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

Muhammad Rizwan,Luc Gwiazdzinski,Ofelia Cervantes,Wan Wanggen

doi:10.3390/ijgi7050196

Abstract

Population density and distribution of services represents the growth and demographic shift of the cities. For urban planners, population density and check-in behavior in space and time are vital factors for planning and development of sustainable cities. Location-based social network (LBSN) data seems to be a complement to many traditional methods (i.e., survey, census) and is used to study check-in behavior, human mobility, activity analysis, and social issues within a city. This check-in phenomenon of sharing location, activities, and time by users has encouraged this research on gender difference and frequency of using LBSN. Therefore, in this study, we investigate the check-in behavior of Chinese microblog Sina Weibo (referred as “Weibo”) in 10 districts of Shanghai, China, for which we observe the gender difference and their frequency of use over a period. The mentioned districts were spatially analyzed for check-in spots by kernel density estimation (KDE) using ArcGIS. Furthermore, our results reveal that female users have a high rate of social media use, and significant difference is observed in check-in behavior during weekdays and weekends in the studied districts of Shanghai. Increase in check-ins is observed during the night as compared to the morning. From the results, it can be assumed that LBSN data can be helpful to observe gender difference.

Highlights

Personal behavior and characteristics are intimately intertwined with city planning and human mobility [1] in past, many traditional methods are used to collect data about human mobility and population density, but these traditional methods are expensive and require more processing time, produce sparse data and not that effective in policymaking.With the introduction of Location-based social network (LBSN)’s (i.e., Weibo [2], Facebook [3], Twitter [4]), users can share their location as well as the activity
We utilized the Weibo check-in data set and used kernel density estimation (KDE) to analyze the density of chFecokr-oinurdaetxap.eTrihmeeonvtse,rawlleduetnilsiizteydofthceheWcke-iibnos cdhuercikn-ginJadnautaarsye–tMaanrdchu2se0d16KcDanE bteo oabnsaelyrvzeedthine density of check-in data
The overall density of check-ins during January–March 2016 can be observed in Figure 4, and it can be observed that the center of the city has a high density of check-ins, which is a normal behavior for a big city due to easy accessibility of transport and living facilities

Summary

Introduction

Personal behavior and characteristics are intimately intertwined with city planning and human mobility [1] in past, many traditional methods (i.e., survey, census) are used to collect data about human mobility and population density, but these traditional methods are expensive and require more processing time, produce sparse data and not that effective in policymaking. Sharing check-ins allows users to announce and discuss places they visit (e.g., eating at local restaurants, shopping, visiting popular area) as part of their social interaction online. This check-in phenomenon and fast sharing of information have attracted more than 222 million subscribers. Compared to the aforementioned traditional methods, LBSN data are highly available and low cost This data contains rich information about geolocation [8], which can be used to study check-in behavior. Geo-location data offers new dimensions toward studying check-in behaviors and helps to create new techniques and approaches to analyze LBSN data.

Related Work

Results and Discussion

Conclusions