I am particularly interested in volunteered geographic information (VGI) and other types of geospatial big data, and their applications in environmental modeling and mapping. I am also interested in geocomputation as an enabler of such endeavors. My research at these fronts has led to quality publications in top GIScience journals including the International Journal of Geographical Information Science and Transactions in GIS. I was also invited to author the topic entry "Volunteered Geographic Information" in The Geographic Information Science & Technology Body of Knowledge compiled by The University Consortium for Geographic Information Science.
Volunteered Geographic Information
Data quality of VGI (and other geospatia big data) is under constant scrutiny as it is a fundamental issue to address when using such kinds of data in geographic research. My research specifically contributes to developing novel methodologies for tackling spatial sampling/observation bias in geospatial big data (one of the prominent data quality issues) to improve the quality of inferences made from them, with practical applications in environmental modeling and mapping (e.g., species distribution modeling and digital soil mapping).
[
GVA/
VGI 31]
Zhang G. (2024). A web-based geovisualization framework for exploratory analysis of individual VGI contributor’s participation characteristics.
Cartography and Geographic Information Science,
accepted.
[Web]
[PDF]
[Demo]
[Code]
[
VGI 30] Huang X, Wang S, Yang D, Hu T, Chen M, Zhang M,
Zhang G, Biljecki F, Lu T, Zou L, Wu C Y, Park Y M, Li X, Liu Y, Fan H, Mitchell J, Li Z and Hohl A. (2024). Crowdsourcing geospatial data for Earth and human observations: a review.
Journal of Remote Sensing,
Accepted.
[Web]
[PDF]
[
VGI/
GVA 29]
Zhang G, Gong X and Zhu D. (2024). Geographic proximity and homophily effects drive social interactions within VGI communities: an example of iNaturalist.
International Journal of Digital Earth,
17(1): 2297948.
[Web]
[PDF]
[
VGI/
GVA 28] Kottwitz M
#,
Zhang G* and Xu J. (2023). The time- and distance-decay effects of hurricane relevancy on social media: an empirical study of three hurricanes in the United States.
Annals of GIS,
29(4): 469-484.
[Web]
[PDF]
[
VGI/
GC/
GVA 26]
Zhang G and Xu J. (2023). Multi-GPU-parallel and tile-based kernel density estimation for large-scale spatial point pattern analysis.
ISPRS International Journal of Geo-Information,
12(2): 31.
[Web]
[PDF]
[Code]
[
VGI/
EM 25]
Zhang G. (2022). Mitigating spatial bias in volunteered geographic information for spatial modeling and prediction." in: Li, B., Shi, X., Zhu, A.X., Wang, C., and Lin, H. (Eds.):
New Thinking in GIScience. Springer Nature, Singapore.
[Web]
[PDF]
[
VGI/
GVA/
GC 23]
Zhang G. (2022). Detecting and visualizing observation hot-spots in massive volunteer-contributed geographic data across spatial scales using GPU-accelerated kernel density estimation.
ISPRS International Journal of Geo-Information,
11(1): 55.
[Web]
[PDF]
[Code]
[
VGI 21]
Zhang G. (2021). Volunteered Geographic Information.
The Geographic Information Science & Technology Body of Knowledge (1st Quarter 2021 Edition), John P. Wilson (Ed.). doi: 10.22224/gistbok/2021.1.1.
[Web]
[
VGI/
GVA 20]
Zhang G. (2020). Spatial and temporal patterns in volunteer data contribution activities: A case study of eBird.
ISPRS International Journal of Geo-Information,
9(10): 597.
[Web]
[PDF]
[
VGI 19]
Zhang G, Zhu A. (2020). Sample size and spatial configuration of volunteered geographic information affect effectiveness of spatial bias mitigation.
Transactions in GIS,
24(5): 1315–1340.
[Web]
[PDF]
[
VGI/
EM 15]
Zhang G, Zhu A. (2019). A representativeness directed approach to spatial bias mitigation in VGI for predictive mapping.
International Journal of Geographical Information Science,
33(9): 1873–1893.
[Web]
[PDF]
[
VGI 17]
Zhang G. (2019). Enhancing VGI application semantics by accounting for spatial bias.
Big Earth Data,
3(3): 255-268.
[Web]
[PDF]
[
VGI/
EM 14]
Zhang G. (2019). Integrating citizen science and GIS for wildlife population monitoring and habitat assessment." in: Ferretti, M. (Eds.):
Wildlife Population Monitoring. IntechOpen Limited, London, UK.
[Web]
[PDF]
[
VGI 13]
Zhang G, Zhu A. (2018). The representativeness and spatial bias of volunteered geographic information: a review.
Annals of GIS,
24(3): 151–162.
[Web]
[PDF]
[
VGI 10]
Zhang G, Zhu A, Huang Z, Ren G, Qin C, Xiao W. (2018). Validity of historical volunteered geographic information: Evaluating citizen data for mapping historical geographic phenomena.
Transactions in GIS,
22(1): 149–164.
