Spatial Differentiation and Perception of Urban Park Cultural Ecosystem Services from a Multiscale Perspective: Evidence from the Changsha–Zhuzhou–Xiangtan Urban Agglomeration

Spatial Differentiation and Perception of Urban Park Cultural Ecosystem Services from a Multiscale Perspective: Evidence from the Changsha–Zhuzhou–Xiangtan Urban Agglomeration | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatial Differentiation and Perception of Urban Park Cultural Ecosystem Services from a Multiscale Perspective: Evidence from the Changsha–Zhuzhou–Xiangtan Urban Agglomeration Jian Wang, Zexuan Zhang, Huan Li, Wenjie Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8178918/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 This study constructs a multi-scale spatial differentiation assessment model by integrating a theoretical framework derived from a systematic literature review with public perceptions reflected in online crowdsourced reviews, to analyze the supply characteristics and perception mechanisms of cultural ecosystem services (CES) provided by urban parks. Taking 577 urban parks in the Chang-Zhu-Tan urban agglomeration as empirical cases, the research synthesizes theoretically coded findings from core literature with semantic information extracted from online reviews, revealing the following key insights: First, public and expert cognition diverge, with public perception concentrating on experiential dimensions such as “Waterfront Experience” and “Environmental Sanitation & Comfort”, whereas experts focus on macro-ecological attributes such as “Ecological Functions” and “Spatial Pattern”. Second, urban parks form a multifunctional network centered on ecological experience, social vitality, and cultural aesthetics, underscoring the spatial heterogeneity of green space systems. Third, basic services including facility convenience and core landscapes including waterfront experience exhibit a dynamic interplay of synergies and trade-offs. Fourth, the distribution of park functions is coupled with urban spatial morphology and the blue–green network, providing support for green infrastructure planning. Multiscale spatial differentiation Cultural ecosystem services Urban parks Public perception Systematic literature review Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Against the backdrop of ongoing global urbanization, urban green spaces (UGS) face increasing development pressure, which weakens their function as critical infrastructure supporting urban sustainability and residents’ well‑being (Desa, 2014 ; Jie Li et al., 2024 ). Despite multiple threats such as landscape fragmentation and spatial encroachment, urban parks and other UGS remain core nodes of ecological networks and play an irreplaceable role in regulating microclimates, maintaining biodiversity, and improving residents’ quality of life (De Groot et al., 2010 ; Europe, 2017 ; Opdam, 2020 ; Tost et al., 2019 ). Meanwhile, as societal demand for mental health and cultural identity continues to rise, cultural ecosystem services (CES), defined as the nonmaterial benefits people obtain from ecosystems, have attracted growing attention from scholars and policymakers (Luo et al., 2025 ; You et al., 2024 ). As important settings for near‑nature environments (Campbell et al., 2016 ), urban parks not only provide spaces for recreation and aesthetic experience but are also associated with benefits for physical and mental health (Bertram & Rehdanz, 2015 ; Dong & Qin, 2017 ). Among the multiple ecosystem services provided by UGS, CES are closely associated with social and cultural value (Jie Li et al., 2024 ). CES is commonly defined as the nonmaterial benefits people obtain from ecosystems, encompassing recreation, aesthetic appreciation, cultural heritage, and educational enrichment (Assessment, 2001 ; TEEB, 2010 ; Tengberg et al., 2012 ; Villamagna et al., 2014 )), with a core emphasis on subjective public perception and value attribution (Andersson et al., 2015 ). However, because CES is intangible, strongly subjective, and highly context dependent, its assessment and quantification pose methodological challenges (Daniel et al., 2012 ; Gómez-Baggethun & Barton, 2013 ). While traditional approaches based on questionnaires and field interviews can capture firsthand perception data, high costs, labor intensity, and limited sample representativeness constrain their capacity to efficiently reveal city-scale spatial patterns and heterogeneity of CES (Bertram & Rehdanz, 2015 ; Zhang et al., 2022 ). Over the past two decades, empirical research aimed at understanding, identifying, and quantifying CES has grown rapidly (Cheng et al., 2019 ; Milcu et al., 2013 ). The rise of UGC has been central, becoming a primary data source for big data–driven CES research. Leveraging the large volume of crowdsourced content on social media platforms, UGC can effectively represent public cognition, emotions, and value judgments regarding natural and cultural landscapes (Zabala et al., 2021 ; Zhu et al., 2020 ). Compared with traditional qualitative methods such as in-depth interviews or focus groups, UGC offers lower cost, broader coverage, and stronger timeliness, serving as an important complement to resource-intensive field surveys. More importantly, UGC functions as a proxy indicator for CES with theoretical validity and empirical reliability, as its production often stems from individuals’ motivations to share pleasurable, memorable, or meaningful experiences (Havinga et al., 2020 ; Oliveira et al., 2020 ). Such data are often presented as first-person narratives and are well suited to text and image content analysis to uncover abstract and highly subjective cultural values manifested in human–environment interactions, including sense of place, psychological fulfillment, and social bonding (Small et al., 2017 ; Zhang et al., 2022 ). To enable systematic identification, researchers commonly build customized lexicons that map specific words or semantic units to a predefined CES typology, supporting fine-grained classification, quantification, and spatial mapping. As the core nonmaterial benefits that urban parks provide, the identification and assessment of CES have become a research focus in recent years (You et al., 2024 ). Based on a systematic review of empirical studies on CES in urban parks, the field has gradually formed an expert-informed CES classification framework that also considers public perception, and has widely applied big data approaches such as UGC to advance CES type identification, mapping of visitor activity hotspots, and value representation (Cheng et al., 2019 ; Milcu et al., 2013 ). For example, Lee and Park (2020) quantified local-scale CES features through text mining (Lee et al., 2020 ); Cabana et al. ( 2020 ) integrated perspectives from ecology, sociology, and planning to build a multidisciplinary CES assessment framework that addresses gaps in data coverage and conceptual operationalization (Cabana et al., 2020 ). At different urban scales, parks may exhibit a division of functions in CES provision: some may emphasize aesthetic experience, whereas others may prioritize social interaction or cultural education, reflecting specific spatial organization patterns. Current research mainly follows two pathways. The first relies on crowdsourced data such as social media content to capture spontaneous public experiences (Derungs & Purves, 2016 ; Hernández-Morcillo et al., 2013 ). This pathway is effective in reflecting actual use preferences and affective expression, but may under-represent value dimensions that require specialized knowledge to perceive, such as biodiversity education or historical and cultural heritage (Gómez-Baggethun & Barton, 2013 ; Li et al., 2023 ). The second pathway is a systematic, expert-based evaluation that integrates indicator systems predefined in the academic literature (Milcu et al., 2013 ). With stronger conceptual systematization and theoretical consistency, it can reveal latent, structural, or long-term value dimensions of CES (Chongxian et al., 2020 ), but may not fully capture the diversity and situational specificity of everyday public experience (Liu et al., 2024 ). Due to differences in methodological perspectives, most existing studies emphasize the identification and classification of CES types, with limited integration of public perception and expert judgment to systematically examine the functional roles of urban parks in CES provision and their interrelationships. In response, this study develops a dual-path assessment framework that integrates data-driven public perception with expert-led evaluation to identify, quantify, and analyze the spatial differentiation of park CES at the scale of a large urban agglomeration. Using the Changsha–Zhuzhou–Xiangtan urban agglomeration as the empirical case, the study addresses three core questions: (a) based on online text data, what types of CES are perceived by the public and how are their intensities distributed; (b) how can we construct a composite CES profile for urban parks by incorporating expert knowledge and identify the dominant CES function of each park; and (c) at the city scale, how do different CES functions exhibit spatial clustering, differentiation, and complementarity. The aim is to provide comprehensive and precise diagnostic tools for urban green space planning and management and to support the development of an urban park system that is functionally diverse, structurally optimized, and spatially equitable, thereby better addressing diverse needs across mental, cultural, and social dimensions. 2. Materials and Methods 2.1. Study Area The case area is the Changsha–Zhuzhou–Xiangtan (CZT) urban agglomeration, a core metropolitan region in central China located in central-eastern Hunan Province (Fig. 1 ). The agglomeration comprises three adjacent prefecture-level cities—the provincial capital Changsha, Zhuzhou, and Xiangtan—and is designated as a nationally prioritized urban agglomeration (Liu et al., 2011 ). Over the past two decades, the region has undergone rapid urban expansion and spatial integration, characterized by sustained population concentration in the core cities, steady growth in economic output, and deepening regional coordination (Peng & Xie, 2024 ). The CZT urban agglomeration lies in the middle reaches of the Yangtze River basin, with the Xiang River running through all three cities (Hu et al., 2025 ). The region has a subtropical monsoon climate with distinct seasons and varied topography, including alluvial plains, hills, and mountain ranges such as Yuelu and Dawei (Luo et al., 2021 ; Luo et al., 2023 ). This geographical and historical context has shaped a diverse system of urban green spaces that reflects natural and cultural heritage (Liang et al., 2024 ), including large scenic areas associated with mountain landforms, riverfront parks along the Xiang River, historic gardens, and modern community parks. Meanwhile, the rapid urbanization of CZT illustrates the tension between development and ecological protection. The interplay between development pressure and a park system serving millions of residents makes the region a suitable case for examining public perceptions of CES and their spatial differentiation. 2.2. Data Sources To capture the multidimensional features of CES, this study adopts a mixed strategy that combines a literature review with empirical data. On the literature side, a systematic review of 30 scholarly articles on ecosystem services in the CZT region is used to derive theoretically grounded assessment dimensions and an indicator system, providing the basis for a comparable analytical framework. On the empirical side, large-scale, spontaneously generated online public reviews are used to mine the public’s lived experience, affective expression, and value perception in everyday use of urban parks. Such UGC is contextual, colloquial, and dynamically evolving, and it complements the limits of conventional approaches in representing subjective perception. By integrating the academic knowledge base with public feedback, the study builds a CES assessment model with stronger coverage and explanatory power. 2.2.1. Online Crowdsourced Text Data This study uses Dianping ( https://www.dianping.com/ ) as the data source and systematically collects publicly available user reviews for 577 urban parks in the CZT region. The time span covers January 2014 to May 2024 to capture long-term dynamics in public perceptions and experiences of urban parks. Each record includes the park name, review text, and publication date. After preprocessing and cleaning, we obtained 170,683 valid reviews and built a text corpus containing 113,695 unique tokens. The distribution of reviews by city is 132,038 for Changsha, 19,820 for Zhuzhou, and 18,825 for Xiangtan. 2.2.2. Park Spatial and Attribute Data We compiled a multisource integrated dataset covering 577 urban parks in the CZT urban agglomeration. First, official park rosters published by the landscape and greening authorities of the three municipalities were used to define the initial sample. We then supplemented and cross‑validated the roster with points‑of‑interest (POI) data from major online mapping platforms by systematically querying keywords such as “park,” “scenic area,” and “forest park” to improve coverage. For each park, we extracted the official name, precise geographic coordinates (latitude and longitude), and administrative address. Using high‑resolution satellite imagery, we manually vectorized park boundaries to produce polygon geometries, ensuring positional accuracy and reliable area estimates. We integrated open remote‑sensing and geospatial datasets to derive multidimensional variables that characterize both natural and built environments, including blue‑ and green‑space density, PM2.5 concentration (NASA SEDAC), normalized difference vegetation index NDVI (USGS), trail density, spatial connectivity, and terrain slope, thereby profiling the ecological and spatial attributes of each park(Table 1 ). Table 1 Variables and Data Sources for Park Characterization Attributes Variables Meaning Data source Natural Blue space density (%) Percentage of water bodies within a 1 km buffer around each park (such as lakes, rivers, ponds, etc.) Remote sensing image analysis (Landsat/Sentinel) Levels of PM2.5 concentrations (µg/m 3 ) Average value of PM 2.5 in each park NASA Socioeconomic Data and Applications Center (SEDAC) Normalized difference vegetation index (NDVI) Average value of NDVI in each park the United States Geological Survey (USGS) Earth Explorer website Trail density Total length of park trails divided by the park area OpenStreetMap (OSM) data Connectivity Number of alternative routes available from each trail segment OpenStreetMap data of 2021 Changsha Terrain slope (degree) Average value of slope in the 20m buffer around each trail segment Geospatial Data Cloud site, Computer Network Information Centre. ( http://www.gscloud.cn ) Elevation (m) Mean elevation within the park boundary Geospatial Data Cloud (SRTM/ASTER DEM) Water features count Inland static and moving water bodies of natural and manmade origin within the park boundary OpenStreetMap data of 2021 Changsha 2.2.3. Literature Review and Extraction of Cultural Ecosystem Services Indicators Using core keywords on the CZT urban agglomeration, urban parks, cultural ecosystem services, and environmental characteristics, we systematically searched CNKI and Web of Science and screened thirty empirical studies with complete research design, data description, and statistical reporting (Appendix A). For each study, we recorded author information, publication year, sample size, spatial analysis unit, research methods, and the definitions and measurements of dependent and independent variables. We then compiled CES-related indicators reported in the literature, calculated their frequencies, and ranked their importance. On this basis, we distilled core assessment indicators that are widely adopted and exhibit higher consensus, which provide the theoretical basis and indicator foundation for the analytical framework of this study. 2.3. Data Preparation and Integration Data integration centers on three core data streams (Fig. 2 ) to build a CES analytical framework that is led by online public perception and bilaterally anchored in the macro-environmental context and the academic knowledge base. First, public perception data constitute the core input. We systematically collected user review texts on the Dianping platform for parks within the CZT urban agglomeration from 2014 to 2024. After data cleaning, tokenization, stop word removal, and sentiment filtering, we applied topic modeling and keyword co-occurrence analysis to extract high-frequency semantic units reflecting lived recreational experience, and derived a CES perception typology with local contextual features. This typology is induced directly from everyday public expression and avoids over-constraining perception structures through a priori theory. Second, to reveal linkages between public CES perception and the macro-environmental context, we assembled multisource spatial context data, including air quality PM2.5, normalized difference vegetation index (NDVI), blue–green space distribution, trail density, terrain relief, transport accessibility, and neighborhood socioeconomic attributes. All variables were harmonized to a common coordinate system, spatially interpolated, and normalized to produce high-resolution environmental covariate layers for subsequent spatial regression between perception hotspots and environmental factors. Third, to connect public cognition with professional discourse, we conducted a systematic review of thirty core studies on CES assessment over the past decade, extracted frequently used assessment themes and indicators, built an expert-oriented CES thematic lexicon, and generated a relative importance ranking based on bibliometric weights (see Section 3.4 ). This knowledge base provides a theoretical reference for public perception dimensions and underpins the subsequent comparison between expert and public value frameworks. 