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Documents authored by Andris, Clio

Document
Short Paper
DOI: 10.4230/LIPIcs.COSIT.2024.19
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, and Clio Andris

Published in: LIPIcs, Volume 315, 16th International Conference on Spatial Information Theory (COSIT 2024)

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.
Cite as

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)

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@InProceedings{mardia_et_al:LIPIcs.COSIT.2024.19,
  author =	{Mardia, Arnav and Jin, Sichen and Carley, Kathleen M. and Lin, Yu-Ru and Neal, Zachary P. and Park, Patrick and Andris, Clio},
  title =	{{The Senators Problem: A Design Space of Node Placement Methods for Geospatial Network Visualization}},
  booktitle =	{16th International Conference on Spatial Information Theory (COSIT 2024)},
  pages =	{19:1--19:9},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-330-0},
  ISSN =	{1868-8969},
  year =	{2024},
  volume =	{315},
  editor =	{Adams, Benjamin and Griffin, Amy L. and Scheider, Simon and McKenzie, Grant},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.COSIT.2024.19},
  URN =		{urn:nbn:de:0030-drops-208346},
  doi =		{10.4230/LIPIcs.COSIT.2024.19},
  annote =	{Keywords: Spatial networks, Political networks, Social networks, Geovisualization, Node placement}
}
Document
Short Paper
DOI: 10.4230/LIPIcs.GIScience.2023.55
Resiliency: A Consensus Data Binning Method (Short Paper)

Authors: Arpit Narechania, Alex Endert, and Clio Andris

Published in: LIPIcs, Volume 277, 12th International Conference on Geographic Information Science (GIScience 2023)

Abstract
Data binning, or data classification, involves grouping quantitative data points into bins (or classes) to represent spatial patterns and show variation in choropleth maps. There are many methods for binning data (e.g., natural breaks, quantile) that may make the same data appear very different on a map. Some of these methods may be more or less appropriate for certain types of data distributions and map purposes. Thus, when designing a map, novice users may be overwhelmed by the number of choices for binning methods and experts may find comparing results from different binning methods challenging. We present resiliency, a new data binning method that assigns areal units to their most agreed-upon, consensus bin as it persists across multiple chosen binning methods. We show how this "smart average" can effectively communicate spatial patterns that are agreed-upon across binning methods. We also measure the variety of bins a single areal unit can be placed in under different binning methods showing fuzziness and uncertainty on a map. We implement resiliency and other binning methods via an open-source JavaScript library, BinGuru.
Cite as

Arpit Narechania, Alex Endert, and Clio Andris. Resiliency: A Consensus Data Binning Method (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 55:1-55:7, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)

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@InProceedings{narechania_et_al:LIPIcs.GIScience.2023.55,
  author =	{Narechania, Arpit and Endert, Alex and Andris, Clio},
  title =	{{Resiliency: A Consensus Data Binning Method}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{55:1--55:7},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-288-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{277},
  editor =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.GIScience.2023.55},
  URN =		{urn:nbn:de:0030-drops-189509},
  doi =		{10.4230/LIPIcs.GIScience.2023.55},
  annote =	{Keywords: data binning, data classification, choropleth maps, geovisualization, geographic information systems, geographic information science, cartography}
}
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