The influence mechanism of urban built environment on cardiovascular diseases

The influence mechanism of urban built environment on cardiovascular diseases | 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 Article The influence mechanism of urban built environment on cardiovascular diseases Shuguang Deng, Jinlong Liang, Ying Peng, Wei Liu, Jinhong Su, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3852583/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Cardiovascular diseases (CVDs) are a major public health concern, and their morbidity is influenced by various built environment elements. This paper aims to investigate the influence mechanisms of different built environment elements on CVDs, and to provide a theoretical foundation for health-oriented urban planning and CVD prevention. We selected the Xixiangtang built-up area of Nanning city as the case study area, and used the distribution data of CVDs and urban point of interests (POIs) as the main data sources. We applied spatial autocorrelation analysis, kernel density analysis, and geographic detector methods to examine the spatial correlation and influence of urban built environment elements on CVD samples. The results show that both the built environment elements and the CVD samples have a spatially clustered distribution, and there is a significant positive correlation between the distribution density of each environmental element and the CVD morbidity. Among the environmental elements, medical care has the largest influence on CVDs, followed by shopping consumption, catering and food, and transportation facilities, while parks and squares and the road network have relatively small influence. Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences Health sciences/Risk factors Figures Figure 1 Figure 2 Figure 3 Introduction Cardiovascular diseases (CVDs) are a leading cause of death worldwide, and their prevalence and mortality have increased in the past two decades. According to statistics, the number of global CVD patients rose from 271 million in 1990 to 523 million in 2019, and the number of deaths due to CVDs also increased from 12.1 million to 18.6 million, accounting for about one-third of the global annual deaths 1 , 2 . CVDs pose a serious global health challenge, exerting enormous pressure on the health care and economic systems 3 . The World Heart Federation projects that the global medical costs of CVDs will grow from about 863 billion USD in 2010 to 1044 billion USD in 2030 4 . To cope with the risk of CVDs, it is essential to understand the influence mechanisms of their occurrence, and to develop sustainable risk reduction and prevention strategies. Urban built environment refers to the combination of physical structure and human-made environment in urban areas, including buildings, transportation networks, infrastructure, land use planning, natural and artificial spaces and other factors 5 . The potential connection between built environment and human health has attracted wide attention, especially the relationship with cardiovascular health. Existing studies indicate that the influence of built environment on cardiovascular health is a complex process, involving multiple factors and pathways. First, built environment affects cardiovascular disease inducing factors such as obesity, diabetes and hypertension 6 , 7 , 8 , 9 , 10 , 11 , environmental pollution issues such as traffic noise and air pollution 12 , 13 , and physical exercise, psychological stress and lifestyle 14 , 15 , 16 , 17 , 18 . Second, these factors then influence the occurrence of cardiovascular diseases 19 , 20 , 21 , 22 . Studies demonstrate that a good urban design, including reasonable land allocation and street layout, can enable people to easily access various life services, and foster the development of a positive attitude and a healthy body, thus lowering the risk of cardiovascular diseases 23 . Compact urban development may encourage physical activity, and decrease the risk of cardiovascular and metabolic disorders 24 . Long commuting time and traffic density may lead to chronic stress and lack of exercise, increasing the risk of obesity and hypertension. However, moderate intersection density, diversified land use, destination convenience and accessibility can encourage walking traffic, and then enhance health, reduce the related risks of cardiovascular diseases such as obesity, diabetes, hypertension and dyslipidemia 25 , 26 , 27 . The density and accessibility of food stores are closely associated with the dietary choices of community residents, Excessive distribution of food stores may increase the risk of obesity and diabetes, and also be linked to blood pressure level 28 . Urban green space and outdoor leisure space have positive impacts on cardiovascular health, Urban green space can provide people with places for exercise and relaxation, help to alleviate stress, improve mental health, and also improve air quality, reduce the harm of air pollution, and further protect the health of heart and blood vessels 29 . There are also studies that suggest that living in urban areas with higher green space coverage can obtain more opportunities and environments to promote physical activity, mental health and healthier lifestyles, and then lower the risk of cardiovascular diseases 30 , 31 . Generally speaking, scientific and reasonable urban planning, including diversified land use, appropriate building density, good street connectivity, easy-to-reach destinations, short-distance transportation and good natural environment, are all important factors for promoting overall health and preventing cardiovascular diseases. The built environment is a key factor affecting the occurrence and development of cardiovascular diseases. However, the relationship between different environmental elements and cardiovascular diseases is complex and diverse, and requires advanced statistical methods and spatial analysis models to reveal. Previous studies have mainly used methods such as spatial autocorrelation analysis (Moran’s I), ordinary least squares (OLS), geographically weighted regression (GWR), multiscale geographically weighted regression (MGWR), and logistic regression model to quantify and evaluate the impact of built environment on cardiovascular diseases 5 , 32 , 33 . These methods can provide useful insights, but they cannot capture the influence size and degree of various environmental elements on cardiovascular diseases. Geodetector is a novel method that can overcome this limitation, but it has rarely been applied in this field of research. Therefore, this study aims to explore the mechanism of urban built environment on cardiovascular diseases using geodetector, based on the distribution data of cardiovascular diseases and urban POI data in the built-up area of the Xixiangtang District in Nanning City. We also use spatial autocorrelation analysis and kernel density analysis to study the spatial distribution and correlation level of various environmental elements and cardiovascular diseases. Materials and methods Study area This study focuses on the relationship between the high-density built environment and cardiovascular diseases in the built-up area of the Xixiangtang District in Nanning City (Fig. 1 ). Nanning City is the capital of the Guangxi Zhuang Autonomous Region of China and has experienced rapid urbanization in recent years. Xixiangtang District is one of the first urban areas to be developed in Nanning City, and it has a highly dense built environment. However, this also leads to a series of environmental issues, such as poor air quality, traffic congestion, noise pollution, and high population density, which may adversely affect the cardiovascular health of residents. Therefore, this study is representative and realistic for exploring the impact of the urban built environment on cardiovascular diseases. Data resources and processing We are uses road data, cardiovascular disease sample data, and POI data to investigate the relationship between high-density built environment and cardiovascular diseases. The cardiovascular disease sample data are derived from the records of the cardiovascular department of Guangxi National Hospital, a well-established and well-equipped grade three A hospital in Nanning City, from January 1, 2020 to December 31, 2022. The residents of Xixiangtang District, where the study area is located, have a high trust in this hospital, and its cardiovascular disease data are representative of the district. Our research mainly used the address data of cardiovascular disease patients authorized by Guangxi Ethnic Hospital to conduct spatial analysis. We have confirmed that our research was conducted in accordance with ethical principles, and did not involve any research that had a substantial impact on the patients. We extracted the address text from the records, and after data cleaning, coordinate transformation, and other processing, we obtained 3472 valid samples, which were displayed on the map using ArcGIS 10.8 software. The built environment elements include road data and other urban POI data, which were selected based on existing studies on the influence of urban environment on cardiovascular diseases. The road data were obtained from the OSM open map website 34 , and filtered by ArcGIS 10.8 software to exclude some small roads that were not well recorded. We only retained five main types of roads, namely, expressways, express roads, trunk roads, secondary roads, and branch roads, for the analysis of the road network environmental element. The other built environment elements consist of six environmental elements: catering and food 35 , parks and squares 36 , transportation facilities 37 , shopping and consumption 38 , sports and fitness 39 , and medical care 40 (Table 1 ). The data of these six elements were obtained from Gaode map, which can reflect the distribution of urban built environment. These detailed data will provide comprehensive background information for the research, and help to examine the correlation between built environment factors and cardiovascular health more thoroughly. Table 1 Description of indicators of built environmental factors Environmental indicators Source Quantity (unit) Indicator description Road network OSM map 692 (lines) Including the distribution of five main types of roads: expressways, express roads, trunk roads, secondary roads and branch roads Catering and food Gaode map 9905 (individuals) Including the distribution of Chinese food, foreign food, fast food restaurants, snack shops, milk tea