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

Jae Min Lee

Environment and Planning B, 2021, vol. 48, issue 2, 331-347

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-to-optional activities.

Keywords: Big data; computer vision; public space; social activities; urban design (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://journals.sagepub.com/doi/10.1177/2399808319869935 (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:sae:envirb:v:48:y:2021:i:2:p:331-347

DOI: 10.1177/2399808319869935

Access Statistics for this article

More articles in Environment and Planning B
Bibliographic data for series maintained by SAGE Publications ().