Investigation of Travel and Activity Patterns Using Location-based Social Network Data: A Case Study of Active Mobile Social Media Users

Abstract

Due to its relatively high availability and low cost, location-based social network (LBSN) (e.g., Foursquare) data (a popular type of volunteered geographic information) seem to be an alternative or complement to survey data in the study of travel behavior and activity analysis. Illustrating this situation, recently, a number of studies attempted to use LBSN data (e.g., Foursquare check-ins) to investigate patterns of human travel and activity. Of particular note is that compared to other individual-level characteristics of users, such as age, profession, education, income and so forth, gender is relatively highly available in the profiles of Foursquare users. Moreover, considering gender differences in travel and activity analysis is a popular research topic and is helpful in better understanding the changes in women's roles in family, labor force participation, society and so forth. Therefore, this paper empirically investigates how gender influences the travel and activity patterns of active local Foursquare users in New York City. Empirical investigations of gender differences in travel and activity patterns are conducted at both the individual and aggregate level. The empirical results reveal that there are gender differences in the travel and activity patterns of active local users in New York City at both the individual and aggregate level. Finally, the results of the empirical study and the extent to which LBSN data can be exploited to produce travel diary data are discussed.

Using Location-Based Social Media Data to Observe Check-In Behavior and Gender Difference: Bringing Weibo Data into Play

“…The gender difference in 10 district of Shanghai is examined by the comparison of male and female users check-ins in 10 districts of Shanghai during January-March 2016. We use a relative difference [68,91] (d r ) to calculate the gender differences in 10 districts of Shanghai, it is often used as a quantitative indicator of quality assurance and quality control in the proportion of all check-ins and is expressed as follows:…”

“…Location based datasets have now been used in many studies for urbanization and its environmental effects [64], development and prediction [65][66][67], travel and activity patterns [68,69] and emergency response [70][71][72] and urban sustainability [73]. Hong [74] Highlighted the use of an LBSN data to observe the willingness of buyers to pay for various factors.…”

Five classes of geospatial data and the barriers to using them

“…Recent research is using the APIs from LBSNs to gather location data and make inferences about human behaviour. Examples are Twitter (Crampton et al, 2013;Korson, 2015;Stephens & Poorthuis, 2015), Foursquare (Aubrecht et al, 2016;Sun & Li, 2015), and Strava (Jestico, Nelson, & Winters, 2016). This move from PPGIS/PGIS to VGI, however, is increasing the disconnection between researchers and contributors.…”

Investigating “Locality” of Intra-Urban Spatial Interactions in New York City Using Foursquare Data

“…When compared to some other data sources (e.g., survey data and mobile phone data), LBSN check-in data has some advantages as an indicator of human activity categories such as dining, working, and shopping, as it provides a fine-grained resolution, and is readily available. Therefore, user-generated geo-referenced check-in data has excellent potential when wishing to study human mobility, as some researchers have already demonstrated [3,[5][6][7][8][9][10][11]. Since spatial interactions are measured through the use of human mobility patterns, so check-in data has significant potential within the study of spatial interaction.…”