shops, etc. Parks and squares 191 (individuals) Including the distribution of parks, squares, attractions, zoos, botanical gardens, etc. Shopping and consumption 14851 (individuals) Including the distribution of department stores, shopping centers, convenience stores, commercial streets, markets, etc. Transportation facilities 2659 (individuals) Transportation facilities include the distribution of bus stops, parking lots, subway entrances, toll stations, bus stations, etc. Sports and fitness 442 (individuals) Including the distribution of fitness centers, basketball courts, badminton courts, swimming pools, gymnasiums, etc. Medical care 2092 (individuals) Including the distribution of emergency centers, clinics, specialty hospitals, general hospitals, pharmacies, etc. Research methods The research framework is shown in Fig. 2 . We followed three main steps to investigate the influence of urban built environment on cardiovascular diseases. First, we reviewed the existing literature and identified the indicators of built environment elements that affect cardiovascular diseases 33 , 41 , 42 . We then collected and processed the relevant data for these indicators. Second, we applied the global spatial autocorrelation model to assess the spatial aggregation degree of each element and the spatial correlation between the built environment elements and cardiovascular diseases. We also used the kernel density tool to display the spatial distribution characteristics of each element and to classify and export the kernel density grid values 43 . Third, we employed the geodetector model to detect the influencing factors of cardiovascular diseases based on the kernel density grid values 44 . Finally, we discussed the influence mechanism of urban built environment on cardiovascular diseases and proposed optimization strategies based on our analysis. Global spatial autocorrelation analysis Univariate global spatial autocorrelation Univariate global spatial autocorrelation analysis is a statistical method to examine the spatial distribution characteristics of the data set and the spatial relationship among all observations. It can use the univariate global Moran’s index (Moran’s I) 45 . to assess whether the whole data set has spatial autocorrelation, that is, whether there is spatial clustering or dispersion 46 , 47 . This study employs global spatial autocorrelation analysis to measure the degree of spatial clustering of cardiovascular disease samples and various environmental factors. The calculation formula is: $$\begin{array}{c}I=\frac{n}{{S}_{0}}\times \frac{\sum _{i=1}^{n}\sum _{j=1}^{n}{W}_{ij}\left({y}_{i}-\stackrel{-}{y}\right)\left({y}_{j}-\stackrel{-}{y}\right)}{\sum _{i=1}^{n}{\left({y}_{i}-\stackrel{-}{y}\right)}^{2}}\#\left(1\right)\end{array}$$ In the formula, I is the Moran’s index, n is the sample number of each environmental element in the study area, y i and y j are the observation values of the spatial units, \(\stackrel{-}{y}\) is the average of the observation values, w ij is the spatial weight matrix, and S 0 is the sum of the spatial weight matrix. Bivariate global spatial autocorrelation. Bivariate global spatial autocorrelation is proposed based on the spatial autocorrelation index (Moran’s I), aiming to examine the spatial correlation among different indicators 48 , 49 . Compared with the traditional OLS linear model, it considers the spatial dependence of the data, focuses on the examination and identification of the spatial association among the variables, and is more applicable for this study. This paper is used to assess the degree of spatial correlation between cardiovascular diseases and various built environments. The calculation formula is: $$\begin{array}{c}I=\frac{\sum _{p=1}^{n}\sum _{q=1}^{n}{Z}_{pq}\left({x}_{p}-\stackrel{-}{x}\right)\left({y}_{q}-\stackrel{-}{y}\right)}{{S}^{2}\sum _{p=1}^{n}\sum _{q=1}^{n}{Z}_{pq}}\#\left(2\right)\end{array}$$ In the formula, I is the bivariate global spatial autocorrelation coefficient, x p denotes the independent variable attribute value of the p spatial unit, that is, each environmental element, y q denotes the dependent variable attribute value of the q spatial unit, that is, the cardiovascular disease distribution, S denotes the attribute value variance, Z pq denotes the weight matrix established by the p and q spatial units. Kernel density analysis Kernel density analysis is a method to compute the unit density of point and line element measurements within a specified neighborhood range. By using kernel functions to assign different weights to the elements in the region, this method can intuitively reveal the distribution characteristics of discrete measurements in continuous regions 50 , 51 , 52 . This study employs kernel density analysis to display the distribution characteristics of cardiovascular disease samples and various environmental elements in the study area. The calculation formula is: $$\begin{array}{c}f\left(x\right)=\frac{1}{nh}\sum _{i=1}^{n}k\left[\frac{1}{h}\left(x-{x}_{i}\right)\right]\#\left(3\right)\end{array}$$ In the formula, f ( x ) is the kernel density estimation at x, h is the search radius, n is the total number of sample points within the search range, ( x - x i ) is the distance between the POI sample point x i and the estimation point x , and K is the weight of the distance. GeoDetector Geographical detector is a spatial analysis method to detect spatial heterogeneity and explain its driving factors. Its principle is based on the assumption that if a variable has a significant impact on another variable, then the spatial distribution of the two variables should be similar. Geographical detector mainly includes four detectors: factor detector, interaction detector, risk detector, and ecological detector 53 , 54 , 55 . This study uses factor detector to measure the influence of environmental factors on the distribution of cardiovascular diseases. The model is based on the following equation: $$\begin{array}{c}q=1-\frac{1}{n{\sigma }^{2}}\sum _{h=1}^{L}{n}^{h}{\sigma }_{X}^{2}\#\left(4\right)\end{array}$$ In the formula, q is the power of the determining indicator, n is the total sample size of the study area, n h​ is the sample size of layer h , L is the stratification of the dependent or independent variables, also known as classification or partition, and σ 2 is the variance of the whole study area. The range of q is [0,1], and the larger the q value, the stronger the explanatory power of the influencing factors on the spatial heterogeneity of the cardiovascular disease samples. Results Spatial autocorrelation characteristics To analyze the spatial characteristics of urban built environment factors and cardiovascular disease distribution, we used Geoda software 56 . We performed univariate and bivariate global spatial autocorrelation analyses on urban built environment factors and cardiovascular disease samples. The univariate analysis tested whether the samples of each factor clustered or dispersed in space, and the bivariate analysis evaluated the spatial correlation between each environmental factor and cardiovascular disease (Table 2 ). Table 2 shows that all variables were significant at the 0.01 level, with P values less than 0.01 and Z values greater than 2.58. This indicated a 99% confidence level to reject the null hypothesis and to consider the spatial autocorrelation results as highly reliable. Table 2 Spatial autocorrelation results of each element in the study area Element Univariate Bivariate Z Value P Value Moran’s I Z Value P Value Moran’s I Shopping and consumption 19.839 0.001 0.504 15.379 0.001 0.305 Catering and food 18.867 0.001 0.466 17.999 0.001 0.355 Road network 7.814 0.001 0.202 3.135 0.005 0.061 Parks and squares 5.567 0.001 0.125 3.785 0.008 0.071 Transportation facilities 18.852 0.001 0.489 13.751 0.001 0.277 Sports and fitness 10.382 0.001 0.261 11.073 0.001 0.216 Medical care 17.401 0.001 0.434 18.267 0.001 0.355 Cardiovascular disease 19.044 0.001 0.435 The univariate spatial autocorrelation results showed that shopping consumption had the highest degree of spatial clustering, with a Moran’s I value of 0.504. Transportation facilities, catering and food, and health care also had high Moran’s I values, ranging from 0.434 to 0.489, indicating strong spatial clustering. Parks and squares had the lowest Moran’s I value, only 0.125, indicating weak spatial clustering. Cardiovascular disease had a Moran’s I value of 0.435, indicating that it also clustered in space. These results suggested that these factors were not randomly distributed in space, but had different patterns of spatial aggregation. The bivariate spatial autocorrelation analysis revealed the spatial correlation between each environmental factor and cardiovascular disease. All environmental factors had a significant positive spatial correlation with cardiovascular disease. Shopping consumption, catering and food, and health care had the strongest spatial association with cardiovascular disease, with Moran’s I values greater than 0.3. This meant that in the study area, areas with high density of shopping, catering, and health care services tended to overlap with areas with high incidence of cardiovascular disease. Road network and parks and squares had the weakest spatial association with cardiovascular disease, with Moran’s I values less than 0.01. This meant that in the study area, the spatial distribution of road network and parks and squares had little influence on cardiovascular disease. Kernel density distribution characteristics Kernel density analysis is a statistical method that can show the spatial distribution and density of various element samples. We used ArcGIS 10.8 software to perform kernel density analysis on cardiovascular disease samples and various environmental elements. We divided the kernel density level into five levels according to the natural break method, and arranged them from high to low, as shown in Fig. 3 . Figure 3 shows that the high-density areas of shopping consumption, catering food, transportation facilities and medical care were mainly concentrated in the southeast. The high-density areas of road network were mainly distributed along the Yongjiang River in the south in a belt shape, and in the central part in a block shape. The high-density areas of parks and squares were mainly near the Yongjiang River in the south. The high-density areas of sports and fitness facilities were distributed in the southeast and central parts. These spatial distribution characteristics reflected that the overall development direction of