Similarity measurement on human mobility data with spatially weighted structural similarity index (SpSSIM)
Similarity measurement on human mobility data with spatially weighted structural similarity index (SpSSIM)
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DOI：
10.1111/tgis.12590
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发表时间：
2019-10
影响因子：
2.4
通讯作者：
Chanwoo Jin;A. Nara;Jiue-An Yang;Ming-Hsiang Tsou
中科院分区：
文献类型：
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作者：
Chanwoo Jin;A. Nara;Jiue-An Yang;Ming-Hsiang Tsou
了解人类流动性的不同特征，有助于深入了解城市动态和复杂性。人类的运动被记录在各种数据源中，每个数据源都描述了独特的运动特征。揭示流动性数据源的相似性和差异性有助于全面把握人类流动性模式。本研究介绍了一种新的方法，通过空间扩展图像评估工具，结构相似性指数（SSIM）来测量两个原点-目的地（OD）矩阵的相似性。新的测量方法，空间加权SSIM (SpSSIM)，通过明确定义空间邻接性，利用权重矩阵克服了由于OD对排序导致的SSIM灵敏度问题。为了评估SpSSIM，我们通过重新采样OD对的顺序来比较SSIM和SpSSIM的性能，并进行自举来检验SpSSIM的统计显著性。作为案例研究，我们比较了加利福尼亚州圣地亚哥县三个数据源生成的OD矩阵：基于美国人口普查的纵向雇主-家庭动态来源-目的地就业统计数据、Twitter和Instagram。案例研究表明，SpSSIM能够捕获随距离变化的数据集之间迁移模式的相似性。一些地区显示出局部差异，而全球指数显示它们是相似的。研究结果增强了对包括社交媒体在内的各种数据集的复杂移动模式的理解。
Understanding diverse characteristics of human mobility provides profound knowledge of urban dynamics and complexity. Human movements are recorded in a variety of data sources and each describes unique mobility characteristics. Revealing similarity and difference in mobility data sources facilitates grasping comprehensive human mobility patterns. This study introduces a new method to measure similarities on two origin–destination (OD) matrices by spatially extending an image‐assessment tool, the structural similarity index (SSIM). The new measurement, spatially weighted SSIM (SpSSIM), utilizes weight matrices to overcome the SSIM sensitivity issue due to the ordering of OD pairs by explicitly defining spatial adjacency. To evaluate SpSSIM, we compared performances between SSIM and SpSSIM with resampling the orders of OD pairs and conducted bootstrapping to test the statistical significance of SpSSIM. As a case study, we compared OD matrices generated from three data sources in San Diego County, CA: U.S. Census‐based Longitudinal Employer–Household Dynamics Origin–Destination employment statistics, Twitter, and Instagram. The case study demonstrated that SpSSIM was able to capture similarities of mobility patterns between datasets that varied by distance. Some regions showed local dissimilarity while the global index indicated they were similar. The results enhance the understanding of complex mobility patterns from various datasets, including social media.