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The Senators Problem: A Design Space of Node Placement Methods for Geospatial Network Visualization (Short Paper)

Authors Arnav Mardia , Sichen Jin , Kathleen M. Carley , Yu-Ru Lin , Zachary P. Neal , Patrick Park , Clio Andris

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Author Details

Arnav Mardia
  • Georgia Institute of Technology, Atlanta, GA, USA
Sichen Jin
  • Georgia Institute of Technology, Atlanta, GA, USA
Kathleen M. Carley
  • Carnegie Mellon University, Pittsburgh, PA, USA
Yu-Ru Lin
  • University of Pittsburgh, PA, USA
Zachary P. Neal
  • Michigan State University, East Lansing, MI, USA
Patrick Park
  • Carnegie Mellon University, Pittsburgh, PA, USA
Clio Andris
  • Georgia Institute of Technology, Atlanta, GA, USA

Funding

The following authors acknowledge funding for this project. {Clio Andris and Sichen Jin: National Science Foundation #2045271.}
  • Carley, Kathleen M.: Office of Naval Research (ONR) #N000142112229.
  • Lin, Yu-Ru: AFOSR, ONR, Minerva, NSF #2318461, and Pitt Cyber Institute’s PCAG awards. Any opinions, findings, and conclusions or recommendations expressed in this material do not necessarily reflect the views of the funding sources.

Cite AsGet BibTex

Arnav Mardia, Sichen Jin, Kathleen M. Carley, Yu-Ru Lin, Zachary P. Neal, Patrick Park, and Clio Andris. The Senators Problem: A Design Space of Node Placement Methods for Geospatial Network Visualization (Short Paper). In 16th International Conference on Spatial Information Theory (COSIT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 315, pp. 19:1-19:9, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024)
https://doi.org/10.4230/LIPIcs.COSIT.2024.19

Abstract

Geographic network visualizations often require assigning nodes to geographic coordinates, but this can be challenging when precise node locations are undefined. We explore this problem using U.S. senators as a case study. Each state has two senators, and thus it is difficult to assign clear individual locations. We devise eight different node placement strategies ranging from geometric approaches such as state centroids and longest axis midpoints to data-driven methods using population centers and home office locations. Through expert evaluation, we found that specific coordinates such as senators’ office locations and state centroids are preferred strategies, while random placements and the longest axis method are least favored. The findings also highlight the importance of aligning node placement with research goals and avoiding potentially misleading encodings. This paper contributes to future advancements in geospatial network visualization software development and aims to facilitate more effective exploratory spatial data analysis.

Subject Classification

ACM Subject Classification
  • Human-centered computing → Geographic visualization
  • Human-centered computing → Graph drawings
Keywords
  • Spatial networks
  • Political networks
  • Social networks
  • Geovisualization
  • Node placement

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