Using LLMs for the multidimensional perception assessment of recreation and leisure spaces: a case study of Hangzhou, China

Abstract

Urban recreation and leisure spaces (RLSs) are vital venues for citizens to relax, socialize, and experience urban life. Public perception of RLSs represents a crucial component of urban studies. Traditional Natural Language Processing (NLP)-based methods for analyzing RLS perceptions from social media data face limitations in semantic understanding, multidimensional sentiment differentiation, and spatial recognition. To overcome these limitations, we propose and validate a novel Large Language Model (LLM)-based analytical framework for multidimensional perception assessment of urban recreation and leisure spaces. Through comparative evaluation of three mainstream LLMs (DeepSeek-R1, Kimi, and QWEN), we selected the best-performing Qwen model (F1-score=0.899) to construct a multidimensional Aspect-Based Sentiment Analysis (ABSA) framework. Taking Hangzhou, China as a case study, we collected note data from the "REDnote" platform and employed the LLM to identify seven perception dimensions and determine sentiment polarities. Building upon the ABSA perception evaluation data generated by the LLM, we further conducted spatial distribution analysis, keyword co-occurrence network analysis, and place-activity bipartite network analysis to reveal perception patterns and their underlying causes. The findings indicate that: a) Hangzhou's recreation and leisure spaces excel in "soft power" dimensions (cultural atmosphere, spatial aesthetics) but require optimization in "hard power" aspects (accessibility, functional configuration); b) Hangzhou's leisure resources are unevenly distributed, with perception evaluations exhibiting a core-periphery structure; c) open outdoor spaces are more positively perceived than enclosed commercial complexes, with "city walking" emerging as the dominant positive activity. This research confirms the transformative value of LLM as core analytical engines rather than preprocessing tools, providing a new research paradigm and practical guidance for the planning and management of urban recreation and leisure spaces.

Keywords

Recreation and leisure spaces / Multidimensional perception / Social media data / Large Language Model / Sentiment analysis

References

[1] Abdul-Rahman M, Chan EHW, Wong MS, Irekponor VE, Abdul-Rahman MO. A framework to simplify pre-processing location-based social media big data for sustainable urban planning and management. Cities, 2021, 109 102986

[2] Agrawal A, Kuriakose PN. Implications of a twitter data-centred methodology for assessing commuters’ perceptions of the Delhi Metro in India. Computational Urban Science, 2022, 2(1 38

[3] Bai Z, Huang S, Wang L, Qi J. Refined understanding of street vitality: Comparing the spatial effects of pedestrian vitality and cycling vitality and the spatial heterogeneity of factors. International Journal of Digital Earth, 2025

[4] Bell PA, Fisher JD, Loomis RJ. Environmental psychology, 1978Saunders

[5] Brown T, Mann B, Ryder N, Subbiah M, Kaplan JD, Dhariwal P, Neelakantan A, Shyam P, Sastry G, Askell A. Language models are few-shot learners. Advances in Neural Information Processing Systems, 2020, 33: 1877-1901

[6] Chen X, Zhang L, Zhao Z, Zhang F, Liu S, Long Y. Characterizing and measuring the environmental amenities of urban recreation leisure regions based on image and text fusion perception: A case study of Nanjing, China. Land, 2023, 1211 11

[7] Chen Z, Tan KJ, Liao Z, Tung VWS. Walking the hidden city: The role of citywalk in shaping destination branding experience. Tourism Recreation Research, 2024

[8] Chen B, Zhang Z, Langrené N, Zhu S. Unleashing the potential of prompt engineering for large language models. Patterns, 2025

[9] Chen X, Sun Y, Ibrahim FIB, Kamarazaly MAB, Abidin SNBZ, Tang S. Social media interaction and built environment effects on urban walking experience: A machine learning analysis of Shanghai citywalk. PLoS ONE, 2025, 204 e0320951

[10] Conti S, Dias Á, Pereira L. Perceived city sustainability and tourist behavioural intentions. Smart Cities, 2023, 62): 692

[11] DiMaggio P, Nag M, Blei D. Exploiting affinities between topic modeling and the sociological perspective on culture: Application to newspaper coverage of U.S. government arts funding. Poetics, 2013, 416570-606

[12] Ding Y, Song Y, Xu Z. Exploring the impact of park features and visitors’ socioeconomic status on park visitation: A case study of Austin, Texas. Journal of Outdoor Recreation and Tourism, 2025, 50 100877

[13] Fábos JG. Greenway planning in the United States: Its origins and recent case studies. Landscape and Urban Planning, 2004, 68(2–3): 321-342

[14] Feng, J., Zhang, J., Liu, T., Zhang, X., Ouyang, T., Yan, J., Du, Y., Guo, S., & Li, Y. (2025). CityBench: Evaluating the capabilities of large language models for urban tasks (No. arXiv:2406.13945). arXiv. https://doi.org/10.48550/arXiv.2406.13945

[15] Fu X, Li C, Quan SJ, Yigitcanlar T, Wasserman D. Large language models in urban planning. Nature Cities, 2025

[16] Ghahremani H, Afsari Bajestani S, McCarthy L, Jalalianhosseini M. Transformation of urban spaces within cities in the context of globalization and urban competitiveness. Journal of Urban Planning and Development, 2021, 147(3): 5021026

[17] Gibson, J. J. (2014). The theory of affordances: (1979). In The People, Place, and Space Reader. Routledge.

