Guiming Zhang

5050 E. Iliff Avenue, Boettcher West #240 · Denver, CO 80208 · (303) 871-7908 · guiming.zhang@du.edu

I am an Associate Professor of GIScience in the Department of Geography & the Environment at the University of Denver. I develop methods and tools for understanding geospatial big data, with applications in social sensing and environmental modeling.

My research connects volunteered geographic information, GeoAI, geovisual analytics, and high-performance geocomputation to study people, places, and environmental processes.

Research at a glance

VGI & Social Sensing

I study how volunteered and human-generated geographic data reveal population behavior, and how spatial biases in those data can be understood and mitigated.

GeoAI & Environmental Modeling

I build transferable machine-learning and ensemble approaches for urban climate, species distribution, habitat suitability, and digital soil mapping.

Geovisual Analytics & Geocomputation

I create interactive visual tools and GPU-accelerated methods that make large, complex spatial processes explorable and computationally tractable.

Recent work

View all publications →
  • Gong X, Liu L, Lu Y, Zhang G, Huang X, and Lin Y. (2026). Sleepless in America: A social sensing study of pandemic-era sleeplessness using nighttime social media data. PLOS One. [Web] [PDF]
  • Mitra B and Zhang G. (2026). Downscaling urban land surface temperature using AlphaEarth satellite GeoAI embeddings: A cross-city evaluation in the US. Urban Climate. [Web] [PDF]
  • Zhang G. (2026). Demystifying Geographic "Laws" for Soil Mapping via Interactive Geovisualization. ISPRS International Journal of Geo-Information. [Web] [PDF]

Appointments

University of Denver

Department of Geography & the Environment
Associate Professor | Assistant Professor

University of Wisconsin-Madison

Department of Geography
Lecturer | Graduate Teaching Assistant

Education

University of Wisconsin-Madison

Ph.D. Geography

University of Wisconsin-Madison

M.S. Computer Sciences

Beijing Normal University

M.S. Geographic Information Science

Beijing Normal University

B.S. Geographic Information Systems

Research Areas

I am particularly interested in volunteered geographic information (VGI) and other forms of geospatial big data, geospatial artificial intelligence (GeoAI), and their applications to social sensing, as well as to 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, and to contribute a chapter on VGI and crowdsourcing to The Geoinformatics Frontier: AI, Big Data, and Crowdsourced Technologies.

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).

[SS/VGI 39]   Gong X, Liu L, Lu Y, Zhang G, Huang X and Lin Y. (2026). Sleepless in America: A social sensing study of pandemic-era sleeplessness using nighttime social media data. PLOS One, 21(9): e0356547. [Web] [PDF]
[SS/VGI 38]   Zhang G. (2026). Volunteered geographic information and crowdsourcing in geographic practice: A unifying conceptual framework. In: Kalogeropoulos, K., Tsatsaris, A., Antoniou, V., and Huang, X. (Eds.): The Geoinformatics Frontier: AI, Big Data, and Crowdsourced Technologies. Elsevier, pp. 423-434. [Web] [PDF]
[SS/VGI 35]   Xu J# and Zhang G. (2026). Changes in Individual OpenStreetMap contributors' contribution behavior under COVID-19: A case study in New York City. ISPRS International Journal of Geo-Information, 15(3): 121. [Web] [PDF]
[SS/VGI 33]   Lu Y, Gong X, Zhang G, Brown C P, Lin Y C and Lin Y. (2026). Exploring contemporary public perceptions of historical redlining practices in the United States. Computational Urban Science, 6(1). [Web] [PDF]
[SS/VGI/GVA 32]   Zhang G, Luo W, Wu M and Ye L. (2025). Exploring social interaction patterns and drivers in VGI communities using a custom geovisual analytics tool. Annals of GIS, 31(3): 413-431. [Web] [PDF] [Demo] [Code]
[SS/VGI/GVA 31]   Zhang G. (2025). A web-based geovisualization framework for exploratory analysis of individual VGI contributor's participation characteristics. Cartography and Geographic Information Science, 52(2): 199-219. [Web] [PDF] [Demo] [Code]
[SS/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, 4: 0105. [Web] [PDF]
[SS/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]
[SS/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, pp. 179-190.  [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]
[SS/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. (Ed.): Wildlife Population Monitoring. IntechOpen Limited, London, UK, pp. 1-14.  [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]
[SS/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]

Social Sensing

My social sensing research uses geotagged social media, VGI, and other human-generated digital traces to observe population-level behaviors, perceptions, interactions, and responses to societal and environmental events. This work develops scalable spatial and temporal approaches for understanding social dynamics and supporting public health, disaster response, and urban research.

