Reference: Liu, X., Huang Q., Gao S. and Xia J., 2020. Activity knowledge discovery: Detecting collective and individual activities with digital footprints and open source geographic data. Computers, Environment and Urban Systems, 85, p.101551. DOI: 1016/j.compenvurbsys.2020.101551. Paper Link >>
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 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 and multi-scaled DBSCAN).
- This research proposes a two-step framework to effectively detect both collective and individual activity zones and activity types. To our best knowledge, this work is the first to examine people’s frequent activities at both levels.
- The activity zone detection methods, with multi-level spatial clustering for the collective level, and location-aware clustering for the individual level, consider the geographic context and enable the accurate detection of travel activities from sparse digital footprints.
- With open source OSM datasets, this work provides a detailed inference of activity types (ten in total; Table 3), contributing to the comprehensive understanding and analysis of human movement behaviours.
Read the full article at: https://www.sciencedirect.com/science/article/pii/S0198971520302842