distance, transportation accessibility, and surrounding activity space also play a role. Research in China has found a strong correlation between the development of institutional eldercare and income levels as well as family compensation functions. In urban areas, this correlation is associated with a combination of factors such as individuals, families, and communities. However, in rural areas, the focus is predominantly on family factors (Luo et al., 2020; Sun et al., 2017). Based on the above findings, relevant research has achieved significant value, but there are still limitations. Firstly, existing research often focuses on overall statistics and policy development, overlooking the explanatory role of geographical spatial structure in the development of institutional eldercare. As a result, spatial factors closely related to its distribution are not adequately reflected in previous studies. Differentiated development of institutional eldercare is the geographical projection of social change, and few studies analyze its development pattern from a spatial perspective. Secondly, current research mainly focuses on micro-surveys of a specific city, and its conclusions cannot be extrapolated to the overall regional context. Therefore, there are limitations to the contribution of planning and top-level design for the development of institutional eldercare. Thirdly, some studies, although they touch upon the spatial distribution of institutional eldercare, are limited to using cross-sectional data from a single year, which prevents them from conducting temporal and spatial evolutionary analysis and refined categorization studies. The IE-YRDUA faces great uncertainty, and the lack of detailed research can easily lead to imprecise targeting of eldercare policies, thereby affecting the accuracy and effectiveness of social resource allocation. To broaden the explanatory levels of the research, this paper bridges the gaps and provide new research data in existing studies by utilizing Python technology to crawl data information from the institutional eldercare service network platform. From a geographical perspective, the dynamic evolution and driving mechanisms of the IE-YRDUA are discussed. This paper is an exploration of the macro shift in current relevant research, aiming to seek a scientific basis for the in-depth development of regional eldercare services integration. 2 Theoretical foundation In new economic geography pioneered by Fujita (1988), Krugman (1993), and Venables (1996), first nature refers to physical geography characteristics and second nature refers to the geography of interactions between economic agents (Venables 2006). They both play an important role in explaining economic development. After entering the 21st century, the influence of new factors on regional development has deepened, highlighting the complexity of geographical influences. Based on this, Liu et al. (2009) and Wang (2016) proposed the concept of “third nature”, with a focus on human capital and information development, breaking the previous limitations of explanations regarding agglomeration economies. The development of institutional eldercare is closely connected to various geographic factors. This study examines the causal relationship between three main geographic nature factors and the development of the IE-YRDUA based on theories in economic geography. Firstly, the concept of “first nature” refers to the inherent characteristics of a region that are independent of human activities. These characteristics, such as topography, temperature, and precipitation, play a fundamental role in regional development (Nause, 2009). Regions with advantageous natural environments and abundant resources are more likely to attract development opportunities (Liu et al. 2009). Secondly, “second nature” encompasses the spatial relationship between human beings and their environment. This includes factors like economies of scale, transportation costs, and the flow of resources, which are important drivers for regional development (Naude, 2009; Wang et al., 2021). The distribution of institutional eldercare facilities is associated with these spatial factors, including transportation, population, and industry. It also reflects social and economic development, market structure, and transportation location. Lastly, “third nature” refers to the improvement of living and production conditions through the creation and reform of facilities, resulting in spillover effects that mobilize social resources. This includes human resources and regional knowledge reserves (Tian et al., 2018). Third nature acts as an internal driving force for the progress of the regional institutional eldercare industry, enhancing quality and efficiency. It is characterized by factors such as talent, information technology, and scientific progress as representative developmental capital. 3 Study area, data sources and research methodology Study area The observation period of this study is 2001-2020. The study area, in accordance with the Approval of the State Council on the Development Plan for the Urban Agglomeration of the Yangtze River Delta (No. 87) in 2016, comprises 26 cities within three provinces and a direct-administered municipality, namely, Shanghai, Nanjing, Wuxi, Changzhou, Suzhou, Nantong, Yancheng, Yangzhou, Zhenjiang, Taizhou, Hangzhou, Ningbo, Jiaxing, Huzhou, Shaoxing, and Jinhua, Zhoushan, Taizhou, Hefei, Wuhu, Maanshan, Tongling, Anqing, Chuzhou, Chizhou andXuancheng. The pressure on the long-term balance of eldercare resources is gradually increasing due to the aging population with fewer children and the steady increase in social security benefits. The YRDUA serves as a powerful and active engine of growth in China’s social development. It plays a pioneering and leading role in promoting the coordinated development of public services. Exploring the development of institutional eldercare and its influencing factors contributes to the establishment of an information-sharing, market-open, and orderly competition public resource trading market system, which effectively supports the high-quality development of regional integration. Data sources China’s traditional eldercare institutions are nursing homes (Xia et al., 2012). This study uses the number of eldercare institutions as a measure of the degree of regional institutional eldercare development. The data is sourced from relevant information obtained through a Python web scraping software from China’s institutional eldercare industry service information platform (www.yanglao.com.cn). The data collection period is until December 2020. The clear time, complete samples, and accurate data provided by this platform are advantageous: First, the platform has long been engaged in the collection of information related to institutional eldercare services, and it can obtain data on eldercare institutions with a long time scale and a wide geographic range. Second, the platform covers a wide range of institutional eldercare types such as senior citizen apartments, eldercare nursing homes, continuing care retirement communities (CCRCs), and community-based small and micro institutions, which realizes the comprehensive collection of information and can effectively avoid the enlargement of spatial research errors due to the lack of samples. This study has made efforts to tackle the challenges related to information collection and ensure the quality of data. To achieve this, we utilized two official enterprise credit platforms, namely “Qichacha” (https://www.qcc.com/) and “Tianyancha” (https://www.tianyancha.com/), to screen abnormal data. Additionally, a random sample of over 1/3 of the data is manually verified to further enhance the reliability of the dataset. Finally, the Gaode coordinate pickup system is used to spatially locate the records, resulting in a total of 4,347 accurate and verified records. It should be noted that the eldercare institutions referred to in this study specifically refer to all institutions that provide residential and nursing beds, including eldercare homes, homes for the aged, senior citizen apartments, nursing homes, continuing care retirement communities (CCRCs), and community-based small and micro institutions. Institutions can be classified into four operational forms: public institutions, private institutions, government-owned private-operated institutions, and government-aided private institutions. Based on practical experience, this study categorizes government-owned, contractor - operated eldercare institutions as public institutions and government-aided private institutions as private institutions. Based on the overall analysis, it proceeds to conduct a specific analysis of the temporal and spatial development and formation mechanisms of these two different types of institutional eldercare. It is worth noting that this study aims to provide a reference for future eldercare planning in the YRD urban agglomeration, therefore, for cities that have undergone administrative adjustments during the research period, such as the Luwan and Zhabei districts in Shanghai, and the Canglang district in Hangzhou, the analysis is based on the current administrative divisions. In addition, temperature and precipitation data are sourced from the historical data of the National Meteorological Center, economic density and industrial structure data are sourced from the annual China City Statistical Yearbook, various provincial and municipal statistical yearbooks, and statistical bulletins, market size data is sourced from previous national population censuses, and terrain data is obtained from the National Geographical Information Center of China. Research methodology Nearest neighbor index This study applies the nearest neighbor index to assess the spatial distribution characteristics of the development of the IE-YRDUA. By comparing the actual distribution of points with the theoretical random distribution (Xiong et al., 2018), the R- value is used. An R- value of 1 indicates a random distribution; R 1 suggests a uniform distribution; and an R- value of 0 represents complete concentration. Kernel density estimation Kernel Density Estimation (KDE) transforms discrete point clusters into continuous density surfaces in order to explore the spatial distribution characteristics of said point clusters (Lin, 2016). In this study, the density at the same locations is superimposed to represent the number of eldercare institutions within a specific spatial distance, thus allowing for an exploration of the spatial development pattern of the IE-YRDUA. Global spatial autocorrelation analysis Global spatial autocorrelation aims to reveal the overall distribution of spatial unit observations to measure the spatial distribution characteristics of the observations across the region (Wang et al., 2020). In this study, Moran’s I is commonly used to measure the spatial correlation of eldercare institutions in the region as a whole, as a way to test whether the IE-YRDUA development is spatially dependent. Geodetector Geodetector is used as a statistical method to detect the drivers of spatial dissimilarity and test for causality between univariate spatial dissimilarity and bivariate (Wang et al., 2023). Compared to other methods, this model has the advantages of not assuming linearity and being immune to multivariate covariate covariance, as long as it has a clear physical meaning (Wang et al., 2017). In this study, the factor detection and interaction detection modules are used to identify the critical factors and their interactions that drive the spatiotemporal evolution of the IE-YRDUA. 4 The spatiotemporal patterns of the IE-YRDUA development Chronological characterization of institutional eldercare development The IE-YRDUA is currently in a state of steady development, but its evolutionary trajectory can still be characterized by phases, as depicted in Figure 1. The total number of eldercare institutions increases from 504 to 4,275 during the period under examination, with an average annual growth rate of 11.96%. Among these institutions, the increase in public ones is greater than that of private institutions, which ultimately determines the overall state of regional development. It is worth noting that the total number of eldercare institutions remained relatively stable between 2001 and 2010, with the highest annual increase of 198 institutions occurring in 2007. This coincides with the emergence of the aging problem in the YRDUA population and the pressing need to construct a socialized eldercare service system aligned with the demographic structure. Consequently, the development of residential institutional eldercare is promoted. From 2011 to 2020, the IE-YRDUA continues to experience rapid development, with public institutions growing by 200 annually and private institutions growing by 60 annually. This phenomenon can be attributed to the government’s policy of directing social resources towards eldercare investments. On one hand, the intensifying aging issue compels governments worldwide to broaden the coverage of universal eldercare and support private capital in renovating and integrating underutilized eldercare resources. On the other hand, the strategy of regional integration of eldercare services is prioritized, leading to the development of regionally competitive eldercare institutions through the joint sharing of eldercare resources across different regions. Evolution of the spatial pattern of institutional eldercare development Types of spatial distribution This study examines the spatial distribution characteristics of different eldercare institutions in the YRDUA using the average nearest distance tool, as depicted in Table 1. The nearest point index R for eldercare institutions in the YRDUA is 0.576 in 2001, demonstrating a significant geographic proximity distribution effect. In terms of internal types, the nearest neighbor point index R for private institutions (0.494) is smaller than that of public institutions (0.611), mainly because private institutions are for-profit and rely more on the local market’s development. In 2011, the nearest neighbor index for eldercare institutions is 0.557, indicating a more pronounced clumped distribution in the spatial development of the IE-YRDUA, particularly the increased agglomeration of private institutions. By 2020, the overall spatial distribution of eldercare institutions in the YRDUA is further strengthened, with the nearest-neighbor index R decreasing to 0.531 for both public and private institutions. The nearest neighbor index R also decreases to 0.569 and 0.346 respectively, suggesting a strengthened spatial agglomeration pattern. Table 1 The nearest neighbor index results for eldercare institutions in the YRDUA. NNI (R-value) E ldercare institutions Public institutions Private institutions Spatial distribution 2001 0.576 0.611 0.494 clumped 2011 0.557 0.604 0.364 clumped 2020 0.531 0.569 0.346 clumped Spatial agglomeration characteristics The allocation of public welfare resources is affected by geographical location and shows spatial correlation (Ma, 2015). To verify the existence of spatial association in the IE-YRDUA, this study quantitatively measured its spatial correlation. The results showed that during the research period, the Moran’s I of eldercare institutions in the YRDUA were consistently positive, ranging from 0.379 to 0.410, and statistically significant at the 1% level. This indicates that the IE-YRDUA development exhibits significant spatial clustering, with clustering