Research on the Synergistic Relationship between Urban Tourist Attractions and Green Infrastructure in Japanese Cities Based on Green Tourism

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As more people choose cities as their travel destinations, this trend not only promotes urban economic development but also impacts urban ecosystems. Based on this, this paper proposes the concept of coordinated planning for urban green infrastructure (UGI) and urban tourist attractions. By studying and conducting a comparative analysis of the synergy between green infrastructure and urban tourist attractions in Tokyo, Kyoto, and Kumamoto, this study explores the relationship between green infrastructure and urban tourist attractions in cities with different levels of economic development and population sizes. The study mainly considers the spatial correlation between UGI and urban tourist attractions. Green Tourism Urban tourist attractions Urban Green Infrastructure Sustainable Development Figures Figure 1 Figure 2 Figure 3 1. Introduction After the pandemic ended, the recovery of the tourism industry spurred economic growth. However, during this process, cities lacked resilience and paid insufficient attention to green tourism, leading to a certain degree of ecological damage caused by the rapid development of urban tourism (Yi-min and Yong-zhi 2024). As early as the 1992 United Nations Conference on Environment and Development, the concept of sustainable development was formally adopted (Cities, Governments et al. 2004 ). Since then, the related concept of green tourism has been proposed and identified as an important strategy to promote sustainable tourism development and environmental protection (Clarke 1997 ).Green tourism not only promotes sustainable development and reduces negative impacts on the environment but also fosters economic development in local communities (Toubes and Araújo-Vila 2022 , Tong and Research 2023 , Aram 2024 ). Green tourism emphasizes the cooperation between tourists and residents, establishing a psychological bond between visitors and the destination (Pourhossein, Baker et al. 2023 ). In some studies in Japan, "green tourism" was initially used interchangeably with "rural tourism" and "ecotourism," mainly referring to tourism activities in rural areas (Jin, Takao et al. 2022 ). As the concept of green tourism continues to evolve, the factors influencing it have also gradually expanded (Al Fahmawee, Jawabreh et al. 2023). Consequently, the scope of the research has grown to include cities as important subjects of research. Meanwhile, the primary destinations for most tourists are cities (Dodds and Joppe 2001, Iwachido, Uchida et al. 2020 ). Existing studies reveal that tourists now have a high willingness to engage in green tourism (Samdin and Management 2008, Rahim, Shamsudin et al. 2018 , Hassin, Jais et al. 2021 ). Therefore, research on green tourism has gradually focus on urban planning and urban tourism design (Chen, Liu et al. 2022 , Theodorou 2022 ). In studies related to urban green tourism, many researchers have emphasized the important role of green infrastructure in the development of green tourism (O’Brien, De Vreese et al. 2017 , da Schio, Phillips et al. 2021 ). The benefits of urban green infrastructure(UGI) to users have been demonstrated through numerous case studies (Terkenli, Bell et al. 2020 ). Existing research results show that UGI has the potential to significantly improve urban environmental quality and residents' well-being (Vujcic, Tomicevic-Dubljevic et al. 2018, Vujcic, Tomicevic-Dubljevic et al. 2019 ). Many green spaces with high recreational, visual, and historical value also tend to attract tourists (Fabos and planning 1995). Studies have shown that urban parks have certain tourism benefits (Hansmann, Whitehead et al. 2016 ). Although some studies suggest that UGI can complement urban attractions, the quantity of related research is limited, and the quality and strength of the evidence are relatively weak. Given that tourists plan and conduct city visits in various ways, this paper selects urban areas as the research region. In the study, green tourism is defined as a mode of visiting urban tourist attractions and UGI through walking and cycling. The goal of the study is to explore the relationship between UGI and urban tourist attractions based on tourists walking and cycling, ultimately promoting the development of green tourism in urban areas. It proposes the concept of coordinated development of UGI and urban tourist attractions. The objective of the study is to explore the relationship between UGI and urban tourist attractions based on tourists walking and cycling, ultimately promoting the development of green tourism in cities. By leveraging the connection between UGI and urban tourist attractions in cities, the study seeks to enhance the infrastructure in green tourism, improve the environment around existing urban tourist attractions, and increase their attractiveness. 2. Data and Methods 2.1. Research Subjects This study selects three representative cities in Japan: Tokyo, Kyoto, and Kumamoto. These cities have high visitor rates in their respective regions. Among them, Tokyo represents a developed central city, Kyoto stands as a representative city rich in cultural tourism resources, and Kumamoto City represents a developing central city. Tokyo, as Japan's capital and largest city, boasts a highly developed economy and a high population density. The degree of urbanization is advanced, and the planning of UGI and urban tourist attractions is relatively well-developed (Okata, Murayama et al. 2011). Kyoto is renowned for its rich cultural heritage and tourism resources. Due to preservation policies for the ancient capital, the city's building density is relatively lower compared to Tokyo (Hiroshi 2020 ). Kumamoto represents a typical scenario for mid-sized cities, aiding in the comparison and analysis of the coordinated planning of green infrastructure and urban tourist attractions across cities of different sizes and development levels. Due to the significant differences in the administrative area sizes of the three selected cities, and because large areas of natural forests and farmland far from the city centers are not considered UGI in this study, we have refined and narrowed the study area to densely populated districts (DID) within the cities. The DID areas of the three cities are shown in Fig. 1 . As shown in Fig. 1 (a), Tokyo is somewhat special in this regard. This study has selected the Tokyo metropolitan area. Due to its large population, all 23 wards selected are DID. 2.2. Data