Senseable City Lab, Massachusetts Institute of Technology, Cambridge, MA, United States of America.
Universidad Carlos III de Madrid, Leganés, Spain.
PLoS One. 2021 Mar 9;16(3):e0247996. doi: 10.1371/journal.pone.0247996. eCollection 2021.
We present a novel metric for measuring relative connection between parts of a city using geotagged Twitter data as a proxy for co-occurrence of city residents. We find that socioeconomic similarity is a significant predictor of this connectivity metric, which we call "linkage strength": neighborhoods that are similar to one another in terms of residents' median income, education level, and (to a lesser extent) immigration history are more strongly connected in terms of the of people who spend time there, indicating some level of homophily in the way that individuals choose to move throughout a city's districts.
我们提出了一种新的度量标准，用于衡量城市各部分之间的相对联系，使用地理标记的 Twitter 数据作为城市居民同时出现的替代指标。我们发现，社会经济相似性是这种连接度量的一个重要预测指标，我们称之为“连接强度”：在居民的中位数收入、教育水平以及（在较小程度上）移民历史方面彼此相似的社区，在人们花费时间的方式上的连接更为紧密，这表明在个人选择在城市各区域移动的方式上存在某种程度的同质性。