Xixiangtang District was from southeast to northwest, while the development level of the northeast region was low, the population distribution was small, and the layout of various facilities was imperfect. Moreover, the kernel density distribution characteristics of cardiovascular disease samples indicated that the high-incidence areas of cardiovascular diseases were mainly concentrated in the southeast region, which overlapped with the high-density areas of most built environmental elements. Geodetector results analysis This study used the factor detector method of geodetector to quantify the influence of different environmental elements on the distribution of cardiovascular disease samples. Table 3 shows the factor detection results, which indicate that the influence of environmental elements on cardiovascular diseases is significant and reliable (p < 0.01), but varies greatly in size (q value). The q value ranges from 0 to 1, and the larger the q value, the stronger the influence of the environmental element. The results show that the environmental elements can be ranked by their influence on cardiovascular diseases as follows: medical care > shopping consumption > catering food > transportation facilities > sports and fitness > parks and squares > road network. Table 3 Geographical detector factor detection results Environmental indicators q Value p Value Catering and food 0.447 0.001 Road network 0.160 0.001 Parks and squares 0.179 0.001 Shopping and consumption 0.492 0.001 Transportation facilities 0.423 0.001 Sports and fitness 0.374 0.001 Medical care 0.529 0.001 Medical care has the strongest influence on cardiovascular disease samples, with a q value of 0.529, which suggests that the spatial distribution of cardiovascular diseases is closely related to the spatial distribution of medical care services. High-density medical facilities provide easier access to medical services, which is crucial for the prevention and treatment of cardiovascular diseases. People with cardiovascular disease risk may prefer to live in areas with convenient medical services57. Shopping consumption, catering food and transportation facilities are the next three influential environmental elements, with q values over 0.4. These elements reflect the agglomeration characteristics of urban buildings and the commercial vitality and population density of an area. This agglomeration phenomenon may expose residents to more choices and temptations in their daily lives, leading to psychological stress and anxiety, and consequently burdening the cardiovascular system. Parks and squares and road network are the two environmental elements with the weakest influence on cardiovascular diseases, with q values less than 0.2. This implies that the areas with dense distribution of these elements have a relatively low incidence of cardiovascular diseases, which may be attributed to the ecological and traffic effects of these elements. Discussion The influence of built environment on cardiovascular diseases This study examined the influence of urban built environment on the distribution of cardiovascular disease samples using various methods, such as spatial autocorrelation analysis, kernel density analysis, and geodetector. The global spatial autocorrelation method was used to analyze the spatial aggregation of each variable, and the spatial correlation between each environmental element and cardiovascular disease was tested. The kernel density analysis tool was used to present the kernel density distribution of each variable in a view. The geodetector analysis was used to study the influence difference of different built environment elements on cardiovascular diseases. This study found that both built environment elements and cardiovascular diseases showed aggregated distribution in space, and most of them were distributed in the southeast direction. The significant positive correlation between each environmental element and cardiovascular disease in space indicated a certain connection between urban built environment and the incidence of cardiovascular disease. The factor detection results showed that different built environments had different impacts on cardiovascular diseases. Medical care had the largest impact on cardiovascular diseases, followed by shopping consumption, food and catering, and transportation facilities, while parks and squares and road network had the lowest impact. The following paragraphs explain the possible mechanisms behind these impacts. Living near medical institutions was beneficial for patients with cardiovascular diseases, as it brought a sense of security to patients, facilitated coping with emergencies, and improved the accessibility of medical services. Shopping consumption elements reflected the distribution of commercial facilities and living service facilities, which included shopping places such as malls, shopping centers, and convenience stores. Residents nearby had more shopping opportunities and living convenience, due to the high-density aggregation of commercial and living service facilities, but it might also lead to bad living habits and lifestyles, and increase the living cost of residents. Food and catering reflected the distribution of catering shops, which included fast food restaurants, snack bars, western restaurants, etc. High-density catering places provided more opportunities to choose fast food and greasy food. Long-term consumption of high-sugar and high-fat foods might increase the potential health risks such as obesity, diabetes, and hypertension. Transportation facilities mainly reflected the distribution of public transportation facilities, which included facilities such as bus stations and parking lots. High-density transportation facilities near residential areas provided transportation convenience for residents, but it might also reduce the willingness of residents to walk, and might also aggravate noise and air pollution. Parks and squares were places for recreation and exercise, which relaxed the body and mind, and alleviated psychological stress. Some residents might have difficulty in obtaining outdoor exercise opportunities, due to the unreasonable distribution of parks and squares. Long-term lack of green environment might increase the risk of cardiovascular diseases. Road network brought traffic noise and air pollution, which had a negative impact on cardiovascular diseases, but the influence of road network density, an environmental element, on the distribution of cardiovascular disease samples was small, according to the research results. This contradicted previous studies 58 , 59 . These five types of roads might not be the main types of roads that affected the distribution of cardiovascular disease samples, or the prevention and control measures for major traffic noise and pollution might be better in the Xixiangtang built-up area. Suggestions on urban built environment planning This study investigates the relationship between environmental factors and cardiovascular diseases in the built-up area of Xixiangtang District in Nanning City. The results reveal that the high-density built environment in this area may adversely affect cardiovascular health. To address this issue, the following suggestions are proposed to optimize the layout of the built environment, improve the living environment of urban residents, and promote the cardiovascular health of residents. Optimize the layout of health care facilities. Encourage the establishment of medical centers in densely populated areas, enhance the accessibility of medical services, and ensure that community patients with cardiovascular diseases have convenient access to high-quality medical services. Reasonably control the density of commercial areas. Balance the density of commercial facilities and living service facilities, rationally plan the layout of malls, shopping centers and convenience stores, and avoid increasing the living pressure of residents and reducing the walking opportunities while ensuring that residents have shopping convenience. Promote the planning of healthy dining places. Encourage reasonable dining industry layout, provide diverse food choices, increase green dining shops, and reduce the supply of fast food and oily food. Optimize the design of transport facilities. Set up noise barriers and green belts around transport roads and hubs, mitigate the spread of noise and air pollution, and enhance the comfort of the surrounding environment. Rationally plan the layout of parks and green spaces. Increase the proportion of parks and plazas in urban land use, increase the opportunities for residents to have outdoor exercise, and protect the rights of residents to access the natural environment. Strengthen comprehensive management and monitoring. Establish a monitoring system for urban built environment and health, regularly evaluate the impact of built environment on cardiovascular health, strengthen the collaboration between urban planning and health departments, form a comprehensive management mechanism, and timely adjust planning strategies to adapt to the changes of urban development and residents’ health needs. The limitations and future prospects of the research This paper has conducted a comprehensive study on how various environmental factors affect cardiovascular diseases, and proposed ways to optimize the urban built environment. However, this paper still has some limitations. The environmental impact on health and disease is complex, and due to the constraints of time and resources, not all possible variables have been fully considered and analyzed, which may introduce some bias to the research results. To further advance the research on the relationship between built environment and cardiovascular health, the future research should consider the following aspects: First, expand the research scope, collect and analyze data from different cities and regions, to better capture the regional variations in the influence of built environment on cardiovascular health; Second, improve the scientific rigor of research methods, use more objective and precise data collection and analysis methods, to enhance the reliability and accuracy of research; Third, explore the mechanisms underlying the relationship between built environment and cardiovascular health, examine the biological and psychological pathways, to better understand how they are connected. Conclusion This paper uses Xixiangtang District of Nanning City as a case study, and applies spatial autocorrelation, kernel density and geodetector methods to investigate how urban built