Collection We obtained the following data for the study from government websites. Tourism Resources (TR) are the tourism resources listed in the "Tourism Resource Registry," which have been reviewed and selected by the "Tourism Resource Evaluation Committee" established by the Japan Transport and Tourism Agency. They include tourism resources rated A or higher and those listed in the "Tourist Attraction List" compiled by the Japan Tourism Agency, which includes information on urban tourist attractions across various prefectures. Local Resources (LR) are natural landscape resources described in the "Third Basic Survey on Natural Environment Conservation" and the "Natural Environment Information Map." Prefectural Designated Cultural Heritage (PDCH) encompasses various types of cultural properties, including tangible cultural properties, historic sites, cultural landscapes and traditional buildings. Urban Parks (UP) refers to the list of urban parks created by the Urban Bureau of the Ministry of Land, Infrastructure, Transport and Tourism. It includes various types of parks such as sports parks, comprehensive parks, botanical and zoological parks, urban green spaces, greenways, and urban forests, based on information collected from prefectures and designated cities nationwide. 2.3. Buffer Analysis Recent trends indicate that the concept of the x-minute city, such as the 15-minute or 20-minute city, has been adopted by many cities worldwide (Jafari, Singh et al. 2023 ). This concept is based on urban planning principles that promote densification, mixed land use, and active transportation modes (Mouratidis 2024 ). Several studies have examined 800m access to daily destinations in established areas of the city (Barbara, Carra et al. 2021 , Logan, Hobbs et al. 2022 , Thornton, Schroers et al. 2022). In this study, based on the concept of green tourism, a 20-minute travel range around urban tourist attractions is established. It's aims for tourists and residents to reach UGI and urban tourist attractions through green transportation methods, walking and cycling. Therefore, using Arc-GIS software, an 800-meter buffer zone is created based on the 20-minute travel radius, with urban tourism resource points as the center. Within the generated buffer zones, urban park data points are identified and overlaid. 2.3. Local Colocation Quotient (LCLQ) LCLQ was designed to detect the spatial association between a given type A point(e.g. tourism attraction) and surrounding a subset of B points (e.g. UGI) (Cromley, Hanink et al. 2014 ). Hence, LCLQ analysis can explore the variations in spatial (in)dependency between two types of points from places to places. The LCLQ is formulated as: $$\:{LCLQ}_{{A}_{i\to\:B}}=\frac{{N}_{{A}_{i\to\:B}}}{{N}_{B}/\left(N-1\right)}$$ 1 Where \(\:{A}_{i}\) presents the ith type A point. In Eq. ( 1 ), the definition of LCLQ is shown in Eq. ( 2 ). Where \(\:{N}_{B}\) is the total number of category B present in the research area, and N is the total number of points in the research area (including all categories present). \(\:{N}_{{A}_{i\to\:B}}\) is the weighted average of the number of category B points in the neighborhood of each category A point ( \(\:{A}_{i}\) ). This is based on a distance decay function that allows closer features to the target feature to weigh heavier in the calculations than features that are farther away. And it is formulated as follows: $$\:{N}_{{A}_{i\to\:B}}=\sum\:_{j=1\left(j\ne\:i\right)}^{N}\frac{{w}_{ij}*{f}_{ij}}{\sum\:_{j=1\left(j\ne\:i\right)}^{N}{w}_{ij}}$$ 2 Where \(\:{f}_{ij}\) is a binary variable indicating whether point j is a category B point. If this is true, it is equal to 1. If not, it is equal to 0. The kernel function equations are given as: $$\:{w}_{ij}=exp(-0.5*\left(\frac{{d}_{ij}^{2}}{{d}_{ib}^{2}}\right))$$ 3 Permutations are used to calculate a p-value for each of the Input Features of Interest to determine whether the observed colocation quotient values are statistically significant. If the p-value is small (less than 0.05), the actual colocation quotient for the feature is statistically significant. 3. Results 3.1 Integration of Urban tourist attractions and UGI By organizing the data on urban tourist attractions and urban green infrastructure in three cities, the following Table 1 was obtained. Using GIS for data analysis, various resources distributed within the DIDs of each city were identified. The selected resources were then used as centers to monitor buffer zones with a radius of 800 meters. Through buffer analysis, the accessible UGI within an 800M range of urban tourist attractions is shown in Fig. 2 . By overlaying the buffer zone, urban tourist attractions, and UGI data, it was found that urban tourist attractions and UGI exist in an integrated form within the 800-meter buffer zone of urban tourist attractions. The analysis shows that UGI and urban tourist attractions frequently appear together within this range. This indicates that within this range, green spaces and urban tourist attractions coexist, forming an integrated urban landscape. In these three cities, visitors can potentially reach surrounding UGIs by walking or cycling after visiting urban tourist attractions. The UGI near urban tourist attractions includes parks, green spaces, and areas with vegetation cover. This form of integration is evident around different urban tourist attractions in multiple cities. Compared to the other two cities, Kyoto exhibits the most noticeable integration of urban tourist attractions and UGI in Fig. 2 (b). 3.2 Spatial Clustering of Urban tourist attractions and UGI Based on ArcGIS's LCLQ, the LCOQ values of various urban tourist attractions within the DID of the three cities were calculated. These values were then matched with the descriptions in Table 2 . Five relationships between urban tourist attractions and UGI were analyzed and interpreted accordingly. A scatter plot is also created and can display the relationship between the local colocation quotients and the p-values calculated. To better compare these three cities, we divided the ratios into three intervals: 2.0. The LCLQ<0 interval indicates the isolation of urban tourist attractions from urban parks, 1.0<LCLQ2.0 indicates strong co-location. To better compare the three cities, we categorized the types of LCLQ into five cases based on Table 2 for analysis. Among these, we focused particularly on data concerning co-location relationships. In Fig .3 (a), within the DID (densely inhabited district) area of Tokyo, urban tourist attractions with significant co-location with UGI (urban green infrastructure) show a maximum LCLQ value