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    <titleInfo>
        <title>ContourDiff: Revealing Differential Trends in Spatiotemporal Data</title>
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    <name type="personal">
        <namePart type="given">Zonayed</namePart>
        <namePart type="family">Ahmed</namePart>
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        <namePart type="given">Michael</namePart>
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    <name type="personal">
        <namePart type="given">Debajyoti</namePart>
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    <name type="personal">
        <namePart type="given">Chanchal</namePart>
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        <namePart type="given">Christopher</namePart>
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        <namePart type="given">Kevin</namePart>
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    <abstract>Changes in spatiotemporal data may often go unnoticed due to their inherent noise and low variability (e.g., geological processes over years). Commonly used approaches such as side-by-side contour plots and spaghetti plots do not provide a clear idea about the temporal changes in such data. We propose ContourDiff, a vector-based visualization over contour plots to visualize the trends of change across spatial regions and temporal domain. Our approach first aggregates for each location, its value differences from the neighboring points over the temporal domain, and then creates a vector field representing the prominent changes. Finally, it overlays the vectors along the contour paths, revealing differential trends that the contour lines experienced over time. We evaluated our visualization using real-life datasets, consisting of millions of data points, where the visualizations were generated in less than a minute in a single-threaded execution. Our experimental results reveal that ContourDiff can reliably visualize the differential trends, and provide a new way to explore the change pattern in spatiotemporal data.</abstract>
    <identifier type="citekey">Ahmed-2021-ContourDiff:</identifier>
    <identifier type="doi">10.1109/iv53921.2021.00016</identifier>
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        <url>https://gwf-uwaterloo.github.io/gwf-publications/G21-12001</url>
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        <date>2021</date>
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%0 Journal Article
%T ContourDiff: Revealing Differential Trends in Spatiotemporal Data
%A Ahmed, Zonayed
%A Beyene, Michael
%A Mondal, Debajyoti
%A Roy, Chanchal K.
%A Dutchyn, Christopher
%A Schneider, Kevin A.
%J 2021 25th International Conference Information Visualisation (IV)
%D 2021
%I IEEE
%F Ahmed-2021-ContourDiff:
%X Changes in spatiotemporal data may often go unnoticed due to their inherent noise and low variability (e.g., geological processes over years). Commonly used approaches such as side-by-side contour plots and spaghetti plots do not provide a clear idea about the temporal changes in such data. We propose ContourDiff, a vector-based visualization over contour plots to visualize the trends of change across spatial regions and temporal domain. Our approach first aggregates for each location, its value differences from the neighboring points over the temporal domain, and then creates a vector field representing the prominent changes. Finally, it overlays the vectors along the contour paths, revealing differential trends that the contour lines experienced over time. We evaluated our visualization using real-life datasets, consisting of millions of data points, where the visualizations were generated in less than a minute in a single-threaded execution. Our experimental results reveal that ContourDiff can reliably visualize the differential trends, and provide a new way to explore the change pattern in spatiotemporal data.
%R 10.1109/iv53921.2021.00016
%U https://gwf-uwaterloo.github.io/gwf-publications/G21-12001
%U https://doi.org/10.1109/iv53921.2021.00016

Markdown (Informal)

[ContourDiff: Revealing Differential Trends in Spatiotemporal Data](https://gwf-uwaterloo.github.io/gwf-publications/G21-12001) (Ahmed et al., GWF 2021)

• ContourDiff: Revealing Differential Trends in Spatiotemporal Data (Ahmed et al., GWF 2021)

ACL

• Zonayed Ahmed, Michael Beyene, Debajyoti Mondal, Chanchal K. Roy, Christopher Dutchyn, Kevin A. Schneider, Zonayed Ahmed, Michael Beyene, Debajyoti Mondal, Chanchal K. Roy, Christopher Dutchyn, and Kevin A. Schneider. 2021. ContourDiff: Revealing Differential Trends in Spatiotemporal Data. 2021 25th International Conference Information Visualisation (IV).