Center for Geospatial Intelligence

The Center for Geospatial Intelligence (CGEOINT) at George Mason University focuses on research that relates to geospatial and spatiotemporal information extraction, analysis, and visualization.

Funded Research Projects

NSF: III: Small: From Spatial Language to Spatial Data - a simulation-based approach (2021-2024).
PIs: Dieter Pfoser, Antonis Anastasopoulos.
Keywords: spatial language, spatial simulation 
Summary: Devising means to better understand people's perception of space by deciphering such spatial language terms. This includes (i) developing a simulation to crowdsource geospatial language expression data by having users interact in a virtual environment and (ii) exploring neural language modeling approaches for the problem of grounding language to this spatial context. 

NSF: Data-Driven Modeling to Improve Understanding of Human Behavior, Mobility, and Disease Spread (2021-2026). PIs: T. Anderson, D. Pfoser, A. Zuefle, H. Kavak, A. Roess, S. Luke.
Keywords: agent-based modeling, disease simulation, spatial simluation, digital twin 
Summary: The goal of this project is to investigate how including spatiotemporal heterogeneity of human behavior and mobility in ABMs of disease spread will 1) better explain variations of human response to disease spread, 2) improve predictions of infectious disease spread, and 3) prescribe the most effective mitigation policies. 

iARPA - Distributed Semi-Supervised Temporal Learning for Global Change Monitoring (DiSSTL) (2021 - 2022). PI: K. Wessels.
Keywords: image analysis, change detection, remote sensing 
Summary: IARPA’s Space-based Machine Automated Recognition Technique (SMART) Program strives to automate the quantitative analysis of space-based imagery to perform broad-area search for natural and anthropogenic events and characterize their extent and progression in time and space by fusing very large data from multiple satellite sensors. 

DARPA - A ground‐truth simulator for socio‐spatial alternate worlds (2018-2020). PIs: A. Zuefle, D. Pfoser, A. Crooks, and C. Wenk (Tulane)
Keywords: agent-based modeling, urban simulation, spatial simluation, digital twin 
Summary: Test and evaluating different methods and tools for analyzing complex social phenomena. 

NSF/AITF: Collaborative Research: Modeling Movement on Transportation Networks Using Uncertain Data (2016-2022). PIs: D. Pfoser, A. Zuefle
Keywords: urban analytics, spatial data science, mobility modeling 
Summary: Creation a unified framework for aggregating and analyzing diverse and uncertain movement data on transportation networks and to provide tools for querying and predicting traffic volume and movement in urban environments. 

NGA - Crowdsourced Gazetteers to Communicate Place Dynamics (2014-2021). PIs: D. Pfoser, A. Stefanidis, A. Croitoru, A. Crooks.
Keywords: geospatial crowdsourcing, map construction, social media mining 
Summary: Traditional gazetteers and maps are artifacts of a geometry-driven view of the world, communicating primarily locations and their coordinates. Human activities may assign multiple meanings to the same geographic location. The activities associated with a space transform it to a sociocultural place, by assigning to it meaning, scope, and function, that far exceed its geometric coordinates. Understanding and mapping places is a key geospatial research challenge, as we are transitioning from mapping the surface of the world to monitoring and assessing geo-located activities. 

L. Zhang, L Zhao, D Pfoser, 2022. Factorized Deep Generative Models for End-to-End Trajectory Generation with Spatiotemporal Validity Constraints. In Proceedings 30th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2022), to appear, 2022. 

L. Zhang, L Zhao, S Qin, D Pfoser, 2021. TG-GAN: Deep Generative Models for Continuously-time Temporal Graph Generation. In Proceedings 30. the Web Conference (WWW) (pp. 2104-2116).

W. Zhang, L. Zhang, D. Pfoser, Z. Liang, 2021. Disentangled Dynamic Graph Deep Generation. In Proceedings SIAM International Conference on Data Mining (SDM) (pp. 738-746). 

X Min, D. Pfoser, A Züfle, Y Sheng, 2021. A Hierarchical Spatial Network Index for Arbitrarily Distributed Spatial Objects. ISPRS International Journal of Geo-Information 10 (12), 814. 

A. Züfle, C. Wenk, D. Pfoser, A. Crooks, H. Kavak, J-S. Kim, and H. Jin, 2021. Urban Life: A Model of People and Places. Computational and Mathematical Organization Theory.

