Author Bios (50 Words for each Author)

Yun Liang is a Ph.D. student in the Department of Recreation, Park and Tourism Management at the Pennsylvania State University. Her academic interest is to combine social media data analysis with conservation in national parks or protected areas.

Junjun Yin, Ph.D., is an assistant research professor at the Social Science Research Institute and Population Research Institute, Pennsylvania State University. His research interests center on computational GIScience with a focus on developing geospatial Big Data analytics for studying urban and population dynamics concerning urban mobility, accessibility, and sustainability.

Bing Pan, Ph.D., is an Associate Professor in the Department of Recreation, Park, and Tourism Management at Pennsylvania State University, University Park. His research interests include data analytics, tourism big data, destination marketing, and benefits of travel.

Michael Lin is a Ph.D. student in the School of Hospitality Management at the Pennsylvania State University. His academic interest is strategy and finance.

Guangqing Chi, Ph.D., is an associate professor of rural sociology and demography and director of the Computational and Spatial Analysis Core of the Pennsylvania State University. His research is focused on socio-environmental systems by developing spatial and Big Data analytic methods. He leads generalizing Twitter data for social science research.

Abstract (150 Words)

Monitoring visitor demographics and temporal visitation patterns will help park managers allocate resources, develop infrastructure, and predict the demands of visitors. Previous studies have only validated temporal visitation patterns with big data and traditional survey data, while research on validation national park visitor demographics using mobile data is scant.

This study compares SafeGraph data (a type of mobile Location-based Service data) and survey/count data, assessing visitor demographics and temporal visitation patterns in Yellowstone National Park. The comparison between two data sources suggests that SafeGraph data can serve as an additional and complementary source of information to traditional visitor use study/count data. However, biases of SafeGraph data, such as data at an aggregation level and data only in the United States, resulting in differences compared to traditional survey/count data. This study contributes to understanding visitors in national parks by validating a new data source.

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Assessing the validity of SafeGraph data for visitor monitoring in Yellowstone National Park

Monitoring visitor demographics and temporal visitation patterns will help park managers allocate resources, develop infrastructure, and predict the demands of visitors. Previous studies have only validated temporal visitation patterns with big data and traditional survey data, while research on validation national park visitor demographics using mobile data is scant.

This study compares SafeGraph data (a type of mobile Location-based Service data) and survey/count data, assessing visitor demographics and temporal visitation patterns in Yellowstone National Park. The comparison between two data sources suggests that SafeGraph data can serve as an additional and complementary source of information to traditional visitor use study/count data. However, biases of SafeGraph data, such as data at an aggregation level and data only in the United States, resulting in differences compared to traditional survey/count data. This study contributes to understanding visitors in national parks by validating a new data source.