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A Visual Analytics Framework for Spatiotemporal Trade Network Analysis

Economic globalization is increasing connectedness among regions of the world, creating complex interdependencies within various supply chains. Recent studies have indicated that changes and disruptions within such networks can serve as indicators for increased risks of violence and armed conflicts.

Research By:

  • Hong Wang
  • Yafeng Lu
  • Shade T. Shutters
  • Michael Steptoe
  • Feng Wang
  • Steven Landis
  • Ross Maciejewski

Abstract:

Economic globalization is increasing connectedness among regions of the world, creating complex interdependencies within various supply chains. Recent studies have indicated that changes and disruptions within such networks can serve as indicators for increased risks of violence and armed conflicts. This is especially true of countries that may not be able to compete for scarce commodities during supply shocks. Thus, network-induced vulnerability to supply disruption is typically exported from wealthier populations to disadvantaged populations. As such, researchers and stakeholders concerned with supply chains, political science, environmental studies, etc. need tools to explore the complex dynamics within global trade networks and how the structure of these networks relates to regional instability. However, the multivariate, spatiotemporal nature of the network structure creates a bottleneck in the extraction and analysis of correlations and anomalies for exploratory data analysis and hypothesis generation. Working closely with experts in political science and sustainability, we have developed a highly coordinated, multi-view framework that utilizes anomaly detection, network analytics, and spatiotemporal visualization methods for exploring the relationship between global trade networks and regional instability. Requirements for analysis and initial research questions to be investigated are elicited from domain experts, and a variety of visual encoding techniques for rapid assessment of analysis and correlations between trade goods, network patterns, and time series signatures are explored. We demonstrate the application of our framework through case studies focusing on armed conflicts in Africa, regional instability measures, and their relationship to international global trade.

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Citation:

Wang, Hong, Lu, Yafeng, Shutters, Shade T., Steptoe, Michael, Wang, Feng, Landis, Steven, & Maciejewski, Ross. (2019). A Visual Analytics Framework for Spatiotemporal Trade Network Analysis. IEEE Transactions on Visualization and Computer Graphics25(1). doi:10.1109/TVCG.2018.2864844