intensity increasing and spatial correlation gradually strengthening. When looking at different types, the Moran’s I of public institutions increased from 0.212 to 0.398, while the Moran’s I of private institutions showed a larger increase from 0.121 to 0.397. This reflects that, compared to public institutional eldercare, which is oriented towards regional balanced development, private institutional eldercare develops more quickly in advantaged areas. In order to further explore the spatial pattern characteristics of the IE-YRDUA, this study conducts a kernel density analysis of the distribution of eldercare institutions from 2001 to 2020, and visualizes the results according to the Jenks natural breaks classification method, as shown in Figure 2. Specifically, the spatial development pattern of the IE-YRDUA presents the following characteristics. First, during the study period, the number of eldercare institutions in the YRDUA region increases dramatically, and the overall distribution density increases, with the spatial aggregation range continuously expanding. As shown in Figures 3a-c, the development gradient of the IE-YRDUA becomes increasingly prominent, starting from the initial stage of Shanghai as the single central hub and other areas in a nascent state, to the appearance of clustering patches of varying depths in the main urban areas of different cities. Subsequently, there is a significant difference in the scale and intensity of clustering, showing a spatial heterogeneity distribution with a stronger concentration in the east and a weaker concentration in the west. Among them, Ningbo’s core status within Zhejiang province is highlighted, becoming the fourth core in addition to Shanghai, Nanjing, and Hangzhou. Nantong consistently maintains a high level of clustering, with slightly less intensity than Ningbo. The overall development level of Suzhou, Wuxi, and Changzhou is strong, and multiple small clustering areas have appeared within the cities, forming a rudimentary clustering axis from east to west. Second, public institutions exhibit a clustering pattern in terms of spatial distribution, as shown in Figures 3d-f. The core positions of Shanghai, Nanjing, and Hangzhou remain stable, while the secondary core positions of Suzhou, Ningbo, and Nantong are increasingly prominent. The clustering intensity continues to increase, and the influence range significantly expands, forming a distribution that connects multiple cores. On the other hand, the overall changes in spatial characteristics of private institutions are relatively small and possess a certain degree of time inertia and spatial stability, as seen in Figures 3g-i. During the study period, the significant gap between Shanghai and other regions narrows, the clustering degree of Nanjing and Hangzhou improves, and a “one core, two secondary cores” pattern gradually forms, indicating an increased level of regional coordinated development. 5 The spatiotemporal evolution mechanism analysis Selection of influencing factors The development of institutional eldercare in the IE-YRDUA region exhibits strong spatial heterogeneity. Exploring these differential characteristics and patterns of change is a prerequisite for understanding its spatiotemporal evolution mechanism. This study, based on the fundamental theories of economic geography, incorporates recent progress in related research and the realities of the new population structure (Luo et al., 2020; Zhang et al., 2021; Cheng et al., 2022; Peng et al., 2022). It constructs a comprehensive factor detection index system: The natural environment is the initial and fundamental development condition of a region, and it is also an inherent factor influencing the location selection of institutional eldercare. Factors such as topography, temperature, and precipitation are considered as first nature in this study. Economic, locational, and transportation factors compensate for the differences in the natural foundation of a region caused by human activities and are also factors that influence the development of institutional eldercare. Factors such as economic density, market size, industrial structure, capacity to consume, and transportation are considered second nature in this study. Human capital level, informatization, openness, and technological advancement are the third geographic factors that influence the spatial fragmentation and recombination of social activities. Variables such as human resources, degree of openness, level of informatization, and technological advancement are used to represent third nature in this study. Additionally, the quantity of eldercare institutions in each city is used as a measure of regional institutional eldercare development. Similarly, the development level of public and private institutional eldercare is represented by the number of corresponding types of institutions in the region. All variables are categorized using the Jenks natural breaks classification method. Dominant factor identification The heterogeneity in the influence of different driving factors on the overall development of the IE-YRDUA is significant, as shown in Table 2. During the sample period, the driving factors with the largest average influence at the independent factor level are as follows: capacity to consume ( X7 : 0.602), human resources ( X9 : 0.584), degree of informatization ( X11 : 0.580), technological level ( X12 : 0.555), and economic level ( X4 : 0.552). These factors belong to the “second nature” and “third nature” dimensions at the system level, respectively, indicating their dominant roles in the development of the IE-YRDUA. On the other hand, factors such as topography ( X1 ) and temperature ( X2 ) have relatively weak explanatory power and did not pass the significance test. Upon further investigation of the influence q- values at three time points (2001, 2011, and 2020) it is observed that the overall influence of first nature factors on the spatial pattern of institutional eldercare has weakened, while the explanatory power of second and third nature factors has gradually increased. Specifically, the influence of capacity to consume ( X7 ) and human resources ( X9 ) consistently ranks among the top, indicating the significant role of social capacity to consume in shaping the spatial pattern of the IE-YRDUA. The improvement in education level also promotes the shift from “family eldercare” to “social eldercare,” thereby driving the development of institutional eldercare. The influence of temperature ( X2 ) and precipitation ( X3 ) gradually decreases, suggesting a weakened influence of natural foundations on the spatial pattern of institutional eldercare. Conversely, the influence of economic level ( X4 ) and degree of informatization ( X11 ) increases from 0.420 and 0.455 to 0.687 and 0.674, respectively. This indicates that social and economic development drives government investment in livelihood-related fields, thus promoting the development of institutional eldercare. Furthermore, the improvement in informatization fosters the intelligent development of eldercare services, amplifying their positive influence. In addition, the q- value of transportation ( X8 ) significantly increases in significance over the study period. This highlights how improved transportation enhance accessibility within and between regions, accelerate the flow of factors between cities, and become an important driving force for the development of institutional eldercare. Table 2: Influencing factor Detection Results for Overall Development of the IE-YRDUA Dimensions Factors Factor connotation 2001 2011 2020 First nature Topography : X 1 Physical features of an area 0.182 0.200 0.152 Temperature : X 2 Annual average temperature (°C) 0.340 0.268 0.286 Precipitation : X 3 Annual average precipitation (mm) 0.226 0.304 0.206 Second nature Economic level : X 4 GDP (in ten thousand yuan) 0.420 * 0.547 ** 0.687 *** Market size : X 5 Number of elderly population (in ten thousand people) 0.371 0.491 ** 0.605 ** Industrial development : X 6 Number of employees in the tertiary industry (in ten thousand people) (%) 0.519 ** 0.521 ** 0.558 ** Capacity to consume : X 7 End-of-year balance of savings for urban and rural residents (in ten thousand yuan) 0.548 ** 0.568 ** 0.689 *** Transportation : X 8 Total passenger volume (in ten thousand people) 0.192 0.343 * 0.593 *** Third nature Human resources : x 9 Number of students in regular higher education institutions (in number) 0.543 ** 0.530 *** 0.680 *** Degree of openness : X 10 Amount of actual foreign investment utilized in the year (in ten thousand US dollars) 0.562 ** 0.564 0.441 * Degree of informatization : X 11 Total volume of telecommunications services (in ten thousand yuan) 0.455 ** 0.611 ** 0.674 *** Technological level : X 12 Scientific expenditure (in ten thousand yuan) 0.543 ** 0.452 * 0.672 *** In light of the varying influence of geographical conditions on the development of different aspects of institutional eldercare, this study further separates public and private institutional types to compare classifications, as shown in Figure 3. Firstly, within the classification of private institutions, the explanatory power of temperature ( X2 ) among the primary natural factors is greater and achieves statistical significance at the 5% level. However, factors such as terrain topography ( X1 ) and precipitation ( X3 ) have weaker explanatory power and do not attain statistical significance. This is mainly because the YRDUA as a whole has a flat terrain and abundant rainfall, resulting in a weaker influence of underlying natural conditions on the spatial differentiation of institutional eldercare development. Given that the elderly population is more sensitive to temperature, site selection for private institutions focuses more on evaluating environmental and health risks, with temperature comfort having a positive influence on the health of the elderly. This factor becomes particularly important for private institutions. However, over time, the clustering effect of temperature on the development of private institutional eldercare has diminished. This can be attributed to the fact that in the later stages of development, the utilization of geographic resources for health has reached its maximum capacity, and spatial development needs to shift towards other influencing factors. Secondly, the q- values of second nature factors are generally high, indicating that the second nature factor supports the development of the IE-YRDUA. Additionally, this factor supports public institutions more than private ones. On one hand, eldercare service is considered a high-level consumption activity that reflects the elderly’s desire for a better quality of life. The development of public institutional eldercare relies on local finances and the resulting cluster space is closely tied to regional socio-economic development. The prosperity of the socio-economy facilitates the construction of inclusive eldercare services by the government. On the other hand, the level of savings among residents directly affects their ability to pay for eldercare services. A higher level of savings enables the elderly to fully utilize their consumption potential and encourages the improvement of eldercare consumption. This, in turn, has a positive influence on the development of eldercare institutions. The third factor of nature serves as a continuous driving force for the development of the IE-YRDUA. The explanatory power of this dimension on the development of institutional eldercare is gradually increasing, and it generally has a higher influence on private institutions compared to public institutions, although there are some differences depending on the stage. In the early stages, human resources ( X9 ) and degree of openness ( X10 ) have a stronger influence on the development of private institutional eldercare, while the degree of informatization ( X11 ) is the main factor for public institutional eldercare. However, over time, the q- value of technological level ( X12 ) increases in both types of institutions, reaching 0.688 in 2020 and passing the 1% significance level test. This indicates that the development of modern technology, particularly digitalization, has contributed to the innovation of eldercare services, promoted the transformation of eldercare services towards intelligent solutions, and become a key driving force for the development of all types of institutional eldercare in the new era. Interaction Analysis of Key Factors The spatial development of institutional eldercare is not simply a simple mapping of geographical factors, but can achieve differential effects through interaction. This study utilizes an interaction detection module to identify the interaction relationship between different factors. Due to space limitations, this article only displays the relationship between the factors with higher explanatory power after two years of interaction during the study period, as shown in Figure 4. It can be seen that the interaction types of various factors during the examination period are relatively stable, primarily characterized by dual-factor enhancement with a secondary non-linear enhancement. This indicates that the spatiotemporal evolution of the IE-YRDUA is the result of the combined effect of multiple factors. After conducting interaction detection, the overall degree of explanation for institutional eldercare development by each factor is enhanced as a whole (Figures 4a-b). This enhancement is particularly concentrated in the first and second nature factors, such as precipitation ( X3 ) and transportation ( X8 ). These findings indicate that the climate environment and transportation accessibility not only directly affect the development of institutional eldercare in urban agglomerations, but they are also the main interactive factors within the system. This highlights the fundamental role and support provided by the natural environment and transportation in the regional institutional eldercare development process. In the analysis of public institutional eldercare factors (Figures 4c-d), the most important interacting factors influencing spatial development in 2001 and 2020 are the intersection of economic level ( X4 ) and transportation ( X8 ), and the intersection of precipitation ( X3 ) and scientific and technological level ( X12 ), with q- values of 0.711 and 0.748, respectively. It is worth noting that although the q- value of precipitation ( X3 ) is small, its interaction is significantly stronger, for example, with capacity to consume ( X7 ) and informatization ( X11 ), where the interaction explanation strengths are above 0.7, and its driving role after interaction with other factors is above 0.5. The influence pattern of climate factors is that they have a highly supportive and slow effect on the development of public institutional eldercare, mainly through indirect means. Therefore, when precipitation, socio-economic development, and scientific and technological development work together, they significantly promote the development of public institutional eldercare. In the interaction analysis of factors influencing private institutional eldercare development (Figures 4e-f), the interaction between temperature ( X2 ) and other factors in 2001 is significantly enhanced, with q- values above 0.4. This indicates that the temperature factor greatly enhances the influence of other indicators on the development of private institutional eldercare, which is consistent with its strong explanatory