2.4. Analysis Methods This study adopts a three-stage framework—perception-driven, environment response, and knowledge benchmarking (Fig. 3 )—to analyze the structure of public cognition of cultural ecosystem services (CES) in urban parks and its formation mechanisms. Based on user reviews of urban parks posted on Dianping, we apply natural language processing and semantic mining to extract and quantify public expressions of CES perception. As the raw reviews are unstructured text, we first implement standardized preprocessing, including text cleaning (removal of punctuation, URLs, special characters, and function words such as a and the), lowercasing, tokenization, lemmatization, and part-of-speech tagging. Named entity recognition (NER) is used to identify and remove place names, personal names, and other irrelevant entities to reduce noise. These procedures are implemented with mature NLP toolkits such as spacyr (Benoit & Matsuo, 2020 ) and udpipe (Wijffels et al., 2018 ). We then build a crowdsourced, phrase-level CES lexicon tailored to urban park contexts. Its categorization scheme follows the definition of CES in the Millennium Ecosystem Assessment (MEa, 2005 ) and is localized to everyday usage among Chinese urban residents. Unlike dictionaries based on single characters or isolated keywords, we focus on semantically complete units such as noun phrases and verb phrases, for example, “walking feels comfortable,” “good for photo check-ins,” and “taking children to learn about plants,” which more accurately capture contextualized value judgments in public experience. By matching preprocessed reviews to the CES lexicon, we automatically identify CES content and compute frequencies for each review, and then aggregate to the park level to construct multidimensional public-perception CES indices. This approach balances semantic depth with scalability and yields quantifiable, interpretable public-facing evidence for fine-grained evaluation and differentiated planning of urban parks. After semantic parsing and quantification of multi-dimensional CES perception, we spatially match each park’s average perception scores across CES dimensions with multisource environmental covariates to probe how ecological and built environments drive public experience. Indicators include green cover measured by NDVI, air quality captured by PM2.5 concentration, proximity to water bodies, park accessibility such as population density within 500-meter walking or cycling buffers, and facility density such as benches, toilets, and trail length. All variables are aligned to a common coordinate system to ensure accurate coupling between park units and their environmental attributes. We use Pearson correlation to preliminarily assess linear associations between environmental factors and CES dimensions, and then estimate multiple linear regression (MLR) models to test the joint explanatory power of covariates for different CES categories such as aesthetic experience, recreational vitality, and comfort. After diagnosing variance inflation factors (VIF) to exclude multicollinearity, the models identify key ecological and built features that significantly shape public preferences, revealing how green, blue, and gray infrastructures jointly influence cultural service provision and offering empirical guidance for targeted design and intervention. Finally, to position public perception within the broader academic discourse, we introduce bibliometric analysis as an external reference. Drawing on thirty core CES studies from Web of Science and Scopus, we conduct topic modeling and compute keyword weights to derive an expert-oriented ranking of relative importance across CES dimensions. Although the perception dimensions reflected in public reviews and the theoretical themes in the literature differ in their levels of abstraction, semantic alignment enables structural comparison—for example, mapping expert terms such as “landscape aesthetics” and “local culture and aesthetics” to the public’s “aesthetic experience,” and aligning “social relations” and “community cohesion” with “social vitality.” This cross-discourse comparison reveals overlapping areas of professional consensus and public cognition, such as shared emphasis on aesthetics and recreation, while highlighting potential tensions, such as identity and spiritual values that are prominent in expert discourse but comparatively marginal in public reviews, thereby providing a theoretical anchor for bridging the knowledge–perception gap in planning practice. 3. Results 3.1. Overall and Regional Distributions of Public Perceptions of Park Cultural Ecosystem Services in the CZT Urban Agglomeration Using high-frequency terms in the public review corpus, we extracted keywords closely related to CES—with a particular focus on evaluative adjectives—and constructed an eight‑dimension CES semantic labeling scheme. We then annotated and tallied online reviews for 577 parks in the CZT urban agglomeration, calculating for each park the share of positive evaluations across the eight CES dimensions and visualizing the distributions with boxplots (Fig. 3 ). To elucidate regional differences, we summarize patterns using the median, IQR, and overall spread. In Xiangtan (Fig. 3 a), Waterfront Experience stands out, with a median of 0.78 (Q1 = 0.62, Q3 = 0.90) and some parks approaching the upper limit, underscoring the importance of waterside landscapes in the city’s park system. By comparison, other dimensions score modestly: Physical Comfort (median = 0.23), Amenity Completeness (0.23), and Place Vitality (0.13) are low; Transportation Accessibility is slightly higher (0.25) but remains conservative overall; and positive mentions of Objective Safety (0.04) and Information Clarity (0.06) are rare, suggesting that these services are not yet well reflected in public experience. In Changsha (Fig. 3 b), overall perception levels are the highest among the three cities. Waterfront Experience reaches a median of 0.90 (Q1 = 0.78, Q3 = 0.96), with several parks near the maximum, indicating strong attraction of waterside settings. Aesthetic Experience also performs well (median = 0.58) with a wide spread, pointing to diverse cultural‑aesthetic value. Notably, although Transportation Accessibility (0.48), Amenity Completeness (0.25), and Place Vitality (0.24) lead within the region, their absolute levels are still modest, implying room for improvement in functional services. Objective Safety (0.20) and Information Clarity (0.17) show progress but still have many parks without positive mentions, indicating scope for further capacity building. In Zhuzhou (Fig. 3 c), Waterfront Experience (median = 0.79) and Aesthetic Experience (0.63) are the main strengths—the latter slightly above the regional average, hinting at distinctive cultural landscapes. Functional dimensions are more muted: Physical Comfort has the lowest median among the three cities (0.08); Amenity Completeness (0.16) and Place Vitality (0.13) are also low; Transportation Accessibility (0.23) is comparable to Xiangtan but with fewer high‑scoring cases; and positive feedback on Objective Safety (0.04) and Information Clarity (0.07) remains limited. At the CZT scale (Fig. 3 d), Waterfront Experience ranks first with a median of 0.81 (Q1 = 0.65, Q3 = 0.93) and a wide spread, showing broad consensus that waterside landscapes are the core attraction of regional parks. Aesthetic Experience (median = 0.58) forms the second tier. The remaining dimensions are generally lower: Transportation Accessibility (0.27) is the highest among functional indicators, likely lifted by Changsha; Amenity Completeness (0.23), Physical Comfort (0.22), and Place Vitality (0.21) have similar, concentrated distributions, indicating limited salience of basic services in public experience; Information Clarity (0.06) and Objective Safety (0.04) remain at the bottom, suggesting that wayfinding and safety are not yet central concerns in current park services. Overall, public perceptions of CES in CZT parks exhibit a pattern of strong concentration in eco‑aesthetic services and comparatively subdued recognition of basic functional services. This highlights the central value of waterfront and aesthetic experiences in urban parks while suggesting that, alongside consolidating these strengths, future efforts should prioritize coordinated enhancements in facilities, environmental comfort, safety order, and information guidance to foster a more balanced provision of cultural ecosystem services. 3.2 Co-occurrence Relationships among CES Perceptions Based on semantic annotations of online reviews for 577 urban parks, Pearson correlation coefficients were computed for all pairs of the eight CES perception dimensions (Table 3 ). The results show varying degrees of linear association across dimensions, with some pairs exhibiting strong covariation and others negative relationships. Among positive associations, the strongest correlation is between Facilities and Convenience and Activities and Social Vitality (r = 0.85, p < 0.001). Safety and Order is strongly and positively correlated with Transportation Accessibility (r = 0.67, p < 0.001), is also positively related to Facilities and Convenience (r = 0.49, p < 0.001), and shows a moderate positive correlation with Activities and Social Vitality (r = 0.41, p < 0.001). Environmental Sanitation and Comfort is highly and positively correlated with Wayfinding and Process Clarity (r = 0.68, p < 0.001), while its correlations with Transportation Accessibility (r = 0.05), Safety and Order (r = − 0.09), and Place‑based Culture and Aesthetics (r = − 0.11) are weak or near zero. Wayfinding and Process Clarity, in addition to its strong link with Environmental Sanitation and Comfort, is positively but weakly correlated with Transportation Accessibility (r = 0.27, p < 0.001), Safety and Order (r = 0.31, p < 0.001), and Facilities and Convenience (r = 0.18, p < 0.01), and shows a small positive correlation with Activities and Social Vitality (r = 0.14, p < 0.05). On the negative side, Ecological Landscape and Waterfront Experience is significantly and negatively correlated with several dimensions: Safety and Order (r = − 0.53, p < 0.001), Facilities and Convenience (r = − 0.32, p < 0.001), Activities and Social Vitality (r = − 0.28, p < 0.001), and Wayfinding and Process Clarity (r = − 0.28, p < 0.001). It is also weakly negative with Transportation Accessibility (r = − 0.18, p < 0.01) and shows a very small positive correlation with Place‑based Culture and Aesthetics (r = 0.05). Correlations involving Place‑based Culture and Aesthetics are generally small or not statistically significant (e.g., r = 0.08 with Safety and Order), with absolute values mostly below 0.11. Overall, the correlation analysis indicates that the CES perception dimensions are not independent in the data but display systematic co‑occurrence patterns: functional service dimensions tend to be positively associated with each other, whereas the nature‑centered experience dimension (Ecological Landscape and Waterfront Experience) exhibits negative associations with several support‑service dimensions. Table 3 Correlation Matrix of the Eight CES Perception Dimensions Accessibility & Transport Safety & Order Local Culture & Aesthetics Environmental Sanitation & Comfort Facilities & Amenities Activity & Social Vitality Process & Guidance Clarity Ecological Landscape & Waterfront Experience Accessibility & Transport 1.00 Safety & Order 0.67 *** 1.00 Local Culture & Aesthetics 0.03 0.08 1.00 Environmental Sanitation & Comfort 0.05 -0.09 -0.11 1.00 Facilities & Amenities 0.43 *** 0.49 *** -0.09 -0.02 1.00 Activity & Social Vitality 0.37 *** 0.41 *** 0.07 -0.04 0.85 *** 1.00 Process & Guidance Clarity 0.27 *** 0.31 *** 0.02 0.68 *** 0.18 ** 0.14* 1.00 Ecological Landscape & Waterfront Experience -0.18 ** -0.53 *** 0.05 -0.11 -0.32 *** -0.28 *** -0.28 *** 1.00 Note: * p < 0.05; ** p < 0.01; *** p < 0.001 3.3. Correlation Analysis of Park Environmental Characteristics and CES Perceptions To quantify the relationships between park environmental characteristics and public perceptions of CES, a correlation analysis was conducted (Table 4 ). The results revealed multiple significant statistical associations between the physical environment of the parks and the visitor experience. The core findings of the analysis detail the influence of natural elements such as topography, vegetation, and water bodies on CES perceptions. Regarding topographical and atmospheric indicators, higher elevation was significantly and positively correlated with better perceptions of “Ecological Landscape & Waterfront Experience” ( r = 0.32, p < 0.001) and higher “Process & Guidance Clarity” ( r = 0.26, p < 0.001). Conversely, PM2.5 concentration was negatively correlated with “Environmental Sanitation & Comfort” ( r = -0.27, p < 0.001) but showed a significant positive correlation with multiple dimensions including Transportation “Accessibility & Transport”, “Safety & Order”, and “Local Culture & Aesthetics”. This may reflect that areas with higher PM2.5 levels are often centrally located urban zones with greater Accessibility & Transport and population density. Regarding vegetation and water features, the Normalized Difference Vegetation Index (NDVI) was positively correlated with “Environmental Sanitation & Comfort” ( r = 0.24, p < 0.001) but negatively correlated with “Local Culture & Aesthetics” ( r = -0.19, p < 0.01). A park’s blue space density, trail density, and connectivity index were all positively and significantly correlated with “Ecological Landscape & Waterfront Experience”, with trail density showing the strongest relationship ( r = 0.20, p < 0.01). Table 4 Correlations between Park Environment and CES Perceptions PM2.5 (µg/m³) Elevation (m) Slope (deg) NDVI (mean) Blue space density (%) Trail density (km/km²) Connectivity index Water features count Transportation accessibility 0.36*** -0.14* -0.13* -0.11 -0.12 -0.08 0.06 -0.08 Objective safety 0.28*** -0.10 -0.07 -0.12 -0.17** -0.08 0.03 -0.15* Aesthetic experience 0.32*** -0.11 0.03 -0.19** 0.05 0.04 0.07 -0.18** Physical comfort -0.27*** 0.32*** 0.20** 0.24*** 0.04 -0.09 -0.03 0.18** Amenity completeness 0.01 -0.08 -0.17* -0.09 -0.13* -0.00 -0.04 -0.07 Place vitality 0.08 -0.08 -0.13* -0.13 -0.17** -0.06 -0.09 -0.09 Information clarity -0.11 0.26*** 0.21** 0.21** -0.08 -0.06 0.04 0.12 Waterfront experience 0.02 -0.01 -0.01 0.01 0.18** 0.20** 0.16* 0.05 Note: *p < 0.05; **p < 0.01; ***p < 0.001 3.4 Spatial Distribution Figure 4 indicates that the dominant park types in the CZT urban agglomeration exhibit a pronounced river-corridor belt pattern and cluster agglomeration. Parks dominated by Ecological Landscape & Waterfront Experience are most numerous, forming a continuous north–south belt along the main stem of the Xiangjiang River and its principal tributaries; densities are highest around built-up areas in the northern and central parts of the region and decline toward secondary towns and peri-urban areas in the south. Comparing the three cities, Changsha contains the densest point distribution and the widest type coverage; Xiangtan is chiefly clustered along the river zone and around the urban core; Zhuzhou is more dispersed overall, with linear chaining along the river corridor and town nodes. In general, multiple high-density patches occur within central urban districts, while the periphery is characterized by low-density, scattered points. The spatial directionality of individual perception types is distinct. Ecological Landscape & Waterfront Experience constitutes the regional backbone, occurring continuously along shorelines and mountain edges. Local Culture & Aesthetics concentrates in Changsha’s urban area and historic landscape precincts, forming several cultural patches. Facilities & Amenities and Accessibility & Transport are mainly aligned with arterial corridors, bridgeheads, and new-town corridors, with substantial overlap in inner Changsha. Wayfinding & Process Clarity is concentrated in landmark riverfront parks and core urban greens. Environmental Sanitation & Comfort is scattered with minor inter-city differences. Activity & Social Vitality is confined to a few central parks. Safety & Order appears as discrete, intermittent points. Overlays of these types indicate a composite, multi-type pattern in Changsha; a waterfront–ecological predominance in Xiangtan supplemented by a small number of cultural and facilities-oriented sites; and, in Zhuzhou, a primarily river-aligned linear chaining with few non-riverine points. Figure 4. Spatial distribution of dominant CES perceptions in parks. 3.5 CES Theme Classification Based on Bibliometric Analysis Results from the bibliometric analysis, as shown in Table 4 , indicate that among 30 core studies published over the past decade focusing on cultural ecosystem services (CES), nine high-frequency thematic categories emerged with distinct weight distributions. Regulating and maintenance functions and land use and spatial patterns are the two most prominent themes, accounting for 33.42% and 27.11% of the total weight, respectively—collectively exceeding 60%. A second tier comprises trade-offs and synergies among ecosystem services (10.97%), blue-green infrastructure and aquatic ecosystem services (8.50%), and aesthetic values (6.28%). In contrast, themes more directly linked to everyday public experience—namely recreation, tourism, and social values (2.20%) and accessibility and spatial integration (1.07%)—appear far less frequently in the academic literature and thus carry the smallest weights. Table 4 Expert-derived CES Theme Weights Ranking Theme Weight (%) 1 Regulating and maintenance functions 33.42 2 Land use and spatial patterns 27.11 3 Trade-offs and synergies among ecosystem services 10.97 4 Blue-green infrastructure and aquatic ecosystem services 8.50 5 Aesthetic values 6.28 6 Governance and management frameworks 5.55 7 Biodiversity and habitat quality 4.90 8 Recreation, tourism, and social values 2.20 9 Accessibility and spatial integration 1.07 4. Discussion 4.1 Differences between Public Experience and Academic Focus This study finds that public perceptions of urban park cultural ecosystem services, CES, center on experiential dimensions that are directly perceived, such as waterfront landscapes, place-based aesthetics, and social vitality. This is consistent with the widely observed public preference for aesthetic values and recreational experience in prior research (Milcu et al., 2013 ). By contrast, core academic literature over the past decade has focused more on structural or process topics such as regulation and maintenance functions and land-use patterns. This difference is not a fundamental opposition; it more likely arises from natural differentiation in cognitive scales and goal orientations among different actors: the public forms experiential, embodied evaluations grounded in everyday use contexts, whereas scholars tend to construct analytical frameworks from the perspectives of ecological processes, spatial structures, or system functions. Notably, basic service dimensions closely linked to recreational convenience — such as accessibility, safety and order, and wayfinding and information — carry relatively low weights in academic themes yet appear frequently in public comments, indicating room for the current research system to better respond to users’ practical needs. Accordingly, future CES research can, while maintaining ecological scientific rigor, further strengthen the integration of social dimensions (Adhikary et al., 2025 ; Stępniewska, 2021 ). For example, incorporating large-scale public semantic data as a complementary evidence source can help identify service links that are represented in ecological models but have not been effectively translated into user-perceived value. By building a mapping between ecological supply and public experience, researchers can more comprehensively reveal the realization mechanisms of CES and, in turn, provide integrated indicator systems for green infrastructure performance evaluation that combine ecological soundness with social sensitivity. 