environment influences the distribution of cardiovascular disease samples, based on the hospital cardiovascular data and urban POI data. The paper reaches the following two main conclusions: Regarding the spatial distribution characteristics, all environmental factors exhibited a clustered distribution in space after passing the global spatial autocorrelation test, and all environmental factors had a significant positive correlation with cardiovascular diseases in space. The spatial distribution characteristics of cardiovascular disease samples were consistent with those of most environmental factors, and they were mainly concentrated in the southeast of the study area, indicating that the occurrence of cardiovascular diseases was related to the urban built environment to some extent. Regarding the factor influence degree, different urban built environment factors had different effects on the distribution of cardiovascular disease samples. Among them, the distribution of health care had the greatest effect on cardiovascular diseases, followed by shopping consumption, catering and food, and transportation facilities, while parks and squares and road networks had less effect on the distribution of cardiovascular disease samples. This implies that the incidence of cardiovascular diseases may be higher in areas with high-density health care, shopping consumption, catering and food, and transportation facilities, while areas with dense parks and squares and main roads have lower incidence of cardiovascular diseases. Therefore, in the process of urban built environment planning, more attention should be given to the potential relationship between different environmental factors and cardiovascular health, and the balance between urban development and health promotion should be achieved. Declarations Author information Authors and Affiliations School of Geographical and Planning, Nanning Normal University, Nanning, Guangxi, 530100, China. Shuguang Deng,Jinlong Liang,Ying Peng,Binglin Liu,Jinhong Su & Shuyan Zhu School of Architecture, Guangxi Arts University, Nanning, Guangxi, 530009, China. Ying Peng Contributions D.S. Provides research topics, conceptual guidance, translation, paper revision and financial support; L.J. Conceived the framework and wrote the original draft; P.Y. Manuscript checking, chart optimization; L.W. Provided suggestions for revision, and reviewed and edited them; S.J. Is responsible for data acquisition and editing; Z.S. Edits the visual map. Corresponding author Correspondence to Jinlong Liang. Acknowledgements The General Project of Humanities and Social Sciences Research of the Ministry of Education in 2020: A Study on the Assessment and Planning of Healthy Cities Based on Spatial Data Mining (No. 20YJA630011). Ethics declarations Competing interests The authors declare no competing interests. Data availability The datasets used or analyzed during the current study are available from the corresponding author upon reasonable request. References Roth, G. A., Mensah, G. A., & Fuster, V. The global burden of cardiovascular diseases and risks: a compass for global action. Journal of the American College of Cardiology. 76(25), 2980–2981 (2020). Masaebi, F. et al. Trend analysis of disability adjusted life years due to cardiovascular diseases: results from the global burden of disease study 2019. BMC Public Health. 21, 1–13 (2021). Murray, C. J. et al. 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Density of green spaces and cardiovascular risk factors in the city of Madrid: the heart healthy hoods study. International journal of environmental research and public health. 16(24), 4918 (2019). Li, Y. et al. Association of long-term near-highway exposure to ultrafine particles with cardiovascular diseases, diabetes and hypertension. International journal of environmental research and public health. 14(5), 461 (2017). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-3852583","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":268574345,"identity":"508a1d56-9c7c-4813-adb6-79ba87ce0b4e","order_by":0,"name":"Shuguang Deng","email":"","orcid":"","institution":"Nanning Normal University","correspondingAuthor":false,"prefix":"","firstName":"Shuguang","middleName":"","lastName":"Deng","suffix":""},{"id":268574346,"identity":"ca88d53a-db57-4aac-8bbe-cbbc1222e331","order_by":1,"name":"Jinlong 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06:59:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3852583/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3852583/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50051511,"identity":"ea5bc108-4273-4160-aed9-6c93e784521d","added_by":"auto","created_at":"2024-01-23 16:47:11","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":4071220,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of study area\u003c/p\u003e","description":"","filename":"Figure1Locationofstudyarea.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3852583/v1/1dec624a322fcfda78545c80.jpg"},{"id":50051510,"identity":"0e5e5c18-5756-4fba-91ab-bdb3b9761586","added_by":"auto","created_at":"2024-01-23 16:47:11","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":735126,"visible":true,"origin":"","legend":"\u003cp\u003eResearch\u003c/p\u003e","description":"","filename":"Figure2Researchframework.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3852583/v1/ff5f4299043ffed68cffca6c.jpg"},{"id":50051512,"identity":"af3742ef-8439-4e2b-ae5f-3186658e446f","added_by":"auto","created_at":"2024-01-23 16:47:11","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":8075575,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of nuclear density of each element in the study area\u003c/p\u003e","description":"","filename":"Figure3Distributionofnucleardensityofeachelementinthestudyarea.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3852583/v1/99d00fd4567db60699640c05.jpg"},{"id":51680550,"identity":"8c1ae7fa-8e51-4d3a-8790-0274d2181c5a","added_by":"auto","created_at":"2024-02-27 06:24:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":702517,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3852583/v1/d31a8ff7-9d19-4a06-af8e-41acd7be8ea0.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The influence mechanism of urban built environment on cardiovascular diseases","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCardiovascular diseases (CVDs) are a leading cause of death worldwide, and their prevalence and mortality have increased in the past two decades. According to statistics, the number of global CVD patients rose from 271 million in 1990 to 523 million in 2019, and the number of deaths due to CVDs also increased from 12.1 million to 18.6 million, accounting for about one-third of the global annual deaths\u003cu\u003e\u003csup\u003e1\u003c/sup\u003e\u003c/u\u003e\u003csup\u003e,\u003c/sup\u003e\u003cu\u003e\u003csup\u003e2\u003c/sup\u003e\u003c/u\u003e. CVDs pose a serious global health challenge, exerting enormous pressure on the health care and economic systems\u003cu\u003e\u003csup\u003e3\u003c/sup\u003e\u003c/u\u003e. The World Heart Federation projects that the global medical costs of CVDs will grow from about 863 billion USD in 2010 to 1044 billion USD in 2030\u003cu\u003e\u003csup\u003e4\u003c/sup\u003e\u003c/u\u003e. To cope with the risk of CVDs, it is essential to understand the influence mechanisms of their occurrence, and to develop sustainable risk reduction and prevention strategies.\u003c/p\u003e\n\u003cp\u003eUrban built environment refers to the combination of physical structure and human-made environment in urban areas, including buildings, transportation networks, infrastructure, land use planning, natural and artificial spaces and other factors\u003cu\u003e\u003csup\u003e5\u003c/sup\u003e\u003c/u\u003e. The potential connection between built environment and human health has attracted wide attention, especially the relationship with cardiovascular health. Existing studies indicate that the influence of built environment on cardiovascular health is a complex process, involving multiple factors and pathways. First, built environment affects cardiovascular disease inducing factors such as obesity, diabetes and hypertension\u003cu\u003e\u003csup\u003e6\u003c/sup\u003e\u003c/u\u003e\u003csup\u003e,\u003c/sup\u003e\u003cu\u003e\u003csup\u003e7\u003c/sup\u003e\u003c/u\u003e\u003csup\u003e,\u003c/sup\u003e\u003cu\u003e\u003csup\u003e8\u003c/sup\u003e\u003c/u\u003e\u003csup\u003e,\u003c/sup\u003e\u003cu\u003e\u003csup\u003e9\u003c/sup\u003e\u003c/u\u003e\u003csup\u003e,\u003c/sup\u003e\u003cu\u003e\u003csup\u003e10\u003c/sup\u003e\u003c/u\u003e\u003csup\u003e,\u003c/sup\u003e\u003cu\u003e\u003csup\u003e11\u003c/sup\u003e\u003c/u\u003e, environmental pollution issues such as traffic noise and air pollution\u003cu\u003e\u003csup\u003e12\u003c/sup\u003e\u003c/u\u003e\u003csup\u003e,\u003c/sup\u003e\u003cu\u003e\u003csup\u003e13\u003c/sup\u003e\u003c/u\u003e, and physical exercise, psychological stress and lifestyle\u003cu\u003e\u003csup\u003e14\u003c/sup\u003e\u003c/u\u003e\u003csup\u003e,\u003c/sup\u003e\u003cu\u003e\u003csup\u003e15\u003c/sup\u003e\u003c/u\u003e\u003csup\u003e,\u003c/sup\u003e\u003cu\u003e\u003csup\u003e16\u003c/sup\u003e\u003c/u\u003e\u003csup\u003e,\u003c/sup\u003e\u003cu\u003e\u003csup\u003e17\u003c/sup\u003e\u003c/u\u003e\u003csup\u003e,\u003c/sup\u003e\u003cu\u003e\u003csup\u003e18\u003c/sup\u003e\u003c/u\u003e. Second, these factors then influence the occurrence of cardiovascular diseases\u003cu\u003e\u003csup\u003e19\u003c/sup\u003e\u003c/u\u003e\u003csup\u003e,\u003c/sup\u003e\u003cu\u003e\u003csup\u003e20\u003c/sup\u003e\u003c/u\u003e\u003csup\u003e,\u003c/sup\u003e\u003cu\u003e\u003csup\u003e21\u003c/sup\u003e\u003c/u\u003e\u003csup\u003e,\u003c/sup\u003e\u003cu\u003e\u003csup\u003e22\u003c/sup\u003e\u003c/u\u003e. Studies demonstrate that a good urban design, including reasonable land allocation and street layout, can enable people to easily access various life services, and foster the development of a positive attitude and a healthy body, thus lowering the risk of cardiovascular diseases\u003cu\u003e\u003csup\u003e23\u003c/sup\u003e\u003c/u\u003e. Compact urban development may encourage physical activity, and decrease the risk of cardiovascular and metabolic disorders\u003cu\u003e\u003csup\u003e24\u003c/sup\u003e\u003c/u\u003e. Long commuting time and traffic density may lead to chronic stress and lack of exercise, increasing the risk of obesity and hypertension. However, moderate intersection density, diversified land use, destination