power as a single factor. In 2020, the interactions of precipitation ( X3 ) and informatization ( X11 ) with other factors become stronger, suggesting that the development of private institutional eldercare may be constrained by the comfort of the natural environment and the degree of social openness. Thus, its spatial distribution is driven by the interaction of natural and social factors. This finding also reflects the successful expansion of eldercare service provision from a family-oriented approach to a more socialized one, where informatization implicitly guides the transformation of eldercare culture and enhances the capacity and level of eldercare socialization. Analysis of evolutionary mechanisms The development of institutional eldercare is a result of the transfer of environmental characteristics to the allocation of eldercare resources. Expanding on this, this study investigates the mechanism behind the spatial pattern evolution of the IE-YRDUA. Through empirical analysis, the study reveals that the three major geographic nature factors represent distinct mechanisms, and the interconnectedness and mutual influence among elements within each dimension collectively shape the spatiotemporal evolution of institutional eldercare. First nature plays a certain pioneering role, especially in the early spatial distribution of private institutions. However, when comprehensively observing the changes over time, it can be seen that the factors driving the development of the IE-YRDUA are becoming more and more complex. On one hand, the continuous promotion of ecological civilization construction and the continuous optimization of the human habitat have increased the flexibility of institutional eldercare development, weakening the unidimensional driving role of innate environmental factors. On the other hand, the interaction between this dimension and other factors has gradually strengthened, transforming the influence of natural geographic characteristics on institutional eldercare development into a multifaceted linkage relying on other geographic characteristics, such as capacity to consume, transportation, and the level of informatization, rather than isolated influence, especially in the case of the private sector, which is more autonomous, flexible, and efficiency-oriented. The supportive function of second nature for the IE-YRDUA development continues to strengthen. The aging of the population, the miniaturization of families, and the lengthening of life expectancy have led to an increase in the uncertainty of demand for institutional eldercare services. As a result, institutional eldercare development has gradually shifted to rely on the acquired conditions of the city and the market base, and as time passes, it will rely more on the optimization of factors such as economic level and capacity to consume. This enhancement will be achieved through the interaction between the first and second geographic factors such as precipitation and the level of science and technology, boosting the development of the IE-YRDUA. The interaction structure of the first and second geographic factors, such as precipitation, science and technology level, strengthens the guarantee ability. Third nature’s overall influence on the spatial development of the IE-YRDUA is high in intensity and closely related to each other. In the early stage of the study, the single factor of human resources has stronger explanatory power and significant interaction with other indicators, reflecting that the evolution of the spatial pattern of institutional eldercare in the early stage is mainly driven by labor resources. In the late stage of the study, the explanatory power of the level of informatization becomes stronger and the interaction effect is significant, indicating that as smart technology plays an increasingly important role in health management, remote diagnosis and treatment, and elderly care, the empowerment of informatization on smart institutional eldercare will be upgraded comprehensively. 6 Conclusions This paper constructs a theoretical analytical framework in a specific research context. It explores the changing characteristics and development laws of the IE-YRDUA from 2001 to 2020 from the perspectives of spatiotemporal dynamic processes and economic geography. This research enriches the empirical research on related content to some extent. The main conclusions are as follows. First, the development of the IE-YRDUA can be divided into different stages, with a dynamic trajectory showing fluctuating growth, slow growth in the early stage, and rapid growth in the later stage. In terms of internal structure, public institutions have a larger increase than private institutions, which determines the overall state of institutional eldercare development in the region. However, with the significant improvement in the supply capacity of social capital in the eldercare service field, the market vitality and social creativity of private institutional eldercare will be fully stimulated. Second, the spatial distribution of the IE-YRDUA tends to be in a state of agglomeration, and the spatial structure belongs to the clumped type. The spatial pattern evolves from a density zone centered around Shanghai to the fragmentation of main urban areas in various cities, ultimately forming a layout with Shanghai as the center radiating to Nantong, Nanjing, Hangzhou, and Ningbo. In terms of types, the spatial agglomeration of public institutions evolves from early concentration in Shanghai, Nanjing, and Hangzhou to later dispersion in various places. On the contrary, the agglomeration form of private institutions is relatively stable, but it shows an evolution direction with Shanghai as the core and Nanjing and Hangzhou as secondary core areas. Third, the geodetector outputs confirm that the driving factors of the IE-YRDUA development have diversified characteristics. From the perspective of the strength of the influencing factors, the development pattern dominated by second nature and third nature gradually emerges. First nature factors such as temperature and precipitation have a certain leading role in the development of private institutional eldercare. Second nature factors such as economic level and capacity to consume provide necessary spatial support for institutional eldercare development. Factors such as human resources, level of informatization, and technological level provide momentum for the sustainable development of the IE-YRDUA. Fourth, the development of the IE-YRDUA is closely related to various geographical factors, but the dominant factors and their combination characteristics that cause spatial structural differences are not the same. The results of interaction detection show that the spatiotemporal evolution of the IE-YRDUA development is associated with complex factors. Although the single-factor explanatory power of first nature is relatively weak, the influence of interaction effects is significantly enhanced. On the other hand, second nature and third nature play a crucial role as the main dependency paths. They not only have strong independent driving forces but also play an important role in the occurrence of multidimensional interactive effects. The research has analyzed the formation mechanism of the spatiotemporal evolution of the IE-YRDUA from both theoretical and practical perspectives. Based on the above conclusions, the following inspirations can be drawn. First, it is important to explore local natural resources and actively promote the development of health-oriented institutional eldercare driven by natural resources. This can be achieved by creating a shared space that integrates cultural tourism and eldercare, exploring the “health tourism” model that promotes the symbiosis of eldercare services and the natural environment, and developing recreational eldercare and tourist eldercare. Second, to achieve common prosperity, the allocation of resources should be optimized to strengthen the support for the development of disadvantaged areas and the protection of vulnerable groups. Efforts should be made to continuously narrow the regional disparities in the development of the IE-YRDUA, to achieve social equity and enhance people’s well-being. Third, attention should be given to the optimization of the transportation for the development of the IE-YRDUA. This includes strengthening the effective connectivity between different transportation modes such as aviation, high-speed rail, and highways. This can help reduce the economic and time costs of using eldercare resources across regions, explore the feasibility of remote eldercare, and promote the integration of peripheral areas of urban agglomerations. Fourth, emphasis should be placed on the intelligent construction of eldercare and improving the intelligent spatial connection to alleviate the gradient differences in the development of the IE-YRDUA. The intelligent advantages of core cities such as Shanghai, Hangzhou, and Nanjing should be fully utilized to create multiple regional growth poles with leading and benefiting capabilities for the surrounding areas. Last, a combination of policy toolkits at both the regional and city levels should be formulated to fully unleash the development potential of the IE-YRDUA. For example, by focusing on the interaction of the natural environment and leveraging the overlapping effects of multiple factors such as transportation, human capital, and informatization, the linkage of resources within the region can be strengthened, and the spatial layout of institutional eldercare can be optimized. In summary, the development of the IE-YRDUA is associated with various geographical factors. To avoid the spatial mismatch of eldercare resources and promote the integrated development of eldercare in the region, it is necessary to fully consider the co-evolution of different geographical units and the resulting shift in relationships. Efforts should be made to actively guide and leverage the synergistic effects between neighboring regions, promoting innovative applications of eldercare services. Additionally, comprehensive and long-term planning should be undertaken for the development of institutional eldercare at multiple scales, emphasizing the dynamic study of specific developmental attributes, to clarify the general process and mechanisms of institutional eldercare resource evolution. 7 Abbreviations IE-YRDUA the institutional eldercare in the Yangtze River Delta Urban Agglomeration 8 Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials The original contributions presented in the study are included in the article/supplementary materials, further inquiries can be directed to the corresponding author. Competing interests The authors declare no competing interests. Funding This work was supported by Ministry of Education Youth Fund for Humanities and Social Sciences Research (23YJCZH331). Authors’ contributions Methodology: RZ, JC. Writing original draft preparation and writing review and editing RZ, JC. Funding acquisition: RZ, JC. Quality assessment: KJC. All authors have read and agreed to the published version of the manuscript. Conflict of Interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Data Availability Statement The original contributions presented in the study are included in the article/supplementary materials, further inquiries can be directed to the corresponding author. 9 References Bi X, Li M. Between equity and efficiency: A spatial analysis of elderly resources in Beijing. Society. 2020;40(3):117-147. doi:10.15992/j.cnki.31-1123/c.2020.03.005 Cheng M, Cui X. Optimization of spatial allocation of elderly care institutions that consider both equity and efficiency: A case study of Minhang District, Shanghai. Regional Research and Development. 2022;41(3):43-48. Dear M. Planning for Mental Health Care: A Reconsideration of Public Facility Location Theory. International Regional Science Review. 1978;3(2):93-111. doi:10.1177/016001767800300201 Ding Q, Zhu L, Luo J. Spatial accessibility analysis of elderly care facilities in Wuhan City. Human Geography. 2016;31(2):36-42. doi:0.13959/j.issn.1003-2398.2016.02.007 Fujita M. 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Elderly care willingness and its rural-urban differences in China: An analysis based on the data of China Longitudinal Aging Social Survey. Population and Economics. 2017;2017(2):11-20. Tian Y, Jiang X, Wang Z. Analysis of the causes of poverty in concentrated contiguous destitute areas in China from a geographical perspective. Journal of China Agricultural University (Social Sciences Edition). 2018;35(5):32-43. doi:10.13240/j.cnki.caujsse.2018.05.002 Venables AJ. Equilibrium locations of vertically linked industries. International Economic Review. 1996;37:341–359. Venables AJ. Shifts in economic geography and their causes. Economic Review, Federal Reserve Bank of Kansas City. 2006;91(Q IV):61-85. Wang J, Xu C. Geodetector: Principle and prospect. Acta Geographica Sinica. 2017;72(1):116-134. Wang J, Li T, Zhu B. Spatial differentiation, influencing factors, and optimization strategies of county-level rural development level in Qinba Mountain area: A case study of Shanyang County, Shaanxi Province. Geographical Research. 2023;42(6):1506-1527. Wang L, Zhou K, Wang Z. Spatial distribution research of community elderly care facilities under the concept of health equity: A case study of downtown Shanghai. Human Geography. 2021;36(1):48-55. doi:10.13959/j.issn.1003-2398.2021.01.007 Wang W, Sun Y, Cheng S. Measurement of location advantages of model rural tourism villages in China. Regional Research and Development. 2021;40(5):88-94. Wang X, Qi W, Liu S. Spatial distribution characteristics and relevant factors of small towns in China. Geographical Research. 2020;39(2):319-336. Wang Z. Geographic Nature: Break and Reconstruction of the Hu Huanyong Line. Exploration and Contention. 2016;(1):43-47. Wu Y, Teng Y, He J. Basic public service-population development-regional economic coupling and spatial-temporal evolution: A case study of the Yangtze River Delta urban agglomeration. Regional Research and Development. 2023;42(4):21-28. Xia H, Wang Z. Evolution of spatial structure differentiation in mainland China. Geographical Research. 2012;31(12):2123-2138. Xiong J, Zhang J, Zhou H, et al. The spatial distribution characteristics of red tourism sites in China. Regional Research and Development. 2018;37(2):83-88. Zhang S. Government behavior and market mechanism in the construction of elderly care service system in China. Social Security Review. 