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Understanding volume and correlations of automated walk count: Predictors for necessary, optional, and social activities in Dilworth Park

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Abstract
In this paper, we explore 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, we 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, we found that the entry?exit correlation is a useful
indicator to assess how people use public space by estimating the ratio of necessary-tooptional
activities.
Author(s)
이재민
Issued Date
2021
Type
Article
Keyword
Big datacomputer visionpublic spacesocial activitiesurban design
DOI
10.1177/2399808319869935
URI
https://oak.ulsan.ac.kr/handle/2021.oak/8786
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_crossref_primary_10_1177_2399808319869935&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Understanding%20volume%20and%20correlations%20of%20automated%20walk%20count:%20Predictors%20for%20necessary,%20optional,%20and%20social%20activities%20in%20Dilworth%20Park&offset=0&pcAvailability=true
Publisher
ENVIRONMENT AND PLANNING B-URBAN ANALYTICS AND CITY SCIENCE
Location
영국
Language
영어
ISSN
2399-8083
Citation Volume
48
Citation Number
2
Citation Start Page
331
Citation End Page
347
Appears in Collections:
Engineering > Architectural Engineering
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