4.2 Service Portfolio Characteristics Under the Dominance of Waterfront Landscapes In the CZT urban agglomeration, strong public recognition of ecological landscapes and waterfront experience constitutes a salient feature of regional perceptions of cultural ecosystem services, CES. This dimension not only receives the highest scores but also forms a continuous distribution belt along the main stem of the Xiang River, underscoring the core role of water systems in shaping the attractiveness of urban parks. Meanwhile, correlation analysis shows significant negative associations between this dimension and several functional services such as facility convenience, safety and order, and clarity of information, reflecting a specific service portfolio pattern: parks with stronger ecological appeal tend to have relatively minimalist supporting service provision. This portfolio may stem from a planning and design priority on conserving natural character and may also relate to management intensity or development constraints in waterfront areas. From a practical standpoint, such parks succeed in creating strong visual and affective appeal yet may face limitations for all‑weather use, age inclusivity, or emergency assurance. For example, waterfront trails lacking lighting or surveillance can affect nighttime safety, and the absence of wayfinding can reduce visitors’ depth of exploration. Notably, the relationship between ecological value and recreational experience varies across contexts. Dai et al. point out that in Chinese urban parks, spiritual value is synergistic with aesthetic and recreational values(Daniel et al., 2012 ), whereas a study in Bangladesh finds conflicts between the two(Sultana & Selim, 2021 ). This contrast suggests that whether ecological advantages translate into comprehensive well‑being depends strongly on local design strategies and management practices. Accordingly, future optimization of waterfront spaces can explore a low‑intervention, high‑responsiveness design strategy: with minimal disturbance to the natural substrate, progressively remedy basic service gaps through smart facilities, flexible nodes, or community co‑governance mechanisms, so that ecological advantages are more fully translated into inclusive and sustainable public well‑being. 4.3 Spatial Distribution of CES in Urban Parks The spatial distribution of perceived park CES shows regional differentiation: Changsha features multi-type clustering and high coverage, Xiangtan forms a linear concentration centered on waterfront ecology, and Zhuzhou presents a dispersed pattern that links river corridors with urban nodes. This differentiation relates to city scale, development sequence, and spatial structure. As the regional center, Changsha operates within a complex built environment and a broad range of user needs, which yields a park system with functional mixing; Xiangtan and Zhuzhou, constrained by urban size and expansion models, rely more on natural elements as the organizing framework for park layout. This pattern indicates that the functional configuration of urban park systems is not an isolated technical decision but is embedded in broader urban form and ecological networks. The performance of blue-green spaces depends on coordination with transport networks, population distribution, and historical context. Therefore, when advancing integrated regional green-space systems, planning should respect each city’s baseline conditions and stage of development and avoid direct replication of core-area models. 4.4 Multi‑source Data Synergy and Future Research Directions Based on the context of the CZT urban agglomeration and combining online reviews with bibliometric methods, this study offers a preliminary exploration of how public perception and academic attention manifest across the dimensions of cultural ecosystem services, CES. It should be noted that the articulation and cognition of CES are strongly context dependent and may display differentiated patterns across regions and cultural settings(Daniel et al., 2012 ; Sultana & Selim, 2021 ). Accordingly, when applying related findings to other areas, adaptive adjustments informed by local ecological baselines and socio‑cultural characteristics are advisable. In addition, the current depiction of public perception relies mainly on textual data from the Dazhong Dianping platform, whose users tend to record tourism or leisure experiences, which may to some extent amplify the visibility of certain experiential dimensions (Taecharungroj & Mathayomchan, 2019 ). To more fully reflect the needs of diverse groups, future research can further integrate multi‑source data and multiple stakeholder perspectives. For example, short narratives collected through local interviews (Bieling & Plieninger, 2017 ), public participation geographic information systems, PPGIS, to obtain residents’ spatial value annotations (Olafsson et al., 2022 ), and historical archival sources (Derungs & Purves, 2016 ) can enrich the social dimension of CES. Building on this foundation, linking remote sensing environmental indicators with behavioral perception data may enable a more dynamic and explanatory integrated evaluation framework and provide more solid support for the collaborative governance of green infrastructure. 5. Conclusion This study adopts a multi-scale perspective and integrates a systematic literature review with online review data from the Dazhong Dianping platform to construct a cultural ecosystem services assessment framework that coordinates public perception and expert cognition through dual pathways, and it systematically analyzes the spatial differentiation and perception mechanisms of CES across 577 parks in the CZT urban agglomeration. The findings indicate that public perceptions of urban park CES are highly concentrated in embodied experiential dimensions such as waterfront experience and environmental comfort, whereas academic research pays greater attention to macro-structural topics such as ecological regulation functions and spatial patterns, revealing a clear misalignment in value focus. At the same time, park functions exhibit a multifunctional network centered on ecological experience, social vitality, and cultural aesthetics, whose spatial distribution is closely coupled with urban form and blue-green ecological networks, with a pronounced north–south belt-like agglomeration along the main stem of the Xiang River. In addition, basic services such as facility convenience and core landscape features such as waterfront experience show both synergies and trade-offs, underscoring that while parks strengthen ecological appeal, the provision of supporting services such as safety, wayfinding, and accessibility remains insufficient. These results provide empirical support and methodological insight for optimizing green infrastructure planning at the urban-agglomeration scale. Future development of urban park systems should move beyond a single ecological or aesthetic orientation toward a coordinated model that balances ecological integrity, service equity, and public experience. On the one hand, planning should respect the development stages and spatial baselines of different cities and coordinate, at the regional level, a park network that is functionally complementary and diverse in type. On the other hand, under the premise of protecting the natural substrate, low-intervention and high-responsiveness design strategies should be used to precisely address gaps in basic services such as safety, information, and accessibility, promoting the effective translation of ecological advantages into welfare outcomes that are shared by all and friendly to all ages. The study also advocates deep integration of multi-source big data with traditional academic knowledge to build a CES assessment system with stronger social sensitivity, thereby bridging theory and practice and supporting human-centered urban sustainability. Declarations Author Contribution J.W. and Z.Z. conceptualized the study and designed the research framework. J.W. and H.L. conducted the data collection and preprocessing, including the integration of online reviews and geospatial datasets. Z.Z. and H.L. developed the dual-pathway analytical model and performed the multiscale spatial differentiation analysis. J.W. led the manuscript writing with critical input from Z.Z. and W.L. on the theoretical interpretation and policy implications. W.L. supervised the project and provided strategic guidance throughout the research process. All authors contributed to the refinement of the methodology and the interpretation of results. Figures 1–5 were prepared by Z.Z., while J.W. and H.L. collaborated on the visualization of spatial patterns. All authors reviewed and approved the final manuscript. References Adhikary, M., Ghosh, D., Mandal, B., & Das, S. (2025). Estimating and mapping the value of cultural ecosystem services in an urban landscape context. Applied Geography , 177 , 103556. Andersson, E., Tengö, M., McPhearson, T., & Kremer, P. (2015). Cultural ecosystem services as a gateway for improving urban sustainability. Ecosystem services , 12 , 165–168. Assessment, M. E. (2001). Millennium ecosystem assessment (Vol. 2). Millennium Ecosystem Assessment. Benoit, K., & Matsuo, A. (2020). A Guide to Using spacyr. In. Bertram, C., & Rehdanz, K. (2015). Preferences for cultural urban ecosystem services: Comparing attitudes, perception, and use. Ecosystem services , 12 , 187–199. Bieling, C., & Plieninger, T. (2017). 19 Leveraging landscape stewardship: Principles and ways forward. The Science and Practice of Landscape Stewardship , 366. Cabana, D., Ryfield, F., Crowe, T. P., & Brannigan, J. (2020). Evaluating and communicating cultural ecosystem services. Ecosystem services , 42 , 101085. Campbell, L. K., Svendsen, E. S., Sonti, N. F., & Johnson, M. L. (2016). A social assessment of urban parkland: Analyzing park use and meaning to inform management and resilience planning. Environmental Science & Policy , 62 , 34–44. Cheng, X., Van Damme, S., Li, L., & Uyttenhove, P. (2019). Evaluation of cultural ecosystem services: A review of methods. Ecosystem services , 37 , 100925. Chongxian, C., Weijing, L., & Yu, X. (2020). A Meta-analysis of Natural Landscape Effects on Physical and Mental Health of the Elderly Population. Landscape Architecture , 27 (11), 90–95. Daniel, T. C., Muhar, A., Arnberger, A., Aznar, O., Boyd, J. W., Chan, K. M., Costanza, R., Elmqvist, T., Flint, C. G., & Gobster, P. H. (2012). Contributions of cultural services to the ecosystem services agenda. Proceedings of the National Academy of Sciences , 109 (23), 8812–8819. De Groot, R. S., Alkemade, R., Braat, L., Hein, L., & Willemen, L. (2010). Challenges in integrating the concept of ecosystem services and values in landscape planning, management and decision making. Ecological complexity , 7 (3), 260–272. Derungs, C., & Purves, R. S. (2016). Characterising landscape variation through spatial folksonomies. Applied Geography , 75 , 60–70. Desa, U. (2014). World urbanization prospects: the 2018 revision. Population Division, department of economic and social affairs, United Nations Secretariat . Dong, H., & Qin, B. (2017). Exploring the link between neighborhood environment and mental wellbeing: A case study in Beijing, China. Landscape and urban planning , 164 , 71–80. Europe, W. (2017). Urban green spaces: a brief for action. World Health Organization. Abgerufen von. Available from https://apps.who.int/iris/handle/10665/344116 Gómez-Baggethun, E., & Barton, D. N. (2013). Classifying and valuing ecosystem services for urban planning. Ecological economics , 86 , 235–245. Havinga, I., Bogaart, P. W., Hein, L., & Tuia, D. (2020). Defining and spatially modelling cultural ecosystem services using crowdsourced data. Ecosystem services , 43 , 101091. He, Y., Lin, Y., Zhou, G., Zhu, Y., & Tang, K. (2021). Spatial pattern and drivers of urbanization in China’s mid-level developing urban agglomeration: A case study of Chang-Zhu-Tan. Regional Sustainability , 2 (1), 83–97. He, Y., Qin, Z., He, Y., Wang, L., & Li, X. (2024). Impact of ecological protection policies on the trade-offs and synergies of ecosystem services: A case study of the Changsha–Zhuzhou–Xiangtan green-core area. Agriculture and Technology , 44 (22), 125–130. Hernández-Morcillo, M., Plieninger, T., & Bieling, C. (2013). An empirical review of cultural ecosystem service indicators. Ecological indicators , 29 , 434–444. Hu, Y., He, K., Yu, C., Tian, Z., Liu, P., & Zhou, M. (2025). Spatiotemporal differentiation of ecosystem service value and its driving factors: A case study of the ecological green-core area in Changsha–Zhuzhou–Xiangtan. Natural Resource Economics of China , 38 (05), 22–27. Hu, Y., Liu, Y., & Li, C. (2022). Multi-scenario simulation of land use change and ecosystem service value in the middle reaches of Yangtze river urban agglomeration. Sustainability , 14 (23), 15738. J, L., L, Z. X., L, L., D, N. Y., L, X. J., L, W. X., M, Y., Y, W., H, Z. Z., B, C., L, C., J, Y. H., B, Y., X, W. Z.,A, W (2021). Evaluation of forest ecosystem service function in ecological green center area of Changsha-Zhuzhou-Xiangtan city group. Hunan ForestryScience ༆ Technology , 48 (06), 50–56. Jiajun, L., Qiulin, L., Shouyun, S., Xiaoru, L., & Jingpeng, L. (2022). Study on the Spatiotemporal Changes of Land Use and Ecosystem Services Value (ESV) of the Green Heart Area in the Changsha-Zhuzhou-Xiangtan (CZT) City Group. Chinese Landscape Architecture , 38 (1), 100. Lee, J., Park, H., Kim, I., & Kwon, H. (2020). s. Analysis of cultural ecosystem services using text mining of residents’ opinions. Ecological indicators , 115 , 106368. Li, J., Fu, J., Gao, J., Zhou, R., Wang, K., & Zhou, K. (2023). Effects of the spatial patterns of urban parks on public satisfaction: Evidence from Shanghai, China. Landscape ecology , 38 (5), 1265–1277. Li, J., Gao, J., Zhang, Z., Fu, J., Shao, G., Zhao, Z., & Yang, P. (2024). Insights into citizens’ experiences of cultural ecosystem services in urban green spaces based on social media analytics. Landscape and urban planning , 244 , 104999. Li, J., Wang, J., & Zhou, W. (2024). Different impacts of urbanization on ecosystem services supply and demand across old, new and non-urban areas in the ChangZhuTan urban agglomeration, China. Landscape ecology , 39 (6), 107. Li, J., & Yao, L. (2024). Evaluating the social value of ecosystem services in the Changsha–Zhuzhou–Xiangtan green core using social media data: A case study of Shiyan Lake Scenic Area and optimization strategies. Agriculture and Technology , 44 (21), 116–121. Li, J., Yao, L., Liu, L., Lian, J., Li, X., & Gan, D. (2025). Social valuation of ecosystem services in the green-core area of the Changsha–Zhuzhou–Xiangtan urban agglomeration based on the SolVES model. Journal of Ecology and Rural Environment , 41 (08), 987–1001. Li, Z., & Dong, T. (2025). Exploring the Mental Health Benefits of Urban Green Spaces Through Social Media Big Data: A Case Study of the Changsha–Zhuzhou–Xiangtan Urban Agglomeration. Sustainability , 17 (8), 3465. Lian, J., Gao, J., Yao, L., Li, X., & Gan, D. (2024). Differences in perceived landscape aesthetic quality between residents and tourists with varying demographic characteristics: A case study of the ecological green core in the Changsha–Zhuzhou–Xiangtan urban agglomeration. Modern Horticulture , 47 (24), 26–27. Liang, S., Yang, F., Zhang, J., Xiong, S., & Xu, Z. (2024). Assessment and management zoning of ecosystem service trade-Off/Synergy based on the Social–Ecological balance: A case of the chang-zhu-tan metropolitan area. Land , 13 (2), 127. Liu, S., Luo, Y., & Xiao, Y. (2011). Research on the Ecological Value of the Green Core of Changsha-Zhuzhou-Xiangtan. Social Sciences in Hunan (05), 125–127. Liu, W., Li, H., Huang, D., He, F., Liu, W., & Sun, Q. (2024). Application of random forest algorithm in the study of microscopic features and visitor experience evaluation in gardens. Environmental Research Communications , 6 (11), 115019. Luo, J., Zhou, X., Zhang, Z., Tian, Y., & Tang, X. (2021). Evaluation of wetland ecosystem services in green core of Changzhutan urban agglomeration, Hunan. IOP Conference Series: Earth and Environmental Science. Luo, M., Du, M., Jing, Y., Feng, C., Yang, L., & Zhao, B. (2023). Comprehensive ecosystem service value analysis of green spaces and wetlands in the Changsha–Zhuzhou–Xiangtan urban agglomeration. Green Science and Technology , 25 (24), 111–118. Luo, S., Naghibi, M., Patuano, A., & van Leeuwen, E. (2025). Cultural ecosystem services of informal green spaces: usage, perception and preference of residents in deprived urban neighbourhoods. Local Environment , 1–21. Luying, W., Xiaoma, L., Dexin, G., Pengao, L., Sheng, G., & Yi, L. (2024). Spatial Heterogeneity and Driving Factors of Ecosystem Service Trade-offs and Synergies in the Changsha-Zhuzhou-Xiangtan Urban Agglomeration. Ecology and Environment , 33 (6), 969. MEa, M. E. A. (2005). Ecosystems and Human Well-Being: wetlands and water synthesis. Meng, Z., & ZengYongnian (2018). Temporal and spatial dynamic changes and driving forces analysis of wetland landscape of Chang-Zhu-Tan urban agglomeration. Transactions of the Chinese Society of Agricultural Engineering , 34 (01), 241–249. Milcu, A. I., Hanspach, J., Abson, D., & Fischer, J. (2013). Cultural ecosystem services: a literature review and prospects for future research. Ecology and society , 18 (3). Muyang, C., Chen, H., Zancai, X., & Ping, Z. (2025). Spatiotemporal evolution of ecosystem service value in the Changsha–Zhuzhou–Xiangtan metropolitan ring and its driving factors. Journal of Jishou University(Social Sciences) , 46 (04), 163–176. Olafsson, A. S., Purves, R. S., Wartmann, F. M., Garcia-Martin, M., Fagerholm, N., Torralba, M., Albert, C., Verbrugge, L. N., Heikinheimo, V., & Plieninger, T. (2022). Comparing landscape value patterns between participatory mapping and geolocated social media content across Europe. Landscape and urban planning , 226 , 104511. Oliveira, T., Araujo, B., & Tam, C. (2020). Why do people share their travel experiences on social media? Tourism management , 78 , 104041. Opdam, P. (2020). Implementing human health as a landscape service in collaborative landscape approaches. Landscape and urban planning , 199 , 103819. Peng, W., & Xie, X. (2024). Response of Ecological Resilience to Urban Renewal in Chang-Zhu-Tan Urban Agglomeration. Resour Environ Yangtze Basin , 33 , 2369–2378. Rongzhong, G., Haijian, S., Kuo, Z., & Huaping, L. (2025). Ecosystem services and their trade-offs and synergies in Chang-Zhu-Tan urban agglomeration. Research of Soil and Water Conservation , 32 (04), 365–374. Rui, C., Can, Y., Jing, T., & Xiangyu, L. (2023). The impact of new urbanization of Changsha-Zhuzhou-Xiangtan urban agglomeration on the spatial differentiation of ecosystem service values. Journal of Central South University of Forestry & Technology , 43 (6), 158–167. Small, N., Munday, M., & Durance, I. (2017). The challenge of valuing ecosystem services that have no material benefits. Global environmental change , 44 , 57–67. Stępniewska, M. (2021). The capacity of urban parks for providing regulating and cultural ecosystem services versus their social perception. Land Use Policy , 111 , 