Qunying Huang

Website

SPATIAL COMPUTING AND DATA MINING LAB

EDUCATION

Ph.D., Earth Systems and Geoinformation Sciences, George Mason University, 2011
M.S., Cartography and GIScience, Peking University, 2007
B.S., Survey and Mapping Engineering, Central South University, 2004

RESEARCH AREAS

My research primarily focuses on spatial computing, and data mining. I leverage and sythesize big data streams from both physical (e.g., remote sensing) and social (e.g., social media) sensing networks for different application domains, such as natural hazards and human mobility. I am also very interested in applying different computing models, including cluster, grid, GPU, citizen computing, and especially cloud computing to address contemporary computing challenges in the GIScience.

CURRENT RESEARCH

Spatial Data Mining, Spatial Computing, Social Media Analytics, Natural Hazards, Human Mobility, Cloud Computing, GIScience, Remote Sensing

COURSES TAUGHT

Geog170, Our Digital Globe: An Overview of GIScience and its Technology
Geog574, Spatial Database
Geog576, Spatial Web and Mobile Programming
Geog970, Seminar in Geographic Information Science

RECENT PUBLICATIONS

BOOKS
Yang C., Yu M., Huang Q. etc., 2016. Introduction to Programming and GIS Algorithms with Python and ArcGIS, CRC Press/Taylor & Francis, 328p. ISBN: 978-1466510081.

Yang C., Huang Q., Li Z., Xu C., Liu K., 2013. Spatial Cloud Computing: A Practical Approach, CRC Press/Taylor & Francis, 304p. ISBN: 978-1466593169.

Huang Q., 2012. Adaptive Nested Models and Cloud Computing for Scientific Simulation: A Case Study Using Dust Storm Forecasting, LAP LAMBERT Academic Publishing, 120p. ISBN: 978-3659154775.

SELECTED REFERRED PUBLICATIONS
*Underlined names are student advisees.

Peng B., Meng Z., Huang Q., Wang C., 2019. Patch Similarity Convolutional Neural Network for Urban Flood Extent Mapping Using Bi-Temporal Satellite Multispectral Imagery. International Journal of Remote Sensing, 11(21), 2492.DOI: 0.3390/rs11212492.2.

Yu M., Huang Q., Scheele C., Han Q., Yang C., 2019. Deep Learning for Real-Time Social Media Text Classification for Situation Awareness – Using Hurricanes Sandy and Harvey as Case Studies. International Journal of Digital Earth, 12(11): 1230-1247.DOI: 10.1080/17538947.2019.1574316.

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.

Huang Q., Li J., Li Z., 2017. A Hybrid Cloud Platform Based on Multi-sourced Computing and Model Resources for Geosciences. International Journal of Digital Earth. DOI: http://dx.doi.org/10.1080/17538947.2017.1385652.

Zhang G, Zhu AX, 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.

Li Z., Huang Q., Carbone G.J., Hu F., 2017. A high performance query analytical framework for supporting data-intensive climate studies. Computers, Environment and Urban Systems, 62: 210-221.

Yang C., Huang Q., Li Z., Liu K., Hu F., 2017. Big Data and cloud computing: innovation opportunities and challenges. International Journal of Digital Earth, 10: 13-53.

Huang Q., 2016. Mining Online Footprints to predict user’s next location. International Journal of Geographic Information Science, 31(3): 523-541.

Huang Q., Wong D., 2016. Activity patterns, socioeconomic status and urban spatial structure: what can social media data tell us? International Journal of Geographic Information Science, 30(9): 1873-1898.

Huang Q., 2016. Mining Online Footprints to predict user’s next location. International Journal of Geographic Information Science, 31(3): 523-541.

Zhang G., Huang Q., Zhu A.X., Keel J.H., 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. doi: 10.1080/2F13658816.2016.1170836

Cervone G., Sava E., Huang Q., Schnebele E., Harrison J., Waters N., 2016. Using Twitter for tasking remote-sensing data collection and damage assessment: 2013 Boulder flood case study. International Journal of Remote Sensing, 37(1): 100-124.

Wang C., Pavlowsky R.T., Huang Q., C. Chang, 2016. Channel bar area extraction for a mining-contaminated river using high-spatial multispectral remote sensing imagery. GIScience and Remote Sensing, 53(3): 283-302. doi: 10.1080/15481603.2016.1148229.