[SS/VGI 39]   Gong X, Liu L, Lu Y, Zhang G, Huang X and Lin Y. (2026). Sleepless in America: A social sensing study of pandemic-era sleeplessness using nighttime social media data. PLOS One, 21(9): e0356547. [Web] [PDF]
[SS/VGI 38]   Zhang G. (2026). Volunteered geographic information and crowdsourcing in geographic practice: A unifying conceptual framework. In: Kalogeropoulos, K., Tsatsaris, A., Antoniou, V., and Huang, X. (Eds.): The Geoinformatics Frontier: AI, Big Data, and Crowdsourced Technologies. Elsevier, pp. 423-434. [Web] [PDF]
[SS/VGI 35]   Xu J# and Zhang G. (2026). Changes in Individual OpenStreetMap contributors' contribution behavior under COVID-19: A case study in New York City. ISPRS International Journal of Geo-Information, 15(3): 121. [Web] [PDF]
[SS/VGI 33]   Lu Y, Gong X, Zhang G, Brown C P, Lin Y C and Lin Y. (2026). Exploring contemporary public perceptions of historical redlining practices in the United States. Computational Urban Science, 6(1). [Web] [PDF]
[SS/VGI/GVA 32]   Zhang G, Luo W, Wu M and Ye L. (2025). Exploring social interaction patterns and drivers in VGI communities using a custom geovisual analytics tool. Annals of GIS, 31(3): 413-431. [Web] [PDF] [Demo] [Code]
[SS/VGI/GVA 31]   Zhang G. (2025). A web-based geovisualization framework for exploratory analysis of individual VGI contributor's participation characteristics. Cartography and Geographic Information Science, 52(2): 199-219. [Web] [PDF] [Demo] [Code]
[SS/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, 4: 0105. [Web] [PDF]
[SS/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]
[SS/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]
[SS/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]
[SS/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]

Geovisualization and Geovisual Analytics

Bias mitigation for VGI (and other geospatial big data) should be grounded in a sound understanding of the processes through which the data is generated. For instance, biases in VGI largely stem from VGI contributors’ observation efforts. My research employs geovisualization and geovisual analytics to examine the patterns and drivers of VGI contributors’ data contribution activities, including inter-contributor social interactions. Such endeavors provide a deeper understanding of VGI data and its quality, which informs bias mitigation and proper use of VGI data. My GVA work also helps make sense of how environmental modeling methods work by making their underlying geographic principles, model mechanics, and parameter effects visually inspectable.

[GVA/EM 36]   Zhang G. (2026). Demystifying Geographic "Laws" for Soil Mapping via Interactive Geovisualization. ISPRS International Journal of Geo-Information, 15(5): 212. [Web] [PDF] [Demo]
[SS/VGI/GVA 32]   Zhang G, Luo W, Wu M and Ye L. (2025). Exploring social interaction patterns and drivers in VGI communities using a custom geovisual analytics tool. Annals of GIS, 31(3): 413-431. [Web] [PDF] [Demo] [Code]
[SS/VGI/GVA 31]   Zhang G. (2025). A web-based geovisualization framework for exploratory analysis of individual VGI contributor's participation characteristics. Cartography and Geographic Information Science, 52(2): 199-219. [Web] [PDF] [Demo] [Code]
[SS/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]
[SS/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]
[SS/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]
[GVA 9]   Roth R, Young S, Nestel C, Sack C, Davidson B, Janicki J, Knoppe-Wetzel V, Ma F, Mead R, Rose C, Zhang G. (2018). Global landscapes: Teaching globalization through responsive mobile map design. The Professional Geographer, 70(3): 395-411. [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.

