Using social media to measure and map visitation to public lands in Utah

Zhang, Hongchao; Berkel, Derek Van; Howe, Peter D.; Miller, Zachary D.; Smith, Jordan W.

doi:10.1016/j.apgeog.2021.102389

Cited by 28 publications

(9 citation statements)

References 53 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are two main measures in spatial autocorrelation analysis: global and local measures. Global spatial autocorrelation is used to detect the distribution characteristics in the whole region, and it can be calculated as follows (Cheng et al 2019 ; Zhang et al 2021 ): where I indicates the global Moran’s I index; n is the number of regions; x i and x j are the values of the observed variables at sites of i and j space; is the mean value of x i ; and W ij represents the spatial weight matrix . The value of Moran’s I generally vary between − 1 and 1.…”

Section: Methodology and Datamentioning

confidence: 99%

See 1 more Smart Citation

Comprehensive partitions and optimisation strategies based on tourism urbanisation and resources environment carrying capacity in the Yellow River Basin, China

Wang

Chen

2021

Environ Sci Pollut Res

View full text Add to dashboard Cite

Section: Methodology and Datamentioning

confidence: 99%

Section: Spatial Autocorrelation Analysismentioning

confidence: 99%

Comprehensive partitions and optimisation strategies based on tourism urbanisation and resources environment carrying capacity in the Yellow River Basin, China

Wang

Chen

2021

Environ Sci Pollut Res

View full text Add to dashboard Cite

“…Social media were collected between the years 2006 and 2014 through the APIs of both the Flickr and Panoramio platforms . The spatial mapping and validation process are detailed in Zhang et al (2021) and publicly available on the openICPSR repository (https://doi.org/10. 3886/E131163V1).…”

Section: Participatory Workhopsmentioning

confidence: 99%

A process for identifying challenges and opportunities for outdoor recreation and tourism development using participatory workshops and big data

Zhang

Smith²

2022

Front. Sustain. Tour.

Self Cite

View full text Add to dashboard Cite

The collaborative development of scientific and practical knowledge is fostered through processes which allow scientists and stakeholders to work together toward common interests and goals. The purpose of this community case study is to document a process by which big data characterizing the volume and spatial distribution of outdoor recreation and tourism behavior can be used to initiate collaborative discussions amongst the diverse stakeholders involved in outdoor recreation and tourism management. The process we document involves engaging a diverse group of stakeholders including public land managers, county and city government officials, and local business owners, in participatory workshops centered around visualizations of geotagged social media. We document how three workshops with outdoor recreation and tourism providers in Utah (USA) aided in the development of a shared understanding of the challenges and opportunities associated with meeting the growing demand for outdoor recreation and tourism opportunities.

show abstract

“…Wood et al (2013) proposed the metric of user-day to estimate visitation rate at recreational sites with Flickr SMD; the authors found this metric to be a good proxy for empirical visitation rates. With this metric, the possibility of using SMD source as a reliable proxy for visitation was demonstrated by statistical correlation and regression analysis methods, including simple nonlinear regressions (Arkema et al , 2021), Spearman's and Pearson's correlation tests (Ghermandi et al , 2020; Wilkins et al , 2021), linear regressions (Zhang et al , 2021), ordinary least squares regressions (Ghermandi et al , 2020), multiple regressions (Sessions et al , 2016), generalized linear models and wavelet analysis (Mancini et al , 2018). The databases of Flickr (Arkema et al , 2021; Wilkins et al , 2021), Instagram (Heikinheimo et al , 2017; Wood et al , 2020) and Twitter (Sim and Miller, 2019; Ghermandi et al , 2020) were most frequently used as SMD sources in the previous studies.…”

Section: Introductionmentioning

confidence: 99%

“…The databases of Flickr (Arkema et al , 2021; Wilkins et al , 2021), Instagram (Heikinheimo et al , 2017; Wood et al , 2020) and Twitter (Sim and Miller, 2019; Ghermandi et al , 2020) were most frequently used as SMD sources in the previous studies. The reliability of using an SMD as a substitution for survey data was also studied for other platforms like Panoramio (Zhang et al , 2021), VKontakte (Ghermandi et al , 2020), Strava Metro (Sun et al , 2017), GPSies, MapMyFitness and Wikiloc (Norman and Pickering, 2017). However, the SMD used in the literature in the fields of attraction or environmental research focused on SMD developed in Western countries.…”

Section: Introductionmentioning

confidence: 99%

Do social media data indicate visits to tourist attractions? A case study of Shanghai, China

Liang

Zhang

2021

OHI

View full text Add to dashboard Cite

PurposeCan Chinese social media data (SMD) be used as an alternative to traditional surveys used to understand tourists' visitation of attractions in Chinese cities? The purpose of this paper is to explore this question.Design/methodology/approachPopular tourism SMD sources in China, such as Ctrip, Weibo and Dazhong Dianping (DZDP), were used as data source, and the relationships between these sources and traditional data sources were studied with statistical methods. Data from Shanghai were used in this study since it is rich in tourism resources and developed in information.FindingsA systematic research method was followed and led to the following conclusions: There were positive correlations for attraction visitation between Chinese SMD and traditional survey data; Chinese SMD source could temporally indicate visits to Shanghai tourist attractions; Ctrip SMD generally performed less well than Weibo or DZDP, and different SMD performed differently depending on the specific attractions and time units in the visitation calculation process; and factors including visitation, distance from the city center and the grade of attractions might affect the prediction performance based on data from the SMD. The findings suggest that Chinese SMD could be used as a cost-efficient and reliable proxy for traditional survey data to predict Chinese attraction visitation.Originality/valueThis study applies and improves the methods of SMD reliability in attraction use studies, supplies the gap for premise, basis and foundation for the large amounts of tourism researches using SMD in China and could promote and inspire more efficient and advanced measures in tourism management and urban development.

show abstract

Using social media to measure and map visitation to public lands in Utah

Cited by 28 publications

References 53 publications

Comprehensive partitions and optimisation strategies based on tourism urbanisation and resources environment carrying capacity in the Yellow River Basin, China

Comprehensive partitions and optimisation strategies based on tourism urbanisation and resources environment carrying capacity in the Yellow River Basin, China

A process for identifying challenges and opportunities for outdoor recreation and tourism development using participatory workshops and big data

Do social media data indicate visits to tourist attractions? A case study of Shanghai, China

Contact Info

Product

Resources

About