Travel analytics: Understanding how destination choice and business clusters are connected based on social media data |
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Affiliation: | 1. Department of Engineering and Computer Science, Tarleton State University, Stephenville, TX 76401, United States;2. Information Science Institute, University of South California, Marina del Rey, CA 90292, United States;1. Cape Peninsula University of Technology, Cape Town, South Africa;2. University of Central Florida, United States;3. Bournemouth University, England;1. Rosen College of Hospitality Management, University of Central Florida, 9907 Universal Boulevard, Orlando, FL 32819, United States;2. The Business School, Edinburgh Napier University, Edinburgh, EH14 1DJ, UK;3. Department of Tourism and Hospitality, Faculty of Management, Bournemouth University, Poole, BH12 5BB, UK;1. Department of Engineering and Computer Science, Tarleton State University, Stephenville, TX 76401, United States;2. Department of Civil, Environmental, and Geo- Engineering, University of Minnesota, Twin Cities, Minneapolis, MN 55455, United States;1. Inha University, South Korea;2. Rutgers University, USA;3. Incheon National University, South Korea |
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Abstract: | Understanding how destination choice and business clusters are connected is of great importance for designing sustainable cities, fostering flourishing business clusters, and building livable communities. As sharing locations and activities on social media platforms becomes increasingly popular, such data can reveal destination choice and activity space which can shed light on human-environment relationships. To this end, this research models the relationship between characteristics of business clusters and check-in activities from Los Angeles County, California. Business clusters are analyzed via two lenses: the supply side (employment data by industry) and the demand side (on-line check-in data). Spatial and statistical analyses are performed to understand how land use and transportation network features affect the popularity of the identified clusters and their relationships. Our results suggest that a cluster with more employment opportunities and more types of employment is associated with more check-ins. A business cluster that has access to parks or recreational services is also more popular. A business cluster with a longer road network and better connectivity of roads is associated with more check-ins. The visualization of the common visitors between clusters reveals that there are a few clusters with outstanding strong ties, while most have modest ties with each other. Our findings have implications on the influence of urban design on the popularity of business clusters. |
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Keywords: | Social media Check-in data Destination choice Land use |
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