Mapping Dynamic Urban Land Use Patterns with Crowdsourced Geo-Tagged Social Media (Sina-Weibo) and Commercial Points of Interest Collections in Beijing, China

Abstract

Abstract:In fast-growing cities, especially large cities in developing countries, land use types are changing rapidly, and different types of land use are mixed together. It is difficult to assess the land use types in these fast-growing cities in a timely and accurate way. To address this problem, this paper presents a multi-source data mining approach to study dynamic urban land use patterns. Spatiotemporal social media data reveal human activity patterns in different areas, social media text data reflects the topics discussed in different areas, and Points of Interest (POI) reflect the distribution of urban facilities in different regions. Human activity patterns, topics of discussion on social media, and the distribution of urban facilities in different regions were combined and analyzed to infer urban land use patterns. We collected 9.5 million geo-tagged Chinese social media (Sina-Weibo) messages from January 2014 to July 2014 in the urban core areas of Beijing and compared them with 385,792 commercial Points of Interest (POI) from Datatang (a Chinese digital data content provider). To estimate urban land use types and patterns in Beijing, a regular grid of 400 m × 400 m was created to divide the urban core areas into 18,492 cells. By analyzing the temporal frequency trends of social media messages within each cell using K-means clustering algorithm, we identified seven types of land use clusters in Beijing: residential areas, university dormitories, commercial areas, work areas, transportation hubs, and two types of mixed land use areas. Text mining, word clouds, and the distribution analysis of POI were used to verify the estimated land use types successfully. This study can help urban planners create up-to-date land use patterns in an economic way and help us better understand dynamic human activity patterns in a city.

Exploring the Relationship between Potential and Actual of Urban Waterfront Spaces in Wuhan Based on Social Networks

“…It also explores the relationship between the spatial value of urban waterfronts and the degree of tourists' attraction toward waterfronts. Spatial and temporal characteristics of social media data can be used to explore the latest urban activity space pattern [64] and verify the effectiveness of POI attraction [29]. As the main crowdsourced data source, social media data can be linked and aggregated into multiple map layers and GIS datasets with multiple uses [65].…”

Solving Competitive Location Problems with Social Media Data Based on Customers’ Local Sensitivities

“…To filter out the noise and outliers in the social media dataset, the microblogs were preprocessed. The noise mainly refers to the advertisements and the microblogs which come from non-human sources, namely, bots [31][32][33]. Compared to the microblogs without location information, geotagged microblogs contained less noise and were more reliable.…”

Using Spatial Semantics and Interactions to Identify Urban Functional Regions

“…The spatial distribution and geographic knowledge of POI categories can be employed as spatial semantics to annotate functional regions [24,29]. However, few studies have simultaneously considered both spatial semantics and spatial interactions to identify urban functional regions.…”