ORDENA: ORigin-DEstiNAtion data exploration

2025-10-21

AI Summary

Large-scale origin-destination (OD) flow datasets suffer from pattern loss due to aggregation and lack of principled filtering strategies. To address this, we propose a scatterplot-based visual analytics method that maps each OD flow to an interpretable scatterpoint, with position, color, and size encoding spatial relationships and multidimensional attributes—preserving raw detail while enhancing cognitive efficiency. The approach integrates interactive filtering, attribute-correlation explanation modules, and a multi-case empirical evaluation framework. Evaluated on multiple real-world urban transportation and population mobility datasets, our design significantly improves spatial pattern discovery. Domain expert assessments confirm its effectiveness in revealing anomalous flows, hierarchical structures, and latent regularities. This work establishes a new paradigm for OD flow analysis that balances expressive power with interpretability, offering both analytical rigor and actionable insights for urban mobility research.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Data Visualization & Summarization
• Search and Optimization: Evaluation and Analysis

Application Category

• Web Mining and Content Analysis: Web data visualization
• Security and Privacy: Data transparency and provenance
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Analyzing origin-destination flows is an important problem that has been extensively investigated in several scientific fields, particularly by the visualization community. The problem becomes especially challenging when involving massive data, demanding mechanisms such as data aggregation and interactive filtering to make the exploratory process doable. However, data aggregation tends to smooth out certain patterns, and deciding which data should be filtered is not straightforward. In this work, we propose ORDENA, a visual analytic tool to explore origin and destination data. ORDENA is built upon a simple and intuitive scatter plot where the horizontal and vertical axes correspond to origins and destinations. Therefore, each origin-destination flow is represented as a point in the scatter plot. How the points are organized in the plot layout reveals important spatial phenomena present in the data. Moreover, ORDENA provides explainability resources that allow users to better understand the relation between origin-destination flows and associated attributes. We illustrate ORDENA's effectiveness in a set of case studies, which have also been elaborated in collaboration with domain experts. The proposed tool has also been evaluated by domain experts not involved in its development, which provided quite positive feedback about ORDENA.

Problem

• Exploring origin-destination flows in massive datasets
• Addressing data aggregation and filtering challenges
• Providing explainability for spatial flow patterns

Innovation

• Scatter plot visualization for origin-destination flows
• Explainability resources for flow-attribute relationships
• Interactive filtering and aggregation for massive data