Analyzing The Density of Residents in Shanghai to Examine the Role of Big Data in the Development of Smart Cities

Abstract

A significant amount of study has been done in recent decades to evaluate data from social networks based on the region in order to emphasize their applicability. This social networking data based on geography may be used to create models and predictions. methods for analyzing and reproducing spatiotemporal patterns and user activity symmetry, as well as volume estimates. Distinct density estimation methods are used in the present research to examine the number of times people checking in in a certain period of time depth using a database of social networks depending on region obtained from Sina-Weibo, commonly known as Weibo, during a particular time time in ten distinct Shanghai areas, China. The goal of this research is to look at the density of users in Shanghai based on Weibo geolocation data and compare it to other cities using univariate and bivariate thickness estimate methods, such as point density and kernel density estimation (KDE). The study's main findings include: I geographical aspects of users' behavior, such as check-in-based activity centers, (ii) the practicality of employing checkin data to describe the interaction between users and social networks, and (iii) the