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        <identifier>oai:drops-oai.dagstuhl.de:20834</identifier>
        <datestamp>2024-09-09T09:21:19Z</datestamp>
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          <dc:title>The Senators Problem: A Design Space of Node Placement Methods for Geospatial Network Visualization (Short Paper)</dc:title>
          <dc:creator>Mardia, Arnav</dc:creator>
          <dc:creator>Jin, Sichen</dc:creator>
          <dc:creator>Carley, Kathleen M.</dc:creator>
          <dc:creator>Lin, Yu-Ru</dc:creator>
          <dc:creator>Neal, Zachary P.</dc:creator>
          <dc:creator>Park, Patrick</dc:creator>
          <dc:creator>Andris, Clio</dc:creator>
          <dc:subject>Spatial networks</dc:subject>
          <dc:subject>Political networks</dc:subject>
          <dc:subject>Social networks</dc:subject>
          <dc:subject>Geovisualization</dc:subject>
          <dc:subject>Node placement</dc:subject>
          <dc:description>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.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Arnav Mardia and Sichen Jin and Kathleen M. Carley and Yu-Ru Lin and Zachary P. Neal and Patrick Park and Clio Andris</dc:contributor>
          <dc:date>2024</dc:date>
          <dc:relation>Is Part Of LIPIcs, Volume 315, 16th International Conference on Spatial Information Theory (COSIT 2024)</dc:relation>
          <dc:type>InProceedings</dc:type>
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          <dc:identifier>doi:10.4230/LIPIcs.COSIT.2024.19</dc:identifier>
          <dc:identifier>urn:nbn:de:0030-drops-208346</dc:identifier>
          <dc:identifier>https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.COSIT.2024.19</dc:identifier>
          <dc:language>eng</dc:language>
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