[GeoAI/EM 37]   Mitra B# and Zhang G. (2026). Downscaling urban land surface temperature using AlphaEarth satellite GeoAI embeddings: A cross-city evaluation in the US. Urban Climate, 103058. [Web] [PDF]
[GVA/EM 36]   Zhang G. (2026). Demystifying Geographic "Laws" for Soil Mapping via Interactive Geovisualization. ISPRS International Journal of Geo-Information, 15(5): 212. [Web] [PDF] [Demo]
[GeoAI/EM 34]   Mitra B# and Zhang G. (2026). GeoAI-Enabled Ensemble Modeling to Assess Land Use and Atmospheric Pollutant Impacts on Land Surface Temperature in the US Southwest. Remote Sensing, 18(5): 746. [Web] [PDF]
[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, pp. 179-190.  [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. (Ed.): Wildlife Population Monitoring. IntechOpen Limited, London, UK, pp. 1-14.  [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] 

Geospatial Artificial Intelligence

My GeoAI research applies machine learning, ensemble modeling, and geospatial foundation-model embeddings to environmental and urban-climate problems. I am particularly interested in developing transferable methods that integrate satellite observations and other geospatial data for fine-scale prediction and cross-city analysis.

[GeoAI/EM 37]   Mitra B# and Zhang G. (2026). Downscaling urban land surface temperature using AlphaEarth satellite GeoAI embeddings: A cross-city evaluation in the US. Urban Climate, 103058. [Web] [PDF]
[GeoAI/EM 34]   Mitra B# and Zhang G. (2026). GeoAI-Enabled Ensemble Modeling to Assess Land Use and Atmospheric Pollutant Impacts on Land Surface Temperature in the US Southwest. Remote Sensing, 18(5): 746. [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]
[SS/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]

Publications

Published: 39 total; 25 first-author; h-index 17; i10-index 25. Google Scholar metrics synchronized September 15, 2026.

[VGI] - Volunteered Geographic Information
[SS] - Social Sensing
[GVA] - Geovisualization and Geovisual Analytics
[EM] - Environmental Modeling
[GeoAI] - Geospatial Artificial Intelligence
[GC] - GeoCompuation

Refereed Journal Articles

* Corresponding Author  # Student Author

[SS/VGI 39]   Gong X, Liu L, Lu Y, Zhang G, Huang X and Lin Y. (2026). Sleepless in America: A social sensing study of pandemic-era sleeplessness using nighttime social media data. PLOS One, 21(9): e0356547. [Web] [PDF]
[GeoAI/EM 37]   Mitra B# and Zhang G. (2026). Downscaling urban land surface temperature using AlphaEarth satellite GeoAI embeddings: A cross-city evaluation in the US. Urban Climate, 103058. [Web] [PDF]
[GVA/EM 36]   Zhang G. (2026). Demystifying Geographic "Laws" for Soil Mapping via Interactive Geovisualization. ISPRS International Journal of Geo-Information, 15(5): 212. [Web] [PDF] [Demo]
[SS/VGI 35]   Xu J# and Zhang G. (2026). Changes in Individual OpenStreetMap contributors' contribution behavior under COVID-19: A case study in New York City. ISPRS International Journal of Geo-Information, 15(3): 121. [Web] [PDF]
[GeoAI/EM 34]   Mitra B# and Zhang G. (2026). GeoAI-Enabled Ensemble Modeling to Assess Land Use and Atmospheric Pollutant Impacts on Land Surface Temperature in the US Southwest. Remote Sensing, 18(5): 746. [Web] [PDF]
[SS/VGI 33]   Lu Y, Gong X, Zhang G, Brown C P, Lin Y C and Lin Y. (2026). Exploring contemporary public perceptions of historical redlining practices in the United States. Computational Urban Science, 6(1). [Web] [PDF]
[SS/VGI/GVA 32]   Zhang G, Luo W, Wu M and Ye L. (2025). Exploring social interaction patterns and drivers in VGI communities using a custom geovisual analytics tool. Annals of GIS, 31(3): 413-431. [Web] [PDF] [Demo] [Code]
[SS/VGI/GVA 31]   Zhang G. (2025). A web-based geovisualization framework for exploratory analysis of individual VGI contributor's participation characteristics. Cartography and Geographic Information Science, 52(2): 199-219. [Web] [PDF] [Demo] [Code]
[SS/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, 4: 0105. [Web] [PDF]
[SS/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]
[SS/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]
[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/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 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]
[SS/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]
[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]
[VGI 17]   Zhang G. (2019). Enhancing VGI application semantics by accounting for spatial bias. Big Earth Data, 3(3): 255-268. [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 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]
[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]
[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]
[GVA 9]   Roth R, Young S, Nestel C, Sack C, Davidson B, Janicki J, Knoppe-Wetzel V, Ma F, Mead R, Rose C, Zhang G. (2018). Global landscapes: Teaching globalization through responsive mobile map design. The Professional Geographer, 70(3): 395-411. [Web] [PDF]
[SS/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]
[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]
[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]
[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] 

