Studying commuting behaviours using collaborative visual analytics
Studying commuting behaviours using collaborative visual analytics
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DOI：
10.1016/j.compenvurbsys.2013.10.007
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发表时间：
2014-09-01
影响因子：
6.8
通讯作者：
Bowerman, Audrey
中科院分区：
文献类型：
--
作者：
Beecham, Roger;Wood, Jo;Bowerman, Audrey
挖掘一个大的起点-目的地数据集的旅程通过伦敦的自行车出租计划（LCHS），我们开发了一种技术，自动分类通勤行为，涉及骑自行车的旅程的空间分析。我们确定了一个潜在的通勤骑自行车的子集，并为每个人定义一个合理的地理区域代表他们的工作场所。所有在早上在这个衍生工作场所附近终止，晚上从这个衍生工作场所出发的高峰时间行程，我们标记为通勤。三种技术，用于创建这些工作区进行比较，使用可视化分析：加权平均中心计算，空间k均值聚类和核密度估计方法。评估这些技术在个人骑自行车的水平，我们发现，通勤者的高峰时间的行程空间比预期的更多样化，对于一个显着的部分通勤者似乎有一个以上的合理的空间工作区。通过对这三种技术的视觉评价，我们选择了密度估计作为我们的首选方法。确定了两种不同类型的通勤活动：居住在伦敦以外的LCHS客户所采取的通勤活动，他们在伦敦的主要铁路枢纽进行高度定期的通勤旅行;以及居住在非常靠近自行车共享对接站的人的通勤行为。我们发现，伦敦大学周围的许多峰间旅行显然是骑自行车的人工作日的一部分。还发现了早上通勤和晚上通勤的数量不平衡，这可能与当地自行车的可用性有关。围绕我们的工作场所分析的重要决策，特别是这些对通勤行为的更广泛的见解，都是通过可视化地探索这种分析来发现的。本文所描述的可视化分析方法是有效的，使不同层次的分析经验的研究团队参与这项研究。我们认为，这种方法是相关的许多应用研究的背景。(C)2013爱思唯尔有限公司版权所有。
Mining a large origin-destination dataset of journeys made through London's Cycle Hire Scheme (LCHS), we develop a technique for automatically classifying commuting behaviour that involves a spatial analysis of cyclists' journeys. We identify a subset of potential commuting cyclists, and for each individual define a plausible geographic area representing their workplace. All peak-time journeys terminating within the vicinity of this derived workplace in the morning, and originating from this derived workplace in the evening, we label commutes. Three techniques for creating these workplace areas are compared using visual analytics: a weighted mean-centres calculation, spatial k-means clustering and a kernel density-estimation method. Evaluating these techniques at the individual cyclist level, we find that commuters' peak-time journeys are more spatially diverse than might be expected, and that for a significant portion of commuters there appears to be more than one plausible spatial workplace area. Evaluating the three techniques visually, we select the density-estimation as our preferred method. Two distinct types of commuting activity are identified: those taken by LCHS customers living outside of London, who make highly regular commuting journeys at London's major rail hubs; and more varied commuting behaviours by those living very close to a bike-share docking station. We find evidence of many interpeak journeys around London's universities apparently being taken as part of cyclists' working day. Imbalances in the number of morning commutes to, and evening commutes from, derived workplaces are also found, which might relate to local availability of bikes. Significant decisions around our workplace analysis, and particularly these broader insights into commuting behaviours, are discovered through exploring this analysis visually. The visual analysis approach described in the paper is effective in enabling a research team with varying levels of analysis experience to participate in this research. We suggest that such an approach is of relevance to many applied research contexts. (C) 2013 Elsevier Ltd. All rights reserved.