Understanding pedestrian behavior is crucial to inform public space design. However, being laborious, Gehl’s Public Space and Public Life (PSPL) framework is restricted to a small scale. Although prior studies have utilized computer vision (CV), they either focused on monitoring social distancing or measuring urban vitality, ignoring the subtle interplay between public space and public life. This study utilizes webcam data to track walk and stay behaviors, investigating their associations with public space features including point of interest, façade quality, and street furniture. Our findings extend PSPL principles. First, pedestrians tend to stand in less private places with good visual connectivity, indicating that privacy matters less to standing than sitting. Second, pedestrians walk along the edge in large-scale spaces while keeping in the middle in small spaces. Third, although all POIs affect vitality, certain types are more effective (i.e., catering). Fourth, a good place to stay must be convenient to walk through. Our CV framework partially automates PSPL without incurring labor costs. Urban design studies can use the operationalized CV pipeline to draw evidence-based design recommendations and monitor people-space interactions at large scale.
AlexanderC (1978) A Pattern Language: Towns, Buildings, Construction. Oxford: Oxford University Press.
2.
BlečićISaiuVTrunfioG (2024) Enhancing urban walkability assessment with multimodal Large Language models. In: GervasiOMurganteBGarauC, et al. (eds) Computational Science and its Applications – ICCSA 2024 Workshops. Nature Switzerland: Springer, 394–411. DOI: 10.1007/978-3-031-65282-0_26.
3.
ChenSBiljeckiF (2023) Automatic assessment of public open spaces using street view imagery. Cities137: 104329. DOI: 10.1016/j.cities.2023.104329.
4.
DubeyANaikNParikhD, et al. (2016) Deep learning the city: quantifying urban perception at a global scale. In: LeibeBMatasJSebeN, et al. (eds) Computer Vision – ECCV 2016. Berlin: Springer International Publishing, 196–212. DOI: 10.1007/978-3-319-46448-0_12.
GehlJSvarreB (2013) How to Study Public Life. Washington: Island Press/Center for Resource Economics. DOI: 10.5822/978-1-61091-525-0.
8.
GehlJSøholtH (2002). Public Spaces and public life: City of adelaide, 2002. Planning SA.
9.
HanSSongDXuL, et al. (2022) Behaviour in public open spaces: a systematic review of studies with quantitative research methods. Building and Environment223: 109444. DOI: 10.1016/j.buildenv.2022.109444.
10.
HillierB (1996) Space Is the Machine: A Configurational Theory of Architecture (1. Publ). Cambridge: Cambridge University Press.
11.
HuangJQingLHanL, et al. (2023) A collaborative perception method of human-urban environment based on machine learning and its application to the case area. Engineering Applications of Artificial Intelligence119: 105746. DOI: 10.1016/j.engappai.2022.105746.
12.
JiHQingLHanL, et al. (2021) A new data-enabled intelligence framework for evaluating urban space perception. ISPRS International Journal of Geo-Information10(6): 400. DOI: 10.3390/ijgi10060400.
13.
KakoSIsobeAMagomeS (2010) Sequential monitoring of beach litter using webcams. Marine Pollution Bulletin60(5): 775–779. DOI: 10.1016/j.marpolbul.2010.03.009.
14.
KellyCMWilsonJSBakerEA, et al. (2013) Using Google Street View to audit the built environment: inter-rater reliability results. Annals of Behavioral Medicine: A Publication of the Society of Behavioral Medicine45 Suppl 1(Suppl 1): S108–S112. DOI: 10.1007/s12160-012-9419-9.
15.
KimD (2020) Pedestrian and bicycle volume data collection using drone technology. Journal of Urban Technology27(2): 45–60. DOI: 10.1080/10630732.2020.1715158.
16.
LiXRattiCSeiferlingI (2017) Mapping urban landscapes along streets using Google street view. Advances in Cartography and GIScience341–356. DOI: 10.1007/978-3-319-57336-6_24.
17.
LiYYabukiNFukudaT (2022) Exploring the association between street built environment and street vitality using deep learning methods. Sustainable Cities and Society79: 103656. DOI: 10.1016/j.scs.2021.103656.
18.
LiZXuJWangS, et al. (2024) StreetviewLLM: extracting geographic information Using a chain-of-thought multimodal Large Language Model (No. arXiv:2411.14476). arXiv. DOI: 10.48550/arXiv.2411.14476.
19.
LingineniD., et al. (2023) Object Detection and Tracking Using DeepSORT, in BuyyaR., et al. (eds) Computational Intelligence and Data Analytics. Singapore: Springer Nature, pp. 303–312. Available at:https://doi.org/10.1007/978-981-19-3391-2_23
20.