Refereed Book Chapters

[SS/VGI 38]   Zhang G. (2026). Volunteered geographic information and crowdsourcing in geographic practice: A unifying conceptual framework. In: Kalogeropoulos, K., Tsatsaris, A., Antoniou, V., and Huang, X. (Eds.): The Geoinformatics Frontier: AI, Big Data, and Crowdsourced Technologies. Elsevier, pp. 423-434.  [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, pp. 179-190.  [Web] [PDF]
[VGI/EM 14]   Zhang G. (2019). Integrating citizen science and GIS for wildlife population monitoring and habitat assessment. In: Ferretti, M. (Ed.): Wildlife Population Monitoring. IntechOpen Limited, London, UK, pp. 1-14.  [Web] [PDF]

Dissertation

Zhang G. (2018). A Representativeness Directed Approach to Spatial Bias Mitigation in VGI for Predictive Mapping. The University of Wisconsin-Madison. [Web]

Teaching & Mentoring

University of Denver
  • GEOG 2000  Geographic Statistics
  • GEOG 2100  Introduction to Geographic Information Systems
  • GEOG 3120/4120  Environmental GIS Modeling
  • GEOG 3140/4140  GIS Database Design
  • GEOG 3165/4165  Geospatial Artificial Intelligence
University of Wisconsin-Madison
  • Geography 377  An Introduction to Geographic Information System  
  • Geography 576 Geospatial Web and Mobile Programming [Online]
  • Geography 579  GIS and Spatial Analysis [Online]  

Mentoring

Doctoral

  • Farzana Jamal (advisor)    2026 – present
  • Jin Xu (advisor)    2021 – present
  • Xuefei Zhang (dissertation committee member)    2025 – present

Masters

  • Bijoy Mitra (advisor)    2025 – present
  • Oblanuju Emmanuel (advisor)    2023 – 2024
  • Mackenzie Kottwitz (advisor)    2020 – 2022
  • Gina Girgente (thesis committee member)    2025 – 2026
  • Mya Moore (thesis committee member)    2025 – 2026
  • Kristen Burgess (thesis committee member)    2025 – 2026
  • Unisha Ghimire (thesis committee member)    2025 – 2026
  • Alex Van De Water (thesis committee member)    2024 – 2025 [StoryMap] [Experience 1] [Experience 2]
  • Ashmita Dhakal (thesis committee member)    2024 – 2025
  • Elena Arroway (thesis committee member)    2023 – 2024
  • Erin Lammott (thesis committee member)    2023 – 2024
  • Joe Hiebert (independent study advisor, thesis committee member)    2021 – 2022 [Flowmap]
  • Jennifer Murdock (thesis committee member)    2020 – 2021
  • Matt Hugel (thesis committee member)    2019 – 2020
  • Sophie-Min Thomson (thesis committee member)    2019 – 2020
  • Hayley Miller (thesis committee member)    2019 – 2020

Undergraduate

  • Gillian Bush (honors thesis co-advisor)    2026
  • Nissa Tapper (independent study advisor)    2026 [StoryMap]
  • Juanlin Liu (independent study advisor)    2022 [StoryMap]
  • Chloe Pepke (honors thesis co-advisor)    2020
  • Mark Ludke (independent study advisor)    2020

Service

University of Denver (DU)

University
  • Committee Member. PROF Selection Committee Social Science Methods    2023 Spring
  • Senator. DU Faculty Senate    2025 Fall - present
  • Committee Member. Faculty Senate Academic Planning Committee    2025 Fall - present
  • Committee Member. Provost's Goal 2 Committee: Improve career outcomes and better prepare our students to succeed and lead in an AI-transformed workforce    2025 Fall - present
  • Proposal Reviewer. FRF Proposal Review Committee    2025 Winter
  • Proposal Reviewer. CEGAIA Curricular Innovation Proposal Review Committee    2025 Winter
College of Natural Sciences and Mathematics (NSM)
  • Department Representative. NSM Center for Innovative Teaching    2021 Fall - 2023 Spring
  • Committee Member. NSM Sustainability Committee    2025 Fall - present
Department of Geography & the Environment
  • Faculty Director. Geovisualization Laboratory    2019 - present
  • Committee Member. Herold Fund Proposal Faculty Review Committee    2025 Spring - present
  • Geography Colloquium Coordinator.    2023 Spring - 2024 Spring
  • Committee Member. Geography Colloquium Committee    2024 Fall - 2025 Spring
  • Committee Member. Visiting Teaching Assistant Professor Search Committee (Urban Geography)    2022 Spring
  • Committee Member. Tenure-Track Assistant Professor Search Committee (Remote Sensing)    2023 Fall - 2024 Winter
  • Committee Member. Visiting Teaching Assistant Professor Search Committee (Remote Sensing)    2024 Spring

