Unsupervised machine learning in urban studies: A systematic review of applications

Wang, Jing; Biljecki, Filip

doi:10.1016/j.cities.2022.103925

Cited by 90 publications

(25 citation statements)

References 181 publications

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“…For example, one direction could be to employ semi-supervised learning algorithms (e.g. graph convolutional networks (GNNs) or generative adversarial networks (GANs)) for data translation tasks (Xu et al, 2018a,b; You et al, 2020; Wu and Biljecki, 2022; Zhao et al, 2022; Wang and Biljecki, 2022). Such learnings have demonstrated huge potential to fill data gaps in under-represented regions.…”

Section: Discussionmentioning

confidence: 99%

Incorporating networks in semantic understanding of streetscapes: Contextualising active mobility decisions

Yap

Chang

Biljecki

2022

Environment and Planning B: Urban Analytics and City Science

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Planning for active mobility satisfies many fundamental tenets of good urban design and planning. However, planning for active mobility is a complex endeavour due to numerous local, place-based factors that influence active mobility decisions. Recent advancements in urban data research have demonstrated the effectiveness of deep learning methods in evaluating active mobility potential for urban environments. At present, the incorporation of semantic information from deep learning models and street view imagery into spatio-temporal contexts remains a challenge. In particular, knowledge extraction from deep learning models remains an open question for urban planning and decision-making. Towards this issue, we propose a functional deep learning and network science workflow that employs open data from OpenStreetMap and Mapillary to assess factors affecting active mobility decisions and route planning. We demonstrate the generalisability of our analytical workflow through two case studies focusing on urban greenery in Nerima city (Japan) and urban visual complexity in Pasir Ris town (Singapore). Our results reveal clear patterns of heterogeneity in urban streetscapes and identify unevenness in street infrastructure provision based on destination types. Using this information, we propose specific areas for design intervention to improve active mobility planning. Our workflow is applicable for a diverse range of use cases making it relevant to a wide range of stakeholders, not limited to, urban researchers, policy makers and urban planners.

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Section: Discussionmentioning

confidence: 99%

Incorporating networks in semantic understanding of streetscapes: Contextualising active mobility decisions

Yap

Chang

Biljecki

2022

Environment and Planning B: Urban Analytics and City Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…This approach is especially suitable for handling and analysing multiple data and variables. Moreover, according to Wang and Biljecki (2022) , applying advanced analytical methods to establish behavioural patterns is crucial in urban studies as they represent the complex real world of urban systems on multiple scales through multi-source data integration.…”

Section: Bsss and The Covid-19 Pandemicmentioning

confidence: 99%

Who changed and who maintained their urban bike-sharing mobility after the COVID-19 outbreak? A within-subjects study

Seifert

Pellicer-Chenoll

Antón-González

et al. 2023

Cities

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“…This approach has been increasingly adopted by researchers in GIScience and urban studies to conduct systematic reviews using clearly specified and reproducible syntax. Examples include systematic reviews on the applications of machine learning approaches to bike-sharing systems (Albuquerque et al, 2021), the role of urban green space in supporting biodiversity (Berthon et al, 2021), the use of unsupervised machine learning and deep learning in urban studies (Grekousis, 2019;J. Wang & Biljecki, 2022), and spatially-explicit GeoAI applications in urban geography (P. Liu & Biljecki, 2022).…”

Section: Approach For Conducting the Reviewmentioning

confidence: 99%

Conflating point of interest (POI) data: A systematic review of matching methods

Sun

et al. 2023

Computers, Environment and Urban Systems

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Unsupervised machine learning in urban studies: A systematic review of applications

Cited by 90 publications

References 181 publications

Incorporating networks in semantic understanding of streetscapes: Contextualising active mobility decisions

Incorporating networks in semantic understanding of streetscapes: Contextualising active mobility decisions

Who changed and who maintained their urban bike-sharing mobility after the COVID-19 outbreak? A within-subjects study

Conflating point of interest (POI) data: A systematic review of matching methods

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