G
Guiming Zhang
Researcher at University of Denver
Publications - 26
Citations - 366
Guiming Zhang is an academic researcher from University of Denver. The author has contributed to research in topics: Volunteered geographic information & Digital soil mapping. The author has an hindex of 10, co-authored 20 publications receiving 243 citations. Previous affiliations of Guiming Zhang include Beijing Normal University & University of Wisconsin-Madison.
Papers
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Journal ArticleDOI
The representativeness and spatial bias of volunteered geographic information: a review
Guiming Zhang,A-Xing Zhu +1 more
TL;DR: A comprehensive survey of the scientific literature from various domains is offered to summarize existing endeavors related to sample representativeness assessment and sample selection bias correction for enlightening the treatment of these issues in VGI applications.
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A cloud-enabled automatic disaster analysis system of multi-sourced data streams: An example synthesizing social media, remote sensing and Wikipedia data
TL;DR: A framework that synthesizes multi-sourced data (e.g., social media, remote sensing, Wikipedia, and Web), spatial data mining and text mining technologies to build an architecturally resilient and elastic solution to support disaster analysis of historical and future events is presented.
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A citizen data-based approach to predictive mapping of spatial variation of natural phenomena
A-Xing Zhu,Guiming Zhang,Wei Wang,Wen Xiao,Zhi-Pang Huang,Ge-Sang Dunzhu,Guopeng Ren,Cheng-Zhi Qin,Lin Yang,Tao Pei,Shengtian Yang +10 more
TL;DR: This approach reduces location imprecision by adopting a frequency-sampling strategy to identify representative presence locations from areas over which citizens observed the geographic phenomenon and compensates for the spatial bias by weighting presence locations with cumulative visibility.
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A GPU-accelerated adaptive kernel density estimation approach for efficient point pattern analysis on spatial big data
TL;DR: This article presents a graphics processing units (GPUs) -accelerated adaptive KDE algorithm for efficient spatial point pattern analysis on spatial big data that contributes to the geospatial computational toolbox that facilitates geographic knowledge discovery from spatialbig data.
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Modelling species habitat suitability from presence-only data using kernel density estimation
TL;DR: In this paper, the authors present an approach for modeling and mapping habitat suitability from species presence-only data that is useful for ecosystem and species monitoring, which is based on the ratio of species presence probability over environmental factors to background probability of environmental factors.