Exploring the complexity of urban walking environments based on pedestrian navigation data

Abstract

The complexity of urban environments influences pedestrians’ walkability, which is especially significant for people living in mega cities. While many studies identify influential factors, how these factors shape pedestrian wayfinding through complex and spatially varied mechanisms remains underexplored. This study addresses this gap by using a novel pedestrian navigation dataset as a proxy to quantify the perceived complexity of walking environments. By integrating multi-scale urban features—four at the macro-level and 14 at the micro-level derived from Street View Imagery—we systematically uncover the key correlates of navigation demand and their underlying effects. The results reveal that a combination of factors such as the number of Points of Interest, transportation accessibility, proportion of people in view, and intersection count are positively associated with pedestrians’ navigation behavior. More importantly, we demonstrate that their relationship is profoundly non-linear and exhibits strong spatial heterogeneity. These results are further validated through population normalization, sensitivity tests, and temporal comparisons between weekdays and weekends. Such analyses confirm the robust and independent association between environmental complexity and navigation behavior. By operationalizing these complex interrelationships, our work advances the theoretical framework for urban environmental complexity. The findings provide crucial evidence for moving beyond a "one-size-fits-all" approach, offering targeted, context-aware insights to foster truly human-centered urban planning and design.

Keywords

Urban complexity / Navigation demand / Pedestrian wayfinding / Multi-scale analysis / GWRF / SHAP

References

[1] Ahmadpoor N, Smith AD. Spatial knowledge acquisition and mobile maps: The role of environmental legibility. Cities, 2020, 101: 102700

[2] Arnold T, Frost M, Timmis A, Dale S, Ison S. Mobility hubs: Review and future research direction. Transportation Research Record: Journal of the Transportation Research Board, 2023, 2677(2): 858-868 Sage CA: Los Angeles, CA)

[3] Biljecki F, Ito K. Street view imagery in urban analytics and GIS: A review. Landscape and Urban Planning, 2021, 215: 104217

[4] Bongiorno C, Zhou Y, Kryven M, Theurel D, Rizzo A, Santi P, Tenenbaum J, Ratti C. Vector-based pedestrian navigation in cities. Nature Computational Science, 2021, 1(10): 678-685

[5] Calabrese F, Diao M, Di Lorenzo G, Ferreira J, Ratti C. Understanding individual mobility patterns from urban sensing data: A mobile phone trace example. Transportation Research Part c: Emerging Technologies, 2013, 26: 301-313

[6] Celik, E., & Yildirim, O. C. (2025). Wayfinding in historical urban landscapes: Unraveling the spatial-behavioral dynamics in Eskisehir’s Odunpazari. Journal of Asian Architecture and Building Engineering. (world). https://www.tandfonline.com/doi/abs/10.1080/13467581.2025.2490298

[7] Cervero R, Kockelman K. Travel demand and the 3Ds: Density, diversity, and design. Transportation Research Part d: Transport and Environment, 1997, 2(3): 199-219

[8] Chen Y, Tao R, Ma Q. Rethinking shrinking cities through urban flows: Network-based insights into coordinated development. Urban Informatics, 2025, 4(1): 14

[9] Choi HS, Bruyns G, Zhang W, Cheng T, Sharma S. Spatial cognition and three-dimensional vertical urban design guidelines—cognitive measurement and modelling for human centre design. Urban Science, 2023, 7(4): 125

[10] De Tinguy D, Van De Maele T, Verbelen T, Dhoedt B. Spatial and temporal hierarchy for autonomous navigation using active inference in minigrid environment. Entropy, 2024, 26(1): 83

[11] Dong F, Li X, Xie Q, Ye R, Cao S. The influence of weather and temperature on pedestrian walking characteristics on the zigzag bridge. International Journal of Biometeorology, 2022, 66(12): 2541-2552

[12] Durst NJ, Sullivan E, Jochem WC. The spatial and social correlates of neighborhood morphology: Evidence from building footprints in five U.S. metropolitan areas. PLoS ONE, 2024, 19(4): e0299713

[13] Ewing R, Cervero R. Travel and the built environment: A meta-analysis. Journal of the American Planning Association, 2010, 76(3): 265-294

[14] Fan P, Xu L, Yue W, Chen J. Accessibility of public urban green space in an urban periphery: The case of Shanghai. Landscape and Urban Planning, 2017, 165: 177-192

[15] Fayaz J, Galasso C. Interpretability and spatial efficacy of a deep-learning-based on-site early warning framework using explainable artificial intelligence and geographically weighted random forests. Geoscience Frontiers, 2024, 15(5): 101839