convenience and accessibility can encourage walking traffic, and then enhance health, reduce the related risks of cardiovascular diseases such as obesity, diabetes, hypertension and dyslipidemia\u003cu\u003e\u003csup\u003e25\u003c/sup\u003e\u003c/u\u003e\u003csup\u003e,\u003c/sup\u003e\u003cu\u003e\u003csup\u003e26\u003c/sup\u003e\u003c/u\u003e\u003csup\u003e,\u003c/sup\u003e\u003cu\u003e\u003csup\u003e27\u003c/sup\u003e\u003c/u\u003e. The density and accessibility of food stores are closely associated with the dietary choices of community residents, Excessive distribution of food stores may increase the risk of obesity and diabetes, and also be linked to blood pressure level\u003cu\u003e\u003csup\u003e28\u003c/sup\u003e\u003c/u\u003e. Urban green space and outdoor leisure space have positive impacts on cardiovascular health, Urban green space can provide people with places for exercise and relaxation, help to alleviate stress, improve mental health, and also improve air quality, reduce the harm of air pollution, and further protect the health of heart and blood vessels\u003cu\u003e\u003csup\u003e29\u003c/sup\u003e\u003c/u\u003e. There are also studies that suggest that living in urban areas with higher green space coverage can obtain more opportunities and environments to promote physical activity, mental health and healthier lifestyles, and then lower the risk of cardiovascular diseases\u003cu\u003e\u003csup\u003e30\u003c/sup\u003e\u003c/u\u003e\u003csup\u003e,\u003c/sup\u003e\u003cu\u003e\u003csup\u003e31\u003c/sup\u003e\u003c/u\u003e. Generally speaking, scientific and reasonable urban planning, including diversified land use, appropriate building density, good street connectivity, easy-to-reach destinations, short-distance transportation and good natural environment, are all important factors for promoting overall health and preventing cardiovascular diseases.\u003c/p\u003e\n\u003cp\u003eThe built environment is a key factor affecting the occurrence and development of cardiovascular diseases. However, the relationship between different environmental elements and cardiovascular diseases is complex and diverse, and requires advanced statistical methods and spatial analysis models to reveal. Previous studies have mainly used methods such as spatial autocorrelation analysis (Moran\u0026rsquo;s I), ordinary least squares (OLS), geographically weighted regression (GWR), multiscale geographically weighted regression (MGWR), and logistic regression model to quantify and evaluate the impact of built environment on cardiovascular diseases\u003cu\u003e\u003csup\u003e5\u003c/sup\u003e\u003c/u\u003e\u003csup\u003e,\u003c/sup\u003e\u003cu\u003e\u003csup\u003e32\u003c/sup\u003e\u003c/u\u003e\u003csup\u003e,\u003c/sup\u003e\u003cu\u003e\u003csup\u003e33\u003c/sup\u003e\u003c/u\u003e. These methods can provide useful insights, but they cannot capture the influence size and degree of various environmental elements on cardiovascular diseases. Geodetector is a novel method that can overcome this limitation, but it has rarely been applied in this field of research. Therefore, this study aims to explore the mechanism of urban built environment on cardiovascular diseases using geodetector, based on the distribution data of cardiovascular diseases and urban POI data in the built-up area of the Xixiangtang District in Nanning City. We also use spatial autocorrelation analysis and kernel density analysis to study the spatial distribution and correlation level of various environmental elements and cardiovascular diseases.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eStudy area\u003c/h2\u003e \u003cp\u003eThis study focuses on the relationship between the high-density built environment and cardiovascular diseases in the built-up area of the Xixiangtang District in Nanning City (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Nanning City is the capital of the Guangxi Zhuang Autonomous Region of China and has experienced rapid urbanization in recent years. Xixiangtang District is one of the first urban areas to be developed in Nanning City, and it has a highly dense built environment. However, this also leads to a series of environmental issues, such as poor air quality, traffic congestion, noise pollution, and high population density, which may adversely affect the cardiovascular health of residents. Therefore, this study is representative and realistic for exploring the impact of the urban built environment on cardiovascular diseases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData resources and processing\u003c/h2\u003e \u003cp\u003eWe are uses road data, cardiovascular disease sample data, and POI data to investigate the relationship between high-density built environment and cardiovascular diseases. The cardiovascular disease sample data are derived from the records of the cardiovascular department of Guangxi National Hospital, a well-established and well-equipped grade three A hospital in Nanning City, from January 1, 2020 to December 31, 2022. The residents of Xixiangtang District, where the study area is located, have a high trust in this hospital, and its cardiovascular disease data are representative of the district. Our research mainly used the address data of cardiovascular disease patients authorized by Guangxi Ethnic Hospital to conduct spatial analysis. We have confirmed that our research was conducted in accordance with ethical principles, and did not involve any research that had a substantial impact on the patients. We extracted the address text from the records, and after data cleaning, coordinate transformation, and other processing, we obtained 3472 valid samples, which were displayed on the map using ArcGIS 10.8 software.\u003c/p\u003e \u003cp\u003eThe built environment elements include road data and other urban POI data, which were selected based on existing studies on the influence of urban environment on cardiovascular diseases. The road data were obtained from the OSM open map website\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e, and filtered by ArcGIS 10.8 software to exclude some small roads that were not well recorded. We only retained five main types of roads, namely, expressways, express roads, trunk roads, secondary roads, and branch roads, for the analysis of the road network environmental element. The other built environment elements consist of six environmental elements: catering and food\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e, parks and squares\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e, transportation facilities\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e, shopping and consumption\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e, sports and fitness\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e, and medical care\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The data of these six elements were obtained from Gaode map, which can reflect the distribution of urban built environment. These detailed data will provide comprehensive background information for the research, and help to examine the correlation between built environment factors and cardiovascular health more thoroughly.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescription of indicators of built environmental factors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironmental indicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQuantity\u003c/p\u003e \u003cp\u003e(unit)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIndicator description\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoad network\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOSM map\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e692\u003c/p\u003e \u003cp\u003e(lines)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncluding the distribution of five main types of roads: expressways, express roads, trunk roads, secondary roads and branch roads\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCatering and food\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003eGaode map\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9905 (individuals)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncluding the distribution of Chinese food, foreign food, fast food restaurants, snack shops, milk tea shops, etc.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParks and squares\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e191 (individuals)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncluding the distribution of parks, squares, attractions, zoos, botanical gardens, etc.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShopping and consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14851 (individuals)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncluding the distribution of department stores, shopping centers, convenience stores, commercial streets, markets, etc.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransportation facilities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2659 (individuals)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTransportation facilities include the distribution of bus stops, parking lots, subway entrances, toll stations, bus stations, etc.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSports and fitness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e442 (individuals)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncluding the distribution of fitness centers, basketball courts, badminton courts, swimming pools, gymnasiums, etc.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2092 (individuals)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIncluding the distribution of emergency centers, clinics, specialty hospitals, general hospitals, pharmacies, etc.