Understanding volume and correlations of automated walk count: Predictors for necessary, optional, and social activities in Dilworth Park
In this paper, the author explores the potential use of automated pedestrian walk count data in urban design research. The Center City District (CCD) research group used computer vision to collect automated pedestrian walk data from Dilworth Park, Philadelphia. By comparing the count data and participant observations of social activities in the park, the author found that the frequencies of social activities in the park could be predicted by the pedestrian count when considering the outdoor thermal comfort index and the types of events taking place in Dilworth Park. By examining correlations among multiple sensors, the author found that the entry–exit correlation is a useful indicator to assess how people use public space by estimating the ratio of necessary-to-optional activities.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/23998083
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Supplemental Notes:
- © Jae Min Lee 2019.
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Authors:
- Lee, Jae Min
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0000-0002-8485-6943
- Publication Date: 2019
Language
- English
Media Info
- Media Type: Web
- Features: References;
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Serial:
- Environment and Planning B: Urban Analytics and City Science
- Publisher: Sage Publications Limited
- ISSN: 2399-8083
- EISSN: 2399-8091
- Serial URL: http://journals.sagepub.com/home/epb
Subject/Index Terms
- TRT Terms: Activity choices; Central business districts; Computer vision; Parks; Pedestrian counts; Social factors; Urban design
- Geographic Terms: Philadelphia (Pennsylvania)
- Subject Areas: Data and Information Technology; Pedestrians and Bicyclists; Planning and Forecasting;
Filing Info
- Accession Number: 01842465
- Record Type: Publication
- Files: TRIS
- Created Date: Apr 14 2022 9:16AM