Resiliency: A Consensus Data Binning Method (Short Paper)

Authors Arpit Narechania , Alex Endert , Clio Andris

Thumbnail PDF


  • Filesize: 3.52 MB
  • 7 pages

Document Identifiers

Author Details

Arpit Narechania
  • Georgia Institute of Technology, Atlanta, GA, USA
Alex Endert
  • Georgia Institute of Technology, Atlanta, GA, USA
Clio Andris
  • Georgia Institute of Technology, Atlanta, GA, USA

Cite AsGet BibTex

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)


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.

Subject Classification

ACM Subject Classification
  • Human-centered computing → Geographic visualization
  • data binning
  • data classification
  • choropleth maps
  • geovisualization
  • geographic information systems
  • geographic information science
  • cartography


  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    PDF Downloads


  1. Marc Armstrong, Ningchuan Xiao, and David Bennett. Using Genetic Algorithms to Create Multicriteria Class Intervals for Choropleth Maps. Annals of the Association of American Geographers, 93:595-623, September 2003. Google Scholar
  2. Arnold Bregt and Marco CS Wopereis. Comparison of complexity measures for choropleth maps. The Cartographic Journal, 27(2):85-91, 1990. Google Scholar
  3. Cynthia A. Brewer and Linda Pickle. Evaluation of Methods for Classifying Epidemiological Data on Choropleth Maps in Series. Annals of the Association of American Geographers, 92(4):662-681, 2002. Google Scholar
  4. Michael John De Smith, Michael F Goodchild, and Paul Longley. Geospatial Analysis: A Comprehensive Guide To Principles, Techniques And Software Tools. Troubador Publishing Ltd., 2007. Google Scholar
  5. ESRI. ArcGIS, 2023. URL:
  6. ESRI. ArcGIS Maps SDK, 2023. URL:
  7. Alan M MacEachren. Map complexity: Comparison and measurement. The American Cartographer, 9(1):31-46, 1982. Google Scholar
  8. Ministry of Finance, Government of India. Economic Survey of India, 2023. URL:
  9. Mark Monmonier. Maximum-Difference Barriers: An Alternative Numerical Regionalization Method. Geographical Analysis, 5(3):245-261, 1973. Google Scholar
  10. Mark Monmonier. Flat laxity, optimization, and rounding in the selection of class intervals. Cartographica: The International Journal for Geographic Information and Geovisualization, 19(1):16-27, 1982. Google Scholar
  11. Mark Monmonier. How To Lie With Maps. University of Chicago Press, 2018. Google Scholar
  12. Serge Rey and Luc Anselin. PySAL, 2005. URL:
  13. Robert E Roth, Andrew W Woodruff, and Zachary F Johnson. Value-By-Alpha Maps: An Alternative Technique To The Cartogram. The Cartographic Journal, 47(2):130-140, 2010. Google Scholar
  14. Vidya Setlur, Michael Correll, and Sarah Battersby. Oscar: A semantic-based data binning approach. In 2022 IEEE Visualization and Visual Analytics (VIS), pages 100-104, Los Alamitos, CA, USA, October 2022. IEEE Computer Society. Google Scholar
  15. Gary Sherman. QGIS, 2002. URL:
  16. Terry A Slocum, Robert B McMaster, Fritz C Kessler, and Hugh H Howard. Thematic Cartography And Geovisualization. CRC Press, 2022. Google Scholar
  17. Richard M Smith. Comparing traditional methods for selecting class intervals on choropleth maps. The Professional Geographer, 38(1):62-67, 1986. Google Scholar
  18. tmap. Tmap, 2023. URL:
  19. Waldo R Tobler. Choropleth Maps Without Class Intervals? Geographical Analysis, 1973. Google Scholar