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eResearch methods\u003c/h2\u003e \u003cp\u003eThe research framework is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. We followed three main steps to investigate the influence of urban built environment on cardiovascular diseases. First, we reviewed the existing literature and identified the indicators of built environment elements that affect cardiovascular diseases\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/span\u003e,\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/span\u003e,\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. We then collected and processed the relevant data for these indicators. Second, we applied the global spatial autocorrelation model to assess the spatial aggregation degree of each element and the spatial correlation between the built environment elements and cardiovascular diseases. We also used the kernel density tool to display the spatial distribution characteristics of each element and to classify and export the kernel density grid values\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Third, we employed the geodetector model to detect the influencing factors of cardiovascular diseases based on the kernel density grid values\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Finally, we discussed the influence mechanism of urban built environment on cardiovascular diseases and proposed optimization strategies based on our analysis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGlobal spatial autocorrelation analysis\u003c/h2\u003e \u003cp\u003eUnivariate global spatial autocorrelation\u003c/p\u003e \u003cp\u003eUnivariate global spatial autocorrelation analysis is a statistical method to examine the spatial distribution characteristics of the data set and the spatial relationship among all observations. It can use the univariate global Moran\u0026rsquo;s index (Moran\u0026rsquo;s I)\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. to assess whether the whole data set has spatial autocorrelation, that is, whether there is spatial clustering or dispersion\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/span\u003e,\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. This study employs global spatial autocorrelation analysis to measure the degree of spatial clustering of cardiovascular disease samples and various environmental factors. The calculation formula is:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\begin{array}{c}I=\\frac{n}{{S}_{0}}\\times \\frac{\\sum _{i=1}^{n}\\sum _{j=1}^{n}{W}_{ij}\\left({y}_{i}-\\stackrel{-}{y}\\right)\\left({y}_{j}-\\stackrel{-}{y}\\right)}{\\sum _{i=1}^{n}{\\left({y}_{i}-\\stackrel{-}{y}\\right)}^{2}}\\#\\left(1\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn the formula, \u003cem\u003eI\u003c/em\u003e is the Moran\u0026rsquo;s index, \u003cem\u003en\u003c/em\u003e is the sample number of each environmental element in the study area, \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003ej\u003c/em\u003e\u003c/sub\u003e are the observation values of the spatial units, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\stackrel{-}{y}\\)\u003c/span\u003e\u003c/span\u003e is the average of the observation values, \u003cem\u003ew\u003c/em\u003e\u003csub\u003e\u003cem\u003eij\u003c/em\u003e\u003c/sub\u003e is the spatial weight matrix, and \u003cem\u003eS\u003c/em\u003e\u003csub\u003e\u003cem\u003e0\u003c/em\u003e\u003c/sub\u003e is the sum of the spatial weight matrix.\u003c/p\u003e \u003cp\u003eBivariate global spatial autocorrelation.\u003c/p\u003e \u003cp\u003eBivariate global spatial autocorrelation is proposed based on the spatial autocorrelation index (Moran\u0026rsquo;s I), aiming to examine the spatial correlation among different indicators\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/span\u003e,\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. Compared with the traditional OLS linear model, it considers the spatial dependence of the data, focuses on the examination and identification of the spatial association among the variables, and is more applicable for this study. This paper is used to assess the degree of spatial correlation between cardiovascular diseases and various built environments. The calculation formula is:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\begin{array}{c}I=\\frac{\\sum _{p=1}^{n}\\sum _{q=1}^{n}{Z}_{pq}\\left({x}_{p}-\\stackrel{-}{x}\\right)\\left({y}_{q}-\\stackrel{-}{y}\\right)}{{S}^{2}\\sum _{p=1}^{n}\\sum _{q=1}^{n}{Z}_{pq}}\\#\\left(2\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn the formula, \u003cem\u003eI\u003c/em\u003e is the bivariate global spatial autocorrelation coefficient, \u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003ep\u003c/em\u003e\u003c/sub\u003e denotes the independent variable attribute value of the \u003cem\u003ep\u003c/em\u003e spatial unit, that is, each environmental element, \u003cem\u003ey\u003c/em\u003e\u003csub\u003e\u003cem\u003eq\u003c/em\u003e\u003c/sub\u003e denotes the dependent variable attribute value of the \u003cem\u003eq\u003c/em\u003e spatial unit, that is, the cardiovascular disease distribution, \u003cem\u003eS\u003c/em\u003e denotes the attribute value variance, \u003cem\u003eZ\u003c/em\u003e\u003csub\u003e\u003cem\u003epq\u003c/em\u003e\u003c/sub\u003e denotes the weight matrix established by the \u003cem\u003ep\u003c/em\u003e and \u003cem\u003eq\u003c/em\u003e spatial units.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eKernel density analysis\u003c/h2\u003e \u003cp\u003eKernel density analysis is a method to compute the unit density of point and line element measurements within a specified neighborhood range. By using kernel functions to assign different weights to the elements in the region, this method can intuitively reveal the distribution characteristics of discrete measurements in continuous regions\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/span\u003e,\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/span\u003e,\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. This study employs kernel density analysis to display the distribution characteristics of cardiovascular disease samples and various environmental elements in the study area. The calculation formula is:\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$\\begin{array}{c}f\\left(x\\right)=\\frac{1}{nh}\\sum _{i=1}^{n}k\\left[\\frac{1}{h}\\left(x-{x}_{i}\\right)\\right]\\#\\left(3\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn the formula, \u003cem\u003ef\u003c/em\u003e (\u003cem\u003ex\u003c/em\u003e) is the kernel density estimation at x, \u003cem\u003eh\u003c/em\u003e is the search radius, \u003cem\u003en\u003c/em\u003e is the total number of sample points within the search range, (\u003cem\u003ex\u003c/em\u003e-\u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e) is the distance between the POI sample point \u003cem\u003ex\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e and the estimation point \u003cem\u003ex\u003c/em\u003e, and \u003cem\u003eK\u003c/em\u003e is the weight of the distance.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eGeoDetector\u003c/h3\u003e\n\u003cp\u003eGeographical detector is a spatial analysis method to detect spatial heterogeneity and explain its driving factors. Its principle is based on the assumption that if a variable has a significant impact on another variable, then the spatial distribution of the two variables should be similar. Geographical detector mainly includes four detectors: factor detector, interaction detector, risk detector, and ecological detector\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/span\u003e,\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/span\u003e,\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. This study uses factor detector to measure the influence of environmental factors on the distribution of cardiovascular diseases. The model is based on the following equation:\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e\n$$\\begin{array}{c}q=1-\\frac{1}{n{\\sigma }^{2}}\\sum _{h=1}^{L}{n}^{h}{\\sigma }_{X}^{2}\\#\\left(4\\right)\\end{array}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn the formula, \u003cem\u003eq\u003c/em\u003e is the power of the determining indicator, \u003cem\u003en\u003c/em\u003e is the total sample size of the study area, \u003cem\u003en\u003c/em\u003e\u003csub\u003e\u003cem\u003eh​\u003c/em\u003e\u003c/sub\u003e is the sample size of layer \u003cem\u003eh\u003c/em\u003e, \u003cem\u003eL\u003c/em\u003e is the stratification of the dependent or independent variables, also known as classification or partition, and \u003cem\u003eσ\u003c/em\u003e\u003csup\u003e\u003cem\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/em\u003e\u003c/sup\u003e is the variance of the whole study area. The range of \u003cem\u003eq\u003c/em\u003e is [0,1], and the larger the \u003cem\u003eq\u003c/em\u003e value, the stronger the explanatory power of the influencing factors on the spatial heterogeneity of the cardiovascular disease samples.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eSpatial autocorrelation characteristics\u003c/h2\u003e \u003cp\u003eTo analyze the spatial characteristics of urban built environment factors and cardiovascular disease distribution, we used Geoda software\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. We performed univariate and bivariate global spatial autocorrelation analyses on urban built environment factors and cardiovascular disease samples. The univariate analysis tested whether the samples of each factor clustered or dispersed in space, and the bivariate analysis evaluated the spatial correlation between each environmental factor and cardiovascular disease (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows that all variables were significant at the 0.01 level, with \u003cem\u003eP\u003c/em\u003e values less than 0.01 and \u003cem\u003eZ\u003c/em\u003e values greater than 2.58. This indicated a 99% confidence level to reject the null hypothesis and to consider the spatial autocorrelation results as highly reliable.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSpatial autocorrelation results of each element in the study area\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eElement\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eUnivariate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e \u003cp\u003eBivariate\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMoran\u0026rsquo;s I\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eZ\u003c/em\u003e Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMoran\u0026rsquo;s I\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShopping and consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e15.379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.305\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCatering and food\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e17.