Professional Community

UCGIS

  • Committee Member, Research Committee: Initiative on CyberGIS and Decision Support Systems [Web], University Consortium for Geographic Information Science 2021–present

AAG

  • Chair, Cyberinfrastructure Specialty Group [Web] 2022–2023
  • Vice Chair, Cyberinfrastructure Specialty Group 2021–2022
  • Board of Director, Cyberinfrastructure Specialty Group 2019–2021
  • Judge, Graduate Student Paper Competition, Great Plains-Rocky Mountain Division Annual Meeting 2022
  • Judge, The Jacques May Thesis Prize, Health and Medical Geography Specialty Group 2019

CPGIS

  • Board of Directors, International Association of Chinese Professionals in Geographic Information Sciences 2024–2025
  • Committee Chair, Election Committee 2024
  • Committee Chair, Social Event Committee 2023
  • Committee Member, Conference Site Selection Committee 2024–present

iEMSs

  • Committee Member, Early Career Scientist Committee, International Environmental Modelling and Software Society 2022–present

Reviewer Board Memberships

  • Reviewer Board Member, ISPRS International Journal of Geo-Information [Web] 2019–present
  • Reviewer Board Member, Remote Sensing [Web] 2019–present

Guest Editorialships

These roles are documented in my verified Web of Science editor record.

  • Lead Guest Editor, Geographical Principles in the New Era of Spatial Analysis and Modeling [Web], Annals of GIS 2026–2027
  • Co-Guest Editor, Advances and Applications of Geospatial Modeling and Analysis in Digital Twins [Web], Frontiers in Earth Science 2022–2023
  • Lead Guest Editor, Remote Sensing and GIS Technologies for Sustainable Ecosystem Management [Web], Remote Sensing 2022–2023
  • Lead Guest Editor, Mapping, Modeling and Prediction with VGI [Web], ISPRS International Journal of Geo-Information 2020–2021
  • Co-Guest Editor, Geospatial Semantic, Ontology and Knowledge Graph [Web], Big Earth Data 2019

Conference Organization

20262 entries
  • Paper Session Lead Organizer and Chair: Geographical Principles in Spatial Analysis and Modeling. 33rd International Conference on Geoinformatics 2026, University Town, National University of Singapore, Singapore Jul 19–22, 2026
  • Paper Session Lead Organizer and Chair: Geographical Principles in Spatial Analysis and Modeling. 2026 AAG Annual Meeting, San Francisco, CA Mar 17–21, 2026
20234 entries
  • Organizing Committee Member: The 9th Symposium on Human Dynamics Research. 2023 AAG Annual Meeting, Denver, CO Mar 23–27, 2023
  • Paper Session Co-organizer: Symposium on Human Dynamics Research: Mining Human Dynamics with Big Data. 2023 AAG Annual Meeting, Denver, CO Mar 23–27, 2023
  • Paper Session Co-organizer: 2023 CISG Robert Raskin Student Competition. 2023 AAG Annual Meeting, Denver, CO Mar 23–27, 2023
  • Paper Session Co-organizer: CyberGIS and Spatial Decision Support Systems. 2023 AAG Annual Meeting, Denver, CO Mar 23–27, 2023
20224 entries
  • Paper Session Organizer/Chair: 2022 CISG Robert Raskin Student Competition. 2022 AAG Annual Meeting, New York City, NY Feb 25–Mar 1, 2022
  • Paper Session Organizer: GeoAI and CyberGIS for Advancing Spatial Decision Making. 2022 AAG Annual Meeting, New York City, NY Feb 25–Mar 1, 2022
  • Organizing Committee Member: The 8th Symposium on Human Dynamics Research. 2022 AAG Annual Meeting, New York City, NY Feb 25–Mar 1, 2022
  • Paper Session (Virtual) Organizer/Chair: Mining Human Dynamics with Big Data. 2022 AAG Annual Meeting, New York City, NY Feb 25–Mar 1, 2022
20212 entries
  • Organizing Committee Member: The 7th Symposium on Human Dynamics Research. 2021 AAG Annual Meeting, Seattle, WA Apr 7–11, 2021
  • Paper Session (Virtual) Organizer/Chair: Mapping, Modeling and Prediction with VGI. 2021 AAG Annual Meeting, Seattle, WA Apr 7–11, 2021
20201 entry
  • Paper Session Organizer/Chair: Mapping, Modeling and Prediction with VGI. 2020 AAG Annual Meeting, Denver, CO Apr 6–10, 2020 [Cancelled due to COVID-19]

