Visualization of Big Spatial Data using Coresets for Kernel Density Estimates

@article{Zheng2017VisualizationOB,
  title={Visualization of Big Spatial Data using Coresets for Kernel Density Estimates},
  author={Yan Zheng and Yi Ou and Alexander Lex and Jeff M. Phillips},
  journal={2017 IEEE Visualization in Data Science (VDS)},
  year={2017},
  pages={23-30}
}
  • Yan Zheng, Yi Ou, +1 author Jeff M. Phillips
  • Published in
    IEEE Visualization in Data…
    2017
  • Computer Science
  • 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… CONTINUE READING

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