Visualization of Big Spatial Data using Coresets for Kernel Density Estimates

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.

1 Introduction

Data is collected at ever-increasing sizes, and for many datasets, each data point has geo-spatial locations (e.g., either (x,y)coordinates, or latitudes and longitudes). Examples include population tracking data, geo-located social media contributions, seismic data, crime data, and weather station data. The availability of such detailed datasets enables analysts to ask more complex and specific questions. These have applications in wide ranging areas including biosurveillance, epidemiology, economics, ecology environmental management, public policy and safety, transportation design and monitoring, geology, and climatology. Truly large datasets, however, cannot be simply plotted, since they typically exceed the number of pixels available for plotting, the available storage space, and/or the available bandwidth necessary to transfer the data.