Journal Reviewer

162 verified peer reviews across 47 journals. Counts synchronized from my Web of Science researcher profile on September 15, 2026.

  • 40Annals of GIS
  • 17Transactions in GIS
  • 16ISPRS International Journal of Geo-Information
  • 11International Journal of Geographical Information Science
  • 4Big Earth Data
  • 4Data
  • 4Diversity and Distributions
  • 4International Journal of Digital Earth
  • 4Sustainability
  • 3Arabian Journal of Geosciences
  • 3Ecological Indicators
  • 3PLOS One
  • 3Remote Sensing
  • 2Applied Sciences
  • 2Earth Science Informatics
  • 2Ecological Informatics
  • 2Frontiers of Computer Science
  • 2Geo-Spatial Information Science
  • 2Geomatics, Natural Hazards and Risk
  • 2IEEE Access
  • 2IEEE Geoscience and Remote Sensing Letters
  • 2International Journal of Environmental Research and Public Health
  • 2PeerJ Computer Science
  • 2Scientific Reports
  • 2The Professional Geographer
  • 1Big Data and Cognitive Computing
  • 1Cartography and Geographic Information Science
  • 1Citizen Science: Theory and Practice
  • 1Computers & Geosciences
  • 1Drones
  • 1Earth System Science Data
  • 1Environment and Planning B: Urban Analytics and City Science
  • 1Geocarto International
  • 1IEEE Transactions on Geoscience and Remote Sensing
  • 1IEEE Transactions on Knowledge and Data Engineering
  • 1Information
  • 1International Journal of Applied Earth Observation and Geoinformation
  • 1International Journal of Image and Data Fusion
  • 1International Journal of Parallel, Emergent and Distributed Systems
  • 1International Journal of Remote Sensing
  • 1Journal of Maps
  • 1Journal of the Royal Statistical Society: Series C (Applied Statistics)
  • 1Natural Hazards
  • 1Pedosphere
  • 1Sensors
  • 1Social Science Computer Review
  • 1Social Sciences

Awards & Honors

  • 2026  DU Faculty Research Fund (FRF) Grant, University of Denver. PI
  • 2026  DU CEGAIA Curricular Innovation Grant, University of Denver. PI
  • 2026  NSM Olin Faculty Development Grant, University of Denver. PI
  • 2025  DU Proposal Incentive Program Grant, University of Denver. PI
  • 2025  NSM Olin Faculty Development Grant, University of Denver. PI
  • 2022  DU Professional Research Opportunities for Faculty (PROF) Grant, University of Denver. PI
  • 2022  DU Faculty Research Fund (FRF) Grant, University of Denver. PI
  • 2020  Microsoft AI for Earth - Azure Compute Credit Grants. PI
  • 2019  DU Faculty Research Fund (FRF) Grant, University of Denver. PI
  • 2019  DU Internationalization Grant, University of Denver. PI
  • 2019  DU Public Good Fund Grant, University of Denver. Co-PI (Cancelled due to COVID)
  • 2017  Best Paper Competition Award (2nd Place): International Symposium on Spatiotemporal Computing
  • 2017  Whitbeck Graduate Dissertator Award, Department of Geography, University of Wisconsin-Madison
  • 2017  Trewartha Conference Travel Award, Department of Geography, University of Wisconsin-Madison
  • 2016  Campus-Wide Capstone Ph.D. Teaching Award, University of Wisconsin-Madison
  • 2009/2010  College Students GIS Software Development Contest (3rd Place): ESRI (China)
  • 2008  Excellent Olympics Volunteer Award, Beijing Summer Olympics
  • 2007/2008  Undergraduate Student Scholarships, Beijing Normal University