Exploropleth

Exploratory Analysis of Data Binning Methods in Choropleth Maps

Arpit Narechania, Alex Endert, Clio Andris

When creating choropleth maps, mapmakers often bin (i.e., group, classify) quantitative data values into groups to help show that certain areas fall within a similar range of values. For instance, a mapmaker may divide counties into groups of high, middle, and low life expectancy (measured in years). It is well known that different binning methods (e.g., natural breaks, quantile) yield different groupings, meaning the same data can be presented differently depending on how it is divided into bins. To help guide a wide variety of users, we present a new, open source, web-based, geospatial visualization tool, Exploropleth, that lets users interact with a catalog of established data binning methods, and subsequently compare, customize, and export custom maps. This tool advances the state of the art by providing multiple binning methods in one view and supporting administrative unit reclassification on-the-fly. We interviewed 16 cartographers and geographic information systems (GIS) experts from 13 government organizations, NGOs, and federal agencies who identified opportunities to integrate Exploropleth into their existing mapmaking workflow, and found that the tool has potential to educate students as well as mapmakers with varying levels of experience. Exploropleth is open-source and publicly available.

    @article{narechania2025exploropleth,
        title={{Exploropleth: Exploratory Analysis of Data Binning Methods in Choropleth Maps}},
        author={Narechania, Arpit and Endert, Alex and Andris, Clio},
        journal={{Cartography and Geographic Information Science}},
        year={2025}
    }
                            

Cartographers in Cubicles

How Training and Preferences of Mapmakers Interplay with Structures and Norms in Not-for-Profit Organizations

Arpit Narechania, Alex Endert, Clio Andris

Choropleth maps are a common and effective way to visualize geographic thematic data. Although cartographers have established many principles about map design, data binning and color usage, less is known about how mapmakers make individual decisions in practice. We interview 16 cartographers and geographic information systems (GIS) experts from 13 government organizations, NGOs, and federal agencies about their choropleth mapmaking decisions and workflows. We categorize our findings and report on how mapmakers follow cartographic guidelines and personal rules of thumb, collaborate with other stakeholders within and outside their organization, and how organizational structures and norms are tied to decision-making during data preparation, data analysis, data binning, map styling, and map post-processing. We find several points of variation as well as regularity across mapmakers and organizations and present takeaways to inform cartographic education and practice, including broader implications and opportunities for CSCW, HCI, and information visualization researchers and practitioners.

    @article{narechania2025cartographersincubicles,
        title = {{Cartographers in Cubicles: How Training and Preferences of Mapmakers Interplay with Structures and Norms in Not-for-Profit Organizations}},
        shorttitle = {{Cartographers in Cubicles}},
        author = {{Narechania}, Arpit and {Endert}, Alex and {Andris}, Clio},
        journal = {ACM CSCW},
        year = {2025},
        publisher = {ACM}
    }
                            

Resiliency

A Consensus Data Binning Method

Arpit Narechania, Alex Endert, Clio Andris

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. Resiliency is available in an open-source JavaScript library, BinGuru, described next.

    @article{narechania2023resiliency,
        title={{Resiliency: A Consensus Data Binning Method}},
        author={Narechania, Arpit and Endert, Alex and Andris, Clio},
        journal={12th International Conference on Geographic Information Science (GIScience)},
        year={{2023}},
        publisher={Leibniz International Proceedings in Informatics},
        url={https://doi.org/10.4230/LIPIcs.GIScience.2023.55}
    }
                            

BinGuru

Open-Source JavaScript Library for Data Binning/Classification

Arpit Narechania, Alex Endert, Clio Andris

BinGuru is a JavaScript package with an API to several established data binning / data classification methods (including Resiliency) that are often used for visualizing data on choropleth maps. Currently supported methods include:

Install the library via pip install binguru

    import { BinGuru } from "binguru";

    let rawData = [1, 45, 65, 23, 65, 87, 54, 45, 31, 21, 12, 12, 98, 56, 76, null, null, "nan", undefined, "", "null"]; // Input array of numbers, strings, nulls, nans, undefineds.
    let binCount = 5; // Desired number of bins (inconsequential for certain binning methods, e.g., boxPlot).
    let binExtent = 10; // Desired bin interval (only applicable for certain binning methods, e.g., definedInterval).
    let precision = 2; // Desired rounding off precision.
    let binGuruObj = new BinGuru(rawData=rawData, binCount=binCount, binExtent=binExtent, precision=precision); // Initialize an instance of BinGuru

    let bins = binGuruObj.fisherJenks(); // Call an endpoint, e.g., fisherJenks() to bin using the FisherJenks / Natural Breaks binning method first.
    console.log(bins);