of 2.08. Additionally, Tokyo has the most urban tourist attractions with co-location relationships with UGI among the three cities. Figure 3 (b) shows that within the DID area of Kyoto, urban tourist attractions with significant co-location with UGI have a maximum LCLQ value of 3.17. Kyoto has the highest number of tourist attractions with strong co-location relationships with UGI. In Fig. 3 (c), within the DID area of Kumamoto, urban tourist attractions with significant co-location with UGI show a maximum LCLQ value of 2.73, with the fewest tourist attractions exhibiting co-location with UGI among the three cities. Table 3 Number of Urban Tourist Attractions in Five relationships City Urban Tourist Attractions Co-located Significant Co-located Not Significant Isolated Significant Isolated Not Significant Undefined Tokyo 205 6 58 80 61 0 Kyoto 158 9 16 54 11 68 Kumamoto 72 7 4 12 1 48 We summarized the data and compiled information on the number of urban tourist attractions with and without co-location relationships with UGI in each city, as shown in Table 3 . The results indicate that Kyoto has the highest number of urban tourist attractions with strong co-location relationships with UGI, but the differences among the three cities are minor in this regard. However, the total number of tourist attractions co-located with UGI varies significantly, with Tokyo having the most (64), followed by Kyoto (25), and Kumamoto with the fewest (11). Additionally, a large number of urban tourist attractions in Kyoto and Kumamoto lack surrounding UGI, making their relationship with UGI undefined. In contrast, no such case is observed in Tokyo. In all three cities, spatial correlation exists between urban tourist attractions and UGI. However, individual tourist attractions may be influenced by their geographic location and facility characteristics. For example, in Kumamoto, several attractions within Kumamoto Castle are separated from the surrounding city by walls, creating an isolated spatial relationship with urban green spaces. This spatial correlation suggests a certain regularity in the spatial layout of urban tourist attractions and UGI in urban areas. The study also indicates that different economic levels and population sizes may impact the relationship between urban tourist attractions and UGI. Further analysis could reveal the implications of this spatial correlation for urban planning and tourism development. 4. Discussion The study found a significant spatial correlation between urban tourist attractions and UGI. This result indicates that the distribution of urban tourist attractions and UGI is not random but follows a certain pattern or regularity. This may reflect tourists' preferences for natural environments and their need for green spaces when choosing travel destinations. Compared to existing literature, this study further confirms the association between urban tourist attractions and UGI in Japan. Previous study has indicated that a comprehensive urban index can improve urban environmental quality, enhance residents' quality of life, and increase happiness. Our results suggest that the comprehensive urban index may also play a crucial role in enhancing the attractiveness of urban tourist attractions and increasing the number of visitors. The study's limitations include not delving deeply into the specific mechanisms behind the spatial correlation between UGI and urban tourist attractions. For instance, whether there are differences in tourists' preferences for different types of UGI and which types are most popular are questions that require further study. Additionally, the limitations of the data sample may impact the results. Although these three cities are used as representatives in this study and can represent some similar cities, there are still certain limitations from the perspective of data analysis. Therefore, suggesting that future studies should consider broader data collection and analysis. Based on the findings, future study could explore the specific mechanisms by which UGI enhances the attractiveness of urban tourist attractions. For example, surveys or interviews could be used to understand tourists' preferences and needs for different types of UGI, providing more specific recommendations for the planning and development of urban tourist attractions and UGI. Overall, this study reveals the spatial association between urban landscapes and UGI. The synergistic development of these elements can not only promote the sustainable development of tourism but also improve urban environmental quality and residents' quality of life to a certain extent. 5. Conclusions This study examines three cities based on a green tourism model incorporating walking and cycling. From the buffer zone analysis, we find that UGI is generally distributed around urban tourist attractions in all three cities. The results indicate a significant spatial correlation between urban tourist attractions and the comprehensive city index within the DID areas. Additionally, based on LCLQ calculations, some urban tourist attractions exhibit a co-location relationship with the comprehensive city index. Tokyo, with a much higher level of urban development than the other two cities, also shows a more pronounced co-location relationship between urban tourist attractions and UGI. In Kyoto, due to government policies on cultural and historical preservation, the city has the highest number of urban tourist attractions with strong co-location relationships with UGI. In Kumamoto, urban tourist attractions co-located with UGI are mainly concentrated in the city center, with significantly fewer occurrences than in the other two cities. This suggests that in cities with more advanced urban planning, co-location relationships are more evident. Thus, we propose that factors such as a city's development level, economic status, and the degree of cultural and historical preservation may also influence the relationship between urban tourist attractions and UGI. Further research is needed to explore these influences in detail. These findings suggest a synergistic relationship between urban tourist attractions and the comprehensive city index based on green tourism. Their cooperative development could further support the advancement of urban green tourism. The collaborative growth of urban tourist attractions and the comprehensive city index will likely have a positive impact on future urban planning, environmental protection, and cultural heritage preservation. Urban planners are advised to further emphasize the collaborative planning of green infrastructure and urban tourist attractions in future