Travel analytics: Understanding how destination choice and business clusters are connected based on social media data. Huang, A., Gallegos, L., & Lerman, K. Transportation Research Part C: Emerging Technologies, 77:245–256, April, 2017.
Travel analytics: Understanding how destination choice and business clusters are connected based on social media data [link]Paper  doi  abstract   bibtex   3 downloads  
We studied the relationship between characteristics of business clusters and check-in activities based on Twitter check-in data in Los Angeles County, California. We not only performed statistical analysis to model how various factors of clusters influence the intensity of check-in activities, but also proposed a visualization framework to understand the relationships among clusters embedded in a network. We discovered new insights on strategies of promoting flourishing business clusters based on social media data. 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.
@article{Huang2017travel,
    abstract = { We studied the relationship between characteristics of business clusters and check-in activities based on Twitter check-in data in Los Angeles County, California. We not only performed statistical analysis to model how various factors of clusters influence the intensity of check-in activities, but also proposed a visualization framework to understand the relationships among clusters embedded in a network. We discovered new insights on strategies of promoting flourishing business clusters based on social media data. 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.},
    author = {Huang, Arthur and Gallegos, Luciano and Lerman, Kristina},
    citeulike-article-id = {14276713},
    citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.trc.2016.12.019},
    doi = {10.1016/j.trc.2016.12.019},
    issn = {0968090X},
    journal = {Transportation Research Part C: Emerging Technologies},
    keywords = {cities, geo, lerman, sentiment, twitter, urban},
    month = apr,
    pages = {245--256},
    posted-at = {2017-02-10 18:07:48},
    priority = {2},
    title = {Travel analytics: Understanding how destination choice and business clusters are connected based on social media data},
    url = {http://dx.doi.org/10.1016/j.trc.2016.12.019},
    volume = {77},
    year = {2017}
}
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