Detecting and visualizing observation hot-spots in massive VGI datasets across spatial scales

Keywords: Volunteered geographic information (VGI), geospatial big data, point pattern analysis, kernel density estimation, iNaturalist, geocomputation
Abstract Type: Paper Abstract

Abstract

Volunteered geographic information (VGI) is an important source of geospatial big data that support a variety of research and applications. A major concern on VGI data quality is that the underlying observation processes are inherently biased. Detecting observation hot-spots helps better understand the bias. Enabled by the parallel kernel density estimation (KDE) computational tool that can run on multiple GPUs (graphics processing units), this study conducted point pattern analyses on tens of millions of iNaturalist observations to detect and visualize volunteers’ observation hot-spots across spatial scales. It was achieved by setting varying KDE bandwidths in accordance with the spatial scales at which hot-spots are to be detected. The succession of estimated density surfaces were then rendered at a sequence of map scales for visual detection of hot-spots. This study offers an effective geovisualization scheme for hierarchically detecting hot-spots in massive VGI datasets, which is useful for understanding the pattern-shaping drivers that operate at multiple spatial scales. This research exemplifies a geocomputation tool that contributes to expanding the toolbox for geospatial big data analytics.