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

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.

Comparison of Twelve Machine Learning Regression Methods for Spatial Decomposition of Demographic Data Using Multisource Geospatial Data: An Experiment in Guangzhou City, China

“…The auxiliary data from various sources can be combined for gridded population mapping via machine learning regression. It was demonstrated that location-based services (LBS) data, derived from mobile phones, Baidu map, Tencent LBS, Sina Weibo, and so on, offer the possibility of illustrating gridded population maps more accurately and finely in urban areas [11,22,[64][65][66][67][68][69][70][71]. In particular, the accuracy of gridded population maps can be improved significantly through the integration of remote sensing data and LBS data [72].…”

“…Second, the data sources for prompting the fine-scale spatial decomposition research need to be enriched. Dynamic geo-data, such as mobile phone communication data [74], social media check-in data [64,67,68], GPS trajectory data [66], and so on, can effectively improve the accuracy of the proposed approach and provide the spatial-temporal information on population distribution on the fine scale, which is very important for issues in urban governance, such as emergency management, public service facility configuration, and so on. In future research, we could focus on revealing the spatial-temporal dynamics of population distribution with more individual-scale trajectory data, such as mobile phone signaling data, non-floating bicycle trajectory data, online car trip hailing data, and so on.…”

Assessing Reliability of Chinese Geotagged Social Media Data for Spatiotemporal Representation of Human Mobility

“…Weibo tweets have been utilized with their geographic and semantic information, wherein the text has been widely used in analyzing public opinion [16], user behavior [17], subjective wellbeing [18], climate change [19], and hazard management [20]. The geotags have been used as an important open data for identifying land use [21], urban function area [22,23], urban spatial structure [24], and delimitating urban boundaries [25]. Recent studies also extract human mobility information based on geotags to analyze regional spatial networks [26] and evaluate spatial segregation patterns [27].…”

Delineating Urban Functional Zones Using U-Net Deep Learning: Case Study of Kuancheng District, Changchun, China

“…Traditional methods have relied on field surveys and remote-sensing images [12,13]. However, crowdsourced geographic information (e.g., point-of-interest, mobile, and traffic-flow data) provides a new channel for describing and understanding urban spatial structures [4,[14][15][16][17][18][19][20][21]. Combining these two types of methods can make the identification of urban functional areas more accurate [22][23][24][25].…”

“…Related studies have typically focused on economically developed cities, such as Beijing [19,26,27,37], Shenzhen [25,36,38], Guangzhou [14,39], Wuxi [40], and New York [41]. While Klapka and Halás [17,42] studied urban functional zones in the Czech Republic, few studies have focused on old industrial cities (except for Wuhan [20]) and Rust Belt cities.…”