[18] Godwyll JM, Buzinde CN, Glover TD, White DD, Frazier AE, Manuel-Navarrete D. When perception is reality: Examining perceived accessibility of recreational public spaces through social space production. World Leisure Journal, 2025

[19] Hou C, Zhang F, Li Y, Li H, Mai G, Kang Y, Yao L, Yu W, Yao Y, Gao S, Chen M, Liu Y. Urban sensing in the era of large language models. The Innovation, 2025, 61 100749

[20] Huang W, Zhao X, Lin G, Wang Z, Chen M. How to quantify multidimensional perception of urban parks? Integrating deep learning-based social media data analysis with questionnaire survey methods. Urban Forestry & Urban Greening, 2025, 107 128754

[21] Jang KM, Kim Y. Crowd-sourced cognitive mapping: A new way of displaying people’s cognitive perception of urban space. PLoS ONE, 2019, 146 e0218590

[22] Jiang Y, Yu R, Chen Q, Zhou Y. Exploring the construction and application of “urban sentiment maps” in urban planning practice: A case study of chengdu. Transactions in Urban Data, Science, and Technology, 2025

[23] Kang, J., Körner, M., Wang, Y., Taubenböck, H., & Zhu, X. X. (2018). Building instance classification using street view images. ISPRS Journal of Photogrammetry and Remote Sensing, 145, 44–59. https://doi.org/10.1016/j.isprsjprs.2018.02.006

[24] Kruse J, Kang Y, Liu Y-N, Zhang F, Gao S. Places for play: Understanding human perception of playability in cities using street view images and deep learning. Computers, Environment and Urban Systems, 2021, 90 101693

[25] Lefebvre H. From the production of space, 2010Routledge

[26] Lemoine-Rodríguez R, Inostroza L, Zepp H. The global homogenization of urban form. An assessment of 194 cities across time. Landscape and Urban Planning, 2020, 204 103949

[27] Li, Q., Dou, S., Shao, K., Chen, C., & Hu, H. (2025). Evaluating scoring bias in LLM-as-a-judge (No. arXiv:2506.22316). arXiv. https://doi.org/10.48550/arXiv.2506.22316

[28] Liu J, Meng B, Shi C. A multi-activity view of intra-urban travel networks: A case study of Beijing. Cities, 2023, 143 104634

[29] Lock O, Pettit C. Social media as passive geo-participation in transportation planning – How effective are topic modeling & sentiment analysis in comparison with citizen surveys?. Geo-Spatial Information Science, 2020

[30] Luo H, Zhang Z, Zhu Q, Houda Ben Ameur NE, Liu X, Ding F, Cai Y. Using large language models to investigate cultural ecosystem services perceptions: A few-shot and prompt method. Landscape and Urban Planning, 2025, 258 105323

[31] Lynch K. The image of the city, 1964MIT Press

[32] Mehta V. Evaluating public space. Journal of Urban Design, 2014, 19153-88

[33] Murtagh, F., & Heck, A. (2012). Multivariate data analysis. Springer Science & Business Media.

[34] Niu H, Silva EA. Understanding temporal and spatial patterns of urban activities across demographic groups through geotagged social media data. Computers, Environment and Urban Systems, 2023, 100 101934

[35] Oldenburg, R. (1999). The Great Good Place: Cafes, Coffee Shops, Bookstores, Bars, Hair Salons, and Other Hangouts at the Heart of a Community. Hachette Books.

[36] Praliya, S., & Garg, P. (2019). Public space quality evaluation: Prerequisite for public space management. The Journal of Public Space, 4(1), 93–126. https://doi.org/10.32891/jps.v4i1.667

[37] Ravi K, Ravi V. A survey on opinion mining and sentiment analysis: Tasks, approaches and applications. Knowledge-Based Systems, 2015, 89: 14-46

[38] Rillig MC, Mansour I, Hempel S, Bi M, König-Ries B, Kasirzadeh A. How widespread use of generative AI for images and video can affect the environment and the science of ecology. Ecology Letters, 2024, 27(3 e14397

[39] Rui J, Cai C. Plausible or misleading? Evaluating the adaption of the place pulse 2.0 dataset for predicting subjective perception in Chinese urban landscapes. Habitat International, 2025, 157 103333

[40] Saelens BE, Frank LD, Auffrey C, Whitaker RC, Burdette HL, Colabianchi N. Measuring physical environments of parks and playgrounds: EAPRS instrument development and inter-rater reliability. Journal of Physical Activity & Health, 2006

[41] Stock K, Jones CB, Russell S, Radke M, Das P, Aflaki N. Detecting geospatial location descriptions in natural language text. International Journal of Geographical Information Science, 2022

