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...
| Main Authors: | , , |
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| Format: | Working Paper |
| Language: | English |
| Published: |
World Bank, Washington, DC
2020
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| Subjects: | |
| 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 |
| Summary: | 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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