Exploring the Spatiotemporal Patterns of Residents’ Daily Activities Using Text-Based Social Media Data: A Case Study of Beijing, China

Jian Liu,Guoqing Zhi,Siyu Chen,Bin Tian,Juan Wang,Bin Meng

doi:10.3390/ijgi10060389

Abstract

The use of social media data provided powerful data support to reveal the spatiotemporal characteristics and mechanisms of human activity, as it integrated rich spatiotemporal and textual semantic information. However, previous research has not fully utilized its semantic and spatiotemporal information, due to its technical and algorithmic limitations. The efficiency of the deep mining of textual semantic resources was also low. In this research, a multi-classification of text model, based on natural language processing technology and the Bidirectional Encoder Representations from Transformers (BERT) framework is constructed. The residents’ activities in Beijing were then classified using the Sina Weibo data in 2019. The results showed that the accuracy of the classifications was more than 90%. The types and distribution of residents’ activities were closely related to the characteristics of the activities and holiday arrangements. From the perspective of a short timescale, the activity rhythm on weekends was delayed by one hour as compared to that on weekdays. There was a significant agglomeration of residents’ activities that presented a spatial co-location cluster pattern, but the proportion of balanced co-location cluster areas was small. The research demonstrated that location conditions, especially the microlocation condition (the distance to the nearest subway station), were the driving factors that affected the resident activity cluster patterns. In this research, the proposed framework integrates textual semantic analysis, statistical method, and spatial techniques, broadens the application areas of social media data, especially text data, and provides a new paradigm for the research of residents’ activities and spatiotemporal behavior.

Highlights

Introduction published maps and institutional affilThe continuous advancements of globalization and informatization have profoundly affected people’s daily lives and behavioral activities, causing tremendous changes in the traditional patterns of residents’ activities
This study aims to introduce the current advanced natural language processing (NLP) technology into the field of resident activity research in order to efficiently extract the rich semantic information from the social media data
User-initiated social media data, based on a social network platform, contain a wealth of information on resident behavior dynamics, which is of great significance for the understanding of the spatiotemporal patterns and dynamic laws of resident activities in the information age [60]

Summary

Introduction

Introduction published maps and institutional affilThe continuous advancements of globalization and informatization have profoundly affected people’s daily lives and behavioral activities, causing tremendous changes in the traditional patterns of residents’ activities. The rapid development of information and communication technologies (ICTs) has changed the temporal and spatial relationships of residents’ daily activities so that some activities are no longer subject to specific temporal and spatial constraints, thereby allowing better flexibility and coordination [2]. In this context, research into residents’ activities has received extensive attention in many disciplines, such as geography, urban planning, transportation, computers, and public health [3,4,5]. By exploring the differences in the distribution scales and types of various resident activities, the urban iations.

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