urban development. Through the coordinated planning of urban tourist attractions and high-quality UGI, the development of urban green tourism can be promoted. This approach enhances the appeal of city attractions while protecting the urban ecological environment, fostering interactions between tourists and communities, and preserving the city’s culture and history. Declarations Contributions Conceptualization: Zhuojing Yang; Methodology: Zhuojing Yang; Formal analysis and investigation: Zhuojing Yang; Writing—original draft preparation: Zhuojing Yang; Writing—review and editing: Zhuojing Yang, Riken Homma; Supervision: Riken Homma. Acknowledgements We would like to express our gratitude to the Amano Institute of Technology for providing a scholarship that supported Ms. Zhuojing Yang's research and studies at Kumamoto University. We also extend our thanks to all the experts and scholars who provided feedback on this paper. Funding The authors did not receive support from any organization for the submitted work. Ethics declarations Ethics approval and consent to participate Informed consent. Competing Interests We declare any competing interests. Availability of data and materials All relevant data are within the paper. References Al Fahmawee, E. A. D., O. J. G. J. o. T. Jawabreh and Geosites (2023). "Sustainability of green tourism by international tourists and its impact on green environmental achievement: Petra heritage, Jordan." 46(1): 27-36. Aram, F. J. R. (2024). Resources of Urban Green Spaces and Sustainable Development, MDPI. 13: 10. Barbara, C., M. Carra, S. Rossetti and Z. J. E. T. T. E. Michele (2021). 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"Chinese-style Urbanization: Theoretical Exploration and Practical Innovation in the Transition from Traditional Practice to a New Model of Urbanization." 43(1): 1-10. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Minor revision 21 Dec, 2024 Reviewers agreed at journal 06 Nov, 2024 Reviewers invited by journal 06 Nov, 2024 Editor assigned by journal 30 Oct, 2024 First submitted to journal 28 Oct, 2024 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-5351247","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":374652759,"identity":"87220c10-b0df-445d-836b-f08141e8bfbc","order_by":0,"name":"Zhuojing YANG","email":"data:image/png;base64,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","orcid":"https://orcid.org/0009-0009-2678-3123","institution":"Kumamoto University: Kumamoto Daigaku","correspondingAuthor":true,"prefix":"","firstName":"Zhuojing","middleName":"","lastName":"YANG","suffix":""},{"id":374652760,"identity":"3f6fa0a3-bd84-455e-a919-345ca2facc98","order_by":1,"name":"Riken Homma","email":"","orcid":"","institution":"Kumamoto University: Kumamoto Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Riken","middleName":"","lastName":"Homma","suffix":""}],"badges":[],"createdAt":"2024-10-29 05:27:44","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5351247/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5351247/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69338092,"identity":"0f596d57-6d35-47ed-93c3-5919dc28c78b","added_by":"auto","created_at":"2024-11-19 10:25:44","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":114621,"visible":true,"origin":"","legend":"\u003cp\u003eThe Densely Inhabited District (DID) in Tokyo (a), Kyoto (b) and Kumamoto (c)\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5351247/v1/9ca196ff26deee8c79a9a78f.png"},{"id":69338090,"identity":"515d0f9b-9f38-4a6d-bbde-799e995d10fa","added_by":"auto","created_at":"2024-11-19 10:25:44","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":324633,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of various resources within the 800-meter buffer zone in Tokyo (a), Kyoto (b) and Kumamoto (c)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5351247/v1/e9dbd71b4fd0b4699d36e564.png"},{"id":69338091,"identity":"5218c1cc-f06a-4781-9f94-eb99dd7d2c80","added_by":"auto","created_at":"2024-11-19 10:25:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":133774,"visible":true,"origin":"","legend":"\u003cp\u003eRelationship between Local Colocation Quotient (LCLQ) and p-value in Tokyo (a), Kyoto (b) and Kumamoto (c)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5351247/v1/7ad8f3ee7230bcf79bb2a48b.png"},{"id":69338259,"identity":"829961ee-61a6-4da0-b637-9937896477a8","added_by":"auto","created_at":"2024-11-19 10:33:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":934237,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5351247/v1/e2b1fef3-ffdb-4d86-948e-acc00dcf84c4.pdf"}],"financialInterests":"","formattedTitle":"Research on the Synergistic Relationship between Urban Tourist Attractions and Green Infrastructure in Japanese Cities Based on Green Tourism","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAfter the pandemic ended, the recovery of the tourism industry spurred economic growth. However, during this process, cities lacked resilience and paid insufficient attention to green tourism, leading to a certain degree of ecological damage caused by the rapid development of urban tourism (Yi-min and Yong-zhi 2024). As early as the 1992 United Nations Conference on Environment and Development, the concept of sustainable development was formally adopted (Cities, Governments et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Since then, the related concept of green tourism has been proposed and identified as an important strategy to promote sustainable tourism development and environmental protection (Clarke \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1997\u003c/span\u003e).Green tourism not only promotes sustainable development and reduces negative impacts on the environment but also fosters economic development in local communities (Toubes and Ara\u0026uacute;jo-Vila \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Tong and Research \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e, Aram \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Green tourism emphasizes the cooperation between tourists and residents, establishing a psychological bond between visitors and the destination (Pourhossein, Baker et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn some studies in Japan, \"green tourism\" was initially used interchangeably with \"rural tourism\" and \"ecotourism,\" mainly referring to tourism activities in rural areas (Jin, Takao et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). As the concept of green tourism continues to evolve, the factors influencing it have also gradually expanded (Al Fahmawee, Jawabreh et al. 