Zhang T., Li J., Liu Q., Huang Q., 2016. A cloud-enabled remote visualization tool for time-varying climate data analytics. Environmental Modeling & Software, 75: 513-518.

Gui Z, Yu M, Yang C, Jiang Y, Chen S, Xia J. Huang Q., Liu K., Li Z., Hassan M., Jin B., 2016. Developing Subdomain Allocation Algorithms Based on Spatial and Communicational Constraints to Accelerate Dust Storm Simulation. PLoS ONE, 11(4): e0152250. doi:10.1371/journal.pone.0152250.

Huang Q., Cervone, G., 2016. Usage of Social Media and Cloud Computing during Natural Hazards, in.In: Vance T., Merati N., Yang C., Yuan M., eds. Cloud Computing for Ocean and Atmospheric Sciences. Academic Press.

Li J., Liu K., Huang Q., 2016. Utilizing cloud computing to support scalable atmospheric modeling: A case study of cloud-enabled ModelE. In: Vance T., Merati N., Yang C., Yuan M., eds. Cloud Computing for Ocean and Atmospheric Sciences. Academic Press.

Huang Q., Wong D., 2015. Modeling and Visualizing Regular Human Mobility Patterns with Uncertainty: An Example Using Twitter Data. Annals of the Association of American Geographers, 105(6): 1179-1197.

Huang Q., Xiao Y., 2015. Geographic Situational Awareness: Mining Tweets for Disaster Preparedness, Emergency Response, Impact, and Recovery. International Journal of Geo-Information, 4(3): 1549-1568. doi:10.3390/ijgi4031549.

Huang Q., Cervone G, Jing D., Chang C., 2015. DisasterMapper: A CyberGIS framework for disaster management using social media data. In Proceedings of the 4th International ACM SIGSPATIAL Workshop on Analytics for Big Geospatial Data (BigSpatial) 2015, ACM SIGSPATIAL 2015, Nov 1-3, Seattle, WA.

Xiao Y., Huang Q., Wu K, 2015. Understanding social media data for disaster management. Natural Hazards. doi:10.1007/s11069-015-1918-0.

Chang C., Ye. Z., Huang Q., and Wang C. 2015. “An Integrative Method for Mapping Urban Land Use Change Using Geo-sensor Data.” UrbanGIS’15: Proceedings of 1st International ACM SIGSPATIAL Workshop on Smart Cities and Urban Analytics, ACM SIGSPATIAL 2015, Nov 1-3, Seattle, WA.

Hultquist C., Simpson M., Cervone G, Huang Q., 2015. Using Nightlight Remote Sensing Imagery and Twitter Data to Study Power Outages. In Proceedings of the 1st ACM SIGSPATIAL International Workshop on the Use of GIS in Emergency Management 2015 (EM-GIS 2015), ACM SIGSPATIAL 2015, Nov 1-3, Seattle, WA.

Huang Q., Cao G., Wang C., 2014. From Where Do Tweets Originate? – A GIS Approach for User Location Inference. In Proceedings of the 7th ACM SIGSPATIAL International Workshop on Location-Based Social Networks (LBSN ’14), ACM SIGSPATIAL 2014, Nov 6-9, Dallas, TX.

Huang Q., C. Xu, 2014. A Data-Driven Framework for Archiving and Exploring Social Media Data, Annals of GIS, 20(4): 265-277

Li R., Feng W., Wu H., Huang, Q., 2014. A replication strategy for a distributed high-speed caching system based on spatiotemporal access patterns of geospatial data. Computers, Environment and Urban Systems. DOI: 10.1016/j.compenvurbsys.2014.02.009

Li Z., Yang C., Huang Q., Liu K., Sun M., Xia J., et al., 2014. Building Model as a Service to Support Geosciences. Computers, Environment and Urban Systems. DOI: 10.1016/j.compenvurbsys.2014.06.004.

Xia J., Yang C., Liu K., Gui Z., Li Z., Huang Q., & Li, R. (2014). Adopting cloud computing to optimize spatial web portals for better performance to support Digital Earth and other global geospatial initiatives. International Journal of Digital Earth, 8(6): 451-475.

