.png){kind=icon}
返回
Visualization of Big Spatial Data Using Coresets for Kernel Density Estimates
.png){kind=icon}
OA
AI
DOI：10.1109/TBDATA.2019.2913655.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
Data visualization
Kernel
Spatial databases
Visualization
Big Data
Geospatial analysis
Sociology
Spatial data visualization
sampling
big data
coresets
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
I
IF:
5.7
论文数:
887
被引数:
3.0K
机构
引用论文
.png){kind=icon}
Genetic Characterization of DNA Region Containing the
trh
and
ure
Genes of
Vibrio parahaemolyticusOAAI
OAAI分享收藏分享收藏PECVD-grown carbon nanotubes on silicon substrates with a nickel-seeded tip-growth structure具有镍种子尖端生长结构的硅衬底上的PECVD生长的碳纳米管
PREAI
分享收藏
查看更多内容