2021;5(1):129-145. Additional Declarations The authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3849846","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":266627248,"identity":"4c8fb77a-854a-4f72-b277-18b3588368ff","order_by":0,"name":"Rong Zhou","email":"","orcid":"","institution":"College of Philosophy, Law \u0026 Political Science, Shanghai Normal University","correspondingAuthor":false,"prefix":"","firstName":"Rong","middleName":"","lastName":"Zhou","suffix":""},{"id":266627249,"identity":"14b1b9d8-f0ed-4252-86bc-4d6c3441497a","order_by":1,"name":"Jinghang Cui","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1UlEQVRIiWNgGAWjYBACxmYGBmYos/FBQoUNaVqaDR6cSSPOJqgWBjbJh22HiFDeznv4dUHNHbv+GcltFQlsBxj427sTCDiML816xrFnyTNuJLbdSOC5wyBx5uwGAlp4zIx52A4nG0iAtEg8YzCQyCVGyz+IloIEg8NEaTF+zNt22A6khSEhgTgtZsy8fYcTJM48bJZIOJDGQ9Avhv1njD/zfDtsz9+e/vDjz382cvztvQS0NDCwSQDpxAaoAA9e5SAgD4yaD0DanqDKUTAKRsEoGLkAAGycSh6e+3tzAAAAAElFTkSuQmCC","orcid":"","institution":"School of Physical Education, Jiangsu University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Jinghang","middleName":"","lastName":"Cui","suffix":""},{"id":271614495,"identity":"24539d22-616e-4792-976b-7d1b28898b71","order_by":2,"name":"K. Jason Crandall","email":"","orcid":"","institution":"Center for Applied Science in Health and Aging, Western Kentucky University","correspondingAuthor":false,"prefix":"","firstName":"K.","middleName":"Jason","lastName":"Crandall","suffix":""}],"badges":[],"createdAt":"2024-01-10 09:29:25","currentVersionCode":2,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-3849846/v2","doiUrl":"https://doi.org/10.21203/rs.3.rs-3849846/v2","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50823800,"identity":"d149a953-d1fd-4704-859a-0759c30c9a19","added_by":"auto","created_at":"2024-02-07 22:36:40","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":37346,"visible":true,"origin":"","legend":"\u003cp\u003eThe development of the IE-YRDUA from 2001 to 2020\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3849846/v2/72f716975b3051f42aef9e8b.png"},{"id":50823801,"identity":"eb84aa1c-91ed-4d7a-a01e-c2e58ea77245","added_by":"auto","created_at":"2024-02-07 22:36:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":554411,"visible":true,"origin":"","legend":"\u003cp\u003eKernel density map of the spatial distribution of the YRDUA eldercare institutions, 2001-2020\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3849846/v2/c5c0e201c9c0cde01d252967.png"},{"id":50823803,"identity":"60585599-40ff-4e98-8bb7-6f193d069036","added_by":"auto","created_at":"2024-02-07 22:36:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":14931,"visible":true,"origin":"","legend":"\u003cp\u003eDetection results of influencing factors based on institution types.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3849846/v2/288c07a48bd52106ca165a47.png"},{"id":50823924,"identity":"ff58b0c8-de83-44cb-9879-e60325ca412c","added_by":"auto","created_at":"2024-02-07 22:44:40","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":71115,"visible":true,"origin":"","legend":"\u003cp\u003eInteraction detection results for key factors\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3849846/v2/7beb18dcb3152de65ee40bf5.png"},{"id":50824246,"identity":"fa70315e-6c6f-44b6-8770-6b1830ca0943","added_by":"auto","created_at":"2024-02-07 22:52:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":882573,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3849846/v2/e0f38ccf-56b0-4db0-b1da-36fa8764b2eb.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eSpatiotemporal Evolution of the Institutional Eldercare in the Yangtze River Delta Urban Agglomeration\u003c/p\u003e","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe high-quality development of eldercare services is an important goal for China as it actively addresses the challenges posed by an aging population. Institutional eldercare, with its strong industry relevance, high degree of service integration, and comprehensive benefits, has become an important lever. The \u0026ldquo;14th Five-Year Plan\u0026rdquo; has raised the prominence of enhancing inclusive eldercare services and advancing the reform of eldercare institutions to the status of long-term objectives at the national level. According to the data from the 7th National Population Census, the proportion of senior citizens in the Yangtze River Delta Urban Agglomeration, YRDUA, with household registration has exceeded 20%, indicating that it has entered a moderately aged society, making it the region with the most severe aging issue in China. However, due to the long-term influence of various factors, the YRDUA still faces development problems such as imbalances and mismatches in basic public services (Wu et al., 2023). With the severe trend of population aging, this widespread gap may gradually widen. To alleviate the social pressure of eldercare, the YRDUA has taken the lead in conducting regional coordinated eldercare practices. With a policy orientation towards regional cooperation in eldercare resources, it aims to develop a relatively mature and stable model to provide experience and reference for the spatial allocation of integrated regional eldercare resources in China.\u003c/p\u003e\n\u003cp\u003eCurrent relevant research primarily focuses on three key areas. Firstly, there is a strong emphasis on accessibility analysis. For instance, Shah et al. utilized the GIS-based accessibility approach and 3SFCA method to investigate the accessibility of eldercare services in Canada. Their findings revealed disparities in the allocation of basic healthcare services concerning the percentage of individuals aged 65 and above, especially within rural and isolated communities (Shah et al., 2017). Ding et al. (2016; Wang at al., 2021; Gao et al., 2018) employed the potential function, Lorenz curve, and spatial distance method respectively. Their research highlighted significant variations in the distribution level of eldercare facilities in major Chinese cities, with spatial accessibility exhibiting a decreasing distribution pattern from the central urban area towards the periphery. Secondly, the research delves into the analysis of resource utilization differences. Joseph et al. (1996) discovered a correlation between eldercare institutions in New Zealand and the geographic evolution of the elderly population, that is, urban centers benefited from an expansion of long-term care driven by private-sector initiatives, while rural communities suffered a broad-based depletion of services. Stubbs et al. (1992) attributed this phenomenon to the exacerbation of social inequality resulting from the privatization of social welfare, leading to a spatial imbalance in resource utilization. Additionally, Liu et al. (2021) verified through their survey in China that both the quality and utilization of institutional eldercare services among urban residents are significantly higher compared to those in rural areas, both in terms of tangibility and effectiveness. The third area of focus is the analysis of influencing factors. Eldercare institutions, as public facilities, have their spatial distribution rooted in political-economic contexts and social processes. They are highly correlated with regional population density and socioeconomic factors (Dear, 1978; Bi et al., 2020). Niefeld (2005) and Ryvicker at al., (2012) further propose that barriers to the use of eldercare resources are more attributable to geographic constraints and information access barriers rather than economic restrictions. Factors such as spatial distance, transportation accessibility, and surrounding activity space also play a role. Research in China has found a strong correlation between the development of institutional eldercare and income levels as well as family compensation functions. In urban areas, this correlation is associated with a combination of factors such as individuals, families, and communities. However, in rural areas, the focus is predominantly on family factors (Luo et al., 2020; Sun et al., 2017).\u003c/p\u003e\n\u003cp\u003eBased on the above findings, relevant research has achieved significant value, but there are still limitations. Firstly, existing research often focuses on overall statistics and policy development, overlooking the explanatory role of geographical spatial structure in the development of institutional eldercare. As a result, spatial factors closely related to its distribution are not adequately reflected in previous studies. Differentiated development of institutional eldercare is the geographical projection of social change, and few studies analyze its development pattern from a spatial perspective. Secondly, current research mainly focuses on micro-surveys of a specific city, and its conclusions cannot be extrapolated to the overall regional context. Therefore, there are limitations to the contribution of planning and top-level design for the development of institutional eldercare. Thirdly, some studies, although they touch upon the spatial distribution of institutional eldercare, are limited to using cross-sectional data from a single year, which prevents them from conducting temporal and spatial evolutionary analysis and refined categorization studies. The IE-YRDUA faces great uncertainty, and the lack of detailed research can easily lead to imprecise targeting of eldercare policies, thereby affecting the accuracy and effectiveness of social resource allocation. To broaden the explanatory levels of the research, this paper bridges the gaps and provide new research data in existing studies by utilizing Python technology to crawl data information from the institutional eldercare service network platform. From a geographical perspective, the dynamic evolution and driving mechanisms of the IE-YRDUA are discussed. This paper is an exploration of the macro shift in current relevant research, aiming to seek a scientific basis for the in-depth development of regional eldercare services integration.\u003c/p\u003e"},{"header":"2 Theoretical foundation","content":"\u003cp\u003eIn new economic geography pioneered by Fujita (1988), Krugman (1993), and Venables (1996), first nature refers to physical geography characteristics and second nature refers to the geography of interactions between economic agents (Venables 2006). They both play an important role in explaining economic development. After entering the 21st century, the influence of new factors on regional development has deepened, highlighting the complexity of geographical influences. Based on this, Liu et al. (2009) and Wang (2016) proposed the concept of \u0026ldquo;third nature\u0026rdquo;, with a focus on human capital and information development, breaking the previous limitations of explanations regarding agglomeration economies.\u003c/p\u003e\n\u003cp\u003eThe development of institutional eldercare is closely connected to various geographic factors. This study examines the causal relationship between three main geographic nature factors and the development of the IE-YRDUA based on theories in economic geography. Firstly, the concept of \u0026ldquo;first nature\u0026rdquo; refers to the inherent characteristics of a region that are independent of human activities. These characteristics, such as topography, temperature, and precipitation, play a fundamental role in regional development (Nause, 2009). Regions with advantageous natural environments and abundant resources are more likely to attract development opportunities (Liu et al. 2009). Secondly, \u0026ldquo;second nature\u0026rdquo; encompasses the spatial relationship between human beings and their environment. This includes factors like economies of scale, transportation costs, and the flow of resources, which are important drivers for regional development (Naude, 2009; Wang et al., 2021). The distribution of institutional eldercare facilities is associated with these spatial factors, including transportation, population, and industry. It also reflects social and economic development, market structure, and transportation location. Lastly, \u0026ldquo;third nature\u0026rdquo; refers to the improvement of living and production conditions through the creation and reform of facilities, resulting in spillover effects that mobilize social resources. This includes human resources and regional knowledge reserves (Tian et al., 2018). Third nature acts as an internal driving force for the progress of the regional institutional eldercare industry, enhancing quality and efficiency. It is characterized by factors such as talent, information technology, and scientific progress as representative developmental capital.\u003c/p\u003e"},{"header":"3\tStudy area, data sources and research methodology","content":"\u003ch2\u003eStudy area\u003c/h2\u003e\n\u003cp\u003eThe observation period of this study is 2001-2020. The study area, in accordance with the Approval of the State Council on the Development Plan for the Urban Agglomeration of the Yangtze River Delta (No. 87) in 2016, comprises 26 cities within three provinces and a direct-administered municipality, namely, Shanghai, Nanjing, Wuxi, Changzhou, Suzhou, Nantong, Yancheng, Yangzhou, Zhenjiang, Taizhou, Hangzhou, Ningbo, Jiaxing, Huzhou, Shaoxing, and Jinhua, Zhoushan, Taizhou, Hefei, Wuhu, Maanshan, Tongling, Anqing, Chuzhou, Chizhou andXuancheng. The pressure on the long-term balance of eldercare resources is gradually increasing due to the aging population with fewer children and the steady increase in social security benefits. The YRDUA serves as a powerful and active engine of growth in China\u0026rsquo;s social development. It plays a pioneering and leading role in promoting the coordinated development of public services. Exploring the development of institutional eldercare and its influencing factors contributes to the establishment of an information-sharing, market-open, and orderly competition public resource trading market system, which effectively supports the high-quality development of regional integration.\u003c/p\u003e\n\u003ch2\u003eData sources\u003c/h2\u003e\n\u003cp\u003eChina\u0026rsquo;s traditional eldercare institutions are nursing homes (Xia et al., 2012). This study uses the number of eldercare institutions as a measure of the degree of regional institutional eldercare development. The data is sourced from relevant information obtained through a Python web scraping software from China\u0026rsquo;s institutional eldercare industry service information platform (www.yanglao.com.cn). The data collection period is until December 2020. The clear time, complete samples, and accurate data provided by this platform are advantageous: First, the platform has long been engaged in the collection of information related to institutional eldercare services, and it can obtain data on eldercare institutions with a long time scale and a wide geographic range. Second, the platform covers a wide range of institutional eldercare types such as senior citizen apartments, eldercare nursing homes, continuing care retirement communities (CCRCs), and community-based small and micro institutions, which realizes the comprehensive collection of information and can effectively avoid the enlargement of spatial research errors due to the lack of samples. This study has made efforts to tackle the challenges related to information collection and ensure the quality of data. To achieve this, we utilized two official enterprise credit platforms, namely \u0026ldquo;Qichacha\u0026rdquo; (https://www.qcc.com/) and \u0026ldquo;Tianyancha\u0026rdquo; (https://www.tianyancha.com/), to screen abnormal data. Additionally, a random sample of over 1/3 of the data is manually verified to further enhance the reliability of the dataset. Finally, the Gaode coordinate pickup system is used to spatially locate the records, resulting in a total of 4,347 accurate and verified records.