2023). Consequently, the scope of the research has grown to include cities as important subjects of research. Meanwhile, the primary destinations for most tourists are cities (Dodds and Joppe 2001, Iwachido, Uchida et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Existing studies reveal that tourists now have a high willingness to engage in green tourism (Samdin and Management 2008, Rahim, Shamsudin et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Hassin, Jais et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, research on green tourism has gradually focus on urban planning and urban tourism design (Chen, Liu et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Theodorou \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn studies related to urban green tourism, many researchers have emphasized the important role of green infrastructure in the development of green tourism (O\u0026rsquo;Brien, De Vreese et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, da Schio, Phillips et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The benefits of urban green infrastructure(UGI) to users have been demonstrated through numerous case studies (Terkenli, Bell et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Existing research results show that UGI has the potential to significantly improve urban environmental quality and residents' well-being (Vujcic, Tomicevic-Dubljevic et al. 2018, Vujcic, Tomicevic-Dubljevic et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Many green spaces with high recreational, visual, and historical value also tend to attract tourists (Fabos and planning 1995). Studies have shown that urban parks have certain tourism benefits (Hansmann, Whitehead et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Although some studies suggest that UGI can complement urban attractions, the quantity of related research is limited, and the quality and strength of the evidence are relatively weak.\u003c/p\u003e \u003cp\u003eGiven that tourists plan and conduct city visits in various ways, this paper selects urban areas as the research region. In the study, green tourism is defined as a mode of visiting urban tourist attractions and UGI through walking and cycling. The goal of the study is to explore the relationship between UGI and urban tourist attractions based on tourists walking and cycling, ultimately promoting the development of green tourism in urban areas. It proposes the concept of coordinated development of UGI and urban tourist attractions.\u003c/p\u003e \u003cp\u003eThe objective of the study is to explore the relationship between UGI and urban tourist attractions based on tourists walking and cycling, ultimately promoting the development of green tourism in cities. By leveraging the connection between UGI and urban tourist attractions in cities, the study seeks to enhance the infrastructure in green tourism, improve the environment around existing urban tourist attractions, and increase their attractiveness.\u003c/p\u003e"},{"header":"2. Data and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Research Subjects\u003c/h2\u003e \u003cp\u003eThis study selects three representative cities in Japan: Tokyo, Kyoto, and Kumamoto. These cities have high visitor rates in their respective regions. Among them, Tokyo represents a developed central city, Kyoto stands as a representative city rich in cultural tourism resources, and Kumamoto City represents a developing central city.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTokyo, as Japan's capital and largest city, boasts a highly developed economy and a high population density. The degree of urbanization is advanced, and the planning of UGI and urban tourist attractions is relatively well-developed (Okata, Murayama et al. 2011). Kyoto is renowned for its rich cultural heritage and tourism resources. Due to preservation policies for the ancient capital, the city's building density is relatively lower compared to Tokyo (Hiroshi \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Kumamoto represents a typical scenario for mid-sized cities, aiding in the comparison and analysis of the coordinated planning of green infrastructure and urban tourist attractions across cities of different sizes and development levels.\u003c/p\u003e \u003cp\u003eDue to the significant differences in the administrative area sizes of the three selected cities, and because large areas of natural forests and farmland far from the city centers are not considered UGI in this study, we have refined and narrowed the study area to densely populated districts (DID) within the cities. The DID areas of the three cities are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e (a), Tokyo is somewhat special in this regard. This study has selected the Tokyo metropolitan area. Due to its large population, all 23 wards selected are DID.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data Collection\u003c/h2\u003e \u003cp\u003eWe obtained the following data for the study from government websites.\u003c/p\u003e \u003cp\u003eTourism Resources (TR) are the tourism resources listed in the \"Tourism Resource Registry,\" which have been reviewed and selected by the \"Tourism Resource Evaluation Committee\" established by the Japan Transport and Tourism Agency. They include tourism resources rated A or higher and those listed in the \"Tourist Attraction List\" compiled by the Japan Tourism Agency, which includes information on urban tourist attractions across various prefectures.\u003c/p\u003e \u003cp\u003eLocal Resources (LR) are natural landscape resources described in the \"Third Basic Survey on Natural Environment Conservation\" and the \"Natural Environment Information Map.\"\u003c/p\u003e \u003cp\u003ePrefectural Designated Cultural Heritage (PDCH) encompasses various types of cultural properties, including tangible cultural properties, historic sites, cultural landscapes and traditional buildings.\u003c/p\u003e \u003cp\u003eUrban Parks (UP) refers to the list of urban parks created by the Urban Bureau of the Ministry of Land, Infrastructure, Transport and Tourism. It includes various types of parks such as sports parks, comprehensive parks, botanical and zoological parks, urban green spaces, greenways, and urban forests, based on information collected from prefectures and designated cities nationwide.