[42] Sun Y, Shao Y. Measuring visitor satisfaction toward peri-urban green and open spaces based on social media data. Urban Forestry & Urban Greening, 2020, 53 126709

[43] Tohidi, K., Dashtipour, K., Rebora, S., & Pourfaramarz, S. (2025). A comparative evaluation of large language models for persian sentiment analysis and emotion detection in social media texts (No. arXiv:2509.14922). arXiv. https://doi.org/10.48550/arXiv.2509.14922

[44] Wang R, Helbich M, Yao Y, Zhang J, Liu P, Yuan Y, Liu Y. Urban greenery and mental wellbeing in adults: Cross-sectional mediation analyses on multiple pathways across different greenery measures. Environmental Research, 2019, 176 108535

[45] Wang S, Hu T, Xiao H, Li Y, Zhang C, Ning H, Zhu R, Li Z, Ye X. GPT, large language models (LLMs) and generative artificial intelligence (GAI) models in geospatial science: A systematic review. International Journal of Digital Earth, 2024

[46] Wankhade M, Rao ACS, Kulkarni C. A survey on sentiment analysis methods, applications, and challenges. Artificial Intelligence Review, 2022, 557): 5731-5780

[47] Wei J, Wang X, Schuurmans D, Bosma M, Xia F, Chi E, Le QV, Zhou D. Chain-of-thought prompting elicits reasoning in large language models. Advances in Neural Information Processing Systems, 2022, 35: 24824-24837

[48] Witte A. Revisiting walking as mobile place-making practice: A discursive perspective. Tourism Geographies, 2023, 25(1): 334-356

[49] Wu C, Liang Y, Zhao M, Teng M, Yue H, Ye Y. Perceiving the fine-scale urban poverty using street view images through a vision-language model. Sustainable Cities and Society, 2025, 123 106267

[50] Wu Y, Bai Z, Qi J. Compensating eco-recreation opportunities based on the supply-demand relationship between large-scale urban eco-spaces and communities. Sustainable Cities and Society, 2025, 126 106423

[51] Xia J, Tong Y, Long Y. Advancements in the application of large language models in urban studies: A systematic review. Cities, 2025, 165 106142

[52] Xiao X, Fang C, Lin H, Liu L, Tian Y, He Q. Exploring spatiotemporal changes in the multi-granularity emotions of people in the city: A case study of Nanchang, China. Computational Urban Science, 2022, 2(1 1

[53] Xu H, Duan J, Ren M, Zhao G, Liu Z. Revealing youth-perceived cultural ecosystem services for high-density urban green space management: A deep learning spatial analysis of social media photographs from central Beijing. Landscape Ecology, 2025, 406): 119

[54] Yao F, Wang Y. Towards resilient and smart cities: A real-time urban analytical and geo-visual system for social media streaming data. Sustainable Cities and Society, 2020, 63 102448

[55] Ye, C., Zhang, F., Mu, L., Gao, Y., & Liu, Y. (2020). Urban function recognition by integrating social media and street-level imagery. Environment and Planning B: Urban Analytics and City Science. https://doi.org/10.1177/2399808320935467

[56] Yildirim I, Paul LA. From task structures to world models: What do LLMs know?. Trends in Cognitive Sciences, 2024, 285404-415

[57] Yuan C, Zhang X, Tang L, Xiong Y. CBEES: A fine-grained commercial built environment perception and evaluation based on social media. Sustainable Cities and Society, 2025, 119 106120

[58] Zhai Y, Fan B, Yu J, Gong R, Yin J. Effects of spatial type and scale of small urban open spaces on perceived restoration: An online survey-based experiment. Land, 2024, 13(9 9

[59] Zhang J, Liu L, Wang J, Dong D, Jiang T, Chen J, Ren Y. Exploring the relationship between the sentiments of young people and urban green space by using a check-in microblog. Forests, 2024, 155 5

[60] Zhang X, Rui J, Xia G, Yang J, Cai C, Zhao W. Revealing disparities and driving factors in leisure activity segregation of residents and tourists: A data-driven analysis of smart phone data. Applied Geography, 2025, 176 103513

[61] Zhao X, Huang H, Yang T, Lu Y, Zhang L, Wang R, Liu Z, Zhong T, Liu T. Urban planning in the age of large language models: Assessing OpenAI o1’s performance and capabilities across 556 tasks. Computers, Environment and Urban Systems, 2025, 121 102332

[62] Zhou, Z., Lin, Y., Jin, D., & Li, Y. (2024). Large language model for participatory urban planning (No. arXiv:2402.17161). arXiv. https://doi.org/10.48550/arXiv.2402.17161

[63] Zhu H, Chang J, An X, Li S. Global and local feature extraction of urban historical spatial perception using large language models: A case study of Harbin Central Street District. Cities, 2025, 165 106183

[64] Zmyslony P, Wędrowicz KA. Cities in the experience economy: The rise and the future of urban leisure formats. Journal of Tourism Futures, 2019, 52): 185-192

RIGHTS & PERMISSIONS

The Author(s)