\u003c/p\u003e\n\u003cp\u003eIt should be noted that the eldercare institutions referred to in this study specifically refer to all institutions that provide residential and nursing beds, including eldercare homes, homes for the aged, senior citizen apartments, nursing homes, continuing care retirement communities (CCRCs), and community-based small and micro institutions. Institutions can be classified into four operational forms: public institutions, private institutions, government-owned private-operated institutions, and government-aided private institutions. Based on practical experience, this study categorizes government-owned, contractor\u003cem\u003e-\u003c/em\u003eoperated eldercare institutions as public institutions and government-aided private institutions as private institutions. Based on the overall analysis, it proceeds to conduct a specific analysis of the temporal and spatial development and formation mechanisms of these two different types of institutional eldercare. It is worth noting that this study aims to provide a reference for future eldercare planning in the YRD urban agglomeration, therefore, for cities that have undergone administrative adjustments during the research period, such as the Luwan and Zhabei districts in Shanghai, and the Canglang district in Hangzhou, the analysis is based on the current administrative divisions. In addition, temperature and precipitation data are sourced from the historical data of the National Meteorological Center, economic density and industrial structure data are sourced from the annual China City Statistical Yearbook, various provincial and municipal statistical yearbooks, and statistical bulletins, market size data is sourced from previous national population censuses, and terrain data is obtained from the National Geographical Information Center of China.\u003c/p\u003e\n\u003ch2\u003eResearch methodology\u003c/h2\u003e\n\u003ch3\u003eNearest neighbor index\u003c/h3\u003e\n\u003cp\u003eThis study applies the nearest neighbor index to assess the spatial distribution characteristics of the development of the IE-YRDUA. By comparing the actual distribution of points with the theoretical random distribution (Xiong et al., 2018), the \u003cem\u003eR-\u003c/em\u003evalue is used. An \u003cem\u003eR-\u003c/em\u003evalue of 1 indicates a random distribution; \u003cem\u003eR\u003c/em\u003e\u0026lt;1 signifies a clumped distribution; \u003cem\u003eR\u003c/em\u003e\u0026gt;1 suggests a uniform distribution; and an \u003cem\u003eR-\u003c/em\u003evalue of 0 represents complete concentration.\u003c/p\u003e\n\u003ch3\u003eKernel density estimation\u003c/h3\u003e\n\u003cp\u003eKernel Density Estimation (KDE) transforms discrete point clusters into continuous density surfaces in order to explore the spatial distribution characteristics of said point clusters (Lin, 2016). In this study, the density at the same locations is superimposed to represent the number of eldercare institutions within a specific spatial distance, thus allowing for an exploration of the spatial development pattern of the IE-YRDUA.\u003c/p\u003e\n\u003ch3\u003eGlobal spatial autocorrelation analysis\u003c/h3\u003e\n\u003cp\u003eGlobal spatial autocorrelation aims to reveal the overall distribution of spatial unit observations to measure the spatial distribution characteristics of the observations across the region (Wang et al., 2020). In this study, Moran\u0026rsquo;s I is commonly used to measure the spatial correlation of eldercare institutions in the region as a whole, as a way to test whether the IE-YRDUA development is spatially dependent.\u003c/p\u003e\n\u003ch3\u003eGeodetector\u003c/h3\u003e\n\u003cp\u003eGeodetector is used as a statistical method to detect the drivers of spatial dissimilarity and test for causality between univariate spatial dissimilarity and bivariate (Wang et al., 2023). Compared to other methods, this model has the advantages of not assuming linearity and being immune to multivariate covariate covariance, as long as it has a clear physical meaning (Wang et al., 2017). In this study, the factor detection and interaction detection modules are used to identify the critical factors and their interactions that drive the spatiotemporal evolution of the IE-YRDUA.\u003c/p\u003e"},{"header":"4 The spatiotemporal patterns of the IE-YRDUA development","content":"\u003ch2\u003eChronological characterization of institutional eldercare development\u003c/h2\u003e\n\u003cp\u003eThe IE-YRDUA is currently in a state of steady development, but its evolutionary trajectory can still be characterized by phases, as depicted in Figure 1. The total number of eldercare institutions increases from 504 to 4,275 during the period under examination, with an average annual growth rate of 11.96%. Among these institutions, the increase in public ones is greater than that of private institutions, which ultimately determines the overall state of regional development. It is worth noting that the total number of eldercare institutions remained relatively stable between 2001 and 2010, with the highest annual increase of 198 institutions occurring in 2007. This coincides with the emergence of the aging problem in the YRDUA population and the pressing need to construct a socialized eldercare service system aligned with the demographic structure. Consequently, the development of residential institutional eldercare is promoted. From 2011 to 2020, the IE-YRDUA continues to experience rapid development, with public institutions growing by 200 annually and private institutions growing by 60 annually. This phenomenon can be attributed to the government\u0026rsquo;s policy of directing social resources towards eldercare investments. On one hand, the intensifying aging issue compels governments worldwide to broaden the coverage of universal eldercare and support private capital in renovating and integrating underutilized eldercare resources. On the other hand, the strategy of regional integration of eldercare services is prioritized, leading to the development of regionally competitive eldercare institutions through the joint sharing of eldercare resources across different regions.\u003c/p\u003e\n\u003ch2\u003eEvolution of the spatial pattern of institutional eldercare development\u003c/h2\u003e\n\u003ch3\u003eTypes of spatial distribution\u003c/h3\u003e\n\u003cp\u003eThis study examines the spatial distribution characteristics of different eldercare institutions in the YRDUA using the average nearest distance tool, as depicted in Table 1. The nearest point index R for eldercare institutions in the YRDUA is 0.576 in 2001, demonstrating a significant geographic proximity distribution effect. In terms of internal types, the nearest neighbor point index \u003cem\u003eR\u003c/em\u003e for private institutions (0.494) is smaller than that of public institutions (0.611), mainly because private institutions are for-profit and rely more on the local market\u0026rsquo;s development. In 2011, the nearest neighbor index for eldercare institutions is 0.557, indicating a more pronounced clumped distribution in the spatial development of the IE-YRDUA, particularly the increased agglomeration of private institutions. By 2020, the overall spatial distribution of eldercare institutions in the YRDUA is further strengthened, with the nearest-neighbor index \u003cem\u003eR\u003c/em\u003e decreasing to 0.531 for both public and private institutions. The nearest neighbor index R also decreases to 0.569 and 0.346 respectively, suggesting a strengthened spatial agglomeration pattern.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp style='margin-top:6.0pt;margin-right:0in;margin-bottom:12.0pt;margin-left:0in;font-size:16px;font-family:\"Times New Roman\",serif;'\u003eTable 1 The nearest neighbor index results for eldercare institutions in the YRDUA.\u003c/p\u003e\n\u003ctable style=\"border-collapse:collapse;border:none;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:65.75pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;background:#E7E6E6;padding:0in 5.4pt 0in 5.4pt;height:12.95pt;\"\u003e\n \u003cp style='margin-top:6.0pt;margin-right:0in;margin-bottom:12.0pt;margin-left:0in;font-size:16px;font-family:\"Times New Roman\",serif;text-align:center;line-height:12.0pt;'\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003eNNI (R-value)\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:59.0pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;background:#E7E6E6;padding:0in 5.4pt 0in 5.4pt;height:12.95pt;\"\u003e\n \u003cp style='margin-top:6.0pt;margin-right:0in;margin-bottom:12.0pt;margin-left:0in;font-size:16px;font-family:\"Times New Roman\",serif;text-align:center;line-height:12.0pt;'\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003eE\u003c/span\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003eldercare\u003c/span\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003e\u0026nbsp;institutions\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:65.35pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;background:#E7E6E6;padding:0in 5.4pt 0in 5.4pt;height:12.95pt;\"\u003e\n \u003cp style='margin-top:6.0pt;margin-right:0in;margin-bottom:12.0pt;margin-left:0in;font-size:16px;font-family:\"Times New Roman\",serif;text-align:center;line-height:12.0pt;'\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003ePublic institutions\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:65.55pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;background:#E7E6E6;padding:0in 5.4pt 0in 5.4pt;height:12.95pt;\"\u003e\n \u003cp style='margin-top:6.0pt;margin-right:0in;margin-bottom:12.0pt;margin-left:0in;font-size:16px;font-family:\"Times New Roman\",serif;text-align:center;line-height:12.0pt;'\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003ePrivate institutions\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:52.45pt;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;background:#E7E6E6;padding:0in 5.4pt 0in 5.4pt;height:12.95pt;\"\u003e\n \u003cp style='margin-top:6.0pt;margin-right:0in;margin-bottom:12.0pt;margin-left:0in;font-size:16px;font-family:\"Times New Roman\",serif;text-align:center;line-height:12.0pt;'\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003eSpatial distribution\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:65.75pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:12.95pt;\"\u003e\n \u003cp style='margin-top:6.0pt;margin-right:0in;margin-bottom:12.0pt;margin-left:0in;font-size:16px;font-family:\"Times New Roman\",serif;text-align:center;line-height:12.0pt;'\u003e\u003cspan style=\"font-size:12px;\"\u003e2001\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:59.0pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:12.95pt;\"\u003e\n \u003cp style='margin-top:6.0pt;margin-right:0in;margin-bottom:12.0pt;margin-left:0in;font-size:16px;font-family:\"Times New Roman\",serif;text-align:center;line-height:12.0pt;'\u003e\u003cspan style=\"font-size:12px;\"\u003e0.576\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:65.35pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:12.95pt;\"\u003e\n \u003cp style='margin-top:6.0pt;margin-right:0in;margin-bottom:12.0pt;margin-left:0in;font-size:16px;font-family:\"Times New Roman\",serif;text-align:center;line-height:12.0pt;'\u003e\u003cspan style=\"font-size:12px;\"\u003e0.611\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:65.55pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:12.95pt;\"\u003e\n \u003cp style='margin-top:6.0pt;margin-right:0in;margin-bottom:12.0pt;margin-left:0in;font-size:16px;font-family:\"Times New Roman\",serif;text-align:center;line-height:12.0pt;'\u003e\u003cspan style=\"font-size:12px;\"\u003e0.494\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:52.45pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:12.95pt;\"\u003e\n \u003cp style='margin-top:6.0pt;margin-right:0in;margin-bottom:12.0pt;margin-left:0in;font-size:16px;font-family:\"Times New Roman\",serif;text-align:center;line-height:12.0pt;'\u003e\u003cspan style=\"font-size:12px;\"\u003eclumped\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:65.75pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:12.95pt;\"\u003e\n \u003cp style='margin-top:6.0pt;margin-right:0in;margin-bottom:12.0pt;margin-left:0in;font-size:16px;font-family:\"Times New Roman\",serif;text-align:center;line-height:12.0pt;'\u003e\u003cspan style=\"font-size:12px;\"\u003e2011\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:59.0pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:12.95pt;\"\u003e\n \u003cp style='margin-top:6.0pt;margin-right:0in;margin-bottom:12.0pt;margin-left:0in;font-size:16px;font-family:\"Times New Roman\",serif;text-align:center;line-height:12.0pt;'\u003e\u003cspan style=\"font-size:12px;\"\u003e0.557\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:65.35pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:12.95pt;\"\u003e\n \u003cp style='margin-top:6.0pt;margin-right:0in;margin-bottom:12.0pt;margin-left:0in;font-size:16px;font-family:\"Times New Roman\",serif;text-align:center;line-height:12.0pt;'\u003e\u003cspan style=\"font-size:12px;\"\u003e0.604\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:65.55pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:12.95pt;\"\u003e\n \u003cp style='margin-top:6.0pt;margin-right:0in;margin-bottom:12.0pt;margin-left:0in;font-size:16px;font-family:\"Times New Roman\",serif;text-align:center;line-height:12.0pt;'\u003e\u003cspan style=\"font-size:12px;\"\u003e0.364\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:52.45pt;border:none;padding:0in 5.4pt 0in 5.4pt;height:12.95pt;\"\u003e\n \u003cp style='margin-top:6.0pt;margin-right:0in;margin-bottom:12.0pt;margin-left:0in;font-size:16px;font-family:\"Times New Roman\",serif;text-align:center;line-height:12.0pt;'\u003e\u003cspan style=\"font-size:12px;\"\u003eclumped\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:65.75pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:12.95pt;\"\u003e\n \u003cp style='margin-top:6.0pt;margin-right:0in;margin-bottom:12.0pt;margin-left:0in;font-size:16px;font-family:\"Times New Roman\",serif;text-align:center;line-height:12.0pt;'\u003e\u003cspan style=\"font-size:12px;\"\u003e2020\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:59.0pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:12.95pt;\"\u003e\n \u003cp style='margin-top:6.0pt;margin-right:0in;margin-bottom:12.0pt;margin-left:0in;font-size:16px;font-family:\"Times New Roman\",serif;text-align:center;line-height:12.0pt;'\u003e\u003cspan style=\"font-size:12px;\"\u003e0.531\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:65.35pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:12.95pt;\"\u003e\n \u003cp style='margin-top:6.0pt;margin-right:0in;margin-bottom:12.0pt;margin-left:0in;font-size:16px;font-family:\"Times New Roman\",serif;text-align:center;line-height:12.0pt;'\u003e\u003cspan style=\"font-size:12px;\"\u003e0.569\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:65.55pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:12.95pt;\"\u003e\n \u003cp style='margin-top:6.0pt;margin-right:0in;margin-bottom:12.0pt;margin-left:0in;font-size:16px;font-family:\"Times New Roman\",serif;text-align:center;line-height:12.0pt;'\u003e\u003cspan style=\"font-size:12px;\"\u003e0.346\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width:52.45pt;border:none;border-bottom:solid windowtext 1.0pt;padding:0in 5.4pt 0in 5.4pt;height:12.95pt;\"\u003e\n \u003cp style='margin-top:6.0pt;margin-right:0in;margin-bottom:12.0pt;margin-left:0in;font-size:16px;font-family:\"Times New Roman\",serif;text-align:center;line-height:12.0pt;'\u003e\u003cspan style=\"font-size:12px;\"\u003eclumped\u0026nbsp;\u003c/span\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ch3\u003eSpatial agglomeration characteristics\u003c/h3\u003e\n\u003cp\u003eThe allocation of public welfare resources is affected by geographical location and shows spatial correlation (Ma, 2015). To verify the existence of spatial association in the IE-YRDUA, this study quantitatively measured its spatial correlation. The results showed that during the research period, the Moran\u0026rsquo;s I of eldercare institutions in the YRDUA were consistently positive, ranging from 0.379 to 0.410, and statistically significant at the 1% level. This indicates that the IE-YRDUA development exhibits significant spatial clustering, with clustering intensity increasing and spatial correlation gradually strengthening. When looking at different types, the Moran\u0026rsquo;s I of public institutions increased from 0.212 to 0.398, while the Moran\u0026rsquo;s I of private institutions showed a larger increase from 0.121 to 0.397. This reflects that, compared to public institutional eldercare, which is oriented towards regional balanced development, private institutional eldercare develops more quickly in advantaged areas.