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.355\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoad network\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParks and squares\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.567\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransportation facilities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.852\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.277\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSports and fitness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.382\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.261\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e11.073\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.216\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.401\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.355\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCardiovascular disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe univariate spatial autocorrelation results showed that shopping consumption had the highest degree of spatial clustering, with a Moran\u0026rsquo;s I value of 0.504. Transportation facilities, catering and food, and health care also had high Moran\u0026rsquo;s I values, ranging from 0.434 to 0.489, indicating strong spatial clustering. Parks and squares had the lowest Moran\u0026rsquo;s I value, only 0.125, indicating weak spatial clustering. Cardiovascular disease had a Moran\u0026rsquo;s I value of 0.435, indicating that it also clustered in space. These results suggested that these factors were not randomly distributed in space, but had different patterns of spatial aggregation.\u003c/p\u003e \u003cp\u003eThe bivariate spatial autocorrelation analysis revealed the spatial correlation between each environmental factor and cardiovascular disease. All environmental factors had a significant positive spatial correlation with cardiovascular disease. Shopping consumption, catering and food, and health care had the strongest spatial association with cardiovascular disease, with Moran\u0026rsquo;s I values greater than 0.3. This meant that in the study area, areas with high density of shopping, catering, and health care services tended to overlap with areas with high incidence of cardiovascular disease. Road network and parks and squares had the weakest spatial association with cardiovascular disease, with Moran\u0026rsquo;s I values less than 0.01. This meant that in the study area, the spatial distribution of road network and parks and squares had little influence on cardiovascular disease.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eKernel density distribution characteristics\u003c/h2\u003e \u003cp\u003eKernel density analysis is a statistical method that can show the spatial distribution and density of various element samples. We used ArcGIS 10.8 software to perform kernel density analysis on cardiovascular disease samples and various environmental elements. We divided the kernel density level into five levels according to the natural break method, and arranged them from high to low, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows that the high-density areas of shopping consumption, catering food, transportation facilities and medical care were mainly concentrated in the southeast. The high-density areas of road network were mainly distributed along the Yongjiang River in the south in a belt shape, and in the central part in a block shape. The high-density areas of parks and squares were mainly near the Yongjiang River in the south. The high-density areas of sports and fitness facilities were distributed in the southeast and central parts. These spatial distribution characteristics reflected that the overall development direction of Xixiangtang District was from southeast to northwest, while the development level of the northeast region was low, the population distribution was small, and the layout of various facilities was imperfect. Moreover, the kernel density distribution characteristics of cardiovascular disease samples indicated that the high-incidence areas of cardiovascular diseases were mainly concentrated in the southeast region, which overlapped with the high-density areas of most built environmental elements.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGeodetector results analysis\u003c/h2\u003e \u003cp\u003eThis study used the factor detector method of geodetector to quantify the influence of different environmental elements on the distribution of cardiovascular disease samples. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the factor detection results, which indicate that the influence of environmental elements on cardiovascular diseases is significant and reliable (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), but varies greatly in size (q value). The q value ranges from 0 to 1, and the larger the q value, the stronger the influence of the environmental element. The results show that the environmental elements can be ranked by their influence on cardiovascular diseases as follows: medical care\u0026thinsp;\u0026gt;\u0026thinsp;shopping consumption\u0026thinsp;\u0026gt;\u0026thinsp;catering food\u0026thinsp;\u0026gt;\u0026thinsp;transportation facilities\u0026thinsp;\u0026gt;\u0026thinsp;sports and fitness\u0026thinsp;\u0026gt;\u0026thinsp;parks and squares\u0026thinsp;\u0026gt;\u0026thinsp;road network.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eGeographical detector factor detection results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironmental indicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eq\u003c/em\u003e Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCatering and food\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRoad network\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParks and squares\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShopping and consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransportation facilities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSports and fitness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedical care\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMedical care has the strongest influence on cardiovascular disease samples, with a q value of 0.529, which suggests that the spatial distribution of cardiovascular diseases is closely related to the spatial distribution of medical care services. High-density medical facilities provide easier access to medical services, which is crucial for the prevention and treatment of cardiovascular diseases. People with cardiovascular disease risk may prefer to live in areas with convenient medical services57. Shopping consumption, catering food and transportation facilities are the next three influential environmental elements, with q values over 0.4. These elements reflect the agglomeration characteristics of urban buildings and the commercial vitality and population density of an area. This agglomeration phenomenon may expose residents to more choices and temptations in their daily lives, leading to psychological stress and anxiety, and consequently burdening the cardiovascular system. Parks and squares and road network are the two environmental elements with the weakest influence on cardiovascular diseases, with q values less than 0.2. This implies that the areas with dense distribution of these elements have a relatively low incidence of cardiovascular diseases, which may be attributed to the ecological and traffic effects of these elements.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eThe influence of built environment on cardiovascular diseases\u003c/h2\u003e \u003cp\u003eThis study examined the influence of urban built environment on the distribution of cardiovascular disease samples using various methods, such as spatial autocorrelation analysis, kernel density analysis, and geodetector. The global spatial autocorrelation method was used to analyze the spatial aggregation of each variable, and the spatial correlation between each environmental element and cardiovascular disease was tested. The kernel density analysis tool was used to present the kernel density distribution of each variable in a view. The geodetector analysis was used to study the influence difference of different built environment elements on cardiovascular diseases.\u003c/p\u003e \u003cp\u003eThis study found that both built environment elements and cardiovascular diseases showed aggregated distribution in space, and most of them were distributed in the southeast direction. The significant positive correlation between each environmental element and cardiovascular disease in space indicated a certain connection between urban built environment and the incidence of cardiovascular disease. The factor detection results showed that different built environments had different impacts on cardiovascular diseases. Medical care had the largest impact on cardiovascular diseases, followed by shopping consumption, food and catering, and transportation facilities, while parks and squares and road network had the lowest impact. The following paragraphs explain the possible mechanisms behind these impacts.\u003c/p\u003e \u003cp\u003eLiving near medical institutions was beneficial for patients with cardiovascular diseases, as it brought a sense of security to patients, facilitated coping with emergencies, and improved the accessibility of medical services. Shopping consumption elements reflected the distribution of commercial facilities and living service facilities, which included shopping places such as malls, shopping centers, and convenience stores. Residents nearby had more shopping opportunities and living convenience, due to the high-density aggregation of commercial and living service facilities, but it might also lead to bad living habits and lifestyles, and increase the living cost of residents. Food and catering reflected the distribution of catering shops, which included fast food restaurants, snack bars, western restaurants, etc. High-density catering places provided more opportunities to choose fast food and greasy food. Long-term consumption of high-sugar and high-fat foods might increase the potential health risks such as obesity, diabetes, and hypertension. Transportation facilities mainly reflected the distribution of public transportation facilities, which included facilities such as bus stations and parking lots. High-density transportation facilities near residential areas provided transportation convenience for residents, but it might also reduce the willingness of residents to walk, and might also aggravate noise and air pollution. Parks and squares were places for recreation and exercise, which relaxed the body and mind, and alleviated psychological stress. Some residents might have difficulty in obtaining outdoor exercise opportunities, due to the unreasonable distribution of parks and squares. Long-term lack of green environment might increase the risk of cardiovascular diseases. Road network brought traffic noise and air pollution, which had a negative impact on cardiovascular diseases, but the influence of road network density, an environmental element, on the distribution of cardiovascular disease samples was small, according to the research results. This contradicted previous studies\u003csup\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/span\u003e,\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/span\u003e\u003c/sup\u003e. These five types of roads might not be the main types of roads that affected the distribution of cardiovascular disease samples, or the prevention and control measures for major traffic noise and pollution might be better in the Xixiangtang built-up area.