105778. Sultana, R., & Selim, S. A. (2021). Residents' perceptions of the role and management of green spaces to provide cultural ecosystem services in Dhaka, Bangladesh. Ecology & Society , 26 (4). Taecharungroj, V., & Mathayomchan, B. (2019). Analysing TripAdvisor reviews of tourist attractions in Phuket, Thailand. Tourism management , 75 , 550–568. TEEB, R. O. (2010). Mainstreaming the economics of nature . TEEB Geneva, Switzerland. Tengberg, A., Fredholm, S., Eliasson, I., Knez, I., Saltzman, K., & Wetterberg, O. (2012). Cultural ecosystem services provided by landscapes: Assessment of heritage values and identity. Ecosystem services , 2 , 14–26. Tost, H., Reichert, M., Braun, U., Reinhard, I., Peters, R., Lautenbach, S., Hoell, A., Schwarz, E., Ebner-Priemer, U., & Zipf, A. (2019). Neural correlates of individual differences in affective benefit of real-life urban green space exposure. Nature neuroscience , 22 (9), 1389–1393. Villamagna, A. M., Mogollón, B., & Angermeier, P. L. (2014). A multi-indicator framework for mapping cultural ecosystem services: The case of freshwater recreational fishing. Ecological indicators , 45 , 255–265. Wang, Z., Xu, J., Wu, W., & Zhang, H. Spatial coupling coordination between transport superiority and ecosystem services in metropolitan areas: A case study of Changsha–Zhuzhou–Xiangtan. Economic Geography , 1–15. Wenbin, P., & Xiaotian, C. (2023). Spatiotemporal differentiation of ecological resilience under urban renewal and its influencing mechanisms around the Changsha-Zhuzhou-Xiangtan Urban Agglomeration. Economic Geography , 43 (10), 44–52. Wijffels, J., Straka, M., & Straková, J. (2018). Package ‘udpipe’. Also available at https://cran.microsoft.com/snapshot/2018-06-12/web/packages/udpipe/udpipe . pdf X, O., X, Z., & Y, H. Q. (2020). Incorporating ecosystem services with ecosystem health for ecological risk assessment: case study in Changsha-Zhuzhou-Xiangtan urban agglomeration, China. Acta Ecologica Sinica , 40 (16), 5478–5489. Xiao, O., Qingyun, H., & Xiang, Z. (2020). Simulation of impacts of urban agglomeration land use change on ecosystem services value under multi-scenarios: Case study in Changsha-Zhuzhou-Xiangtan urban agglomeration. Economic Geography , 40 (1), 93–102. Y, S. Z., G, W. S., & B, C. (2024). Construction of ecological security pattern and assessment of ecological resilience based on ecosystem services: A case study of Changsha-Zhuzhou-Xiangtan urban agglomeration. Acta Ecologica Sinica , 44 (11), 4650–4661. Yang, Z., Haiyan, Z., & Zhipeng, W. (2024). Research on the Quality Evaluation and Spatial Differentiation of Human Settlement Environment in the Changsha Zhuzhou Xiangtan Urban Agglomeration. Research of Soil & Water Conservation , 31 (5). Yizhang, C., Guohua, Z., Hua, W., Shuqiang, C., Fangze, B., & Xuexia, Y. (2023). The Evolution Characteristics and Enhancement Paths of the Economy-Society-Ecology Coordinated Development Level of Changsha-Zhuzhou-Xiangtan Urban Ag-glomeration. Tropical Geography , 43 (03), 519–531. You, C., Qu, H., Feng, C. C., & Guo, L. (2024). Evaluating the match between natural ecosystem service supply and cultural ecosystem service demand: Perspectives on spatiotemporal heterogeneity. Environmental Impact Assessment Review , 108 , 107592. Zabala, J. A., Martínez-Paz, J. M., & Alcon, F. (2021). A comprehensive approach for agroecosystem services and disservices valuation. Science of the Total Environment , 768 , 144859. Zhan, W., Cheng, H., & Shen, S. (2020). Evaluation of urban wetland ecosystem service value in Zhuzhou City. Nature Environment and Pollution Technology , 19 (2), 453–467. Zhang, H., Huang, R., Zhang, Y., & Buhalis, D. (2022). Cultural ecosystem services evaluation using geolocated social media data: A review. Tourism Geographies , 24 (4–5), 646–668. Zhao, C., Xiao, P., Qian, P., Xu, J., Yang, L., & Wu, Y. (2022). Spatiotemporal differentiation and balance pattern of ecosystem service supply and demand in the Yangtze River Economic Belt. International journal of environmental research and public health (Vol , 19 , 7223. Zhao, W., Xie, Y., & Xiang, P. (2020). Evaluation and quantitative criteria of urban ecological space: A case study of the Changsha–Zhuzhou–Xiangtan urban agglomeration. Journal of Central South University of Forestry & Technology , 40 (04), 72–79. Zhu, C., Zhang, X., Zhou, M., He, S., Gan, M., Yang, L., & Wang, K. (2020). Impacts of urbanization and landscape pattern on habitat quality using OLS and GWR models in Hangzhou, China. Ecological indicators , 117 , 106654. Appendix, A. Detailed data from the selected 30 sample literature. Additional Declarations No competing interests reported. Supplementary Files AppendixA.docx 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8178918","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":595840966,"identity":"29f63561-9918-4555-a464-750d7ba32758","order_by":0,"name":"Jian Wang","email":"","orcid":"","institution":"Changsha University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Wang","suffix":""},{"id":595840967,"identity":"853b446e-68d0-4126-93dc-acdf1cec405a","order_by":1,"name":"Zexuan Zhang","email":"","orcid":"","institution":"Changsha University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Zexuan","middleName":"","lastName":"Zhang","suffix":""},{"id":595840968,"identity":"f423ba8e-eb8b-4cf7-ac33-970f7ef5b43d","order_by":2,"name":"Huan Li","email":"","orcid":"","institution":"Macau University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Huan","middleName":"","lastName":"Li","suffix":""},{"id":595840969,"identity":"a6d76742-b026-4eba-a5d9-c8757fd0cf0d","order_by":3,"name":"Wenjie Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIie3OMQrCMBTG8RcCTq1d6+IZMrkI7VVaMniD4uAQl04eoKJ4hk7OLzxwknbt4KC7Q0cXwRYRNxM3wfynL/B+EACX6ycjhTB/PQZWRHfk2A9uTZgCln9DhNZL8ndRLOoaoc0Igo0yENSK/L1My0YCKyqC8ISfyeRJMBENB+7nBCJMbMgWu48R8Ls9UchKlMCZDYk7orcHma4bKfSqmnlhYyCjgqi9LqJ4WOvL+ZZNx0FhIBDie/fTM9x3Bcp843K5XH/eA7u2TG+MrgtkAAAAAElFTkSuQmCC","orcid":"","institution":"Changsha University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Wenjie","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2025-11-22 08:23:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8178918/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8178918/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103349190,"identity":"3a96c317-c8c0-4c1f-9d09-965506907e1b","added_by":"auto","created_at":"2026-02-24 16:41:22","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":171267,"visible":true,"origin":"","legend":"\u003cp\u003eStudy area\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8178918/v1/216e69950f940576a0434f5f.jpg"},{"id":103349206,"identity":"2183cb95-ce98-4149-a3f3-af8c61d69f3c","added_by":"auto","created_at":"2026-02-24 16:41:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":381740,"visible":true,"origin":"","legend":"\u003cp\u003eData Preprocessing and Analytical Framework\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8178918/v1/755819b1aa50dcd6efcbcdbd.png"},{"id":103349197,"identity":"e1b5a7c0-5cf8-4c20-a46e-46f053c84f66","added_by":"auto","created_at":"2026-02-24 16:41:23","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":166924,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of Eight CES Perception Weights in CZT Urban Parks. (a) Overall distribution for Xiangtan; (b) Distribution for Changsha; (c) Distribution for Zhuzhou; (d) Distribution for CZT.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8178918/v1/b743bd97591122fdc8e9d730.jpg"},{"id":103349192,"identity":"6c71318c-8f6c-4a83-9dd7-f18abea35418","added_by":"auto","created_at":"2026-02-24 16:41:22","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":141642,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of dominant CES perceptions in parks.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8178918/v1/d34de65557e41ff1803bbcfc.jpg"},{"id":103349237,"identity":"705a7cb2-3f6f-41ff-bcf3-b863ef17dee2","added_by":"auto","created_at":"2026-02-24 16:41:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2060026,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8178918/v1/787c0afc-cfae-4418-bd52-7d8c2ce65008.pdf"},{"id":103349201,"identity":"4b3059ca-e7f7-4061-a2da-398f4ae7bc16","added_by":"auto","created_at":"2026-02-24 16:41:25","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":32292,"visible":true,"origin":"","legend":"","description":"","filename":"AppendixA.docx","url":"https://assets-eu.researchsquare.com/files/rs-8178918/v1/92477ad9be5afef80eb54136.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatial Differentiation and Perception of Urban Park Cultural Ecosystem Services from a Multiscale Perspective: Evidence from the Changsha–Zhuzhou–Xiangtan Urban Agglomeration","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAgainst the backdrop of ongoing global urbanization, urban green spaces (UGS) face increasing development pressure, which weakens their function as critical infrastructure supporting urban sustainability and residents\u0026rsquo; well‑being (Desa, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Jie Li et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Despite multiple threats such as landscape fragmentation and spatial encroachment, urban parks and other UGS remain core nodes of ecological networks and play an irreplaceable role in regulating microclimates, maintaining biodiversity, and improving residents\u0026rsquo; quality of life (De Groot et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Europe, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Opdam, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tost et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Meanwhile, as societal demand for mental health and cultural identity continues to rise, cultural ecosystem services (CES), defined as the nonmaterial benefits people obtain from ecosystems, have attracted growing attention from scholars and policymakers (Luo et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; You et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). As important settings for near‑nature environments (Campbell et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), urban parks not only provide spaces for recreation and aesthetic experience but are also associated with benefits for physical and mental health (Bertram \u0026amp; Rehdanz, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Dong \u0026amp; Qin, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAmong the multiple ecosystem services provided by UGS, CES are closely associated with social and cultural value (Jie Li et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). CES is commonly defined as the nonmaterial benefits people obtain from ecosystems, encompassing recreation, aesthetic appreciation, cultural heritage, and educational enrichment (Assessment, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; TEEB, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Tengberg et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Villamagna et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2014\u003c/span\u003e)), with a core emphasis on subjective public perception and value attribution (Andersson et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, because CES is intangible, strongly subjective, and highly context dependent, its assessment and quantification pose methodological challenges (Daniel et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; G\u0026oacute;mez-Baggethun \u0026amp; Barton, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). While traditional approaches based on questionnaires and field interviews can capture firsthand perception data, high costs, labor intensity, and limited sample representativeness constrain their capacity to efficiently reveal city-scale spatial patterns and heterogeneity of CES (Bertram \u0026amp; Rehdanz, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOver the past two decades, empirical research aimed at understanding, identifying, and quantifying CES has grown rapidly (Cheng et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Milcu et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The rise of UGC has been central, becoming a primary data source for big data\u0026ndash;driven CES research. Leveraging the large volume of crowdsourced content on social media platforms, UGC can effectively represent public cognition, emotions, and value judgments regarding natural and cultural landscapes (Zabala et al., \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Zhu et al., \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Compared with traditional qualitative methods such as in-depth interviews or focus groups, UGC offers lower cost, broader coverage, and stronger timeliness, serving as an important complement to resource-intensive field surveys. More importantly, UGC functions as a proxy indicator for CES with theoretical validity and empirical reliability, as its production often stems from individuals\u0026rsquo; motivations to share pleasurable, memorable, or meaningful experiences (Havinga et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Oliveira et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Such data are often presented as first-person narratives and are well suited to text and image content analysis to uncover abstract and highly subjective cultural values manifested in human\u0026ndash;environment interactions, including sense of place, psychological fulfillment, and social bonding (Small et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Zhang et al., \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). To enable systematic identification, researchers commonly build customized lexicons that map specific words or semantic units to a predefined CES typology, supporting fine-grained classification, quantification, and spatial mapping.\u003c/p\u003e \u003cp\u003eAs the core nonmaterial benefits that urban parks provide, the identification and assessment of CES have become a research focus in recent years (You et al., \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Based on a systematic review of empirical studies on CES in urban parks, the field has gradually formed an expert-informed CES classification framework that also considers public perception, and has widely applied big data approaches such as UGC to advance CES type identification, mapping of visitor activity hotspots, and value representation (Cheng et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Milcu et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). For example, Lee and Park (2020) quantified local-scale CES features through text mining (Lee et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2020\u003c/span\u003e); Cabana et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) integrated perspectives from ecology, sociology, and planning to build a multidisciplinary CES assessment framework that addresses gaps in data coverage and conceptual operationalization (Cabana et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAt different urban scales, parks may exhibit a division of functions in CES provision: some may emphasize aesthetic experience, whereas others may prioritize social interaction or cultural education, reflecting specific spatial organization patterns. Current research mainly follows two pathways. The first relies on crowdsourced data such as social media content to capture spontaneous public experiences (Derungs \u0026amp; Purves, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Hern\u0026aacute;ndez-Morcillo et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This pathway is effective in reflecting actual use preferences and affective expression, but may under-represent value dimensions that require specialized knowledge to perceive, such as biodiversity education or historical and cultural heritage (G\u0026oacute;mez-Baggethun \u0026amp; Barton, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Li et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The second pathway is a systematic, expert-based evaluation that integrates indicator systems predefined in the academic literature (Milcu et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). With stronger conceptual systematization and theoretical consistency, it can reveal latent, structural, or long-term value dimensions of CES (Chongxian et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), but may not fully capture the diversity and situational specificity of everyday public experience (Liu et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Due to differences in methodological perspectives, most existing studies emphasize the identification and classification of CES types, with limited integration of public perception and expert judgment to systematically examine the functional roles of urban parks in CES provision and their interrelationships.\u003c/p\u003e \u003cp\u003eIn response, this study develops a dual-path assessment framework that integrates data-driven public perception with expert-led evaluation to identify, quantify, and analyze the spatial differentiation of park CES at the scale of a large urban agglomeration. Using the Changsha\u0026ndash;Zhuzhou\u0026ndash;Xiangtan urban agglomeration as the empirical case, the study addresses three core questions: (a) based on online text data, what types of CES are perceived by the public and how are their intensities distributed; (b) how can we construct a composite CES profile for urban parks by incorporating expert knowledge and identify the dominant CES function of each park; and (c) at the city scale, how do different CES functions exhibit spatial clustering, differentiation, and complementarity. The aim is to provide comprehensive and precise diagnostic tools for urban green space planning and management and to support the development of an urban park system that is functionally diverse, structurally optimized, and spatially equitable, thereby better addressing diverse needs across mental, cultural, and social dimensions.