Assessing Physical Environment of TOD Communities around Metro Stations : Using Big Data and Machine Learning

Assessing Physical Environment of TOD Communities around Metro Stations : Using Big Data and Machine Learning

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 repeate...

Full description

Bibliographic Details
Main Authors: Fang, Wanli, Liu, Liu, Zhou, Jianhao
Format: Working Paper
Language:English
Published: World Bank, Washington, DC 2020
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
http://hdl.handle.net/10986/33343
id okr-10986-33343
recordtype oai_dc
spelling okr-10986-333432021-05-25T09:32:37Z 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 http://documents.worldbank.org/curated/en/433621581930100479/Assessing-Physical-Environment-of-TOD-Communities-around-Metro-Stations-Using-Big-Data-and-Machine-Learning http://hdl.handle.net/10986/33343 English CC BY 3.0 IGO http://creativecommons.org/licenses/by/3.0/igo World Bank World Bank, Washington, DC Publications & Research Publications & Research :: Working Paper East Asia and Pacific China
repository_type Digital Repository
institution_category Foreign Institution
institution Digital Repositories
building World Bank Open Knowledge Repository
collection World Bank
language English
topic 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
Fang, Wanli
Liu, Liu
Zhou, Jianhao
Assessing Physical Environment of TOD Communities around Metro Stations : Using Big Data and Machine Learning
geographic_facet East Asia and Pacific
China
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
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 : Using Big Data and Machine Learning
title_short Assessing Physical Environment of TOD Communities around Metro Stations : Using Big Data and Machine Learning
title_full Assessing Physical Environment of TOD Communities around Metro Stations : Using Big Data and Machine Learning
title_fullStr Assessing Physical Environment of TOD Communities around Metro Stations : Using Big Data and Machine Learning
title_full_unstemmed Assessing Physical Environment of TOD Communities around Metro Stations : Using Big Data and Machine Learning
title_sort assessing physical environment of tod communities around metro stations : using big data and machine learning
publisher World Bank, Washington, DC
publishDate 2020
url http://documents.worldbank.org/curated/en/433621581930100479/Assessing-Physical-Environment-of-TOD-Communities-around-Metro-Stations-Using-Big-Data-and-Machine-Learning
http://hdl.handle.net/10986/33343
_version_ 1764478526168236032

Similar Items