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Buffer Analysis\u003c/h2\u003e \u003cp\u003eRecent trends indicate that the concept of the x-minute city, such as the 15-minute or 20-minute city, has been adopted by many cities worldwide (Jafari, Singh et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This concept is based on urban planning principles that promote densification, mixed land use, and active transportation modes (Mouratidis \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Several studies have examined 800m access to daily destinations in established areas of the city (Barbara, Carra et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Logan, Hobbs et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e, Thornton, Schroers et al. 2022). In this study, based on the concept of green tourism, a 20-minute travel range around urban tourist attractions is established. It's aims for tourists and residents to reach UGI and urban tourist attractions through green transportation methods, walking and cycling. Therefore, using Arc-GIS software, an 800-meter buffer zone is created based on the 20-minute travel radius, with urban tourism resource points as the center. Within the generated buffer zones, urban park data points are identified and overlaid.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Local Colocation Quotient (LCLQ)\u003c/h2\u003e \u003cp\u003eLCLQ was designed to detect the spatial association between a given type A point(e.g. tourism attraction) and surrounding a subset of B points (e.g. UGI) (Cromley, Hanink et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Hence, LCLQ analysis can explore the variations in spatial (in)dependency between two types of points from places to places. The LCLQ is formulated as:\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:{LCLQ}_{{A}_{i\\to\\:B}}=\\frac{{N}_{{A}_{i\\to\\:B}}}{{N}_{B}/\\left(N-1\\right)}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{A}_{i}\\)\u003c/span\u003e\u003c/span\u003e presents the ith type A point. In Eq.\u0026nbsp;(\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), the definition of LCLQ is shown in Eq.\u0026nbsp;(\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{N}_{B}\\)\u003c/span\u003e\u003c/span\u003e is the total number of category B present in the research area, and N is the total number of points in the research area (including all categories present). \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{N}_{{A}_{i\\to\\:B}}\\)\u003c/span\u003e\u003c/span\u003e is the weighted average of the number of category B points in the neighborhood of each category A point (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{A}_{i}\\)\u003c/span\u003e\u003c/span\u003e). This is based on a distance decay function that allows closer features to the target feature to weigh heavier in the calculations than features that are farther away. And it is formulated as follows:\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:{N}_{{A}_{i\\to\\:B}}=\\sum\\:_{j=1\\left(j\\ne\\:i\\right)}^{N}\\frac{{w}_{ij}*{f}_{ij}}{\\sum\\:_{j=1\\left(j\\ne\\:i\\right)}^{N}{w}_{ij}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{f}_{ij}\\)\u003c/span\u003e\u003c/span\u003e is a binary variable indicating whether point j is a category B point. If this is true, it is equal to 1. If not, it is equal to 0. The kernel function equations are given as:\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:{w}_{ij}=exp(-0.5*\\left(\\frac{{d}_{ij}^{2}}{{d}_{ib}^{2}}\\right))$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ePermutations are used to calculate a p-value for each of the Input Features of Interest to determine whether the observed colocation quotient values are statistically significant. If the p-value is small (less than 0.05), the actual colocation quotient for the feature is statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Integration of Urban tourist attractions and UGI\u003c/h2\u003e\n \u003cp\u003eBy organizing the data on urban tourist attractions and urban green infrastructure in three cities, the following Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e was obtained. Using GIS for data analysis, various resources distributed within the DIDs of each city were identified. The selected resources were then used as centers to monitor buffer zones with a radius of 800 meters. Through buffer analysis, the accessible UGI within an 800M range of urban tourist attractions is shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. By overlaying the buffer zone, urban tourist attractions, and UGI data, it was found that urban tourist attractions and UGI exist in an integrated form within the 800-meter buffer zone of urban tourist attractions.\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\" width=\"884\" height=\"190\"\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eThe analysis shows that UGI and urban tourist attractions frequently appear together within this range. This indicates that within this range, green spaces and urban tourist attractions coexist, forming an integrated urban landscape. In these three cities, visitors can potentially reach surrounding UGIs by walking or cycling after visiting urban tourist attractions. The UGI near urban tourist attractions includes parks, green spaces, and areas with vegetation cover. This form of integration is evident around different urban tourist attractions in multiple cities. Compared to the other two cities, Kyoto exhibits the most noticeable integration of urban tourist attractions and UGI in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e (b).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Spatial Clustering of Urban tourist attractions and UGI\u003c/h2\u003e\n \u003cp\u003eBased on ArcGIS\u0026apos;s LCLQ, the LCOQ values of various urban tourist attractions within the DID of the three cities were calculated. These values were then matched with the descriptions in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Five relationships between urban tourist attractions and UGI were analyzed and interpreted accordingly. A scatter plot is also created and can display the relationship between the local colocation quotients and the p-values calculated.\u003c/p\u003e\n \u003cp\u003e\u003cimg 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\" width=\"904\" height=\"241\"\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eTo better compare these three cities, we divided the ratios into three intervals: \u0026lt;0, 1.0\u0026ndash;2.0, and \u0026gt;2.0. The LCLQ\u0026lt;0 interval indicates the isolation of urban tourist attractions from urban parks, 1.0\u0026lt;LCLQ\u0026lt;2.0 indicates co-location, and LCLQ\u0026gt;2.0 indicates strong co-location.\u003c/p\u003e\n \u003cp\u003eTo better compare the three cities, we categorized the types of LCLQ into five cases based on Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e for analysis. Among these, we focused particularly on data concerning co-location relationships.