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Study Area\u003c/h2\u003e \u003cp\u003eThe case area is the Changsha\u0026ndash;Zhuzhou\u0026ndash;Xiangtan (CZT) urban agglomeration, a core metropolitan region in central China located in central-eastern Hunan Province (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The agglomeration comprises three adjacent prefecture-level cities\u0026mdash;the provincial capital Changsha, Zhuzhou, and Xiangtan\u0026mdash;and is designated as a nationally prioritized urban agglomeration (Liu et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Over the past two decades, the region has undergone rapid urban expansion and spatial integration, characterized by sustained population concentration in the core cities, steady growth in economic output, and deepening regional coordination (Peng \u0026amp; Xie, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe CZT urban agglomeration lies in the middle reaches of the Yangtze River basin, with the Xiang River running through all three cities (Hu et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The region has a subtropical monsoon climate with distinct seasons and varied topography, including alluvial plains, hills, and mountain ranges such as Yuelu and Dawei (Luo et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Luo et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This geographical and historical context has shaped a diverse system of urban green spaces that reflects natural and cultural heritage (Liang et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), including large scenic areas associated with mountain landforms, riverfront parks along the Xiang River, historic gardens, and modern community parks. Meanwhile, the rapid urbanization of CZT illustrates the tension between development and ecological protection. The interplay between development pressure and a park system serving millions of residents makes the region a suitable case for examining public perceptions of CES and their spatial differentiation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data Sources\u003c/h2\u003e \u003cp\u003eTo capture the multidimensional features of CES, this study adopts a mixed strategy that combines a literature review with empirical data. On the literature side, a systematic review of 30 scholarly articles on ecosystem services in the CZT region is used to derive theoretically grounded assessment dimensions and an indicator system, providing the basis for a comparable analytical framework. On the empirical side, large-scale, spontaneously generated online public reviews are used to mine the public\u0026rsquo;s lived experience, affective expression, and value perception in everyday use of urban parks. Such UGC is contextual, colloquial, and dynamically evolving, and it complements the limits of conventional approaches in representing subjective perception. By integrating the academic knowledge base with public feedback, the study builds a CES assessment model with stronger coverage and explanatory power.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1. Online Crowdsourced Text Data\u003c/h2\u003e \u003cp\u003eThis study uses Dianping (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.dianping.com/\u003c/span\u003e\u003cspan address=\"https://www.dianping.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) as the data source and systematically collects publicly available user reviews for 577 urban parks in the CZT region. The time span covers January 2014 to May \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2024\u003c/span\u003e to capture long-term dynamics in public perceptions and experiences of urban parks. Each record includes the park name, review text, and publication date. After preprocessing and cleaning, we obtained 170,683 valid reviews and built a text corpus containing 113,695 unique tokens. The distribution of reviews by city is 132,038 for Changsha, 19,820 for Zhuzhou, and 18,825 for Xiangtan.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2. Park Spatial and Attribute Data\u003c/h2\u003e \u003cp\u003eWe compiled a multisource integrated dataset covering 577 urban parks in the CZT urban agglomeration. First, official park rosters published by the landscape and greening authorities of the three municipalities were used to define the initial sample. We then supplemented and cross‑validated the roster with points‑of‑interest (POI) data from major online mapping platforms by systematically querying keywords such as \u0026ldquo;park,\u0026rdquo; \u0026ldquo;scenic area,\u0026rdquo; and \u0026ldquo;forest park\u0026rdquo; to improve coverage. For each park, we extracted the official name, precise geographic coordinates (latitude and longitude), and administrative address.\u003c/p\u003e \u003cp\u003eUsing high‑resolution satellite imagery, we manually vectorized park boundaries to produce polygon geometries, ensuring positional accuracy and reliable area estimates. We integrated open remote‑sensing and geospatial datasets to derive multidimensional variables that characterize both natural and built environments, including blue‑ and green‑space density, PM2.5 concentration (NASA SEDAC), normalized difference vegetation index NDVI (USGS), trail density, spatial connectivity, and terrain slope, thereby profiling the ecological and spatial attributes of each park(Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\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\u003eVariables and Data Sources for Park Characterization\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\u003eAttributes\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMeaning\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eData source\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003eNatural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlue space density (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePercentage of water bodies within a 1 km buffer around each park (such as lakes, rivers, ponds, etc.)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRemote sensing image analysis (Landsat/Sentinel)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevels of PM2.5\u0026nbsp;concentrations (\u0026micro;g/m\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage value of PM\u003csub\u003e2.5\u003c/sub\u003e in each park\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNASA Socioeconomic Data and Applications Center (SEDAC)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNormalized difference vegetation index (NDVI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage value of NDVI in each park\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ethe United States Geological Survey (USGS) Earth Explorer website\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrail density\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal length of park trails divided by the park area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOpenStreetMap (OSM) data\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eConnectivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of alternative routes available from each trail segment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOpenStreetMap data of 2021 Changsha\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTerrain slope (degree)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage value of slope in the 20m buffer around each trail segment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGeospatial Data Cloud site, Computer Network Information Centre. (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.gscloud.cn\u003c/span\u003e\u003cspan address=\"http://www.gscloud.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElevation (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u0026nbsp;elevation\u0026nbsp;within\u0026nbsp;the\u0026nbsp;park boundary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGeospatial\u0026nbsp;Data\u0026nbsp;Cloud\u0026nbsp;(SRTM/ASTER\u0026nbsp;DEM)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWater features count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInland static and moving water bodies of natural and manmade origin within the park boundary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOpenStreetMap data of 2021 Changsha\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=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3. Literature Review and Extraction of Cultural Ecosystem Services Indicators\u003c/h2\u003e \u003cp\u003eUsing core keywords on the CZT urban agglomeration, urban parks, cultural ecosystem services, and environmental characteristics, we systematically searched CNKI and Web of Science and screened thirty empirical studies with complete research design, data description, and statistical reporting (Appendix A). For each study, we recorded author information, publication year, sample size, spatial analysis unit, research methods, and the definitions and measurements of dependent and independent variables. We then compiled CES-related indicators reported in the literature, calculated their frequencies, and ranked their importance. On this basis, we distilled core assessment indicators that are widely adopted and exhibit higher consensus, which provide the theoretical basis and indicator foundation for the analytical framework of this study.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Data Preparation and Integration\u003c/h2\u003e \u003cp\u003eData integration centers on three core data streams (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) to build a CES analytical framework that is led by online public perception and bilaterally anchored in the macro-environmental context and the academic knowledge base.\u003c/p\u003e \u003cp\u003eFirst, public perception data constitute the core input. We systematically collected user review texts on the Dianping platform for parks within the CZT urban agglomeration from 2014 to 2024. After data cleaning, tokenization, stop word removal, and sentiment filtering, we applied topic modeling and keyword co-occurrence analysis to extract high-frequency semantic units reflecting lived recreational experience, and derived a CES perception typology with local contextual features. This typology is induced directly from everyday public expression and avoids over-constraining perception structures through a priori theory.\u003c/p\u003e \u003cp\u003eSecond, to reveal linkages between public CES perception and the macro-environmental context, we assembled multisource spatial context data, including air quality PM2.5, normalized difference vegetation index (NDVI), blue\u0026ndash;green space distribution, trail density, terrain relief, transport accessibility, and neighborhood socioeconomic attributes. All variables were harmonized to a common coordinate system, spatially interpolated, and normalized to produce high-resolution environmental covariate layers for subsequent spatial regression between perception hotspots and environmental factors.\u003c/p\u003e \u003cp\u003eThird, to connect public cognition with professional discourse, we conducted a systematic review of thirty core studies on CES assessment over the past decade, extracted frequently used assessment themes and indicators, built an expert-oriented CES thematic lexicon, and generated a relative importance ranking based on bibliometric weights (see Section \u003cspan refid=\"Sec13\" class=\"InternalRef\"\u003e3.4\u003c/span\u003e). This knowledge base provides a theoretical reference for public perception dimensions and underpins the subsequent comparison between expert and public value frameworks.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Analysis Methods\u003c/h2\u003e \u003cp\u003eThis study adopts a three-stage framework\u0026mdash;perception-driven, environment response, and knowledge benchmarking (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e)\u0026mdash;to analyze the structure of public cognition of cultural ecosystem services (CES) in urban parks and its formation mechanisms.\u003c/p\u003e \u003cp\u003eBased on user reviews of urban parks posted on Dianping, we apply natural language processing and semantic mining to extract and quantify public expressions of CES perception. As the raw reviews are unstructured text, we first implement standardized preprocessing, including text cleaning (removal of punctuation, URLs, special characters, and function words such as a and the), lowercasing, tokenization, lemmatization, and part-of-speech tagging. Named entity recognition (NER) is used to identify and remove place names, personal names, and other irrelevant entities to reduce noise. These procedures are implemented with mature NLP toolkits such as spacyr (Benoit \u0026amp; Matsuo, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and udpipe (Wijffels et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe then build a crowdsourced, phrase-level CES lexicon tailored to urban park contexts. Its categorization scheme follows the definition of CES in the Millennium Ecosystem Assessment (MEa, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) and is localized to everyday usage among Chinese urban residents. Unlike dictionaries based on single characters or isolated keywords, we focus on semantically complete units such as noun phrases and verb phrases, for example, \u0026ldquo;walking feels comfortable,\u0026rdquo; \u0026ldquo;good for photo check-ins,\u0026rdquo; and \u0026ldquo;taking children to learn about plants,\u0026rdquo; which more accurately capture contextualized value judgments in public experience. By matching preprocessed reviews to the CES lexicon, we automatically identify CES content and compute frequencies for each review, and then aggregate to the park level to construct multidimensional public-perception CES indices. This approach balances semantic depth with scalability and yields quantifiable, interpretable public-facing evidence for fine-grained evaluation and differentiated planning of urban parks.\u003c/p\u003e \u003cp\u003eAfter semantic parsing and quantification of multi-dimensional CES perception, we spatially match each park\u0026rsquo;s average perception scores across CES dimensions with multisource environmental covariates to probe how ecological and built environments drive public experience. Indicators include green cover measured by NDVI, air quality captured by PM2.5 concentration, proximity to water bodies, park accessibility such as population density within 500-meter walking or cycling buffers, and facility density such as benches, toilets, and trail length. All variables are aligned to a common coordinate system to ensure accurate coupling between park units and their environmental attributes. We use Pearson correlation to preliminarily assess linear associations between environmental factors and CES dimensions, and then estimate multiple linear regression (MLR) models to test the joint explanatory power of covariates for different CES categories such as aesthetic experience, recreational vitality, and comfort. After diagnosing variance inflation factors (VIF) to exclude multicollinearity, the models identify key ecological and built features that significantly shape public preferences, revealing how green, blue, and gray infrastructures jointly influence cultural service provision and offering empirical guidance for targeted design and intervention.\u003c/p\u003e \u003cp\u003eFinally, to position public perception within the broader academic discourse, we introduce bibliometric analysis as an external reference. Drawing on thirty core CES studies from Web of Science and Scopus, we conduct topic modeling and compute keyword weights to derive an expert-oriented ranking of relative importance across CES dimensions. Although the perception dimensions reflected in public reviews and the theoretical themes in the literature differ in their levels of abstraction, semantic alignment enables structural comparison\u0026mdash;for example, mapping expert terms such as \u0026ldquo;landscape aesthetics\u0026rdquo; and \u0026ldquo;local culture and aesthetics\u0026rdquo; to the public\u0026rsquo;s \u0026ldquo;aesthetic experience,\u0026rdquo; and aligning \u0026ldquo;social relations\u0026rdquo; and \u0026ldquo;community cohesion\u0026rdquo; with \u0026ldquo;social vitality.\u0026rdquo; This cross-discourse comparison reveals overlapping areas of professional consensus and public cognition, such as shared emphasis on aesthetics and recreation, while highlighting potential tensions, such as identity and spiritual values that are prominent in expert discourse but comparatively marginal in public reviews, thereby providing a theoretical anchor for bridging the knowledge\u0026ndash;perception gap in planning practice.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e \u003cem\u003e3.1. Overall and Regional Distributions of Public Perceptions of Park Cultural Ecosystem Services in the CZT Urban Agglomeration\u003c/em\u003e \u003c/p\u003e \u003cp\u003eUsing high-frequency terms in the public review corpus, we extracted keywords closely related to CES\u0026mdash;with a particular focus on evaluative adjectives\u0026mdash;and constructed an eight‑dimension CES semantic labeling scheme. We then annotated and tallied online reviews for 577 parks in the CZT urban agglomeration, calculating for each park the share of positive evaluations across the eight CES dimensions and visualizing the distributions with boxplots (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). To elucidate regional differences, we summarize patterns using the median, IQR, and overall spread.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn Xiangtan (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea), Waterfront Experience stands out, with a median of 0.78 (Q1\u0026thinsp;=\u0026thinsp;0.62, Q3\u0026thinsp;=\u0026thinsp;0.90) and some parks approaching the upper limit, underscoring the importance of waterside landscapes in the city\u0026rsquo;s park system. By comparison, other dimensions score modestly: Physical Comfort (median\u0026thinsp;=\u0026thinsp;0.23), Amenity Completeness (0.23), and Place Vitality (0.13) are low; Transportation Accessibility is slightly higher (0.25) but remains conservative overall; and positive mentions of Objective Safety (0.04) and Information Clarity (0.06) are rare, suggesting that these services are not yet well reflected in public experience.\u003c/p\u003e \u003cp\u003eIn Changsha (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb), overall perception levels are the highest among the three cities. Waterfront Experience reaches a median of 0.90 (Q1\u0026thinsp;=\u0026thinsp;0.78, Q3\u0026thinsp;=\u0026thinsp;0.96), with several parks near the maximum, indicating strong attraction of waterside settings. Aesthetic Experience also performs well (median\u0026thinsp;=\u0026thinsp;0.58) with a wide spread, pointing to diverse cultural‑aesthetic value. Notably, although Transportation Accessibility (0.48), Amenity Completeness (0.25), and Place Vitality (0.24) lead within the region, their absolute levels are still modest, implying room for improvement in functional services. Objective Safety (0.20) and Information Clarity (0.17) show progress but still have many parks without positive mentions, indicating scope for further capacity building.\u003c/p\u003e \u003cp\u003eIn Zhuzhou (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec), Waterfront Experience (median\u0026thinsp;=\u0026thinsp;0.79) and Aesthetic Experience (0.63) are the main strengths\u0026mdash;the latter slightly above the regional average, hinting at distinctive cultural landscapes. Functional dimensions are more muted: Physical Comfort has the lowest median among the three cities (0.08); Amenity Completeness (0.16) and Place Vitality (0.13) are also low; Transportation Accessibility (0.23) is comparable to Xiangtan but with fewer high‑scoring cases; and positive feedback on Objective Safety (0.04) and Information Clarity (0.07) remains limited.