Assessing Physical Environment of TOD Communities around Metro Stations
Policy makers and city planning professionals who work on transit-oriented development are often interested in evaluating the quality of physical environment around metro stations. How to carry out this task comprehensively, effectively and repeatedly, with limited time and budget? Under the GEF Sustainable Cities Integrated Approach Pilot Project (P156507), the task team has explored the possibility of utilizing street view photos and machine learning models. The analysis measures physical environment from four aspects, i.e., convenience, comfort, vibrancy and characteristics using 14 subsets of indicators. It covers 201 stations within the 5th Ring Road of Beijing and all indicators are measured for areas within 10-minute walking distance from the metro stations. The analytic results can be used to support data-driven and evidence-based city planning and zoning.
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| Format: | Working Paper biblioteca |
| Language: | English |
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World Bank, Washington, DC
2020-01
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| Subjects: | TRANSIT, BIG DATA, MACHINE LEARNING, URBAN TRANSPORT, URBAN TRANSIT, SPATIAL ECONOMICS, PUBLIC TRANSPORT, |
| Online Access: | http://documents.worldbank.org/curated/en/433621581930100479/Assessing-Physical-Environment-of-TOD-Communities-around-Metro-Stations-Using-Big-Data-and-Machine-Learning https://hdl.handle.net/10986/33343 |
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dig-okr-10986333432024-08-07T18:57:32Z Assessing Physical Environment of TOD Communities around Metro Stations Using Big Data and Machine Learning Fang, Wanli Liu, Liu Zhou, Jianhao TRANSIT BIG DATA MACHINE LEARNING URBAN TRANSPORT URBAN TRANSIT SPATIAL ECONOMICS PUBLIC TRANSPORT Policy makers and city planning professionals who work on transit-oriented development are often interested in evaluating the quality of physical environment around metro stations. How to carry out this task comprehensively, effectively and repeatedly, with limited time and budget? Under the GEF Sustainable Cities Integrated Approach Pilot Project (P156507), the task team has explored the possibility of utilizing street view photos and machine learning models. The analysis measures physical environment from four aspects, i.e., convenience, comfort, vibrancy and characteristics using 14 subsets of indicators. It covers 201 stations within the 5th Ring Road of Beijing and all indicators are measured for areas within 10-minute walking distance from the metro stations. The analytic results can be used to support data-driven and evidence-based city planning and zoning. 2020-02-19T16:39:50Z 2020-02-19T16:39:50Z 2020-01 Working Paper Document de travail Documento de trabajo http://documents.worldbank.org/curated/en/433621581930100479/Assessing-Physical-Environment-of-TOD-Communities-around-Metro-Stations-Using-Big-Data-and-Machine-Learning https://hdl.handle.net/10986/33343 English CC BY 3.0 IGO http://creativecommons.org/licenses/by/3.0/igo World Bank application/pdf text/plain World Bank, Washington, DC |
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| country |
Estados Unidos |
| countrycode |
US |
| component |
Bibliográfico |
| access |
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| databasecode |
dig-okr |
| tag |
biblioteca |
| region |
America del Norte |
| libraryname |
Biblioteca del Banco Mundial |
| language |
English |
| topic |
TRANSIT BIG DATA MACHINE LEARNING URBAN TRANSPORT URBAN TRANSIT SPATIAL ECONOMICS PUBLIC TRANSPORT TRANSIT BIG DATA MACHINE LEARNING URBAN TRANSPORT URBAN TRANSIT SPATIAL ECONOMICS PUBLIC TRANSPORT |
| spellingShingle |
TRANSIT BIG DATA MACHINE LEARNING URBAN TRANSPORT URBAN TRANSIT SPATIAL ECONOMICS PUBLIC TRANSPORT TRANSIT BIG DATA MACHINE LEARNING URBAN TRANSPORT URBAN TRANSIT SPATIAL ECONOMICS PUBLIC TRANSPORT Fang, Wanli Liu, Liu Zhou, Jianhao Assessing Physical Environment of TOD Communities around Metro Stations |
| description |
Policy makers and city planning
professionals who work on transit-oriented development are
often interested in evaluating the quality of physical
environment around metro stations. How to carry out this
task comprehensively, effectively and repeatedly, with
limited time and budget? Under the GEF Sustainable Cities
Integrated Approach Pilot Project (P156507), the task team
has explored the possibility of utilizing street view photos
and machine learning models. The analysis measures physical
environment from four aspects, i.e., convenience, comfort,
vibrancy and characteristics using 14 subsets of indicators.
It covers 201 stations within the 5th Ring Road of Beijing
and all indicators are measured for areas within 10-minute
walking distance from the metro stations. The analytic
results can be used to support data-driven and
evidence-based city planning and zoning. |
| format |
Working Paper |
| topic_facet |
TRANSIT BIG DATA MACHINE LEARNING URBAN TRANSPORT URBAN TRANSIT SPATIAL ECONOMICS PUBLIC TRANSPORT |
| author |
Fang, Wanli Liu, Liu Zhou, Jianhao |
| author_facet |
Fang, Wanli Liu, Liu Zhou, Jianhao |
| author_sort |
Fang, Wanli |
| title |
Assessing Physical Environment of TOD Communities around Metro Stations |
| title_short |
Assessing Physical Environment of TOD Communities around Metro Stations |
| title_full |
Assessing Physical Environment of TOD Communities around Metro Stations |
| title_fullStr |
Assessing Physical Environment of TOD Communities around Metro Stations |
| title_full_unstemmed |
Assessing Physical Environment of TOD Communities around Metro Stations |
| title_sort |
assessing physical environment of tod communities around metro stations |
| publisher |
World Bank, Washington, DC |
| publishDate |
2020-01 |
| url |
http://documents.worldbank.org/curated/en/433621581930100479/Assessing-Physical-Environment-of-TOD-Communities-around-Metro-Stations-Using-Big-Data-and-Machine-Learning https://hdl.handle.net/10986/33343 |
| work_keys_str_mv |
AT fangwanli assessingphysicalenvironmentoftodcommunitiesaroundmetrostations AT liuliu assessingphysicalenvironmentoftodcommunitiesaroundmetrostations AT zhoujianhao assessingphysicalenvironmentoftodcommunitiesaroundmetrostations AT fangwanli usingbigdataandmachinelearning AT liuliu usingbigdataandmachinelearning AT zhoujianhao usingbigdataandmachinelearning |
| _version_ |
1807157414742458368 |