Location based social media data analysis for observing check-in behavior and city rhythm in Shanghai

Abstract

The acquisition of location-based services (LBS) has become a powerful tool to connect and link people with similar interest across long distances. To observe human mobility behavior and patterns it is very important to understand and measure the frequency of location based social network (LBSN) use. In this paper, we investigate the check-in behavior difference during middle week of the month, for whom we observe the gender and their frequency of using Chinese microblog Sina Weibo over a period of time in Shanghai. Current study allows us to examine how check-in behavior vary in same weeks but in different years, it also helps study mobility patterns and practices in terms of time & space in Shanghai. In order to produce smooth density surface of check-ins, we analyze the overall spatial patterns by using the kernel density estimation (KDE). Initial results indicates difference in social media usage behavior during middle week in different years. We interpret these findings as suggestive evidence that location-based social media data can provide a new outlook to observe mobility patterns and intensity of check-ins. It can also help to observe variations in population density over the period of time and act as a tool to estimate mobility demand in the city.