\u003c/p\u003e \u003cp\u003eAt the CZT scale (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed), Waterfront Experience ranks first with a median of 0.81 (Q1\u0026thinsp;=\u0026thinsp;0.65, Q3\u0026thinsp;=\u0026thinsp;0.93) and a wide spread, showing broad consensus that waterside landscapes are the core attraction of regional parks. Aesthetic Experience (median\u0026thinsp;=\u0026thinsp;0.58) forms the second tier. The remaining dimensions are generally lower: Transportation Accessibility (0.27) is the highest among functional indicators, likely lifted by Changsha; Amenity Completeness (0.23), Physical Comfort (0.22), and Place Vitality (0.21) have similar, concentrated distributions, indicating limited salience of basic services in public experience; Information Clarity (0.06) and Objective Safety (0.04) remain at the bottom, suggesting that wayfinding and safety are not yet central concerns in current park services.\u003c/p\u003e \u003cp\u003eOverall, public perceptions of CES in CZT parks exhibit a pattern of strong concentration in eco‑aesthetic services and comparatively subdued recognition of basic functional services. This highlights the central value of waterfront and aesthetic experiences in urban parks while suggesting that, alongside consolidating these strengths, future efforts should prioritize coordinated enhancements in facilities, environmental comfort, safety order, and information guidance to foster a more balanced provision of cultural ecosystem services.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Co-occurrence Relationships among CES Perceptions\u003c/h2\u003e \u003cp\u003eBased on semantic annotations of online reviews for 577 urban parks, Pearson correlation coefficients were computed for all pairs of the eight CES perception dimensions (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The results show varying degrees of linear association across dimensions, with some pairs exhibiting strong covariation and others negative relationships.\u003c/p\u003e \u003cp\u003eAmong positive associations, the strongest correlation is between Facilities and Convenience and Activities and Social Vitality (r\u0026thinsp;=\u0026thinsp;0.85, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Safety and Order is strongly and positively correlated with Transportation Accessibility (r\u0026thinsp;=\u0026thinsp;0.67, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), is also positively related to Facilities and Convenience (r\u0026thinsp;=\u0026thinsp;0.49, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and shows a moderate positive correlation with Activities and Social Vitality (r\u0026thinsp;=\u0026thinsp;0.41, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Environmental Sanitation and Comfort is highly and positively correlated with Wayfinding and Process Clarity (r\u0026thinsp;=\u0026thinsp;0.68, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), while its correlations with Transportation Accessibility (r\u0026thinsp;=\u0026thinsp;0.05), Safety and Order (r = \u0026minus;\u0026thinsp;0.09), and Place‑based Culture and Aesthetics (r = \u0026minus;\u0026thinsp;0.11) are weak or near zero. Wayfinding and Process Clarity, in addition to its strong link with Environmental Sanitation and Comfort, is positively but weakly correlated with Transportation Accessibility (r\u0026thinsp;=\u0026thinsp;0.27, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Safety and Order (r\u0026thinsp;=\u0026thinsp;0.31, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and Facilities and Convenience (r\u0026thinsp;=\u0026thinsp;0.18, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and shows a small positive correlation with Activities and Social Vitality (r\u0026thinsp;=\u0026thinsp;0.14, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003eOn the negative side, Ecological Landscape and Waterfront Experience is significantly and negatively correlated with several dimensions: Safety and Order (r = \u0026minus;\u0026thinsp;0.53, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Facilities and Convenience (r = \u0026minus;\u0026thinsp;0.32, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), Activities and Social Vitality (r = \u0026minus;\u0026thinsp;0.28, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and Wayfinding and Process Clarity (r = \u0026minus;\u0026thinsp;0.28, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). It is also weakly negative with Transportation Accessibility (r = \u0026minus;\u0026thinsp;0.18, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and shows a very small positive correlation with Place‑based Culture and Aesthetics (r\u0026thinsp;=\u0026thinsp;0.05). Correlations involving Place‑based Culture and Aesthetics are generally small or not statistically significant (e.g., r\u0026thinsp;=\u0026thinsp;0.08 with Safety and Order), with absolute values mostly below 0.11.\u003c/p\u003e \u003cp\u003eOverall, the correlation analysis indicates that the CES perception dimensions are not independent in the data but display systematic co‑occurrence patterns: functional service dimensions tend to be positively associated with each other, whereas the nature‑centered experience dimension (Ecological Landscape and Waterfront Experience) exhibits negative associations with several support‑service dimensions.\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 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation Matrix of the Eight CES Perception Dimensions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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=\"char\" char=\".\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccessibility \u0026amp; Transport\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSafety \u0026amp; Order\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLocal Culture \u0026amp; Aesthetics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEnvironmental Sanitation \u0026amp; Comfort\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFacilities \u0026amp; Amenities\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eActivity \u0026amp; Social Vitality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eProcess \u0026amp; Guidance Clarity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eEcological Landscape \u0026amp; Waterfront Experience\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAccessibility \u0026amp; Transport\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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 \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSafety \u0026amp; Order\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.67\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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 \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLocal Culture \u0026amp; Aesthetics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.00\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 \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnvironmental Sanitation \u0026amp; Comfort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.00\u003c/p\u003e \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 \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFacilities \u0026amp; Amenities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.43\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.49\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eActivity \u0026amp; Social Vitality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.37\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.41\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.85\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProcess \u0026amp; Guidance Clarity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.27\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.31\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.68\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.18\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.14*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEcological Landscape \u0026amp; Waterfront Experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.18\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.53\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.32\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.28\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.28\u003csup\u003e***\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: \u003csup\u003e*\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05; \u003csup\u003e**\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01; \u003csup\u003e***\u003c/sup\u003e\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Correlation Analysis of Park Environmental Characteristics and CES Perceptions\u003c/h2\u003e \u003cp\u003eTo quantify the relationships between park environmental characteristics and public perceptions of CES, a correlation analysis was conducted (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The results revealed multiple significant statistical associations between the physical environment of the parks and the visitor experience.\u003c/p\u003e \u003cp\u003eThe core findings of the analysis detail the influence of natural elements such as topography, vegetation, and water bodies on CES perceptions. Regarding topographical and atmospheric indicators, higher elevation was significantly and positively correlated with better perceptions of \u0026ldquo;Ecological Landscape \u0026amp; Waterfront Experience\u0026rdquo; (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.32, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and higher \u0026ldquo;Process \u0026amp; Guidance Clarity\u0026rdquo; (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.26, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Conversely, PM2.5 concentration was negatively correlated with \u0026ldquo;Environmental Sanitation \u0026amp; Comfort\u0026rdquo; (\u003cem\u003er\u003c/em\u003e = -0.27, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) but showed a significant positive correlation with multiple dimensions including Transportation \u0026ldquo;Accessibility \u0026amp; Transport\u0026rdquo;, \u0026ldquo;Safety \u0026amp; Order\u0026rdquo;, and \u0026ldquo;Local Culture \u0026amp; Aesthetics\u0026rdquo;. This may reflect that areas with higher PM2.5 levels are often centrally located urban zones with greater Accessibility \u0026amp; Transport and population density.\u003c/p\u003e \u003cp\u003eRegarding vegetation and water features, the Normalized Difference Vegetation Index (NDVI) was positively correlated with \u0026ldquo;Environmental Sanitation \u0026amp; Comfort\u0026rdquo; (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.24, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) but negatively correlated with \u0026ldquo;Local Culture \u0026amp; Aesthetics\u0026rdquo; (\u003cem\u003er\u003c/em\u003e = -0.19, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01). A park\u0026rsquo;s blue space density, trail density, and connectivity index were all positively and significantly correlated with \u0026ldquo;Ecological Landscape \u0026amp; Waterfront Experience\u0026rdquo;, with trail density showing the strongest relationship (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.20, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01).\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 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelations between Park Environment and CES Perceptions\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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=\"char\" char=\".\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePM2.5 (\u0026micro;g/m\u0026sup3;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eElevation (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSlope (deg)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNDVI (mean)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBlue space density (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eTrail density (km/km\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eConnectivity index\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eWater features count\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTransportation accessibility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.36***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.14*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.13*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObjective safety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.28***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.17**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.15*\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAesthetic experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.32***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.19**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.18**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePhysical comfort\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.27***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.32***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.20**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.24***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.18**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAmenity completeness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.17*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.13*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlace vitality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.13*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.17**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e-0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInformation clarity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.26***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.21**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.21**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e-0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaterfront experience\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.18**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.20**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.16*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e\u003cem\u003eNote: *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Spatial Distribution\u003c/h2\u003e \u003cp\u003eFigure 4 indicates that the dominant park types in the CZT urban agglomeration exhibit a pronounced river-corridor belt pattern and cluster agglomeration. Parks dominated by Ecological Landscape \u0026amp; Waterfront Experience are most numerous, forming a continuous north\u0026ndash;south belt along the main stem of the Xiangjiang River and its principal tributaries; densities are highest around built-up areas in the northern and central parts of the region and decline toward secondary towns and peri-urban areas in the south. Comparing the three cities, Changsha contains the densest point distribution and the widest type coverage; Xiangtan is chiefly clustered along the river zone and around the urban core; Zhuzhou is more dispersed overall, with linear chaining along the river corridor and town nodes. In general, multiple high-density patches occur within central urban districts, while the periphery is characterized by low-density, scattered points.\u003c/p\u003e \u003cp\u003eThe spatial directionality of individual perception types is distinct. Ecological Landscape \u0026amp; Waterfront Experience constitutes the regional backbone, occurring continuously along shorelines and mountain edges. Local Culture \u0026amp; Aesthetics concentrates in Changsha\u0026rsquo;s urban area and historic landscape precincts, forming several cultural patches. Facilities \u0026amp; Amenities and Accessibility \u0026amp; Transport are mainly aligned with arterial corridors, bridgeheads, and new-town corridors, with substantial overlap in inner Changsha. Wayfinding \u0026amp; Process Clarity is concentrated in landmark riverfront parks and core urban greens. Environmental Sanitation \u0026amp; Comfort is scattered with minor inter-city differences. Activity \u0026amp; Social Vitality is confined to a few central parks. Safety \u0026amp; Order appears as discrete, intermittent points. Overlays of these types indicate a composite, multi-type pattern in Changsha; a waterfront\u0026ndash;ecological predominance in Xiangtan supplemented by a small number of cultural and facilities-oriented sites; and, in Zhuzhou, a primarily river-aligned linear chaining with few non-riverine points.\u003c/p\u003e \u003cp\u003eFigure 4. Spatial distribution of dominant CES perceptions in parks.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.5 CES Theme Classification Based on Bibliometric Analysis\u003c/h2\u003e \u003cp\u003eResults from the bibliometric analysis, as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, indicate that among 30 core studies published over the past decade focusing on cultural ecosystem services (CES), nine high-frequency thematic categories emerged with distinct weight distributions. Regulating and maintenance functions and land use and spatial patterns are the two most prominent themes, accounting for 33.42% and 27.11% of the total weight, respectively\u0026mdash;collectively exceeding 60%. A second tier comprises trade-offs and synergies among ecosystem services (10.97%), blue-green infrastructure and aquatic ecosystem services (8.50%), and aesthetic values (6.28%). In contrast, themes more directly linked to everyday public experience\u0026mdash;namely recreation, tourism, and social values (2.20%) and accessibility and spatial integration (1.07%)\u0026mdash;appear far less frequently in the academic literature and thus carry the smallest weights.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eExpert-derived CES Theme Weights\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=\"left\" 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\u003eRanking\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTheme\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWeight (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegulating and maintenance functions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLand use and spatial patterns\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrade-offs and synergies among ecosystem services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlue-green infrastructure and aquatic ecosystem services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAesthetic values\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.28\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGovernance and management frameworks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBiodiversity and habitat quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRecreation, tourism, and social values\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAccessibility and spatial integration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.07\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"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Differences between Public Experience and Academic Focus\u003c/h2\u003e \u003cp\u003eThis study finds that public perceptions of urban park cultural ecosystem services, CES, center on experiential dimensions that are directly perceived, such as waterfront landscapes, place-based aesthetics, and social vitality. This is consistent with the widely observed public preference for aesthetic values and recreational experience in prior research (Milcu et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). By contrast, core academic literature over the past decade has focused more on structural or process topics such as regulation and maintenance functions and land-use patterns. This difference is not a fundamental opposition; it more likely arises from natural differentiation in cognitive scales and goal orientations among different actors: the public forms experiential, embodied evaluations grounded in everyday use contexts, whereas scholars tend to construct analytical frameworks from the perspectives of ecological processes, spatial structures, or system functions. Notably, basic service dimensions closely linked to recreational convenience \u0026mdash; such as accessibility, safety and order, and wayfinding and information \u0026mdash; carry relatively low weights in academic themes yet appear frequently in public comments, indicating room for the current research system to better respond to users\u0026rsquo; practical needs.