\u003c/p\u003e\n\u003cp\u003eIn order to further explore the spatial pattern characteristics of the IE-YRDUA, this study conducts a kernel density analysis of the distribution of eldercare institutions from 2001 to 2020, and visualizes the results according to the Jenks natural breaks classification method, as shown in Figure 2. Specifically, the spatial development pattern of the IE-YRDUA presents the following characteristics. First, during the study period, the number of eldercare institutions in the YRDUA region increases dramatically, and the overall distribution density increases, with the spatial aggregation range continuously expanding. As shown in Figures 3a-c, the development gradient of the IE-YRDUA becomes increasingly prominent, starting from the initial stage of Shanghai as the single central hub and other areas in a nascent state, to the appearance of clustering patches of varying depths in the main urban areas of different cities. Subsequently, there is a significant difference in the scale and intensity of clustering, showing a spatial heterogeneity distribution with a stronger concentration in the east and a weaker concentration in the west. Among them, Ningbo\u0026rsquo;s core status within Zhejiang province is highlighted, becoming the fourth core in addition to Shanghai, Nanjing, and Hangzhou. Nantong consistently maintains a high level of clustering, with slightly less intensity than Ningbo. The overall development level of Suzhou, Wuxi, and Changzhou is strong, and multiple small clustering areas have appeared within the cities, forming a rudimentary clustering axis from east to west. Second, public institutions exhibit a clustering pattern in terms of spatial distribution, as shown in Figures 3d-f. The core positions of Shanghai, Nanjing, and Hangzhou remain stable, while the secondary core positions of Suzhou, Ningbo, and Nantong are increasingly prominent. The clustering intensity continues to increase, and the influence range significantly expands, forming a distribution that connects multiple cores. On the other hand, the overall changes in spatial characteristics of private institutions are relatively small and possess a certain degree of time inertia and spatial stability, as seen in Figures 3g-i. During the study period, the significant gap between Shanghai and other regions narrows, the clustering degree of Nanjing and Hangzhou improves, and a \u0026ldquo;one core, two secondary cores\u0026rdquo; pattern gradually forms, indicating an increased level of regional coordinated development.\u003c/p\u003e"},{"header":"5 The spatiotemporal evolution mechanism analysis","content":"\u003ch2\u003eSelection of influencing factors\u003c/h2\u003e\n\u003cp\u003eThe development of institutional eldercare in the IE-YRDUA region exhibits strong spatial heterogeneity. Exploring these differential characteristics and patterns of change is a prerequisite for understanding its spatiotemporal evolution mechanism. This study, based on the fundamental theories of economic geography, incorporates recent progress in related research and the realities of the new population structure (Luo et al., 2020; Zhang et al., 2021; Cheng et al., 2022; Peng et al., 2022). It constructs a comprehensive factor detection index system: The natural environment is the initial and fundamental development condition of a region, and it is also an inherent factor influencing the location selection of institutional eldercare. Factors such as topography, temperature, and precipitation are considered as first nature in this study. Economic, locational, and transportation factors compensate for the differences in the natural foundation of a region caused by human activities and are also factors that influence the development of institutional eldercare. Factors such as economic density, market size, industrial structure, capacity to consume, and transportation are considered second nature in this study. Human capital level, informatization, openness, and technological advancement are the third geographic factors that influence the spatial fragmentation and recombination of social activities. Variables such as human resources, degree of openness, level of informatization, and technological advancement are used to represent third nature in this study. Additionally, the quantity of eldercare institutions in each city is used as a measure of regional institutional eldercare development. Similarly, the development level of public and private institutional eldercare is represented by the number of corresponding types of institutions in the region. All variables are categorized using the Jenks natural breaks classification method.\u003c/p\u003e\n\u003ch2\u003eDominant factor identification\u003c/h2\u003e\n\u003cp\u003eThe heterogeneity in the influence of different driving factors on the overall development of the IE-YRDUA is significant, as shown in Table 2. During the sample period, the driving factors with the largest average influence at the independent factor level are as follows: capacity to consume (\u003cem\u003eX7\u003c/em\u003e: 0.602), human resources (\u003cem\u003eX9\u003c/em\u003e: 0.584), degree of informatization (\u003cem\u003eX11\u003c/em\u003e: 0.580), technological level (\u003cem\u003eX12\u003c/em\u003e: 0.555), and economic level (\u003cem\u003eX4\u003c/em\u003e: 0.552). These factors belong to the \u0026ldquo;second nature\u0026rdquo; and \u0026ldquo;third nature\u0026rdquo; dimensions at the system level, respectively, indicating their dominant roles in the development of the IE-YRDUA. On the other hand, factors such as topography (\u003cem\u003eX1\u003c/em\u003e) and temperature (\u003cem\u003eX2\u003c/em\u003e) have relatively weak explanatory power and did not pass the significance test. Upon further investigation of the influence \u003cem\u003eq-\u003c/em\u003evalues at three time points (2001, 2011, and 2020) it is observed that the overall influence of first nature factors on the spatial pattern of institutional eldercare has weakened, while the explanatory power of second and third nature factors has gradually increased. Specifically, the influence of capacity to consume (\u003cem\u003eX7\u003c/em\u003e) and human resources (\u003cem\u003eX9\u003c/em\u003e) consistently ranks among the top, indicating the significant role of social capacity to consume in shaping the spatial pattern of the IE-YRDUA. The improvement in education level also promotes the shift from \u0026ldquo;family eldercare\u0026rdquo; to \u0026ldquo;social eldercare,\u0026rdquo; thereby driving the development of institutional eldercare. The influence of temperature (\u003cem\u003eX2\u003c/em\u003e) and precipitation (\u003cem\u003eX3\u003c/em\u003e) gradually decreases, suggesting a weakened influence of natural foundations on the spatial pattern of institutional eldercare. Conversely, the influence of economic level (\u003cem\u003eX4\u003c/em\u003e) and degree of informatization (\u003cem\u003eX11\u003c/em\u003e) increases from 0.420 and 0.455 to 0.687 and 0.674, respectively. This indicates that social and economic development drives government investment in livelihood-related fields, thus promoting the development of institutional eldercare. Furthermore, the improvement in informatization fosters the intelligent development of eldercare services, amplifying their positive influence. In addition, the \u003cem\u003eq-\u003c/em\u003evalue of transportation (\u003cem\u003eX8\u003c/em\u003e) significantly increases in significance over the study period. This highlights how improved transportation enhance accessibility within and between regions, accelerate the flow of factors between cities, and become an important driving force for the development of institutional eldercare.\u003c/p\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eTable 2: Influencing factor Detection Results for Overall Development of the IE-YRDUA\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cdiv align=\"center\" style='margin-top:6.0pt;margin-right:0in;margin-bottom:12.0pt;margin-left:0in;font-size:16px;font-family:\"Times New Roman\",serif;'\u003e\n \u003ctable style=\"width:100.0%;border-collapse:collapse;border:none;\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:13.38%;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;\"\u003e\u003cspan style=\"font-size:12px;\"\u003eDimensions\u0026nbsp;\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:18.86%;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;\"\u003e\u003cspan style=\"font-size:12px;\"\u003e\u0026nbsp;Factors\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:34.8%;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;\"\u003e\u003cspan style=\"font-size:12px;\"\u003eFactor connotation\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:10.98%;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;\"\u003e\u003cspan style=\"font-size:12px;\"\u003e2001\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:10.98%;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;\"\u003e\u003cspan style=\"font-size:12px;\"\u003e2011\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:10.98%;border-top:solid windowtext 1.0pt;border-left:none;border-bottom:solid windowtext 1.0pt;border-right:none;padding:0in 5.4pt 0in 5.4pt;\"\u003e\u003cspan style=\"font-size:12px;\"\u003e2020\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" style=\"width:13.38%;border:none;background:#E7E6E6;padding:0in 5.4pt 0in 5.4pt;\"\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003eFirst nature\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:18.86%;border:none;background:#E7E6E6;padding:0in 5.4pt 0in 5.4pt;\"\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003eTopography \u003cem\u003e: X\u003c/em\u003e\u003c/span\u003e\u003cem\u003e\u003csub\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003e1\u003c/span\u003e\u003c/sub\u003e\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:34.8%;border:none;background:#E7E6E6;padding:0in 5.4pt 0in 5.4pt;\"\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003ePhysical features of an area\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.98%;border: medium;background: rgb(231, 230, 230);padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003e0.182\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.98%;border: medium;background: rgb(231, 230, 230);padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003e0.200\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.98%;border: medium;background: rgb(231, 230, 230);padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003e0.152\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:18.86%;border:none;background:#E7E6E6;padding:0in 5.4pt 0in 5.4pt;\"\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003eTemperature\u003c/span\u003e\u003cem\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003e\u0026nbsp;: X\u003csub\u003e2\u003c/sub\u003e\u003c/span\u003e\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:34.8%;border:none;background:#E7E6E6;padding: 0in 5.4pt 0in 5.4pt;\"\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003eAnnual average temperature\u003c/span\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003e\u0026nbsp;(\u0026deg;C)\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.98%;border: medium;background: rgb(231, 230, 230);padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003e0.340\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.98%;border: medium;background: rgb(231, 230, 230);padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003e0.268\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.98%;border: medium;background: rgb(231, 230, 230);padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003e0.286\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:18.86%;border:none;background:#E7E6E6;padding:0in 5.4pt 0in 5.4pt;\"\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003ePrecipitation \u003cem\u003e: X\u003csub\u003e3\u003c/sub\u003e\u003c/em\u003e\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:34.8%;border:none;background:#E7E6E6;padding: 0in 5.4pt 0in 5.4pt;\"\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003eAnnual average precipitation\u003c/span\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003e\u0026nbsp;(mm)\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.98%;border: medium;background: rgb(231, 230, 230);padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003e0.226\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.98%;border: medium;background: rgb(231, 230, 230);padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003e0.304\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.98%;border: medium;background: rgb(231, 230, 230);padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003e0.206\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"5\" style=\"width:13.38%;border:none;padding:0in 5.4pt 0in 5.4pt;\"\u003e\u003cspan style=\"font-size:12px;\"\u003eSecond nature\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:18.86%;border:none;padding:0in 5.4pt 0in 5.4pt;\"\u003e\u003cspan style=\"font-size:12px;\"\u003eEconomic level \u003cem\u003e: X\u003csub\u003e4\u003c/sub\u003e\u003c/em\u003e\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 34.8%;border: medium;padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;\"\u003eGDP (in ten thousand yuan)\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.98%;border: medium;padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;\"\u003e0.420\u003c/span\u003e\u003csup\u003e\u003cspan style=\"font-size:12px;\"\u003e*\u003c/span\u003e\u003c/sup\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.98%;border: medium;padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;\"\u003e0.547\u003csup\u003e**\u003c/sup\u003e\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.98%;border: medium;padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;\"\u003e0.687\u003csup\u003e***\u003c/sup\u003e\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:18.86%;border:none;padding:0in 5.4pt 0in 5.4pt;\"\u003e\u003cspan style=\"font-size:12px;\"\u003eMarket size \u003cem\u003e: X\u003csub\u003e5\u003c/sub\u003e\u003c/em\u003e\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 34.8%;border: medium;padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;\"\u003eNumber of elderly population (in ten thousand people)\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.98%;border: medium;padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;\"\u003e0.371\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.98%;border: medium;padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;\"\u003e0.491\u003csup\u003e**\u003c/sup\u003e\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.98%;border: medium;padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;\"\u003e0.605\u003csup\u003e**\u003c/sup\u003e\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:18.86%;border:none;padding:0in 5.4pt 0in 5.4pt;\"\u003e\u003cspan style=\"font-size:12px;\"\u003eIndustrial development \u003cem\u003e: X\u003csub\u003e6\u003c/sub\u003e\u003c/em\u003e\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 34.8%;border: medium;padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;\"\u003eNumber of employees in the tertiary industry (in ten thousand people) (%)\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.98%;border: medium;padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;\"\u003e0.519\u003csup\u003e**\u003c/sup\u003e\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.98%;border: medium;padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;\"\u003e0.521\u003csup\u003e**\u003c/sup\u003e\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.98%;border: medium;padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;\"\u003e0.558\u003csup\u003e**\u003c/sup\u003e\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:18.86%;border:none;padding:0in 5.4pt 0in 5.4pt;\"\u003e\u003cspan style=\"font-size:12px;\"\u003eCapacity to consume \u003cem\u003e: X\u003c/em\u003e\u003c/span\u003e\u003cem\u003e\u003csub\u003e\u003cspan style=\"font-size:12px;\"\u003e7\u003c/span\u003e\u003c/sub\u003e\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 34.8%;border: medium;padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;\"\u003eEnd-of-year balance of savings for urban and rural residents (in ten thousand yuan)\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.98%;border: medium;padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;\"\u003e0.548\u003csup\u003e**\u003c/sup\u003e\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.98%;border: medium;padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;\"\u003e0.568\u003csup\u003e**\u003c/sup\u003e\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.98%;border: medium;padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;\"\u003e0.689\u003csup\u003e***\u003c/sup\u003e\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:18.86%;border:none;padding:0in 5.4pt 0in 5.4pt;\"\u003e\u003cspan style=\"font-size:12px;\"\u003eTransportation \u003cem\u003e: X\u003csub\u003e8\u003c/sub\u003e\u003c/em\u003e\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 34.8%;border: medium;padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;\"\u003eTotal passenger volume (in ten thousand people)\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.98%;border: medium;padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;\"\u003e0.192\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.98%;border: medium;padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;\"\u003e0.343\u003csup\u003e*\u003c/sup\u003e\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.98%;border: medium;padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;\"\u003e0.593\u003csup\u003e***\u003c/sup\u003e\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" style=\"width:13.38%;border:none;border-bottom:solid windowtext 1.0pt;background:#E7E6E6;padding:0in 5.4pt 0in 5.4pt;\"\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003eThird nature\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width:18.86%;border:none;background:#E7E6E6;padding:0in 5.4pt 0in 5.4pt;\"\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003eHuman resources \u003cem\u003e: x\u003c/em\u003e\u003c/span\u003e\u003cem\u003e\u003csub\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003e9\u003c/span\u003e\u003c/sub\u003e\u003c/em\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 34.8%;border: medium;background: rgb(231, 230, 230);padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003eNumber of students in regular higher education institutions (in number)\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.98%;border: medium;background: rgb(231, 230, 230);padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003e0.543\u003csup\u003e**\u003c/sup\u003e\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.98%;border: medium;background: rgb(231, 230, 230);padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003e0.530\u003csup\u003e***\u003c/sup\u003e\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.98%;border: medium;background: rgb(231, 230, 230);padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003e0.680\u003csup\u003e***\u003c/sup\u003e\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:18.86%;border:none;background:#E7E6E6;padding:0in 5.4pt 0in 5.4pt;\"\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003eDegree of openness \u003cem\u003e: X\u003csub\u003e10\u003c/sub\u003e\u003c/em\u003e\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 34.8%;border: medium;background: rgb(231, 230, 230);padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003eAmount of actual foreign investment utilized in the year (in ten thousand US dollars)\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.98%;border: medium;background: rgb(231, 230, 230);padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003e0.562\u003csup\u003e**\u003c/sup\u003e\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.98%;border: medium;background: rgb(231, 230, 230);padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003e0.564\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.98%;border: medium;background: rgb(231, 230, 230);padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003e0.441\u003csup\u003e*\u003c/sup\u003e\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:18.86%;border:none;background:#E7E6E6;padding:0in 5.4pt 0in 5.4pt;\"\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003eDegree of informatization \u003cem\u003e: X\u003csub\u003e11\u003c/sub\u003e\u003c/em\u003e\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 34.8%;border: medium;background: rgb(231, 230, 230);padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003eTotal volume of telecommunications services (in ten thousand yuan)\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.98%;border: medium;background: rgb(231, 230, 230);padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003e0.455\u003csup\u003e**\u003c/sup\u003e\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.98%;border: medium;background: rgb(231, 230, 230);padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003e0.611\u003csup\u003e**\u003c/sup\u003e\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.98%;border: medium;background: rgb(231, 230, 230);padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003e0.674\u003csup\u003e***\u003c/sup\u003e\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width:18.86%;border:none;border-bottom:solid windowtext 1.0pt;background:#E7E6E6;padding:0in 5.4pt 0in 5.4pt;\"\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003eTechnological level\u003cem\u003e: X\u003csub\u003e12\u003c/sub\u003e\u003c/em\u003e\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 34.8%;border-width: medium medium 1pt;border-style: none none solid;border-color: currentcolor currentcolor windowtext;border-image: none;background: rgb(231, 230, 230);padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003eScientific expenditure (in ten thousand yuan)\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.98%;border-width: medium medium 1pt;border-style: none none solid;border-color: currentcolor currentcolor windowtext;border-image: none;background: rgb(231, 230, 230);padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003e0.543\u003csup\u003e**\u003c/sup\u003e\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.98%;border-width: medium medium 1pt;border-style: none none solid;border-color: currentcolor currentcolor windowtext;border-image: none;background: rgb(231, 230, 230);padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003e0.452\u003csup\u003e*\u003c/sup\u003e\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 10.98%;border-width: medium medium 1pt;border-style: none none solid;border-color: currentcolor currentcolor windowtext;border-image: none;background: rgb(231, 230, 230);padding: 0in 5.4pt;vertical-align: top;\"\u003e\u003cspan style=\"font-size:12px;color:black;\"\u003e0.672\u003csup\u003e***\u003c/sup\u003e\u003c/span\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eIn light of the varying influence of geographical conditions on the development of different aspects of institutional eldercare, this study further separates public and private institutional types to compare classifications, as shown in Figure 3. Firstly, within the classification of private institutions, the explanatory power of temperature (\u003cem\u003eX2\u003c/em\u003e) among the primary natural factors is greater and achieves statistical significance at the 5% level. However, factors such as terrain topography (\u003cem\u003eX1\u003c/em\u003e) and precipitation (\u003cem\u003eX3\u003c/em\u003e) have weaker explanatory power and do not attain statistical significance. This is mainly because the YRDUA as a whole has a flat terrain and abundant rainfall, resulting in a weaker influence of underlying natural conditions on the spatial differentiation of institutional eldercare development. Given that the elderly population is more sensitive to temperature, site selection for private institutions focuses more on evaluating environmental and health risks, with temperature comfort having a positive influence on the health of the elderly. This factor becomes particularly important for private institutions. However, over time, the clustering effect of temperature on the development of private institutional eldercare has diminished. This can be attributed to the fact that in the later stages of development, the utilization of geographic resources for health has reached its maximum capacity, and spatial development needs to shift towards other influencing factors.\u003c/p\u003e\n\u003cp\u003eSecondly, the \u003cem\u003eq-\u003c/em\u003evalues of second nature factors are generally high, indicating that the second nature factor supports the development of the IE-YRDUA. Additionally, this factor supports public institutions more than private ones. On one hand, eldercare service is considered a high-level consumption activity that reflects the elderly\u0026rsquo;s desire for a better quality of life. The development of public institutional eldercare relies on local finances and the resulting cluster space is closely tied to regional socio-economic development. The prosperity of the socio-economy facilitates the construction of inclusive eldercare services by the government. On the other hand, the level of savings among residents directly affects their ability to pay for eldercare services. A higher level of savings enables the elderly to fully utilize their consumption potential and encourages the improvement of eldercare consumption. This, in turn, has a positive influence on the development of eldercare institutions.\u003c/p\u003e\n\u003cp\u003eThe third factor of nature serves as a continuous driving force for the development of the IE-YRDUA. The explanatory power of this dimension on the development of institutional eldercare is gradually increasing, and it generally has a higher influence on private institutions compared to public institutions, although there are some differences depending on the stage. In the early stages, human resources (\u003cem\u003eX9\u003c/em\u003e) and degree of openness (\u003cem\u003eX10\u003c/em\u003e) have a stronger influence on the development of private institutional eldercare, while the degree of informatization (\u003cem\u003eX11\u003c/em\u003e) is the main factor for public institutional eldercare. However, over time, the \u003cem\u003eq-\u003c/em\u003evalue of technological level (\u003cem\u003eX12\u003c/em\u003e) increases in both types of institutions, reaching 0.688 in 2020 and passing the 1% significance level test. This indicates that the development of modern technology, particularly digitalization, has contributed to the innovation of eldercare services, promoted the transformation of eldercare services towards intelligent solutions, and become a key driving force for the development of all types of institutional eldercare in the new era.\u003c/p\u003e\n\u003ch2\u003eInteraction Analysis of Key Factors\u003c/h2\u003e\n\u003cp\u003eThe spatial development of institutional eldercare is not simply a simple mapping of geographical factors, but can achieve differential effects through interaction. This study utilizes an interaction detection module to identify the interaction relationship between different factors. Due to space limitations, this article only displays the relationship between the factors with higher explanatory power after two years of interaction during the study period, as shown in Figure 4. It can be seen that the interaction types of various factors during the examination period are relatively stable, primarily characterized by dual-factor enhancement with a secondary non-linear enhancement. This indicates that the spatiotemporal evolution of the IE-YRDUA is the result of the combined effect of multiple factors.\u003c/p\u003e\n\u003cp\u003eAfter conducting interaction detection, the overall degree of explanation for institutional eldercare development by each factor is enhanced as a whole (Figures 4a-b). This enhancement is particularly concentrated in the first and second nature factors, such as precipitation (\u003cem\u003eX3\u003c/em\u003e) and transportation (\u003cem\u003eX8\u003c/em\u003e). These findings indicate that the climate environment and transportation accessibility not only directly affect the development of institutional eldercare in urban agglomerations, but they are also the main interactive factors within the system. This highlights the fundamental role and support provided by the natural environment and transportation in the regional institutional eldercare development process. In the analysis of public institutional eldercare factors (Figures 4c-d), the most important interacting factors influencing spatial development in 2001 and 2020 are the intersection of economic level (\u003cem\u003eX4\u003c/em\u003e) and transportation (\u003cem\u003eX8\u003c/em\u003e), and the intersection of precipitation (\u003cem\u003eX3\u003c/em\u003e) and scientific and technological level (\u003cem\u003eX12\u003c/em\u003e), with \u003cem\u003eq-\u003c/em\u003evalues of 0.711 and 0.748, respectively. It is worth noting that although the \u003cem\u003eq-\u003c/em\u003evalue of precipitation (\u003cem\u003eX3\u003c/em\u003e) is small, its interaction is significantly stronger, for example, with capacity to consume (\u003cem\u003eX7\u003c/em\u003e) and informatization (\u003cem\u003eX11\u003c/em\u003e), where the interaction explanation strengths are above 0.7, and its driving role after interaction with other factors is above 0.5. The influence pattern of climate factors is that they have a highly supportive and slow effect on the development of public institutional eldercare, mainly through indirect means. Therefore, when precipitation, socio-economic development, and scientific and technological development work together, they significantly promote the development of public institutional eldercare. In the interaction analysis of factors influencing private institutional eldercare development (Figures 4e-f), the interaction between temperature (\u003cem\u003eX2\u003c/em\u003e) and other factors in 2001 is significantly enhanced, with \u003cem\u003eq-\u003c/em\u003evalues above 0.4. This indicates that the temperature factor greatly enhances the influence of other indicators on the development of private institutional eldercare, which is consistent with its strong explanatory power as a single factor. In 2020, the interactions of precipitation (\u003cem\u003eX3\u003c/em\u003e) and informatization (\u003cem\u003eX11\u003c/em\u003e) with other factors become stronger, suggesting that the development of private institutional eldercare may be constrained by the comfort of the natural environment and the degree of social openness. Thus, its spatial distribution is driven by the interaction of natural and social factors. This finding also reflects the successful expansion of eldercare service provision from a family-oriented approach to a more socialized one, where informatization implicitly guides the transformation of eldercare culture and enhances the capacity and level of eldercare socialization.