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    Visualization of Big Spatial Data using Coresets for Kernel Density Estimates (English)

    In: 2017 IEEE Visualization in Data Science (VDS) ; 23-30  ; 2017

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    The size of large, geo-located datasets has reached scales where visualization of all data points is inefficient. Random sampling is a method to reduce the size of a dataset, yet it can introduce unwanted errors. We describe a method for subsampling of spatial data suitable for creating kernel density estimates from very large data and demonstrate that it results in less error than random sampling. We also introduce a method to ensure that thresholding of low values based on sampled data does not omit any regions above the desired threshold when working with sampled data. We demonstrate the effectiveness of our approach using both, artificial and real-world large geospatial datasets.

    Table of contents conference proceedings

    The tables of contents are generated automatically and are based on the data records of the individual contributions available in the index of the TIB portal. The display of the Tables of Contents may therefore be incomplete.

    1
    Visual Analysis of Spatio-Temporal Event Predictions: Investigating the Spread Dynamics of Invasive Species
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    Visual Integration of Data and Model Space in Ensemble Learning
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    23
    Visualization of Big Spatial Data using Coresets for Kernel Density Estimates
    Zheng, Yan / Ou, Yi / Lex, Alexander / Phillips, Jeff M. et al. | 2017
    31
    Visual Progression Analysis of Student Records Data
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    Crop Planning using Stochastic Visual Optimization
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    52
    Visualizing Sensor Network Coverage with Location Uncertainty
    Sodergren, Tim / Hair, Jessica / Phillips, Jeff M. / Wang, Bei et al. | 2017
    i
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    Table of contents
    | 2017

    How to get this title?

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    Commercial Copyright fee: €30.54 Basic fee: €4.00 Total price: €34.54
    Academic Copyright fee: €30.54 Basic fee: €2.00 Total price: €32.54

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