Gui Z., Yang C., Huang Q. et al., 2014. A Service Brokering and Recommendation Mechanism for Better Selecting Cloud Services, PLOS ONE , 9(8): e105297. doi: 10.1371/journal.pone.0105297.

Huang Q., Li Z., Liu K., Xia J., Xu C., Jiang Y., Yu M., Yang C., 2014. Accelerating Geocomputation with Cloud Computing. In: Shi X., Kindratenko, V., Yang C., eds. Modern Accelerating Technologies for GIScience. Springer.

Huang Q., Yang C., Liu K., Xia J., Xu C., Li J., Gui Z., Sun M., Li Z., 2013. Evaluating Open Source Cloud Computing Solutions for Geosciences, Computers & Geosciences, 59(9): 41-52.

Huang Q., Yang C., Benedict K., Chen, S., Rezgui A., Xie J., 2013. Utilize Cloud Computing to Support Dust Storm Forecasting, International Journal of Digital Earth, 6(4): 338-355.

Huang Q., Yang C., Benedict K., Rezgui A., Xie J., Xia J., Chen, S., 2013. Using Adaptively Coupled Models and High-performance Computing for Enabling the Computability of Dust Storm Forecasting, International Journal of Geographic Information Science, 27(4): 765-784.

Huang Q., Yang C., 2011. Optimizing Grid Configuration to Support Geospatial Processing – An Example with DEM Interpolation, Computer & Geosciences, 37(2): 165-176.

Li J., Jiang Y., Yang C., Huang Q., Rice M., 2013. Visualizing 3D/4D environmental data using many-core graphics processing units (GPUs) and multi-core central processing units (CPUs), Computers & Geosciences. DOI: j.cageo.2013.04.029.

Huang Q., Xia J., Yang C., Hassan M., Chen S., 2012. An Experimental Study of Open-Source Cloud Platforms for Dust Storm Forecasting, Proceedings of the ACM SIGSPATIAL 2012, Nov 6-9, Redondo Beach, CA, pp.534-537.

Sun M., Li J., Yang C., Schmidt G.A., Bambacus M., Cahalan R., Huang Q., Xu C., Noble E.U., Li Z., 2012. A Web-based Geovisual Analytical Tool for Spatiotemporal Climate Data, Future Internet. DOI: 10.3390/fi4041069.

Yang C., Wu H., Huang Q., Li Z., Li J., 2011. Spatial Computing for Supporting Physical Sciences, Proceedings of National Academy of Sciences, 108(14):5498-5503.

Yang C., Goodchild M., Huang Q., Nebert D., Raskin R., Bambacus M., Xu Y., Fay D., 2011. Spatial Cloud Computing – How Can Geospatial Sciences Use and Help to Shape Cloud Computing, International Journal of Digital Earth, 4(4): 305-329.

Xie J., Yang C., Zhou B., Huang Q., 2010. High Performance Computing for the Simulation of Dust Storms. Computers, Environment, and Urban Systems, 34(4): 278-290.

Huang Q., Yang C., Nebert D., Liu K., Wu H., 2010. Cloud Computing for Geosciences: Deployment of GEOSS Clearinghouse on Amazon’s EC2, Proceedings of the ACM.

AWARDS AND HONORS

2020: Vilas Associates Award;
2016: Madison Teaching and Learning Excellence (MTLE) Faculty Fellow;
2015: Best paper award for or the 1st International Symposium on Spatiotemporal Computing (ISSC);
2014: Next Generation of Hazards & Disasters Researchers, National Science Foundation;
2014: CyberGIS Fellow, NCSA;
2012: Outstanding Graduate Student, GMU;
2011: Summer Student Best Paper Travel Award, UCGIS;
2010: AAG CISG Student Best Paper Award

GRADUATE STUDENTS AND POSTDOCS

Current: Bo Peng (Ph.D), Chenxiao (Atlas) Guo (Ph.D), Xinyi Liu (Ph.D), Jirapa (Jam) Vongkusolkit (M.S), Yuhan Qin (B.S);
Graduated: Chris Scheele (M.S), Duanyang Jing (M.S) , Zidong Zhang (M.S), Meiliu Wu (M.S), Zonglin Meng (B.S)