[Web]
[PDF]
[
VGI/
GC 8] Huang Q, Cervone G,
Zhang G. (2017). A cloud-enabled automatic disaster analysis system of multi-sourced data streams: An example synthesizing social media, remote sensing and Wikipedia data.
Computers, Environment and Urban Systems,
66: 23-37.
[Web]
[PDF]
[
VGI/
EM 2] Zhu A,
Zhang G*, Wang W, Xiao W, Huang Z, Dunzhu G, Ren G, Qin C, Yang L, Pei T, Yang S. (2015). A citizen data-based approach to predictive mapping of spatial variation of natural phenomena.
International Journal of Geographical Information Science,
29(10): 1864–1886.
[Web]
[PDF]
Geovisualization and Geovisual Analytics
Bias mitigation for VGI (and other geospatia big data) should be grounded in a sound understanding of the processes through which the data is generated. For instance, biases in VGI largely root from VGI contributors’ observation efforts. My research also employs geovisualization and geovisual analytics to examine the patterns (and drivers) of VGI contributors’ data contribution activities (including inter-contributor social interactions). Such endeavors help gain a deeper understanding of VGI data and its quality, which informs bias mitigation and proper use of VGI data.
[
GVA/
VGI 31]
Zhang G. (2024). A web-based geovisualization framework for exploratory analysis of individual VGI contributor’s participation characteristics.
Cartography and Geographic Information Science,
accepted.
[Web]
[PDF]
[Demo]
[Code]
[
VGI/
GVA 29]
Zhang G, Gong X and Zhu D. (2024). Geographic proximity and homophily effects drive social interactions within VGI communities: an example of iNaturalist.
International Journal of Digital Earth,
17(1): 2297948.
[Web]
[PDF]
[
VGI/
GVA 28] Kottwitz M
#,
Zhang G* and Xu J. (2023). The time- and distance-decay effects of hurricane relevancy on social media: an empirical study of three hurricanes in the United States.
Annals of GIS,
29(4): 469-484.
[Web]
[PDF]
[
VGI/
GC/
GVA 26]
Zhang G and Xu J. (2023). Multi-GPU-parallel and tile-based kernel density estimation for large-scale spatial point pattern analysis.
ISPRS International Journal of Geo-Information,
12(2): 31.
[Web]
[PDF]
[Code]
[
VGI/
GVA/
GC 23]
Zhang G. (2022). Detecting and visualizing observation hot-spots in massive volunteer-contributed geographic data across spatial scales using GPU-accelerated kernel density estimation.
ISPRS International Journal of Geo-Information,
11(1): 55.
[Web]
[PDF]
[Code]
[
VGI/
GVA 20]
Zhang G. (2020). Spatial and temporal patterns in volunteer data contribution activities: A case study of eBird.
ISPRS International Journal of Geo-Information,
9(10): 597.
[Web]
[PDF]
Environmental Modeling
My research develops new methods and computational tools for enviornmental modeling (e.g., species distribution modeling and digital soil mapping). The developed methods and tools are capable of accounting for spatial sampling/observation bias and integrating multi-source data and can exploit heterogeneous computing resources for parallel computing to accelerate modeling involving geospatial big data.
[
EM 27] Luo W. and
Zhang G. (2023). Advances and applications of geospatial modeling and analysis in digital twins.
Frontiers in Earth Science,
11: 1226466.
[Web]
[PDF]
[
VGI/
EM 25]
Zhang G. (2022). Mitigating spatial bias in volunteered geographic information for spatial modeling and prediction." in: Li, B., Shi, X., Zhu, A.X., Wang, C., and Lin, H. (Eds.):
New Thinking in GIScience. Springer Nature, Singapore.
[Web]
[PDF]
[
GC/
EM 24]
Zhang G. (2022). PyCLKDE: A big data-enabled high-performance computational framework for species habitat suitability modeling and mapping.
Transactions in GIS, 26(4): 1754-1774.
[Web]
[PDF]
[Code]
[
GC/
EM 22]
Zhang G, Zhu, A, Liu J, Guo S, Zhu Y. (2021). PyCLiPSM: Harnessing heterogeneous computing resources on CPUs and GPUs for accelerated digital soil mapping.
Transactions in GIS,
25(3): 1396-1418.
[Web]
[PDF]
[Code]
[
EM 18]
Zhang G, Zhu A, He Y, Huang Z, Ren G, Xiao W. (2020). Integrating multi-source data for wildlife habitat mapping: A case study of the black-and-white snub-nosed monkey (Rhinopithecus bieti) in Yunnan, China.
Ecological Indicators,
118: 106735.
[Web]
[PDF]
[
EM 16]
Zhang G, Zhu A. (2019). A representativeness heuristic for mitigating spatial bias in existing soil samples for digital soil mapping.
Geoderma,
351: 130–143.
[Web]
[PDF]
[
VGI/
EM 15]
Zhang G, Zhu A. (2019). A representativeness directed approach to spatial bias mitigation in VGI for predictive mapping.
International Journal of Geographical Information Science,
33(9): 1873–1893.
