Visualization, Spatiotemporal Patterns, and Directional Analysis of Urban Activities Using Geolocation Data Extracted from LBSN

Abstract

Location-based social networks (LBSNs) have rapidly prevailed in China with the increase in smart devices use, which has provided a wide range of opportunities to analyze urban behavior in terms of the use of LBSNs. In a LBSN, users socialize by sharing their location (also referred to as “geolocation”) in the form of a tweet (also referred to as a “check-in”), which contains information in the form of, but is not limited to, text, audio, video, etc., which records the visited place, movement patterns, and activities performed (e.g., eating, living, working, or leisure). Understanding the user’s activities and behavior in space and time using LBSN datasets can be achieved by archiving the daily activities, movement patterns, and social media behavior patterns, thus representing the user’s daily routine. The current research observing and analyzing urban activities behavior was often supported by the volunteered sharing of geolocation and the activity performed in space and time. The objective of this research was to observe the spatiotemporal and directional trends and the distribution differences of urban activities at the city and district levels using LBSN data. The density was estimated, and the spatiotemporal trend of activities was observed, using kernel density estimation (KDE); for spatial regression analysis, geographically weighted regression (GWR) analysis was used to observe the relationship between different activities in the study area. Finally, for the directional analysis, to observe the principle orientation and direction, and the spatiotemporal movement and extension trends, a standard deviational ellipse (SDE) analysis was used. The results of the study show that women were more inclined to use social media compared with men. However, the activities of male users were different during weekdays and weekends compared to those of female users. The results of the directional analysis at the district level reflect the change in the trajectory and spatiotemporal dynamics of activities. The directional analysis at the district level reveals its fine spatial structure in comparison to the whole city level. Therefore, LBSN can be considered as a supplementary and reliable source of social media big data for observing urban activities and behavior within a city in space and time.

Spatiotemporal data mining: a survey on challenges and open problems

“…The authors in (Rizwan et al. 2020 ) visualised the spatiotemporal and directional trends in urban activities. They examined both city and district levels using location-based social data.…”

“…( 2012 ); Kastner and Samet ( 2020 ); Sakaue and Sato ( 2020 ); Rizwan et al. ( 2020 ); Salcedo-Gonzalez et al. ( 2020 ); Sha et al.…”

Role of Big Data in the Development of Smart City by Analyzing the Density of Residents in Shanghai

“…Taking into account the privacy of the users, no private information is available. Therefore, the check-in data shows the day-to-day activity patterns of users and their behaviors, and it exhibits the average person's everyday life operations [31,39]. Shanghai was chosen as the study area since it has a large volume of check-ins and active users.…”

Human Mobility Prediction With Region-Based Flows and Water Consumption

“…The development of predictors leveraging such coarse-grained mobility data is still scarce in the mobility mining domain. • On the other hand, the mobility behaviour of a pop-ulation is strongly related to their latent activities at each moment [13], [14]. However, existing solutions for human-mobility prediction usually neglect the usage of human-activity data which is not directly related to movement or displacement actions, that is, the spatiotemporal traces generated by the target moving objects.…”