2017/09/13 by Yan Zheng, Yi Ou, Zheng, Yan +5
Computer Science · Social Sciences · #Computational Geometry (cs.CG) #Data Analysis with R #Data Visualization and Analytics #FOS: Computer and information sciences #Human Mobility and Location-Based Analysis #Human-Computer Interaction (cs.HC)
paper · pdf · doi:10.48550/arxiv.1709.04453
openalex publication_date 2017/09/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01
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.