[Web]
[PDF]
[
VGI/
EM 14]
Zhang G. (2019). Integrating citizen science and GIS for wildlife population monitoring and habitat assessment." in: Ferretti, M. (Eds.):
Wildlife Population Monitoring. IntechOpen Limited, London, UK.
[Web]
[PDF]
[
EM 12]
Zhang G, Zhu A, Windels S, Qin C. (2018). Modelling species habitat suitability from presence-only data using kernel density estimation.
Ecological Indicators,
93: 387-396.
[Web]
[PDF]
[
EM 11]
Zhang G, Zhu A, Huang Z, Xiao W. (2018). A heuristic-basedapproach to mitigating positional errors in patrol data for species distribution modeling.
Transactions in GIS,
22(1): 202-216.
[Web]
[PDF]
[
GC/
EM 5] Jiang J, Zhu A, Qin C, Zhu T, Liu J, Du F, Liu J,
Zhang G, An Y. (2016). CyberSoLIM: A cyber platform for digital soil mapping.
Geoderma,
263: 234-243.
[Web]
[PDF]
[
EM 4] Guo S, Meng L, Zhu A, Burt J, Du F, Liu J,
Zhang G. (2015).Unification of soil feedback patterns under different evaporation conditions to improve soil differentiation over flat area.
International Journal of Applied Earth Observation and Geoinformation,
49: 126-137.
[Web]
[PDF]
[
EM 3] Guo S, Meng L, Zhu A, Burt J, Du F, Liu J,
Zhang G. (2015). Data-gap filling to understand the dynamic feedback pattern of soil..
Remote Sensing,
7: 11801–11820.
[Web]
[PDF]
[
EM 1]
张桂铭, 朱阿兴, 杨胜天, 秦承志, 肖文, Steve K. Windels. (2013). 基于核密度估计的动物生境适宜度制图方法.
生态学报,
33(23): 7590-7600.
Zhang G, Zhu A, Yang S, Qin C, Xiao W, Windels S. (2013). Mapping wildlife habitat suitability using kernel density estimation.
Acta Ecologica Sinica,
33(23): 7590-7600.
[Web]
[PDF]
Geo-computation
There is an increasing need to address computational challenges associated with geospatial big data analytics in order to keep pace with the ever-faster-growing big data volume and analytical complexity. Traditional spatial analysis tools often are unable to handle big geospatial data efficiently, and therefore computational challenges occur when applying these methods on geospatial big data. My research with this regard develops algorithmic optimizations for spatial analysis methods and utilizes cutting-edge computing technologies such as cloud computing and GPU (graphics processing units) computing to accelerate the algorithms to support geospatial big data analytics (i.e., spatial point pattern analysis of massive VGI data).
[
VGI/
GC/
GVA 26]
Zhang G and Xu J. (2023). Multi-GPU-parallel and tile-based kernel density estimation for large-scale spatial point pattern analysis.
ISPRS International Journal of Geo-Information,
12(2): 31.
[Web]
[PDF]
[Code]
[
GC/
EM 24]
Zhang G. (2022). PyCLKDE: A big data-enabled high-performance computational framework for species habitat suitability modeling and mapping.
Transactions in GIS, 26(4): 1754-1774.
[Web]
[PDF]
[Code]
[
VGI/
GVA/
GC 23]
Zhang G. (2022). Detecting and visualizing observation hot-spots in massive volunteer-contributed geographic data across spatial scales using GPU-accelerated kernel density estimation.
ISPRS International Journal of Geo-Information,
11(1): 55.
[Web]
[PDF]
[Code]
[
GC/
EM 22]
Zhang G, Zhu, A, Liu J, Guo S, Zhu Y. (2021). PyCLiPSM: Harnessing heterogeneous computing resources on CPUs and GPUs for accelerated digital soil mapping.
Transactions in GIS,
25(3): 1396-1418.
[Web]
[PDF]
[Code]
[
VGI/
GC 8] Huang Q, Cervone G,
Zhang G. (2017). A cloud-enabled automatic disaster analysis system of multi-sourced data streams: An example synthesizing social media, remote sensing and Wikipedia data.
Computers, Environment and Urban Systems,
66: 23-37.
[Web]
[PDF]
[
GC 7]
Zhang G, Zhu A, Huang Q. (2017). A GPU-accelerated adaptive kernel density estimation approach for efficient point pattern analysis on spatial big data.
International Journal of Geographical Information Science,
31(10): 2068-2097.
[Web]
[PDF]
[Code]
[
GC 6]
Zhang G, Huang Q, Zhu A, Keel J. (2016). Enabling point pattern analysis on spatial big data using cloud computing: Optimizing and accelerating Ripley’s K function.
International Journal of Geographical Information Science,
30(11): 2230–2252.
[Web]
[PDF]
[Code]
[
GC/
EM 5] Jiang J, Zhu A, Qin C, Zhu T, Liu J, Du F, Liu J,
Zhang G, An Y. (2016). CyberSoLIM: A cyber platform for digital soil mapping.
Geoderma,
263: 234-243.
[Web]
[PDF]