Analyzing Urban Spatial Patterns and Functional Zones Using Sina Weibo POI Data: A Case Study of Beijing

Ruomu Miao,Yuxia Wang,Shuang Li

doi:10.3390/su13020647

Abstract

With the development of Web2.0 and mobile Internet, urban residents, a new type of “sensor”, provide us with massive amounts of volunteered geographic information (VGI). Quantifying the spatial patterns of VGI plays an increasingly important role in the understanding and development of urban spatial functions. Using VGI and social media activity data, this article developed a method to automatically extract and identify urban spatial patterns and functional zones. The method is put forward based on the case of Beijing, China, and includes the following three steps: (1) Obtain multi-source urban spatial data, such as Weibo data (equivalent to Twitter in Chinese), OpenStreetMap, population data, etc.; (2) Use the hierarchical clustering algorithm, term frequency-inverse document frequency (TF-IDF) method, and improved k-means clustering algorithms to identify functional zones; (3) Compare the identified results with the actual urban land uses and verify its accuracy. The experiment results proved that our method can effectively identify urban functional zones, and the results provide new ideas for the study of urban spatial patterns and have great significance in optimizing urban spatial planning.

Highlights

In the era of Web2.0 and mobile Internet, people often use Weibo, online comments, photo sharing, travel records, and social media to generate, process, and share a large amount of information [1,2,3]
With the popularization of global positioning systems (GPS) and wireless cellular positioning technology in mobile devices, most of the information spontaneously created by users automatically carries spatial information [4]
We selected a large number of Weibo points of interests (POI) for analysis, and used the checkin_num kernel density analysis weight of each POI point to perform kernel density analysis, and obtained the following results

Summary

Introduction

In the era of Web2.0 and mobile Internet, people often use Weibo (equivalent to Twitter in Chinese), online comments, photo sharing, travel records, and social media to generate, process, and share a large amount of information [1,2,3]. With the popularization of global positioning systems (GPS) and wireless cellular positioning technology in mobile devices, most of the information spontaneously created by users automatically carries spatial information [4] This kind of spatial information is called volunteered geographic information (VGI) in academia [5]. VGI’s real-time, diversity, and content creativity have huge application potential in the fields of spatio-temporal analysis, urban planning, environmental monitoring, disaster warning, and public information services [6,7,8,9]. These massive data are gradually being mined and analyzed, and people have truly entered the era of big data. Goodchild pointed out that we are rapidly entering an era where ordinary citizens are both consumers and producers of geographic information [1]

Methods

Results

Discussion

Conclusion