[16] Guan F, Fang Z, Wang L, Zhang X, Zhong H, Huang H. Modelling people’s perceived scene complexity of real-world environments using street-view panoramas and open geodata. ISPRS Journal of Photogrammetry and Remote Sensing, 2022, 186: 315-331

[17] He X, He SY. Using open data and deep learning to explore walkability in Shenzhen, China. Transportation Research Part d: Transport and Environment, 2023, 118: 103696

[18] Hegarty M, He C, Boone AP, Yu S, Jacobs EG, Chrastil ER. Understanding differences in wayfinding strategies. Topics in Cognitive Science, 2023, 15(1): 102-119

[19] Hosseini, R., Tong, D., Lim, S., Sohn, G., & Gidófalvi, G. (2025). A framework for performance analysis of OpenStreetMap data in navigation applications: The case of a well-developed road network in Australia. Annals of GIS. https://www.tandfonline.com/doi/abs/10.1080/19475683.2025.2468184. (world).

[20] Ibrahim A, Ali HH, Mahmoud Z. Perception beyond sight: Investigating the cognitive maps of congenitally blind individuals in urban environments. Frontiers of Architectural Research, 2024, 13(4): 809-821

[21] Jamshidi, S., Ensafi, M., & Pati, D. (2020). Wayfinding in interior environments: an integrative review. Frontiers in Psychology, 11. https://doi.org/10.3389/fpsyg.2020.549628

[22] Jiang H, Dong L, Qiu B. How are macro-scale and micro-scale built environments associated with running activity? The application of strava data and deep learning in inner london. ISPRS International Journal of Geo-Information, 2022, 11(10): 504

[23] Jiao W, Huang W, Fan H. Evaluating spatial accessibility to healthcare services from the lens of emergency hospital visits based on floating car data. International Journal of Digital Earth, 2022, 15(1): 108-133

[24] Kong X, Li M, Ma K, Tian K, Wang M, Ning Z, Xia F. Big trajectory data: A survey of applications and services. IEEE Access, 2018, 6: 58295-58306

[25] Lan L, Ghasemifard H, Yuan Y, Hachinger S, Zhao X, Bhattacharjee S, Bi X, Bai Y, Menzel A, Chen J. Assessment of urban CO2 measurement and source attribution in munich based on TDLAS-WMS and trajectory analysis. Atmosphere, 2020, 11(1): Article 1

[26] Li Y, Peng L, Wu C, Zhang J. Street View Imagery (SVI) in the built environment: A theoretical and systematic review. Buildings, 2022, 12(8): Article 8

[27] Li J, Koohsari MJ, Zhao J, Kaczynski AT, McCormack GR, Oka K, Nakaya T, Tanimoto R, Watanabe R, Hanibuchi T. The impact of objective urban features on perception of neighbourhood environments. Scientific Reports, 2025, 15(1): 30322

[28] Liu Y, Chen M, Wang M, Huang J, Thomas F, Rahimi K, Mamouei M. An interpretable machine learning framework for measuring urban perceptions from panoramic street view images. iScience, 2023, 26(3): 106132

[29] Lohmeier, C., Schulze, L., Schöning, J., & Savino, G.-L. (2025). Navigating beyond places: The significance of navigation tools in everyday media use. New Media & Society, 14614448251359632. https://doi.org/10.1177/14614448251359632

[30] Long Y, Wu Y, Huang L, Aleksejeva J, Iossifova D, Dong N, Gasparatos A. Assessing urban livability in Shanghai through an open source data-driven approach. Npj Urban Sustainability, 2024, 4(1): 7

[31] Makanadar A. Neuro-adaptive architecture: Buildings and city design that respond to human emotions, cognitive states. Research in Globalization, 2024, 8: 100222

[32] Millward, H. (2018). Trip chaining and tour mode choice of non-workers grouped by daily activity patterns. Journal of Transport Geography. https://www.academia.edu/111178575/Trip_chaining_and_tour_mode_choice_of_non_workers_grouped_by_daily_activity_patterns

[33] Moura F, Cambra P, Gonçalves AB. Measuring walkability for distinct pedestrian groups with a participatory assessment method: A case study in Lisbon. Landscape and Urban Planning, 2017, 157: 282-296

[34] Nori R, Zucchelli MM, Palmiero M, Piccardi L. Environmental cognitive load and spatial anxiety: What matters in navigation?. Journal of Environmental Psychology, 2023, 88: 102032

[35] Ramanoël S, Durteste M, Bizeul A, Ozier-Lafontaine A, Bécu M, Sahel J, Habas C, Arleo A. Selective neural coding of object, feature, and geometry spatial cues in humans. Human Brain Mapping, 2022, 43(17): 5281-5295

