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- Publisher Website: 10.1016/j.cities.2019.102495
- Scopus: eid_2-s2.0-85075263099
- WOS: WOS:000510082600009
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Article: “Familiar strangers” in the big data era: An exploratory study of Beijing metro encounters
Title | “Familiar strangers” in the big data era: An exploratory study of Beijing metro encounters |
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Authors | |
Keywords | Familiar stranger Big data era Implications Odds Distribution |
Issue Date | 2020 |
Publisher | Pergamon. The Journal's web site is located at http://www.elsevier.com/locate/cities |
Citation | Cities, 2020, v. 97, p. article no. 102495 How to Cite? |
Abstract | Traditionally, familiar strangers are defined as those we encounter and observe repeatedly in the city but never interact with. They are common to most urban dwellers. They also have various socioeconomic, sociopsychological and public-policy implications, which have only been sporadically mentioned and/or examined in existing studies across different disciplines. In this manuscript, we first summarize fragmental existing studies on familiar strangers that are defined in the traditional manner based on “small data” such as survey responses. Then we reconceptualize “familiar strangers” against the backdrop of the emergence and increased availability of big and open data. Such familiar strangers are called “familiar strangers in the big data era” (FSiBDE). After this, we have done the following: (a) synthesized and hypothesized factors influencing the distribution and quantity of the FSiBDE; (b) conducted an empirical study in the context of Beijing to embody and operationalize a special type of the FSiBDE among metro riders and to study its possible influencers. We find that across metro stations, it is spatial structure, population distribution, and transport network that significantly influence the count and odds of FSiBDE among millions of metro riders. In addition, the FSiBDE also can have important policy and planning implications for operating metro services and managing metro station. |
Persistent Identifier | http://hdl.handle.net/10722/289093 |
ISSN | 2023 Impact Factor: 6.0 2023 SCImago Journal Rankings: 1.733 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | Zhou, J | - |
dc.contributor.author | Yang, Y | - |
dc.contributor.author | Ma, H | - |
dc.contributor.author | Li, Y | - |
dc.date.accessioned | 2020-10-22T08:07:44Z | - |
dc.date.available | 2020-10-22T08:07:44Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | Cities, 2020, v. 97, p. article no. 102495 | - |
dc.identifier.issn | 0264-2751 | - |
dc.identifier.uri | http://hdl.handle.net/10722/289093 | - |
dc.description.abstract | Traditionally, familiar strangers are defined as those we encounter and observe repeatedly in the city but never interact with. They are common to most urban dwellers. They also have various socioeconomic, sociopsychological and public-policy implications, which have only been sporadically mentioned and/or examined in existing studies across different disciplines. In this manuscript, we first summarize fragmental existing studies on familiar strangers that are defined in the traditional manner based on “small data” such as survey responses. Then we reconceptualize “familiar strangers” against the backdrop of the emergence and increased availability of big and open data. Such familiar strangers are called “familiar strangers in the big data era” (FSiBDE). After this, we have done the following: (a) synthesized and hypothesized factors influencing the distribution and quantity of the FSiBDE; (b) conducted an empirical study in the context of Beijing to embody and operationalize a special type of the FSiBDE among metro riders and to study its possible influencers. We find that across metro stations, it is spatial structure, population distribution, and transport network that significantly influence the count and odds of FSiBDE among millions of metro riders. In addition, the FSiBDE also can have important policy and planning implications for operating metro services and managing metro station. | - |
dc.language | eng | - |
dc.publisher | Pergamon. The Journal's web site is located at http://www.elsevier.com/locate/cities | - |
dc.relation.ispartof | Cities | - |
dc.subject | Familiar stranger | - |
dc.subject | Big data era | - |
dc.subject | Implications | - |
dc.subject | Odds | - |
dc.subject | Distribution | - |
dc.title | “Familiar strangers” in the big data era: An exploratory study of Beijing metro encounters | - |
dc.type | Article | - |
dc.identifier.email | Zhou, J: zhoujp@hku.hk | - |
dc.identifier.authority | Zhou, J=rp02236 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1016/j.cities.2019.102495 | - |
dc.identifier.scopus | eid_2-s2.0-85075263099 | - |
dc.identifier.hkuros | 316205 | - |
dc.identifier.volume | 97 | - |
dc.identifier.spage | article no. 102495 | - |
dc.identifier.epage | article no. 102495 | - |
dc.identifier.isi | WOS:000510082600009 | - |
dc.publisher.place | United Kingdom | - |
dc.identifier.issnl | 0264-2751 | - |