Work

Multi-granularity spatiotemporal flow patterns

Year: 2026

Type: article

Abstract: Analyzing flow of objects or data at different granularities of space and time can unveil interesting insights or trends. For example, transportation companies, by aggregating passenger travel data (e.g., counting passengers traveling from one region to another), can analyze movement behavior. In this paper, we study the problem of finding important trends in passenger movements between regions at different granularities. We define Origin ( O ), Destination ( D ), and Time ( T ) patterns (ODT patterns) and propose a bottom-up algorithm that enumerates them. We suggest and employ optimizations that greatly reduce the search space and the computational cost of pattern enumeration. We also propose pattern variants (constrained patterns and top- k patterns) that could be useful to different applications scenarios. Finally, we propose an approximate solution that fast identifies ODT patterns of specific sizes, following a generate-and-test approach. We evaluate the efficiency and effectiveness of our methods on three real datasets and showcase interesting ODT flow patterns in them.

Source: GeoInformatica

DOI: 10.1007/s10707-026-00592-0

Authors: Chrysanthi Kosyfaki, Nikos Mamoulis, Reynold Cheng, Ben Kao

Institutions: Hong Kong University of Science and Technology, University of Ioannina, University of Hong Kong

Language: English

Topic: Data Management and Algorithms

Subfield: Signal Processing

Field: Computer Science

Domain: Physical Sciences

SDG: Sustainable cities and communities

Open Access status: hybrid

Estimated APC paid: $2,590