论文标题
一种统计方法，用于识别用于向新西兰国家通勤数据的高流动性领域
A Statistical Method for Identifying Areas of High Mobility Applied to Commuting Data for the Country of New Zealand
论文作者
论文摘要
人类流动性描述了空间系统中人们运动的物理模式。这些模式中的许多模式，包括日常通勤，都是循环且可量化的。这些模式捕获了与流行病学以及其他社会，行为和经济科学研究过程相关的身体现象。本文通过提出了一种统计方法来提高人类流动性研究，该方法使用新西兰国家的通勤数据作为案例研究来识别个人向上迁移和通过的位置。这些位置被称为流动性基因座，它们捕获了人们通勤的社区的全球财产。该方法利用了定向图表表示，其中顶点与位置之间的位置和流量相对应与边缘权重相对应。归一化后，该图被视为可以计算固定分布的马尔可夫链。然后，考虑到定向图的结构和位置之间的流量，提出的置换过程将应用于确定哪些固定分布比预期的要大。评估了该方法的结果，包括与该地区通勤模式已经知道的比较以及与类似特征的比较。
Human mobility describes physical patterns of movement of people within a spatial system. Many of these patterns, including daily commuting, are cyclic and quantifiable. These patterns capture physical phenomena tied to processes studied in epidemiology, and other social, behavioral, and economic sciences. This paper advances human mobility research by proposing a statistical method for identifying locations that individual move to and through at a rate proportionally higher than other locations, using commuting data for the country of New Zealand as a case study. These locations are termed mobility loci and they capture a global property of communities in which people commute. The method makes use of a directed-graph representation where vertices correspond to locations and traffic between locations correspond to edge weights. Following a normalization, the graph can be regarded as a Markov chain whose stationary distribution can be calculated. The proposed permutation procedure is then applied to determine which stationary distributions are larger than what would be expected, given the structure of the directed graph and traffic between locations. The results of this method are evaluated, including a comparison to what is already known about commuting patterns in the area as well as a comparison with similar features.
