Exploring the uncertainty of activity zone detection using digital footprints with multi-scaled DBSCAN

ABSTRACT

The density-based spatial clustering of applications with noise (DBSCAN) method is often used to identify individual activity clusters (i.e., zones) using digital footprints captured from social networks. However, DBSCAN is sensitive to the two parameters, eps and minpts. This paper introduces an improved density-based clustering algorithm, Multi-Scaled DBSCAN (M-DBSCAN), to mitigate the detection uncertainty of clusters produced by DBSCAN at different scales of density and cluster size. M-DBSCAN iteratively calibrates suitable local eps and minpts values instead of using one global parameter setting as DBSCAN for detecting clusters of varying densities, and proves to be effective for detecting potential activity zones. Besides, M-DBSCAN can significantly reduce the noise ratio by identifying all points capturing the activities performed in each zone. Using the historic geo-tagged tweets of users in Washington, D.C. and in Madison, Wisconsin, the results reveal that: 1) M-DBSCAN can capture dispersed clusters with low density of points, and therefore detecting more activity zones for each user; 2) A value of 40 m or higher should be used for eps to reduce the possibility of collapsing distinctive activity zones; and 3) A value between 200 and 300 m is recommended for eps while using DBSCAN for detecting activity zones.

KEYWORDS:

• Activity zones
• Twitter
• spatial clustering
• social networks
• human mobility

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

Support for this research was provided by the University of Wisconsin - Madison Office of the Vice Chancellor for Research and Graduate Education with funding from the Wisconsin Alumni Research Foundation.

Notes on contributors

Xinyi Liu

Xinyi Liu is a first year PhD student in the Department of Geography at University of Wisconsin-Madison. She earned a master’s degree in GIScience/Cartography from the same department. Her doctoral research investigates spatial data mining and its applications in different study areas, especially human mobility.

Qunying Huang

Qunying Huang is an assistant professor in the Department of Geography at University of Wisconsin-Madison. Her fields of expertise include Spatial Computing, Spatiotemporal Big Data Analytics, Spatial Data Science, and Geographic Information Science (GIScience). She employs different computing models, such as Cluster, Grid, GPU, Citizen computing, and especially Cloud Computing to address big data and computing challenges in the GIScience, and leverages social media data for various applications, such as disaster management and human mobility.

Song Gao

Dr. Song Gao, is an Assistant Professor in GIScience at the Department of Geography, University of Wisconsin-Madison. He holds a Ph.D. in Geography at the University of California, Santa Barbara. His main research interests include Place-Based GIS, Geospatial Big Data Analytics, GeoAI, Geospatial Semantics, Human Mobility and Urban Computing.