\u003c/p\u003e \u003cp\u003eAccordingly, future CES research can, while maintaining ecological scientific rigor, further strengthen the integration of social dimensions (Adhikary et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Stępniewska, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For example, incorporating large-scale public semantic data as a complementary evidence source can help identify service links that are represented in ecological models but have not been effectively translated into user-perceived value. By building a mapping between ecological supply and public experience, researchers can more comprehensively reveal the realization mechanisms of CES and, in turn, provide integrated indicator systems for green infrastructure performance evaluation that combine ecological soundness with social sensitivity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Service Portfolio Characteristics Under the Dominance of Waterfront Landscapes\u003c/h2\u003e \u003cp\u003eIn the CZT urban agglomeration, strong public recognition of ecological landscapes and waterfront experience constitutes a salient feature of regional perceptions of cultural ecosystem services, CES. This dimension not only receives the highest scores but also forms a continuous distribution belt along the main stem of the Xiang River, underscoring the core role of water systems in shaping the attractiveness of urban parks. Meanwhile, correlation analysis shows significant negative associations between this dimension and several functional services such as facility convenience, safety and order, and clarity of information, reflecting a specific service portfolio pattern: parks with stronger ecological appeal tend to have relatively minimalist supporting service provision. This portfolio may stem from a planning and design priority on conserving natural character and may also relate to management intensity or development constraints in waterfront areas.\u003c/p\u003e \u003cp\u003eFrom a practical standpoint, such parks succeed in creating strong visual and affective appeal yet may face limitations for all‑weather use, age inclusivity, or emergency assurance. For example, waterfront trails lacking lighting or surveillance can affect nighttime safety, and the absence of wayfinding can reduce visitors\u0026rsquo; depth of exploration. Notably, the relationship between ecological value and recreational experience varies across contexts. Dai et al. point out that in Chinese urban parks, spiritual value is synergistic with aesthetic and recreational values(Daniel et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), whereas a study in Bangladesh finds conflicts between the two(Sultana \u0026amp; Selim, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This contrast suggests that whether ecological advantages translate into comprehensive well‑being depends strongly on local design strategies and management practices. Accordingly, future optimization of waterfront spaces can explore a low‑intervention, high‑responsiveness design strategy: with minimal disturbance to the natural substrate, progressively remedy basic service gaps through smart facilities, flexible nodes, or community co‑governance mechanisms, so that ecological advantages are more fully translated into inclusive and sustainable public well‑being.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Spatial Distribution of CES in Urban Parks\u003c/h2\u003e \u003cp\u003eThe spatial distribution of perceived park CES shows regional differentiation: Changsha features multi-type clustering and high coverage, Xiangtan forms a linear concentration centered on waterfront ecology, and Zhuzhou presents a dispersed pattern that links river corridors with urban nodes. This differentiation relates to city scale, development sequence, and spatial structure. As the regional center, Changsha operates within a complex built environment and a broad range of user needs, which yields a park system with functional mixing; Xiangtan and Zhuzhou, constrained by urban size and expansion models, rely more on natural elements as the organizing framework for park layout.\u003c/p\u003e \u003cp\u003eThis pattern indicates that the functional configuration of urban park systems is not an isolated technical decision but is embedded in broader urban form and ecological networks. The performance of blue-green spaces depends on coordination with transport networks, population distribution, and historical context. Therefore, when advancing integrated regional green-space systems, planning should respect each city\u0026rsquo;s baseline conditions and stage of development and avoid direct replication of core-area models.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Multi‑source Data Synergy and Future Research Directions\u003c/h2\u003e \u003cp\u003eBased on the context of the CZT urban agglomeration and combining online reviews with bibliometric methods, this study offers a preliminary exploration of how public perception and academic attention manifest across the dimensions of cultural ecosystem services, CES. It should be noted that the articulation and cognition of CES are strongly context dependent and may display differentiated patterns across regions and cultural settings(Daniel et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Sultana \u0026amp; Selim, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Accordingly, when applying related findings to other areas, adaptive adjustments informed by local ecological baselines and socio‑cultural characteristics are advisable.\u003c/p\u003e \u003cp\u003eIn addition, the current depiction of public perception relies mainly on textual data from the Dazhong Dianping platform, whose users tend to record tourism or leisure experiences, which may to some extent amplify the visibility of certain experiential dimensions (Taecharungroj \u0026amp; Mathayomchan, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). To more fully reflect the needs of diverse groups, future research can further integrate multi‑source data and multiple stakeholder perspectives. For example, short narratives collected through local interviews (Bieling \u0026amp; Plieninger, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), public participation geographic information systems, PPGIS, to obtain residents\u0026rsquo; spatial value annotations (Olafsson et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and historical archival sources (Derungs \u0026amp; Purves, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) can enrich the social dimension of CES. Building on this foundation, linking remote sensing environmental indicators with behavioral perception data may enable a more dynamic and explanatory integrated evaluation framework and provide more solid support for the collaborative governance of green infrastructure.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study adopts a multi-scale perspective and integrates a systematic literature review with online review data from the Dazhong Dianping platform to construct a cultural ecosystem services assessment framework that coordinates public perception and expert cognition through dual pathways, and it systematically analyzes the spatial differentiation and perception mechanisms of CES across 577 parks in the CZT urban agglomeration. The findings indicate that public perceptions of urban park CES are highly concentrated in embodied experiential dimensions such as waterfront experience and environmental comfort, whereas academic research pays greater attention to macro-structural topics such as ecological regulation functions and spatial patterns, revealing a clear misalignment in value focus. At the same time, park functions exhibit a multifunctional network centered on ecological experience, social vitality, and cultural aesthetics, whose spatial distribution is closely coupled with urban form and blue-green ecological networks, with a pronounced north\u0026ndash;south belt-like agglomeration along the main stem of the Xiang River. In addition, basic services such as facility convenience and core landscape features such as waterfront experience show both synergies and trade-offs, underscoring that while parks strengthen ecological appeal, the provision of supporting services such as safety, wayfinding, and accessibility remains insufficient.\u003c/p\u003e \u003cp\u003eThese results provide empirical support and methodological insight for optimizing green infrastructure planning at the urban-agglomeration scale. Future development of urban park systems should move beyond a single ecological or aesthetic orientation toward a coordinated model that balances ecological integrity, service equity, and public experience. On the one hand, planning should respect the development stages and spatial baselines of different cities and coordinate, at the regional level, a park network that is functionally complementary and diverse in type. On the other hand, under the premise of protecting the natural substrate, low-intervention and high-responsiveness design strategies should be used to precisely address gaps in basic services such as safety, information, and accessibility, promoting the effective translation of ecological advantages into welfare outcomes that are shared by all and friendly to all ages. The study also advocates deep integration of multi-source big data with traditional academic knowledge to build a CES assessment system with stronger social sensitivity, thereby bridging theory and practice and supporting human-centered urban sustainability.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eJ.W. and Z.Z. conceptualized the study and designed the research framework. J.W. and H.L. conducted the data collection and preprocessing, including the integration of online reviews and geospatial datasets. Z.Z. and H.L. developed the dual-pathway analytical model and performed the multiscale spatial differentiation analysis. J.W. led the manuscript writing with critical input from Z.Z. and W.L. on the theoretical interpretation and policy implications. W.L. supervised the project and provided strategic guidance throughout the research process. All authors contributed to the refinement of the methodology and the interpretation of results. Figures 1\u0026ndash;5 were prepared by Z.Z., while J.W. and H.L. collaborated on the visualization of spatial patterns. All authors reviewed and approved the final manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdhikary, M., Ghosh, D., Mandal, B., \u0026amp; Das, S. (2025). Estimating and mapping the value of cultural ecosystem services in an urban landscape context. \u003cem\u003eApplied Geography\u003c/em\u003e, \u003cem\u003e177\u003c/em\u003e, 103556.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndersson, E., Teng\u0026ouml;, M., McPhearson, T., \u0026amp; Kremer, P. (2015). Cultural ecosystem services as a gateway for improving urban sustainability. \u003cem\u003eEcosystem services\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e, 165\u0026ndash;168.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAssessment, M. E. (2001). \u003cem\u003eMillennium ecosystem assessment\u003c/em\u003e (Vol. 2). Millennium Ecosystem Assessment.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenoit, K., \u0026amp; Matsuo, A. (2020). A Guide to Using spacyr. In.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBertram, C., \u0026amp; Rehdanz, K. (2015). Preferences for cultural urban ecosystem services: Comparing attitudes, perception, and use. \u003cem\u003eEcosystem services\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e, 187\u0026ndash;199.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBieling, C., \u0026amp; Plieninger, T. (2017). 19 Leveraging landscape stewardship: Principles and ways forward. \u003cem\u003eThe Science and Practice of Landscape Stewardship\u003c/em\u003e, 366.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCabana, D., Ryfield, F., Crowe, T. P., \u0026amp; Brannigan, J. (2020). Evaluating and communicating cultural ecosystem services. \u003cem\u003eEcosystem services\u003c/em\u003e, \u003cem\u003e42\u003c/em\u003e, 101085.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCampbell, L. K., Svendsen, E. S., Sonti, N. F., \u0026amp; Johnson, M. L. (2016). A social assessment of urban parkland: Analyzing park use and meaning to inform management and resilience planning. \u003cem\u003eEnvironmental Science \u0026amp; Policy\u003c/em\u003e, \u003cem\u003e62\u003c/em\u003e, 34\u0026ndash;44.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng, X., Van Damme, S., Li, L., \u0026amp; Uyttenhove, P. (2019). Evaluation of cultural ecosystem services: A review of methods. \u003cem\u003eEcosystem services\u003c/em\u003e, \u003cem\u003e37\u003c/em\u003e, 100925.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChongxian, C., Weijing, L., \u0026amp; Yu, X. (2020). A Meta-analysis of Natural Landscape Effects on Physical and Mental Health of the Elderly Population. \u003cem\u003eLandscape Architecture\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(11), 90\u0026ndash;95.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDaniel, T. C., Muhar, A., Arnberger, A., Aznar, O., Boyd, J. W., Chan, K. M., Costanza, R., Elmqvist, T., Flint, C. G., \u0026amp; Gobster, P. H. (2012). Contributions of cultural services to the ecosystem services agenda. \u003cem\u003eProceedings of the National Academy of Sciences\u003c/em\u003e, \u003cem\u003e109\u003c/em\u003e(23), 8812\u0026ndash;8819.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Groot, R. S., Alkemade, R., Braat, L., Hein, L., \u0026amp; Willemen, L. (2010). Challenges in integrating the concept of ecosystem services and values in landscape planning, management and decision making. \u003cem\u003eEcological complexity\u003c/em\u003e, \u003cem\u003e7\u003c/em\u003e(3), 260\u0026ndash;272.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDerungs, C., \u0026amp; Purves, R. S. (2016). Characterising landscape variation through spatial folksonomies. \u003cem\u003eApplied Geography\u003c/em\u003e, \u003cem\u003e75\u003c/em\u003e, 60\u0026ndash;70.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDesa, U. (2014). World urbanization prospects: the 2018 revision. \u003cem\u003ePopulation Division, department of economic and social affairs, United Nations Secretariat\u003c/em\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDong, H., \u0026amp; Qin, B. (2017). Exploring the link between neighborhood environment and mental wellbeing: A case study in Beijing, China. \u003cem\u003eLandscape and urban planning\u003c/em\u003e, \u003cem\u003e164\u003c/em\u003e, 71\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEurope, W. (2017). Urban green spaces: a brief for action. \u003cem\u003eWorld Health Organization. Abgerufen von. Available from\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://apps.who.int/iris/handle/10665/344116\u003c/span\u003e\u003cspan address=\"https://apps.who.int/iris/handle/10665/344116\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eG\u0026oacute;mez-Baggethun, E., \u0026amp; Barton, D. N. (2013). Classifying and valuing ecosystem services for urban planning. \u003cem\u003eEcological economics\u003c/em\u003e, \u003cem\u003e86\u003c/em\u003e, 235\u0026ndash;245.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHavinga, I., Bogaart, P. W., Hein, L., \u0026amp; Tuia, D. (2020). Defining and spatially modelling cultural ecosystem services using crowdsourced data. \u003cem\u003eEcosystem services\u003c/em\u003e, \u003cem\u003e43\u003c/em\u003e, 101091.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe, Y., Lin, Y., Zhou, G., Zhu, Y., \u0026amp; Tang, K. (2021). Spatial pattern and drivers of urbanization in China\u0026rsquo;s mid-level developing urban agglomeration: A case study of Chang-Zhu-Tan. \u003cem\u003eRegional Sustainability\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(1), 83\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe, Y., Qin, Z., He, Y., Wang, L., \u0026amp; Li, X. (2024). Impact of ecological protection policies on the trade-offs and synergies of ecosystem services: A case study of the Changsha\u0026ndash;Zhuzhou\u0026ndash;Xiangtan green-core area. \u003cem\u003eAgriculture and Technology\u003c/em\u003e, \u003cem\u003e44\u003c/em\u003e(22), 125\u0026ndash;130.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHern\u0026aacute;ndez-Morcillo, M., Plieninger, T., \u0026amp; Bieling, C. (2013). An empirical review of cultural ecosystem service indicators. \u003cem\u003eEcological indicators\u003c/em\u003e, \u003cem\u003e29\u003c/em\u003e, 434\u0026ndash;444.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu, Y., He, K., Yu, C., Tian, Z., Liu, P., \u0026amp; Zhou, M. (2025). Spatiotemporal differentiation of ecosystem service value and its driving factors: A case study of the ecological green-core area in Changsha\u0026ndash;Zhuzhou\u0026ndash;Xiangtan. \u003cem\u003eNatural Resource Economics of China\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(05), 22\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu, Y., Liu, Y., \u0026amp; Li, C. (2022). Multi-scenario simulation of land use change and ecosystem service value in the middle reaches of Yangtze river urban agglomeration. \u003cem\u003eSustainability\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(23), 15738.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJ, L., L, Z. X., L, L., D, N. Y., L, X. J., L, W. X., M, Y., Y, W., H, Z. Z., B, C., L, C., J, Y. H., B, Y., X, W. Z.,A, W (2021). Evaluation of forest ecosystem service function in ecological green center area of Changsha-Zhuzhou-Xiangtan city group. \u003cem\u003eHunan ForestryScience ༆ Technology\u003c/em\u003e, \u003cem\u003e48\u003c/em\u003e(06), 50\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiajun, L., Qiulin, L., Shouyun, S., Xiaoru, L., \u0026amp; Jingpeng, L. (2022). Study on the Spatiotemporal Changes of Land Use and Ecosystem Services Value (ESV) of the Green Heart Area in the Changsha-Zhuzhou-Xiangtan (CZT) City Group. \u003cem\u003eChinese Landscape Architecture\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(1), 100.