LooBPYZhangF (2024) Design of public open space: site features, playing, and physical activity. Health & Place85: 103149. DOI: 10.1016/j.healthplace.2023.103149.
MagomeSYamashitaTKohamaT, et al. (2007) Jellyfish patch formation investigated by aerial photography and drifter experiment. Journal of Oceanography63(5): 761–773. DOI: 10.1007/s10872-007-0065-y.
23.
MarcusCCFrancisC (1998). In: MarcusCCFrancisCSin TangS, et al. (eds) People Places: Design Guidelines for Urban Open Space. 2nd edition. Hoboken: Wiley.
24.
NiuTQingLHanL, et al. (2022) Small public space vitality analysis and evaluation based on human trajectory modeling using video data. Building and Environment225: 109563. DOI: 10.1016/j.buildenv.2022.109563.
25.
PetrasovaAHippJAMitasovaH (2019) Visualization of pedestrian density dynamics using data extracted from public webcams. ISPRS International Journal of Geo-Information8(12): 559. DOI: 10.3390/ijgi8120559.
26.
PowersDMW (2020) Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. (No. arXiv:2010.16061). arXiv. DOI: 10.48550/arXiv.2010.16061.
27.
PunnNSSonbhadraSKAgarwalS, et al. (2021) Monitoring COVID-19 social distancing with person detection and tracking via fine-tuned YOLO v3 and Deepsort techniques (No. arXiv:2005.01385). arXiv. DOI: 10.48550/arXiv.2005.01385.
28.
QiuWZhangZLiuX, et al. (2022) Subjective or objective measures of street environment, which are more effective in explaining housing prices?Landscape and Urban Planning221: 104358. DOI: 10.1016/j.landurbplan.2022.104358.
29.
QiuWLiWLiuX, et al. (2023) Subjective and objective measures of streetscape perceptions: relationships with property value in Shanghai. Cities132: 104037. DOI: 10.1016/j.cities.2022.104037.
30.
RezaeiMAzarmiM (2020) DeepSOCIAL: social distancing monitoring and infection risk assessment in COVID-19 pandemic. Applied Sciences10(21): 7514. DOI: 10.3390/app10217514.
31.
Salazar-MirandaAFanZBaickM, et al. (2024) Shifting patterns of social interaction: exploring the social life of urban spaces through A.I. Social Science Research Network, (SSRN Scholarly Paper No. 5032549). DOI: 10.2139/ssrn.5032549.
32.
SalessesPSchechtnerKHidalgoCA (2013) The collaborative image of the city: mapping the inequality of urban perception. PLoS One8(7): e68400. DOI: 10.1371/journal.pone.0068400.
33.
SongQLiuYQiuW, et al. (2022) Investigating the impact of perceived micro-level neighborhood characteristics on housing prices in Shanghai. Land11(11): 2002. DOI: 10.3390/land11112002.
34.
SongQDouZQiuW, et al. (2023) The evaluation of urban spatial quality and utility trade-offs for Post-COVID working preferences: a case study of Hong Kong. Architectural Intelligence2(1): 1. DOI: 10.1007/s44223-022-00020-x.
35.
TanYLiWChenD, et al. (2023) Identifying urban park events through computer vision-assisted categorization of publicly-available imagery. ISPRS International Journal of Geo-Information12(10): 419. DOI: 10.3390/ijgi12100419.
36.
ThuanD (2021) “Evolution of YOLO Algorithm and YOLOv5: The State-of-the-art Object Detection Algorithm”, Oulu University of Applied Sciences, 2021.
37.
WhyteWH (1980) The Social Life of Small Urban Spaces. Naperville: Conservation Foundation.
38.
YangSKrenzKQiuW, et al. (2023) The role of subjective perceptions and objective measurements of the urban environment in explaining house prices in greater London: A multi-scale urban morphology analysis. ISPRS International Journal of Geo-Information12(6): 249. DOI: 10.3390/ijgi12060249.
39.
YingSLiLGaoY, et al. (2008) Analysis of relationship between pedestrian facilities and urban morphology based on visibility. Geoinformatics 2008 and Joint Conference on GIS and Built Environment: The Built Environment and Its Dynamics7144: 71441C. DOI: 10.1117/12.812743.
40.
ZhaoQHeYWangY, et al. (2024) Investigating the civic emotion dynamics during the COVID-19 lockdown: evidence from social media. Sustainable Cities and Society107: 105403. DOI: 10.1016/j.scs.2024.105403.
41.
ZuckerP (1966) Town and Square from the Agora to the Village Green. Second Printing edition. Columbia University Press.
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