\u003c/p\u003e\n\u003ch2\u003eAnalysis of evolutionary mechanisms\u003c/h2\u003e\n\u003cp\u003eThe development of institutional eldercare is a result of the transfer of environmental characteristics to the allocation of eldercare resources. Expanding on this, this study investigates the mechanism behind the spatial pattern evolution of the IE-YRDUA. Through empirical analysis, the study reveals that the three major geographic nature factors represent distinct mechanisms, and the interconnectedness and mutual influence among elements within each dimension collectively shape the spatiotemporal evolution of institutional eldercare.\u003c/p\u003e\n\u003cp\u003eFirst nature plays a certain pioneering role, especially in the early spatial distribution of private institutions. However, when comprehensively observing the changes over time, it can be seen that the factors driving the development of the IE-YRDUA are becoming more and more complex. On one hand, the continuous promotion of ecological civilization construction and the continuous optimization of the human habitat have increased the flexibility of institutional eldercare development, weakening the unidimensional driving role of innate environmental factors. On the other hand, the interaction between this dimension and other factors has gradually strengthened, transforming the influence of natural geographic characteristics on institutional eldercare development into a multifaceted linkage relying on other geographic characteristics, such as capacity to consume, transportation, and the level of informatization, rather than isolated influence, especially in the case of the private sector, which is more autonomous, flexible, and efficiency-oriented. The supportive function of second nature for the IE-YRDUA development continues to strengthen. The aging of the population, the miniaturization of families, and the lengthening of life expectancy have led to an increase in the uncertainty of demand for institutional eldercare services. As a result, institutional eldercare development has gradually shifted to rely on the acquired conditions of the city and the market base, and as time passes, it will rely more on the optimization of factors such as economic level and capacity to consume. This enhancement will be achieved through the interaction between the first and second geographic factors such as precipitation and the level of science and technology, boosting the development of the IE-YRDUA. The interaction structure of the first and second geographic factors, such as precipitation, science and technology level, strengthens the guarantee ability. Third nature\u0026rsquo;s overall influence on the spatial development of the IE-YRDUA is high in intensity and closely related to each other. In the early stage of the study, the single factor of human resources has stronger explanatory power and significant interaction with other indicators, reflecting that the evolution of the spatial pattern of institutional eldercare in the early stage is mainly driven by labor resources. In the late stage of the study, the explanatory power of the level of informatization becomes stronger and the interaction effect is significant, indicating that as smart technology plays an increasingly important role in health management, remote diagnosis and treatment, and elderly care, the empowerment of informatization on smart institutional eldercare will be upgraded comprehensively.\u003c/p\u003e"},{"header":"6 Conclusions","content":"\u003cp\u003eThis paper constructs a theoretical analytical framework in a specific research context. It explores the changing characteristics and development laws of the IE-YRDUA from 2001 to 2020 from the perspectives of spatiotemporal dynamic processes and economic geography. This research enriches the empirical research on related content to some extent. The main conclusions are as follows.\u003c/p\u003e\n\u003cp\u003eFirst,\u0026nbsp;the development of the IE-YRDUA can be divided into different stages, with a dynamic trajectory showing fluctuating growth, slow growth in the early stage, and rapid growth in the later stage. In terms of internal structure, public institutions have a larger increase than private institutions, which determines the overall state of institutional eldercare development in the region. However, with the significant improvement in the supply capacity of social capital in the eldercare service field, the market vitality and social creativity of private institutional eldercare will be fully stimulated.\u003c/p\u003e\n\u003cp\u003eSecond, the spatial distribution of the IE-YRDUA tends to be in a state of agglomeration, and the spatial structure belongs to the clumped type. The spatial pattern evolves from a density zone centered around Shanghai to the fragmentation of main urban areas in various cities, ultimately forming a layout with Shanghai as the center radiating to Nantong, Nanjing, Hangzhou, and Ningbo. In terms of types, the spatial agglomeration of public institutions evolves from early concentration in Shanghai, Nanjing, and Hangzhou to later dispersion in various places. On the contrary, the agglomeration form of private institutions is relatively stable, but it shows an evolution direction with Shanghai as the core and Nanjing and Hangzhou as secondary core areas.\u003c/p\u003e\n\u003cp\u003eThird, the geodetector outputs confirm that the driving factors of the IE-YRDUA development have diversified characteristics. From the perspective of the strength of the influencing factors, the development pattern dominated by second nature and third nature gradually emerges. First nature factors such as temperature and precipitation have a certain leading role in the development of private institutional eldercare. Second nature factors such as economic level and capacity to consume provide necessary spatial support for institutional eldercare development. Factors such as human resources, level of informatization, and technological level provide momentum for the sustainable development of the IE-YRDUA.\u003c/p\u003e\n\u003cp\u003eFourth, the development of the IE-YRDUA is closely related to various geographical factors, but the dominant factors and their combination characteristics that cause spatial structural differences are not the same. The results of interaction detection show that the spatiotemporal evolution of the IE-YRDUA development is associated with complex factors. Although the single-factor explanatory power of first nature is relatively weak, the influence of interaction effects is significantly enhanced. On the other hand, second nature and third nature play a crucial role as the main dependency paths. They not only have strong independent driving forces but also play an important role in the occurrence of multidimensional interactive effects.\u003c/p\u003e\n\u003cp\u003eThe research has analyzed the formation mechanism of the spatiotemporal evolution of the IE-YRDUA from both theoretical and practical perspectives. Based on the above conclusions, the following inspirations can be drawn. First, it is important to explore local natural resources and actively promote the development of health-oriented institutional eldercare driven by natural resources. This can be achieved by creating a shared space that integrates cultural tourism and eldercare, exploring the \u0026ldquo;health tourism\u0026rdquo; model that promotes the symbiosis of eldercare services and the natural environment, and developing recreational eldercare and tourist eldercare. Second, to achieve common prosperity, the allocation of resources should be optimized to strengthen the support for the development of disadvantaged areas and the protection of vulnerable groups. Efforts should be made to continuously narrow the regional disparities in the development of the IE-YRDUA, to achieve social equity and enhance people\u0026rsquo;s well-being. Third, attention should be given to the optimization of the transportation for the development of the IE-YRDUA. This includes strengthening the effective connectivity between different transportation modes such as aviation, high-speed rail, and highways. This can help reduce the economic and time costs of using eldercare resources across regions, explore the feasibility of remote eldercare, and promote the integration of peripheral areas of urban agglomerations. Fourth, emphasis should be placed on the intelligent construction of eldercare and improving the intelligent spatial connection to alleviate the gradient differences in the development of the IE-YRDUA. The intelligent advantages of core cities such as Shanghai, Hangzhou, and Nanjing should be fully utilized to create multiple regional growth poles with leading and benefiting capabilities for the surrounding areas. Last, a combination of policy toolkits at both the regional and city levels should be formulated to fully unleash the development potential of the IE-YRDUA. For example, by focusing on the interaction of the natural environment and leveraging the overlapping effects of multiple factors such as transportation, human capital, and informatization, the linkage of resources within the region can be strengthened, and the spatial layout of institutional eldercare can be optimized.\u003c/p\u003e\n\u003cp\u003eIn summary, the development of the IE-YRDUA is associated with various geographical factors. To avoid the spatial mismatch of eldercare resources and promote the integrated development of eldercare in the region, it is necessary to fully consider the co-evolution of different geographical units and the resulting shift in relationships. Efforts should be made to actively guide and leverage the synergistic effects between neighboring regions, promoting innovative applications of eldercare services. Additionally, comprehensive and long-term planning should be undertaken for the development of institutional eldercare at multiple scales, emphasizing the dynamic study of specific developmental attributes, to clarify the general process and mechanisms of institutional eldercare resource evolution.\u003c/p\u003e"},{"header":"7 Abbreviations ","content":"\u003cp\u003eIE-YRDUA \u0026nbsp; \u0026nbsp;the institutional eldercare in the Yangtze River Delta Urban Agglomeration\u003c/p\u003e"},{"header":"8 Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article/supplementary materials, further inquiries can be directed to the corresponding author.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was supported by Ministry of Education Youth Fund for Humanities and Social Sciences Research (23YJCZH331).\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026rsquo; contributions\u003c/h2\u003e\n\u003cp\u003eMethodology: RZ, JC. Writing original draft preparation and writing review and editing RZ, JC. Funding acquisition: RZ, JC. Quality assessment: KJC. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003ch2\u003eConflict of Interest\u003c/h2\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003ch2\u003eData Availability Statement\u003c/h2\u003e\n\u003cp\u003eThe original contributions presented in the study are included in the article/supplementary materials, further inquiries can be directed to the corresponding author.\u003c/p\u003e"},{"header":"9 References","content":"\u003cp\u003eBi X, Li M. Between equity and efficiency: A spatial analysis of elderly resources in Beijing. Society. 2020;40(3):117-147. doi:10.15992/j.cnki.31-1123/c.2020.03.005\u003c/p\u003e\n\u003cp\u003eCheng M, Cui X. 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Social Security Review. 2021;5(1):129-145.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"institutional eldercare, economic geography, geographical nature, the Yangtze River Delta urban agglomeration","lastPublishedDoi":"10.21203/rs.3.rs-3849846/v2","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3849846/v2","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: The objective of this study is to examine the spatiotemporal evolution mechanism of the institutional eldercare in the Yangtze River Delta Urban Agglomeration (IE-YRDUA). It aims to analyze the developmental patterns and spatial correlations of these institutions over a twenty-year period, in order to shed light on the increasing demand for eldercare services in this economically significant region.\u003c/p\u003e\n\u003cp\u003eMethods: This study utilizes spatial analysis and factor detection methods within an economic geography framework to analyze data from 2001 to 2020. The focus of the analysis is on understanding spatial correlations and identifying factors that influence the evolution of eldercare services.\u003c/p\u003e\n\u003cp\u003eResults: The findings indicate a significant growth and distribution of eldercare institutions, with notable spatial correlations suggesting a trend towards regional agglomeration. The study also reveals imbalances in the spatial development of eldercare, with a concentration of facilities in central urban areas and a decline on the periphery. Additionally, factors such as economic level and capacity to consume have a significant impact on the spatial evolution of eldercare services.\u003c/p\u003e\n\u003cp\u003eConclusions: This study emphasizes the dynamic nature of institutional eldercare in the Yangtze River Delta, highlighting the necessity for strategic planning and resource allocation to address spatial imbalances in eldercare provision. The insights gained from this study are crucial for policymakers and stakeholders in optimizing eldercare infrastructure and meeting the growing demands of the population.\u003c/p\u003e","manuscriptTitle":"Spatiotemporal Evolution of the Institutional Eldercare in the Yangtze River Delta Urban Agglomeration","msid":"","msnumber":"","nonDraftVersions":[{"code":2,"date":"2024-02-07 22:36:36","doi":"10.21203/rs.3.rs-3849846/v2","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}},{"code":1,"date":"2024-01-12 06:32:31","doi":"10.21203/rs.3.rs-3849846/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c7a163f5-477a-42f7-99b7-4a9b1426e859","owner":[],"postedDate":"February 7th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28638494,"name":"Health Economics \u0026 Outcomes Research"},{"id":28638495,"name":"Health Policy"}],"tags":[],"updatedAt":"2024-02-04T17:14:25+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-07 22:36:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v2","identity":"rs-3849846","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3849846","identity":"rs-3849846","version":["v2"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}