In this study using data from the 2017 National Household Travel Survey in California from 26,078 survey participants, sequence analysis is used to estimate a fragmentation indicator of people's daily schedules. Then, spatial clustering is used to find groups of observations with similarly high or low fragmentation using the longitude and latitude of their residential locations. This is followed by a hierarchical sequence clustering within each spatial cluster to identify distinct patterns of time allocation. Using the Local Indicator of Spatial Association (LISA) we find a large portion (approximately 30%) of the sample with significant spatial clustering of fragmentation. We also find systematic and significant differences in membership to these clusters based on land use, county of residence, household and personal characteristics, and travel modes used. Sequence analysis pattern recognition within LISA spatial clusters shows systematically repeating time allocation patterns that include typical work and school schedules as well as staying at home patterns. However, each spatial LISA cluster is composed of different time allocation clusters. All this analysis taken together points out substantial and measurable heterogeneity in spatial clustering of fragmentation and the need for customized policy actions in different geographies.

在这项研究中，使用来自 2017 年加利福尼亚州全国家庭旅行调查的 26,078 名调查参与者的数据，序列分析用于估计人们日常日程安排的碎片化指标。然后，空间聚类用于使用其居住位置的经度和纬度来寻找具有相似高或低碎片的观察组。随后是每个空间集群内的层次序列聚类，以识别不同的时间分配模式。使用空间关联的局部指标 (LISA)，我们发现大部分（大约 30%）样本具有显着的碎片空间聚类。我们还发现，根据土地利用、居住县、家庭和个人特征，这些集群的成员存在系统性和显着差异，和使用的出行方式。LISA 空间集群内的序列分析模式识别显示了系统地重复的时间分配模式，包括典型的工作和学校时间表以及呆在家里的模式。然而，每个空间 LISA 簇由不同的时间分配簇组成。所有这些分析综合起来指出了碎片化空间聚类的实质性和可衡量的异质性，以及在不同地区制定定制政策行动的必要性。