Guiming Zhang

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

I am an Assistant Professor of GIScience in the Department of Geography & the Environment at the University of Denver, USA. My research interests are GIScience, volunteered geographic information (VGI), geospatial big data analytics, geovisualization/geovisual analytics and high-performance geocomputation, and their applications in environmental modeling and mapping (species distribution modeling, habitat suitability mapping, digital soil mapping, etc.).

Appointments

University of Denver

Department of Geography & the Environment
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 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]

Publications

[VGI - Volunteered Geographic Information]
[GVA - Geovisualization and Geovisual Analytics]
[EM - Environmental Modeling]
[GC - GeoCompuation]
[OT - Other]

Refereed Journal Articles

* Corresponding Author  # Student Author

[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]
[EM 27]   Luo W. and Zhang G. (2023). Advances and applications of geospatial modeling and analysis in digital twins. Frontiers in Eartch 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]
[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]
[OT 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]
[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

[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/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]

Dissertation

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

Teaching

University of Denver
  • GEOG 2000  Geographic Statistics
  • GEOG 2100  Introduction to Geographic Information Systems
  • GEOG 3120  Environmental GIS Modeling
  • GEOG 3140  GIS Database Design
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]  

Students

Doctoral

  • Jin Xu (advisor)    2021 – present

Masters

  • Oblanuju Emmanuel (advisor)    2023 – present
  • Mackenzie Kottwitz (advisor)    2020 – 2022
  • Erin Lammott (thesis committee member)    2023 – present
  • Joe Hiebert (independent study advisor, thesis committee member)    2021 – 2022
  • 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

  • Juanlin Liu (independent study advisor)    2022
  • Chloe Pepke (honors thesis co-advisor)    2020
  • Mark Ludke (independent study advisor)    2020

Service

Professional Community

  • Committee Member. Research Committee: Initiative on CyberGIS and Decision Support Systems [Web]. University Consortium for Geographic Information Science (UCGIS)    2021 - present
  • Chair. American Association of Geographers (AAG) Cyberinfrastructure Specialty Group (CISG) [Web]    2022 – 2023
  • Vice Chair. AAG CISG    2021 – 2022
  • Board of Director. AAG CISG    2019 – 2021
  • Judge. The Jacques May Thesis Prize. AAG Health and Medical Geography Specialty Group    2019

  • Co-Guest Editor. Special Issue: Advances and Applications of Geospatial Modeling and Analysis in Digital Twins [Web]. Frontiers in Earth Science    2022 -
  • Co-Guest Editor. Special Issue: Remote Sensing and GIS Technologies for Sustainable Ecosystem Management [Web]. Remote Sensing    2022 -
  • Guest Editor. Special Issue: Mapping, Modeling and Prediction with VGI [Web]. ISPRS International Journal of Geo-Information    2020 - 2021
  • Guest Editor. Special Issue: Geospatial Semantic, Ontology and Knowledge Graph [Web]. Big Earth Data    2019
  • Reviewer Board. Remote Sensing Journal [Web]    2019 – present

Conference Organization

  • 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: Symposium on Data-Intensive Geospatial Understanding in the Era of AI and CyberGIS: UCGIS GeoAI & CyberGIS Research Initiative - 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: Symposium on Human Dynamics Research: Mining Human Dynamics with Big Data. 2022 AAG Annual Meeting, New York City, NY    Feb 25 - Mar 1, 2022
  • 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: Symposium on Human Dynamics Research: Mapping, Modeling and Prediction with VGI. 2021 AAG Annual Meeting, Seattle, WA    Apr 7 - 11, 2021
  • 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

  • 10+    5+    2+   
  • Annals of GIS
  • Applied Sciences
  • Arabian Journal of Geosciences
  • Big Earth Data
  • Big Data and Cognitive Computing
  • Computers & Geosciences
  • Data
  • Diversity and Distributions
  • Earth Science Informatics
  • Geocarto International
  • Geo-Spatial Information Science
  • Natural Hazards
  • IEEE Access
  • Information
  • ISPRS International Journal of Geo-Information
  • International Journal of Environmental Research and Public Health
  • International Journal of Geographical Information Science
  • International Journal of Image and Data Fusion
  • ISPRS International Journal of Geo-Information
  • Journal of Maps
  • Journal of the Royal Statistical Society: Series C (Applied Statistics)
  • Pedosphere
  • Plos One
  • Remote Sensing
  • Scientific Reports
  • Sensors
  • Social Sciences
  • Sustainability
  • Scientific Reports
  • The Professional Geographer
  • The 2nd International Conference on Physics, Mathematics and Statistics
  • Transactions in GIS

Awards & Honors