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, J., Park, H., Kim, I., \u0026amp; Kwon, H. (2020). s. Analysis of cultural ecosystem services using text mining of residents\u0026rsquo; opinions. \u003cem\u003eEcological indicators\u003c/em\u003e, \u003cem\u003e115\u003c/em\u003e, 106368.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, J., Fu, J., Gao, J., Zhou, R., Wang, K., \u0026amp; Zhou, K. (2023). Effects of the spatial patterns of urban parks on public satisfaction: Evidence from Shanghai, China. \u003cem\u003eLandscape ecology\u003c/em\u003e, \u003cem\u003e38\u003c/em\u003e(5), 1265\u0026ndash;1277.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, J., Gao, J., Zhang, Z., Fu, J., Shao, G., Zhao, Z., \u0026amp; Yang, P. (2024). Insights into citizens\u0026rsquo; experiences of cultural ecosystem services in urban green spaces based on social media analytics. \u003cem\u003eLandscape and urban planning\u003c/em\u003e, \u003cem\u003e244\u003c/em\u003e, 104999.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, J., Wang, J., \u0026amp; Zhou, W. (2024). Different impacts of urbanization on ecosystem services supply and demand across old, new and non-urban areas in the ChangZhuTan urban agglomeration, China. \u003cem\u003eLandscape ecology\u003c/em\u003e, \u003cem\u003e39\u003c/em\u003e(6), 107.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, J., \u0026amp; Yao, L. (2024). Evaluating the social value of ecosystem services in the Changsha\u0026ndash;Zhuzhou\u0026ndash;Xiangtan green core using social media data: A case study of Shiyan Lake Scenic Area and optimization strategies. \u003cem\u003eAgriculture and Technology\u003c/em\u003e, \u003cem\u003e44\u003c/em\u003e(21), 116\u0026ndash;121.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, J., Yao, L., Liu, L., Lian, J., Li, X., \u0026amp; Gan, D. (2025). Social valuation of ecosystem services in the green-core area of the Changsha\u0026ndash;Zhuzhou\u0026ndash;Xiangtan urban agglomeration based on the SolVES model. \u003cem\u003eJournal of Ecology and Rural Environment\u003c/em\u003e, \u003cem\u003e41\u003c/em\u003e(08), 987\u0026ndash;1001.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, Z., \u0026amp; Dong, T. (2025). Exploring the Mental Health Benefits of Urban Green Spaces Through Social Media Big Data: A Case Study of the Changsha\u0026ndash;Zhuzhou\u0026ndash;Xiangtan Urban Agglomeration. \u003cem\u003eSustainability\u003c/em\u003e, \u003cem\u003e17\u003c/em\u003e(8), 3465.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLian, J., Gao, J., Yao, L., Li, X., \u0026amp; Gan, D. (2024). Differences in perceived landscape aesthetic quality between residents and tourists with varying demographic characteristics: A case study of the ecological green core in the Changsha\u0026ndash;Zhuzhou\u0026ndash;Xiangtan urban agglomeration. \u003cem\u003eModern Horticulture\u003c/em\u003e, \u003cem\u003e47\u003c/em\u003e(24), 26\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiang, S., Yang, F., Zhang, J., Xiong, S., \u0026amp; Xu, Z. (2024). Assessment and management zoning of ecosystem service trade-Off/Synergy based on the Social\u0026ndash;Ecological balance: A case of the chang-zhu-tan metropolitan area. \u003cem\u003eLand\u003c/em\u003e, \u003cem\u003e13\u003c/em\u003e(2), 127.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, S., Luo, Y., \u0026amp; Xiao, Y. (2011). Research on the Ecological Value of the Green Core of Changsha-Zhuzhou-Xiangtan. \u003cem\u003eSocial Sciences in Hunan\u003c/em\u003e(05), 125\u0026ndash;127.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, W., Li, H., Huang, D., He, F., Liu, W., \u0026amp; Sun, Q. (2024). Application of random forest algorithm in the study of microscopic features and visitor experience evaluation in gardens. \u003cem\u003eEnvironmental Research Communications\u003c/em\u003e, \u003cem\u003e6\u003c/em\u003e(11), 115019.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo, J., Zhou, X., Zhang, Z., Tian, Y., \u0026amp; Tang, X. (2021). Evaluation of wetland ecosystem services in green core of Changzhutan urban agglomeration, Hunan. IOP Conference Series: Earth and Environmental Science.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo, M., Du, M., Jing, Y., Feng, C., Yang, L., \u0026amp; Zhao, B. (2023). Comprehensive ecosystem service value analysis of green spaces and wetlands in the Changsha\u0026ndash;Zhuzhou\u0026ndash;Xiangtan urban agglomeration. \u003cem\u003eGreen Science and Technology\u003c/em\u003e, \u003cem\u003e25\u003c/em\u003e(24), 111\u0026ndash;118.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuo, S., Naghibi, M., Patuano, A., \u0026amp; van Leeuwen, E. (2025). Cultural ecosystem services of informal green spaces: usage, perception and preference of residents in deprived urban neighbourhoods. \u003cem\u003eLocal Environment\u003c/em\u003e, 1\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuying, W., Xiaoma, L., Dexin, G., Pengao, L., Sheng, G., \u0026amp; Yi, L. (2024). Spatial Heterogeneity and Driving Factors of Ecosystem Service Trade-offs and Synergies in the Changsha-Zhuzhou-Xiangtan Urban Agglomeration. \u003cem\u003eEcology and Environment\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e(6), 969.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMEa, M. E. A. (2005). Ecosystems and Human Well-Being: wetlands and water synthesis.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeng, Z., \u0026amp; ZengYongnian (2018). Temporal and spatial dynamic changes and driving forces analysis of wetland landscape of Chang-Zhu-Tan urban agglomeration. \u003cem\u003eTransactions of the Chinese Society of Agricultural Engineering\u003c/em\u003e, \u003cem\u003e34\u003c/em\u003e(01), 241\u0026ndash;249.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMilcu, A. I., Hanspach, J., Abson, D., \u0026amp; Fischer, J. (2013). Cultural ecosystem services: a literature review and prospects for future research. \u003cem\u003eEcology and society\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e(3).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuyang, C., Chen, H., Zancai, X., \u0026amp; Ping, Z. (2025). Spatiotemporal evolution of ecosystem service value in the Changsha\u0026ndash;Zhuzhou\u0026ndash;Xiangtan metropolitan ring and its driving factors. \u003cem\u003eJournal of Jishou University(Social Sciences)\u003c/em\u003e, \u003cem\u003e46\u003c/em\u003e(04), 163\u0026ndash;176.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlafsson, A. S., Purves, R. S., Wartmann, F. M., Garcia-Martin, M., Fagerholm, N., Torralba, M., Albert, C., Verbrugge, L. N., Heikinheimo, V., \u0026amp; Plieninger, T. (2022). Comparing landscape value patterns between participatory mapping and geolocated social media content across Europe. \u003cem\u003eLandscape and urban planning\u003c/em\u003e, \u003cem\u003e226\u003c/em\u003e, 104511.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOliveira, T., Araujo, B., \u0026amp; Tam, C. (2020). Why do people share their travel experiences on social media? \u003cem\u003eTourism management\u003c/em\u003e, \u003cem\u003e78\u003c/em\u003e, 104041.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOpdam, P. (2020). Implementing human health as a landscape service in collaborative landscape approaches. \u003cem\u003eLandscape and urban planning\u003c/em\u003e, \u003cem\u003e199\u003c/em\u003e, 103819.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng, W., \u0026amp; Xie, X. (2024). Response of Ecological Resilience to Urban Renewal in Chang-Zhu-Tan Urban Agglomeration. \u003cem\u003eResour Environ Yangtze Basin\u003c/em\u003e, \u003cem\u003e33\u003c/em\u003e, 2369\u0026ndash;2378.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRongzhong, G., Haijian, S., Kuo, Z., \u0026amp; Huaping, L. (2025). Ecosystem services and their trade-offs and synergies in Chang-Zhu-Tan urban agglomeration. \u003cem\u003eResearch of Soil and Water Conservation\u003c/em\u003e, \u003cem\u003e32\u003c/em\u003e(04), 365\u0026ndash;374.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRui, C., Can, Y., Jing, T., \u0026amp; Xiangyu, L. (2023). The impact of new urbanization of Changsha-Zhuzhou-Xiangtan urban agglomeration on the spatial differentiation of ecosystem service values. \u003cem\u003eJournal of Central South University of Forestry \u0026amp; Technology\u003c/em\u003e, \u003cem\u003e43\u003c/em\u003e(6), 158\u0026ndash;167.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmall, N., Munday, M., \u0026amp; Durance, I. (2017). The challenge of valuing ecosystem services that have no material benefits. \u003cem\u003eGlobal environmental change\u003c/em\u003e, \u003cem\u003e44\u003c/em\u003e, 57\u0026ndash;67.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStępniewska, M. (2021). The capacity of urban parks for providing regulating and cultural ecosystem services versus their social perception. \u003cem\u003eLand Use Policy\u003c/em\u003e, \u003cem\u003e111\u003c/em\u003e, 105778.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSultana, R., \u0026amp; Selim, S. A. (2021). Residents' perceptions of the role and management of green spaces to provide cultural ecosystem services in Dhaka, Bangladesh. \u003cem\u003eEcology \u0026amp; Society\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(4).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaecharungroj, V., \u0026amp; Mathayomchan, B. (2019). Analysing TripAdvisor reviews of tourist attractions in Phuket, Thailand. \u003cem\u003eTourism management\u003c/em\u003e, \u003cem\u003e75\u003c/em\u003e, 550\u0026ndash;568.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTEEB, R. O. (2010). \u003cem\u003eMainstreaming the economics of nature\u003c/em\u003e. TEEB Geneva, Switzerland.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTengberg, A., Fredholm, S., Eliasson, I., Knez, I., Saltzman, K., \u0026amp; Wetterberg, O. (2012). Cultural ecosystem services provided by landscapes: Assessment of heritage values and identity. \u003cem\u003eEcosystem services\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e, 14\u0026ndash;26.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTost, H., Reichert, M., Braun, U., Reinhard, I., Peters, R., Lautenbach, S., Hoell, A., Schwarz, E., Ebner-Priemer, U., \u0026amp; Zipf, A. (2019). Neural correlates of individual differences in affective benefit of real-life urban green space exposure. \u003cem\u003eNature neuroscience\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(9), 1389\u0026ndash;1393.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVillamagna, A. M., Mogoll\u0026oacute;n, B., \u0026amp; Angermeier, P. L. (2014). A multi-indicator framework for mapping cultural ecosystem services: The case of freshwater recreational fishing. \u003cem\u003eEcological indicators\u003c/em\u003e, \u003cem\u003e45\u003c/em\u003e, 255\u0026ndash;265.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, Z., Xu, J., Wu, W., \u0026amp; Zhang, H. Spatial coupling coordination between transport superiority and ecosystem services in metropolitan areas: A case study of Changsha\u0026ndash;Zhuzhou\u0026ndash;Xiangtan. \u003cem\u003eEconomic Geography\u003c/em\u003e, 1\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWenbin, P., \u0026amp; Xiaotian, C. (2023). Spatiotemporal differentiation of ecological resilience under urban renewal and its influencing mechanisms around the Changsha-Zhuzhou-Xiangtan Urban Agglomeration. \u003cem\u003eEconomic Geography\u003c/em\u003e, \u003cem\u003e43\u003c/em\u003e(10), 44\u0026ndash;52.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWijffels, J., Straka, M., \u0026amp; Strakov\u0026aacute;, J. (2018). Package \u0026lsquo;udpipe\u0026rsquo;. \u003cem\u003eAlso available at\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.microsoft.com/snapshot/2018-06-12/web/packages/udpipe/udpipe\u003c/span\u003e\u003cspan address=\"https://cran.microsoft.com/snapshot/2018-06-12/web/packages/udpipe/udpipe\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. \u003cem\u003epdf\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eX, O., X, Z., \u0026amp; Y, H. Q. (2020). Incorporating ecosystem services with ecosystem health for ecological risk assessment: case study in Changsha-Zhuzhou-Xiangtan urban agglomeration, China. \u003cem\u003eActa Ecologica Sinica\u003c/em\u003e, \u003cem\u003e40\u003c/em\u003e(16), 5478\u0026ndash;5489.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiao, O., Qingyun, H., \u0026amp; Xiang, Z. (2020). Simulation of impacts of urban agglomeration land use change on ecosystem services value under multi-scenarios: Case study in Changsha-Zhuzhou-Xiangtan urban agglomeration. \u003cem\u003eEconomic Geography\u003c/em\u003e, \u003cem\u003e40\u003c/em\u003e(1), 93\u0026ndash;102.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eY, S. Z., G, W. S., \u0026amp; B, C. (2024). Construction of ecological security pattern and assessment of ecological resilience based on ecosystem services: A case study of Changsha-Zhuzhou-Xiangtan urban agglomeration. \u003cem\u003eActa Ecologica Sinica\u003c/em\u003e, \u003cem\u003e44\u003c/em\u003e(11), 4650\u0026ndash;4661.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang, Z., Haiyan, Z., \u0026amp; Zhipeng, W. (2024). Research on the Quality Evaluation and Spatial Differentiation of Human Settlement Environment in the Changsha Zhuzhou Xiangtan Urban Agglomeration. \u003cem\u003eResearch of Soil \u0026amp; Water Conservation\u003c/em\u003e, \u003cem\u003e31\u003c/em\u003e(5).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYizhang, C., Guohua, Z., Hua, W., Shuqiang, C., Fangze, B., \u0026amp; Xuexia, Y. (2023). The Evolution Characteristics and Enhancement Paths of the Economy-Society-Ecology Coordinated Development Level of Changsha-Zhuzhou-Xiangtan Urban Ag-glomeration. \u003cem\u003eTropical Geography\u003c/em\u003e, \u003cem\u003e43\u003c/em\u003e(03), 519\u0026ndash;531.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYou, C., Qu, H., Feng, C. C., \u0026amp; Guo, L. (2024). Evaluating the match between natural ecosystem service supply and cultural ecosystem service demand: Perspectives on spatiotemporal heterogeneity. \u003cem\u003eEnvironmental Impact Assessment Review\u003c/em\u003e, \u003cem\u003e108\u003c/em\u003e, 107592.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZabala, J. A., Mart\u0026iacute;nez-Paz, J. M., \u0026amp; Alcon, F. (2021). A comprehensive approach for agroecosystem services and disservices valuation. \u003cem\u003eScience of the Total Environment\u003c/em\u003e, \u003cem\u003e768\u003c/em\u003e, 144859.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhan, W., Cheng, H., \u0026amp; Shen, S. (2020). Evaluation of urban wetland ecosystem service value in Zhuzhou City. \u003cem\u003eNature Environment and Pollution Technology\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e(2), 453\u0026ndash;467.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, H., Huang, R., Zhang, Y., \u0026amp; Buhalis, D. (2022). Cultural ecosystem services evaluation using geolocated social media data: A review. \u003cem\u003eTourism Geographies\u003c/em\u003e, \u003cem\u003e24\u003c/em\u003e(4\u0026ndash;5), 646\u0026ndash;668.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao, C., Xiao, P., Qian, P., Xu, J., Yang, L., \u0026amp; Wu, Y. (2022). Spatiotemporal differentiation and balance pattern of ecosystem service supply and demand in the Yangtze River Economic Belt. \u003cem\u003eInternational journal of environmental research and public health (Vol\u003c/em\u003e, \u003cem\u003e19\u003c/em\u003e, 7223.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao, W., Xie, Y., \u0026amp; Xiang, P. (2020). Evaluation and quantitative criteria of urban ecological space: A case study of the Changsha\u0026ndash;Zhuzhou\u0026ndash;Xiangtan urban agglomeration. \u003cem\u003eJournal of Central South University of Forestry \u0026amp; Technology\u003c/em\u003e, \u003cem\u003e40\u003c/em\u003e(04), 72\u0026ndash;79.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu, C., Zhang, X., Zhou, M., He, S., Gan, M., Yang, L., \u0026amp; Wang, K. (2020). Impacts of urbanization and landscape pattern on habitat quality using OLS and GWR models in Hangzhou, China. \u003cem\u003eEcological indicators\u003c/em\u003e, \u003cem\u003e117\u003c/em\u003e, 106654.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAppendix, A. Detailed data from the selected 30 sample literature.\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":"Multiscale spatial differentiation, Cultural ecosystem services, Urban parks, Public perception, Systematic literature review","lastPublishedDoi":"10.21203/rs.3.rs-8178918/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8178918/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study constructs a multi-scale spatial differentiation assessment model by integrating a theoretical framework derived from a systematic literature review with public perceptions reflected in online crowdsourced reviews, to analyze the supply characteristics and perception mechanisms of cultural ecosystem services (CES) provided by urban parks. Taking 577 urban parks in the Chang-Zhu-Tan urban agglomeration as empirical cases, the research synthesizes theoretically coded findings from core literature with semantic information extracted from online reviews, revealing the following key insights: First, public and expert cognition diverge, with public perception concentrating on experiential dimensions such as \u0026ldquo;Waterfront Experience\u0026rdquo; and \u0026ldquo;Environmental Sanitation \u0026amp; Comfort\u0026rdquo;, whereas experts focus on macro-ecological attributes such as \u0026ldquo;Ecological Functions\u0026rdquo; and \u0026ldquo;Spatial Pattern\u0026rdquo;. Second, urban parks form a multifunctional network centered on ecological experience, social vitality, and cultural aesthetics, underscoring the spatial heterogeneity of green space systems. Third, basic services including facility convenience and core landscapes including waterfront experience exhibit a dynamic interplay of synergies and trade-offs. Fourth, the distribution of park functions is coupled with urban spatial morphology and the blue\u0026ndash;green network, providing support for green infrastructure planning.\u003c/p\u003e","manuscriptTitle":"Spatial Differentiation and Perception of Urban Park Cultural Ecosystem Services from a Multiscale Perspective: Evidence from the Changsha–Zhuzhou–Xiangtan Urban Agglomeration","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-24 16:40:50","doi":"10.21203/rs.3.rs-8178918/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":"5ca152aa-a136-4cee-8a6e-0bf46163502f","owner":[],"postedDate":"February 24th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-02-24T16:40:50+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-24 16:40:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8178918","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8178918","identity":"rs-8178918","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}