\u003c/p\u003e\n \u003cp\u003eIn Fig .3 (a), within the DID (densely inhabited district) area of Tokyo, urban tourist attractions with significant co-location with UGI (urban green infrastructure) show a maximum LCLQ value of 2.08. Additionally, Tokyo has the most urban tourist attractions with co-location relationships with UGI among the three cities. Figure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e (b) shows that within the DID area of Kyoto, urban tourist attractions with significant co-location with UGI have a maximum LCLQ value of 3.17. Kyoto has the highest number of tourist attractions with strong co-location relationships with UGI. In Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e (c), within the DID area of Kumamoto, urban tourist attractions with significant co-location with UGI show a maximum LCLQ value of 2.73, with the fewest tourist attractions exhibiting co-location with UGI among the three cities.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\u0026nbsp;\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eNumber of Urban Tourist Attractions in Five relationships\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCity\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUrban Tourist Attractions\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCo-located\u003c/p\u003e\n \u003cp\u003eSignificant\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCo-located\u003c/p\u003e\n \u003cp\u003eNot Significant\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIsolated\u003c/p\u003e\n \u003cp\u003eSignificant\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIsolated\u003c/p\u003e\n \u003cp\u003eNot Significant\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUndefined\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTokyo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKyoto\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eKumamoto\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eWe summarized the data and compiled information on the number of urban tourist attractions with and without co-location relationships with UGI in each city, as shown in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. The results indicate that Kyoto has the highest number of urban tourist attractions with strong co-location relationships with UGI, but the differences among the three cities are minor in this regard. However, the total number of tourist attractions co-located with UGI varies significantly, with Tokyo having the most (64), followed by Kyoto (25), and Kumamoto with the fewest (11). Additionally, a large number of urban tourist attractions in Kyoto and Kumamoto lack surrounding UGI, making their relationship with UGI undefined. In contrast, no such case is observed in Tokyo.\u003c/p\u003e\n \u003cp\u003eIn all three cities, spatial correlation exists between urban tourist attractions and UGI. However, individual tourist attractions may be influenced by their geographic location and facility characteristics. For example, in Kumamoto, several attractions within Kumamoto Castle are separated from the surrounding city by walls, creating an isolated spatial relationship with urban green spaces.\u003c/p\u003e\n \u003cp\u003eThis spatial correlation suggests a certain regularity in the spatial layout of urban tourist attractions and UGI in urban areas. The study also indicates that different economic levels and population sizes may impact the relationship between urban tourist attractions and UGI. Further analysis could reveal the implications of this spatial correlation for urban planning and tourism development.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe study found a significant spatial correlation between urban tourist attractions and UGI. This result indicates that the distribution of urban tourist attractions and UGI is not random but follows a certain pattern or regularity. This may reflect tourists' preferences for natural environments and their need for green spaces when choosing travel destinations.\u003c/p\u003e \u003cp\u003eCompared to existing literature, this study further confirms the association between urban tourist attractions and UGI in Japan. Previous study has indicated that a comprehensive urban index can improve urban environmental quality, enhance residents' quality of life, and increase happiness. Our results suggest that the comprehensive urban index may also play a crucial role in enhancing the attractiveness of urban tourist attractions and increasing the number of visitors.\u003c/p\u003e \u003cp\u003eThe study's limitations include not delving deeply into the specific mechanisms behind the spatial correlation between UGI and urban tourist attractions. For instance, whether there are differences in tourists' preferences for different types of UGI and which types are most popular are questions that require further study.\u003c/p\u003e \u003cp\u003eAdditionally, the limitations of the data sample may impact the results. Although these three cities are used as representatives in this study and can represent some similar cities, there are still certain limitations from the perspective of data analysis. Therefore, suggesting that future studies should consider broader data collection and analysis.\u003c/p\u003e \u003cp\u003eBased on the findings, future study could explore the specific mechanisms by which UGI enhances the attractiveness of urban tourist attractions. For example, surveys or interviews could be used to understand tourists' preferences and needs for different types of UGI, providing more specific recommendations for the planning and development of urban tourist attractions and UGI.\u003c/p\u003e \u003cp\u003eOverall, this study reveals the spatial association between urban landscapes and UGI. The synergistic development of these elements can not only promote the sustainable development of tourism but also improve urban environmental quality and residents' quality of life to a certain extent.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThis study examines three cities based on a green tourism model incorporating walking and cycling. From the buffer zone analysis, we find that UGI is generally distributed around urban tourist attractions in all three cities. The results indicate a significant spatial correlation between urban tourist attractions and the comprehensive city index within the DID areas.