[36] Shen Y, Karimi K. Urban function connectivity: Characterisation of functional urban streets with social media check-in data. Cities, 2016, 55: 9-21

[37] Shen Y, Wu Z. Functional visibility graph analysis: Quantifying visuofunctional space with social media check-in data. Environment and Planning b: Urban Analytics and City Science, 2022, 49(1): 41-57 Sage UK: London, England)

[38] Shi Y, Yan Z. A spatiotemporal analysis of metro-stop based accessibility to bus services in Shanghai, China. Transactions in GIS, 2023, 27(7): 1881-1897

[39] Tahir R, Krogstie J. Impact of navigation aid and spatial ability skills on wayfinding performance and workload in indoor-outdoor campus navigation: Challenges and design. Applied Sciences, 2023, 13(17): 9508

[40] Wang D, Miwa T, Morikawa T. Big trajectory data mining: A survey of methods, applications, and services. Sensors (Basel), 2020, 20(16): 4571

[41] Wang L, Zhou K, Zhang S, Moudon AV, Wang J, Zhu Y-G, Sun W, Lin J, Tian C, Liu M. Designing bike-friendly cities: Interactive effects of built environment factors on bike-sharing. Transportation Research Part d, Transport and Environment, 2023, 117: 103670

[42] Wu H, Jin H, Xu Z, Liu C, Li J, Huang W. Travel mode classification based on GNSS trajectories and open geospatial data. Transactions in GIS, 2022, 26(6): 2598-2620

[43] Xiao Z, Wang Y, Fu K, Wu F. Identifying different transportation modes from trajectory data using tree-based ensemble classifiers. ISPRS International Journal of Geo-Information, 2017, 6(2): Article 2

[44] Xiao D, Kim I, Zheng N. Does built environment have impact on traffic congestion? —A bootstrap mediation analysis on a case study of Melbourne. Transportation Research Part a, Policy and Practice, 2024, 190: 104297

[45] Xu TB, Mirza H, Yang Q, Kalantari S. Evaluating age-related differences in wayfinding in a real-world indoor setting. Environment and Behavior, 2024, Sage CA: Los Angeles, CA)

[46] Yang W, Hu J, Liu Y, Guo W. Examining the influence of neighborhood and street-level built environment on fitness jogging in Chengdu, China: A massive GPS trajectory data analysis. Journal of Transport Geography, 2023, 108: 103575

[47] Yang L, Yang H, Yu B, Lu Y, Cui J, Lin D. Exploring non-linear and synergistic effects of green spaces on active travel using crowdsourced data and interpretable machine learning. Travel Behaviour and Society, 2024, 34: 100673

[48] Yang W, Li Y, Liu Y, Fan P, Yue W. Environmental factors for outdoor jogging in Beijing: Insights from using explainable spatial machine learning and massive trajectory data. Landscape and Urban Planning, 2024, 243: 104969

[49] Ye X, Rogerson P. The impacts of the modifiable areal unit problem (MAUP) on omission error. Geographical Analysis, 2021,

[50] Yu JJQ. Travel mode identification with GPS trajectories using wavelet transform and deep learning. IEEE Transactions on Intelligent Transportation Systems, 2021, 22(2): 1093-1103

[51] Zhan D, Wang Y, Wu Q, Zhang W. Nonlinear effects of the urban built environment on urban vitality: A case study of Hangzhou, China. Journal of Geographical Sciences, 2025, 35(6): 1183-1203

[52] Zhang, F. (2018). Measuring human perceptions of a large-scale urban region using machine learning. Landscape and Urban Planning.

[53] Zhang W, Fang C, Zhou L, Zhu J. Measuring megaregional structure in the Pearl River Delta by mobile phone signaling data: A complex network approach. Cities, 2020, 104: 102809

[54] Zhang A, Zhai S, Liu X, Song G, Feng Y. Investigating the association between streetscapes and mental health in Zhanjiang, China: Using baidu Street View Images and deep learning. International Journal of Environmental Research and Public Health, 2022, 19(24): 16634

[55] Zhao M, Crossley T, Shinohara H. The implications of EEG neurophysiological data in human-centered architectural design: A systematic review and bibliometric analysis. Journal of Environmental Psychology, 2025, 103: 102550

[56] Zhou H, Wang J, Widener M, Wilson K. Examining the relationship between active transport and exposure to streetscape diversity during travel: A study using GPS data and street view imagery. Computers, Environment and Urban Systems, 2024, 110: 102105

Funding

National Natural Science Foundation of China

RIGHTS & PERMISSIONS

The Author(s)