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

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

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 b...

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Bibliographic Details
Main Author: Guiming Zhang
Format: Article
Language:English
Published: MDPI AG 2022-01-01
Series:ISPRS International Journal of Geo-Information
Subjects:
volunteered geographic information (VGI)
geospatial big data
point pattern analysis
kernel density estimation
hot-spot detection and visualization
spatial bias
Online Access:https://www.mdpi.com/2220-9964/11/1/55

Internet

https://www.mdpi.com/2220-9964/11/1/55

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