Multi-granularity Spatiotemporal Flow Patterns

TL;DR

This paper introduces a comprehensive approach to identify and analyze multi-granularity spatiotemporal flow patterns, optimizing pattern enumeration and providing variants for diverse applications, validated on real datasets.

Contribution

It proposes a novel bottom-up algorithm with optimizations for discovering ODT patterns at multiple granularities, including variants and an approximate method for efficiency.

Findings

Efficient algorithms reduce search space and computational cost.

Effective identification of meaningful flow patterns in real datasets.

Variants enable tailored analysis for different scenarios.

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.