Activity patterns, socioeconomic status and urban spatial structure: what can social media data tell us?
Activity patterns, socioeconomic status and urban spatial structure: what can social media data tell us?
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DOI：
10.1080/13658816.2016.1145225
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发表时间：
2016-01-01
影响因子：
5.7
通讯作者：
Wong, David W. S.
中科院分区：
文献类型：
--
作者：
Huang, Qunying;Wong, David W. S.
个体活动模式受多种因素影响。更重要的因素包括社会经济地位和城市空间结构。虽然大多数以前的研究严重依赖于昂贵的旅行日记类型的数据，使用社交媒体数据来支持活动模式分析的可行性尚未得到评估。尽管社交媒体数据有各种吸引人的方面，包括低获取成本和相对广泛的地理和国际覆盖范围，但这些数据也有许多局限性，包括缺乏用户的背景信息，如家庭位置和SES。本研究的一个主要目的是探索Twitter数据可以用于支持活动模式分析的程度。我们介绍了一种方法来确定用户的家庭和工作地点，以检查个人的活动模式。为了推断个人的社会经济地位，我们将美国社区调查（ACS）的数据。使用华盛顿的Twitter数据，我们分析了不同SES的Twitter用户的活动模式。该研究清楚地表明，虽然SES非常重要，但城市空间结构，特别是主要找到工作的地方和该地区的地理布局，在影响来自不同社区的用户之间的活动模式变化方面发挥着关键作用。
Individual activity patterns are influenced by a wide variety of factors. The more important ones include socioeconomic status (SES) and urban spatial structure. While most previous studies relied heavily on the expensive travel-diary type data, the feasibility of using social media data to support activity pattern analysis has not been evaluated. Despite the various appealing aspects of social media data, including low acquisition cost and relatively wide geographical and international coverage, these data also have many limitations, including the lack of background information of users, such as home locations and SES. A major objective of this study is to explore the extent that Twitter data can be used to support activity pattern analysis. We introduce an approach to determine users' home and work locations in order to examine the activity patterns of individuals. To infer the SES of individuals, we incorporate the American Community Survey (ACS) data. Using Twitter data for Washington, DC, we analyzed the activity patterns of Twitter users with different SESs. The study clearly demonstrates that while SES is highly important, the urban spatial structure, particularly where jobs are mainly found and the geographical layout of the region, plays a critical role in affecting the variation in activity patterns between users from different communities.