Detecting and Visualizing Observation Hot-Spots in Massive Volunteer-Contributed Geographic Data across Spatial Scales Using GPU-Accelerated Kernel Density Estimation

Guiming Zhang

doi:10.3390/ijgi11010055

Abstract

Volunteer-contributed geographic data (VGI) is an important source of geospatial big data that support research and applications. A major concern on VGI data quality is that the underlying observation processes are inherently biased. Detecting observation hot-spots thus 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 computational tool that is supported by high-performance computing and capable of efficiently detecting and visualizing multi-scale hot-spots in geospatial big data and contributes to expanding the toolbox for geospatial big data analytics.

Highlights

Academic Editor: Wolfgang KainzVolunteer-contributed geographic data, often termed ‘volunteered geographic information’ (VGI) [1], have flourished over the past two decades or so due to the vast advancements in geospatial and communication technologies that enable ordinary citizens to collect and share georeferenced observations of the world [2]
Citizen science [6] has existed for centuries, and geographic citizen science [7,8] has been a major source of volunteer-contributed geographic data, even long before the term VGI was coined in 2007 [1]
Empowered by the big data-enabled point pattern analysis tools, this study aims to detect and visualize multi-scale observation hot-spots in massive volunteer-contributed geographic data to the global extent using the kernel density estimation (KDE) method accelerated with graphics processing unit (GPU) parallel computing [50]

Summary

Introduction

Volunteer-contributed geographic data, often termed ‘volunteered geographic information’ (VGI) [1], have flourished over the past two decades or so due to the vast advancements in geospatial and communication technologies (e.g., location-aware smart phones, social media) that enable ordinary citizens to collect and share georeferenced observations of the world [2]. VGI represents a paradigm shift in how geographic data is created and shared and in its content and characteristics [14].

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