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Exploring public values through Twitter data associated with urban parks pre- and post- COVID-19.
Huang, Jing-Huei; Floyd, Myron F; Tateosian, Laura G; Aaron Hipp, J.
Afiliação
  • Huang JH; Department of Parks, Recreation and Tourism Management, North Carolina State University, United States.
  • Floyd MF; Center for Geospatial Analytics, North Carolina State University, United States.
  • Tateosian LG; Department of Parks, Recreation and Tourism Management, North Carolina State University, United States.
  • Aaron Hipp J; Department of Parks, Recreation and Tourism Management, North Carolina State University, United States.
Landsc Urban Plan ; 227: 104517, 2022 Nov.
Article em En | MEDLINE | ID: mdl-35966883
ABSTRACT
Since school and business closures due to the evolving COVID-19 outbreak, urban parks have been a popular destination, offering spaces for daily fitness activities and an escape from the home environment. There is a need for evidence for parks and recreation departments and agencies to base decisions when adapting policies in response to the rapid change in demand and preferences during the pandemic. The application of social media data analytic techniques permits a qualitative and quantitative big-data approach to gain unobtrusive and prompt insights on how parks are valued. This study investigates how public values associated with NYC parks has shifted between pre- COVID (i.e., from March 2019 to February 2020) and post- COVID (i.e., from March 2020 to February 2021) through a social media microblogging platform -Twitter. A topic modeling technique for short text identified common traits of the changes in Twitter topics regarding impressions and values associated with the parks over two years. While the NYC lockdown resulted in much fewer social activities in parks, some parks continued to be valued for physical activity and nature contact during the pandemic. Concerns about people not keeping physical distance arose in parks where frequent human interactions and crowding seemed to cause a higher probability of the coronavirus transmission. This study demonstrates social media data could be used to capture park values and be specific per park. Results could inform park management during disruptions when use is altered and the needs of the public may be changing.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Qualitative_research / Risk_factors_studies Idioma: En Revista: Landsc Urban Plan Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Qualitative_research / Risk_factors_studies Idioma: En Revista: Landsc Urban Plan Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Estados Unidos