Soliman Aiman, Soltani Kiumars, Yin Junjun, Padmanabhan Anand, Wang Shaowen
CyberGIS Center for Advanced Digital and Spatial Studies, National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Champaign, Illinois, United States of America.
Illinois Informatics Institute, University of Illinois at Urbana-Champaign, Champaign, Illinois, United States of America.
PLoS One. 2017 Jul 19;12(7):e0181657. doi: 10.1371/journal.pone.0181657. eCollection 2017.
A number of recent studies showed that digital footprints around built environments, such as geo-located tweets, are promising data sources for characterizing urban land use. However, challenges for achieving this purpose exist due to the volume and unstructured nature of geo-located social media. Previous studies focused on analyzing Twitter data collectively resulting in coarse resolution maps of urban land use. We argue that the complex spatial structure of a large collection of tweets, when viewed through the lens of individual-level human mobility patterns, can be simplified to a series of key locations for each user, which could be used to characterize urban land use at a higher spatial resolution. Contingent issues that could affect our approach, such as Twitter users' biases and tendencies at locations where they tweet the most, were systematically investigated using 39 million geo-located Tweets and two independent datasets of the City of Chicago: 1) travel survey and 2) parcel-level land use map. Our results support that the majority of Twitter users show a preferential return, where their digital traces are clustered around a few key locations. However, we did not find a general relation among users between the ranks of locations for an individual-based on the density of tweets-and their land use types. On the contrary, temporal patterns of tweeting at key locations were found to be coherent among the majority of users and significantly associated with land use types of these locations. Furthermore, we used these temporal patterns to classify key locations into generic land use types with an overall classification accuracy of 0.78. The contribution of our research is twofold: a novel approach to resolving land use types at a higher resolution, and in-depth understanding of Twitter users' location-related and temporal biases, promising to benefit human mobility and urban studies in general.
最近的一些研究表明，围绕建成环境的数字足迹，如地理位置推文，是用于描述城市土地利用的有前景的数据来源。然而，由于地理位置社交媒体的数量和非结构化性质，实现这一目的存在挑战。以往的研究集中于对推特数据进行总体分析，从而得出城市土地利用的粗略分辨率地图。我们认为，从个体层面的人类移动模式角度来看，大量推文的复杂空间结构可以简化为每个用户的一系列关键位置，这些关键位置可用于以更高的空间分辨率描述城市土地利用。我们使用3900万条地理位置推文以及芝加哥市的两个独立数据集：1）出行调查和2）地块层面的土地利用地图，系统地研究了可能影响我们方法的偶然问题，如推特用户在其推文最多的位置的偏差和倾向。我们的结果支持这样的观点，即大多数推特用户表现出优先返回的情况，他们的数字痕迹聚集在几个关键位置周围。然而，我们没有发现基于推文密度的个体位置排名与其土地利用类型之间在用户中有普遍关系。相反，发现大多数用户在关键位置的推文时间模式是一致的，并且与这些位置的土地利用类型显著相关。此外，我们利用这些时间模式将关键位置分类为一般土地利用类型，总体分类准确率为0.78。我们研究的贡献有两方面：一种以更高分辨率解析土地利用类型的新方法，以及对推特用户与位置相关的和时间偏差的深入理解，有望总体上有益于人类移动性和城市研究。