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSuggestions on urban built environment planning\u003c/h2\u003e \u003cp\u003eThis study investigates the relationship between environmental factors and cardiovascular diseases in the built-up area of Xixiangtang District in Nanning City. The results reveal that the high-density built environment in this area may adversely affect cardiovascular health. To address this issue, the following suggestions are proposed to optimize the layout of the built environment, improve the living environment of urban residents, and promote the cardiovascular health of residents.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eOptimize the layout of health care facilities. Encourage the establishment of medical centers in densely populated areas, enhance the accessibility of medical services, and ensure that community patients with cardiovascular diseases have convenient access to high-quality medical services.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eReasonably control the density of commercial areas. Balance the density of commercial facilities and living service facilities, rationally plan the layout of malls, shopping centers and convenience stores, and avoid increasing the living pressure of residents and reducing the walking opportunities while ensuring that residents have shopping convenience.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePromote the planning of healthy dining places. Encourage reasonable dining industry layout, provide diverse food choices, increase green dining shops, and reduce the supply of fast food and oily food.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eOptimize the design of transport facilities. Set up noise barriers and green belts around transport roads and hubs, mitigate the spread of noise and air pollution, and enhance the comfort of the surrounding environment.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eRationally plan the layout of parks and green spaces. Increase the proportion of parks and plazas in urban land use, increase the opportunities for residents to have outdoor exercise, and protect the rights of residents to access the natural environment.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eStrengthen comprehensive management and monitoring. Establish a monitoring system for urban built environment and health, regularly evaluate the impact of built environment on cardiovascular health, strengthen the collaboration between urban planning and health departments, form a comprehensive management mechanism, and timely adjust planning strategies to adapt to the changes of urban development and residents\u0026rsquo; health needs.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eThe limitations and future prospects of the research\u003c/h2\u003e \u003cp\u003eThis paper has conducted a comprehensive study on how various environmental factors affect cardiovascular diseases, and proposed ways to optimize the urban built environment. However, this paper still has some limitations. The environmental impact on health and disease is complex, and due to the constraints of time and resources, not all possible variables have been fully considered and analyzed, which may introduce some bias to the research results. To further advance the research on the relationship between built environment and cardiovascular health, the future research should consider the following aspects: First, expand the research scope, collect and analyze data from different cities and regions, to better capture the regional variations in the influence of built environment on cardiovascular health; Second, improve the scientific rigor of research methods, use more objective and precise data collection and analysis methods, to enhance the reliability and accuracy of research; Third, explore the mechanisms underlying the relationship between built environment and cardiovascular health, examine the biological and psychological pathways, to better understand how they are connected.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis paper uses Xixiangtang District of Nanning City as a case study, and applies spatial autocorrelation, kernel density and geodetector methods to investigate how urban built environment influences the distribution of cardiovascular disease samples, based on the hospital cardiovascular data and urban POI data. The paper reaches the following two main conclusions: Regarding the spatial distribution characteristics, all environmental factors exhibited a clustered distribution in space after passing the global spatial autocorrelation test, and all environmental factors had a significant positive correlation with cardiovascular diseases in space. The spatial distribution characteristics of cardiovascular disease samples were consistent with those of most environmental factors, and they were mainly concentrated in the southeast of the study area, indicating that the occurrence of cardiovascular diseases was related to the urban built environment to some extent. Regarding the factor influence degree, different urban built environment factors had different effects on the distribution of cardiovascular disease samples. Among them, the distribution of health care had the greatest effect on cardiovascular diseases, followed by shopping consumption, catering and food, and transportation facilities, while parks and squares and road networks had less effect on the distribution of cardiovascular disease samples. This implies that the incidence of cardiovascular diseases may be higher in areas with high-density health care, shopping consumption, catering and food, and transportation facilities, while areas with dense parks and squares and main roads have lower incidence of cardiovascular diseases. Therefore, in the process of urban built environment planning, more attention should be given to the potential relationship between different environmental factors and cardiovascular health, and the balance between urban development and health promotion should be achieved.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSchool of Geographical and Planning, Nanning Normal University, Nanning, Guangxi, 530100, China.\u003c/p\u003e\n\u003cp\u003eShuguang Deng,Jinlong Liang,Ying Peng,Binglin Liu,Jinhong Su\u0026nbsp;\u0026amp; Shuyan Zhu\u003c/p\u003e\n\u003cp\u003eSchool of Architecture, Guangxi Arts University, Nanning, Guangxi, 530009, China.\u003c/p\u003e\n\u003cp\u003eYing Peng\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eD.S. Provides research topics, conceptual guidance, translation, paper revision and financial support; L.J. Conceived the framework and wrote the original draft; P.Y. Manuscript checking, chart optimization; L.W. Provided suggestions for revision, and reviewed and edited them; S.J. Is responsible for data acquisition and editing; Z.S. Edits the visual map.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCorrespondence to Jinlong Liang.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe General Project of Humanities and Social Sciences Research of the Ministry of Education in 2020: A Study on the Assessment and Planning of Healthy Cities Based on Spatial Data Mining (No. 20YJA630011).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe datasets used or analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e \u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eRoth, G. 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International journal of environmental research and public health. 14(5), 461 (2017).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"","lastPublishedDoi":"10.21203/rs.3.rs-3852583/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3852583/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eCardiovascular diseases (CVDs) are a major public health concern, and their morbidity is influenced by various built environment elements. This paper aims to investigate the influence mechanisms of different built environment elements on CVDs, and to provide a theoretical foundation for health-oriented urban planning and CVD prevention. We selected the Xixiangtang built-up area of Nanning city as the case study area, and used the distribution data of CVDs and urban point of interests (POIs) as the main data sources. We applied spatial autocorrelation analysis, kernel density analysis, and geographic detector methods to examine the spatial correlation and influence of urban built environment elements on CVD samples. The results show that both the built environment elements and the CVD samples have a spatially clustered distribution, and there is a significant positive correlation between the distribution density of each environmental element and the CVD morbidity. Among the environmental elements, medical care has the largest influence on CVDs, followed by shopping consumption, catering and food, and transportation facilities, while parks and squares and the road network have relatively small influence.\u003c/p\u003e","manuscriptTitle":"The influence mechanism of urban built environment on cardiovascular diseases","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-23 16:47:06","doi":"10.21203/rs.3.rs-3852583/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":"0dec6c54-0525-4848-86bf-e78694fc3870","owner":[],"postedDate":"January 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":28290240,"name":"Earth and environmental sciences/Environmental sciences"},{"id":28290241,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":28290242,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2024-02-27T06:16:28+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-23 16:47:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3852583","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3852583","identity":"rs-3852583","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}