\u003c/p\u003e \u003cp\u003eAdditionally, based on LCLQ calculations, some urban tourist attractions exhibit a co-location relationship with the comprehensive city index. Tokyo, with a much higher level of urban development than the other two cities, also shows a more pronounced co-location relationship between urban tourist attractions and UGI. In Kyoto, due to government policies on cultural and historical preservation, the city has the highest number of urban tourist attractions with strong co-location relationships with UGI. In Kumamoto, urban tourist attractions co-located with UGI are mainly concentrated in the city center, with significantly fewer occurrences than in the other two cities. This suggests that in cities with more advanced urban planning, co-location relationships are more evident. Thus, we propose that factors such as a city's development level, economic status, and the degree of cultural and historical preservation may also influence the relationship between urban tourist attractions and UGI. Further research is needed to explore these influences in detail.\u003c/p\u003e \u003cp\u003eThese findings suggest a synergistic relationship between urban tourist attractions and the comprehensive city index based on green tourism. Their cooperative development could further support the advancement of urban green tourism. The collaborative growth of urban tourist attractions and the comprehensive city index will likely have a positive impact on future urban planning, environmental protection, and cultural heritage preservation.\u003c/p\u003e \u003cp\u003eUrban planners are advised to further emphasize the collaborative planning of green infrastructure and urban tourist attractions in future urban development. Through the coordinated planning of urban tourist attractions and high-quality UGI, the development of urban green tourism can be promoted. This approach enhances the appeal of city attractions while protecting the urban ecological environment, fostering interactions between tourists and communities, and preserving the city\u0026rsquo;s culture and history.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: Zhuojing Yang; Methodology: Zhuojing Yang; Formal analysis and investigation: Zhuojing Yang; Writing\u0026mdash;original draft preparation: Zhuojing Yang; Writing\u0026mdash;review and editing: Zhuojing Yang, Riken Homma; Supervision: Riken Homma.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to express our gratitude to the Amano Institute of Technology for providing a scholarship that supported Ms. Zhuojing Yang\u0026apos;s research and studies at Kumamoto University. We also extend our thanks to all the experts and scholars who provided feedback on this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors did not receive support from any organization for the submitted work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eInformed consent.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe declare any competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll relevant data are within the paper.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAl Fahmawee, E. A. D., O. J. G. J. o. T. 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Tomicevic-Dubljevic and economics (2018). \u0026quot;Urban forest benefits to the younger population: The case study of the city of Belgrade, Serbia.\u0026quot; \u0026nbsp;96: 54-62.\u003c/li\u003e\n \u003cli\u003eYi-min, H. and H. J. J. o. X. U. Yong-zhi (2024). \u0026quot;Chinese-style Urbanization: Theoretical Exploration and Practical Innovation in the Transition from Traditional Practice to a New Model of Urbanization.\u0026quot; \u0026nbsp; 43(1): 1-10.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"city-and-built-environment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cben","sideBox":"Learn more about [City and Built Environment](https://www.springer.com/journal/44213/)","snPcode":"44213","submissionUrl":"https://www.editorialmanager.com/cben/default.aspx","title":"City and Built Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"EM KeenBeans","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Green Tourism, Urban tourist attractions, Urban Green Infrastructure, Sustainable Development","lastPublishedDoi":"10.21203/rs.3.rs-5351247/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5351247/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn the context of sustainable development, the concept of green tourism has garnered widespread attention. As more people choose cities as their travel destinations, this trend not only promotes urban economic development but also impacts urban ecosystems. Based on this, this paper proposes the concept of coordinated planning for urban green infrastructure (UGI) and urban tourist attractions. By studying and conducting a comparative analysis of the synergy between green infrastructure and urban tourist attractions in Tokyo, Kyoto, and Kumamoto, this study explores the relationship between green infrastructure and urban tourist attractions in cities with different levels of economic development and population sizes. The study mainly considers the spatial correlation between UGI and urban tourist attractions.\u003c/p\u003e","manuscriptTitle":"Research on the Synergistic Relationship between Urban Tourist Attractions and Green Infrastructure in Japanese Cities Based on Green Tourism","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-19 10:25:39","doi":"10.21203/rs.3.rs-5351247/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Minor revision","date":"2024-12-22T04:05:35+00:00","index":"","fulltext":""},{"type":"reviewerAgreed","content":"","date":"2024-11-06T07:42:56+00:00","index":0,"fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-11-06T05:33:24+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-30T15:24:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"City and Built Environment","date":"2024-10-29T01:27:13+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"city-and-built-environment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"cben","sideBox":"Learn more about [City and Built Environment](https://www.springer.com/journal/44213/)","snPcode":"44213","submissionUrl":"https://www.editorialmanager.com/cben/default.aspx","title":"City and Built Environment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"EM KeenBeans","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"95f2fd1c-335d-461d-b1be-f73a9cbc99c1","owner":[],"postedDate":"November 19th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-01-28T20:43:57+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-19 10:25:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5351247","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5351247","identity":"rs-5351247","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}