Abstract Digital footprints collected from social media platforms are often clustered using methods such as the density-based spatial clustering of applications with noise (DBSCAN) and its variants to identify daily travel activities (e.g., dwelling, working, entertainment, and eating). However, these clustering methods mostly only consider the spatial distribution of travel activity points while ignoring their geographic context, resulting in the aggregation of digital footprints representing different activity types into one cluster. In addition, existing works only focus on examining people's travel activities at either the collective (i.e., macro) or individual (i.e., micro) level. To this end, this study utilizes geographic context information and develops a novel activity knowledge discovery framework to better detect frequent travel activities at both levels. First, we develop a multi-level spatial clustering method to aggregate digital footprints of a group of users into collective clusters (i.e., activity zones) by inferring and integrating the underlying activities performed at each zone with OpenStreetMap (OSM) datasets that can inform geographic context of the activity zones. Next, we introduce a location-aware clustering method to detect activity zones and associate activity types at the individual level by aggregating individual footprints based on the collective results. As case studies, digital footprints from 49 selected Twitter users are analyzed to evaluate the proposed framework. The results reveal that: (1) The multi-level spatial clustering method can often detect significant collective activity zones; and (2) The location-aware clustering method can aggregate individual digital footprints into activity zones more effectively compared with existing density-based spatial clustering methods (e.g., DBSCAN).

中文翻译：

---

活动知识发现：使用数字足迹和开源地理数据检测集体和个人活动

摘要 从社交媒体平台收集的数字足迹通常使用基于密度的噪声应用空间聚类（DBSCAN）及其变体等方法进行聚类，以识别日常旅行活动（例如，居住、工作、娱乐和饮食）。然而，这些聚类方法大多只考虑出行活动点的空间分布，而忽略了其地理背景，导致代表不同活动类型的数字足迹聚合为一个聚类。此外，现有的工作只关注在集体（即宏观）或个人（即微观）层面上检查人们的旅行活动。为此，本研究利用地理背景信息并开发了一种新颖的活动知识发现框架，以更好地检测两个级别的频繁旅行活动。首先，我们开发了一种多级空间聚类方法，通过推断和集成在每个区域执行的基础活动与可以告知地理信息的 OpenStreetMap (OSM) 数据集，将一组用户的数字足迹聚合到集体集群（即活动区域）中。活动区域的背景。接下来，我们引入了一种位置感知聚类方法，通过基于集体结果聚合个人足迹来检测活动区域并在个人层面关联活动类型。作为案例研究，分析了来自 49 个选定 Twitter 用户的数字足迹以评估提议的框架。结果表明：(1)多层次空间聚类方法往往可以检测出显着的集体活动区；(2) 与现有的基于密度的空间聚类方法（例如 DBSCAN）相比，位置感知聚类方法可以更有效地将个人数字足迹聚合到活动区域中。