Lyu, H., Pfoser, D. and Sheng, Y., 2021. Movement-aware map construction. International Journal of Geographical Information Science, pp.1-29.

Yang, C., et al., 2020. Taking the pulse of COVID-19: A spatiotemporal perspective. International journal of digital earth, 13(10), pp.1186-1211.

Kim, J.S., Kavak, H., Rouly, C.O., Jin, H., Crooks, A., Pfoser, D., Wenk, C. and Züfle, A., 2020. Location-based social simulation for prescriptive analytics of disease spread. SIGSPATIAL Special, 12(1), pp.53-61.

Kim, J.S., Pfoser, D. and Züfle, A., 2020, October. Vehicle Relocation for Ride-Hailing. In Proceedings of the IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA) (pp. 589-598).

Gkountouna, O., Pfoser, D. and Züfle, A., 2020, October. Traffic Flow Estimation using Probe Vehicle Data. In Proceedings of the IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA) (pp. 579-588).

L. Zhang, A. Züfle and D. Pfoser, 2020. Station-to-User Transfer Learning: Towards Explainable User Clustering Through Latent Trip Signatures Using Tidal-Regularized Non-Negative Matrix Factorization. In Proc. 28th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL), (pp. 303-313).

Züfle, A., Trajcevski, G., Pfoser, D. and Kim, J.S., 2020. Managing uncertainty in evolving geo-spatial data. In 2020 21st IEEE International Conference on Mobile Data Management (MDM) (pp. 5-8).

J. S. Kim, H. Jin, H. Kavak, O.C. Rouly, A. Crooks, D. Pfoser, and A. Züfle, 2020. Location-based Social Network Data Generation Based on Patterns of Life. In Proc. IEEE International Conference on Mobile Data Management (MDM’20). 

K. Cui, Y. Jiang, Y. Li, and D. Pfoser, 2019. A vocabulary recommendation method for spatiotemporal data discovery based on Bayesian network and ontologies. Big Earth Data, 3(3):220-231.

L. Zhao, O. Gkountouna, D. Pfoser, 2019. Spatial Auto-regressive Dependency Learning Based on Spatial Topological Constraints. ACM Transactions on Spatial Algorithms and Systems (TSAS), 5(3):1-28.

L. Zhang and D. Pfoser, 2019. Using OpenStreetMap point-of-interest data to model urban change—A feasibility study. PloS one 14(2).

B. Weaver and D. Pfoser, 2019. Investigation Design: The Structural Elements of Knowledge-seeking Efforts. Data & Knowledge Engineering Journal, 119:71-88.

X. Fei, O. Gkountouna, D. Pfoser, and A. Züfle, 2019. Spatiotemporal Bus Route Profiling using Odometer Data. In Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 369-378.

J.S. Kim, H. Kavak, U. Manzoor, A. Crooks, D. Pfoser, C. Wenk, C. and A. Züfle, 2019, November. Simulating Urban Patterns of Life: A Geo-Social Data Generation Framework. In Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 576-579.

H. Kavak, J.S. Kim, A. Crooks, D. Pfoser, C. Wenk, and A. Züfle, 2019. Location-based social simulation. In Proceedings of the 16th International Symposium on Spatial and Temporal Databases, pp. 218-221.

D. Cheng, O. Gkountouna, A. Züfle, D. Pfoser, and C. Wenk, 2019. Shortest-Path Diversification through Network Penalization: A Washington DC Area Case Study. In Proceedings of the 12th ACM SIGSPATIAL International Workshop on Computational Transportation Science, 10:1-10.

J.S. Kim, D. Pfoser, and A. Züfle, 2019. Distance-Aware Competitive Spatiotemporal Searching Using Spatiotemporal Resource Matrix Factorization (GIS Cup). In Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 624-627.

T.S. Khan, A. Kabir, D. Pfoser, and A. Züfle, 2019. CrowdZIP: A System to Improve Reverse ZIP Code Geocoding using Spatial and Crowdsourced Data (Demo Paper). In Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, pp. 588-591.

R. Truong, O. Gkountouna, D. Pfoser, and A. Züfle, A., 2018. Towards a Better Understanding of Public Transportation Traffic: A Case Study of the Washington, DC Metro. Urban Science, 2(3), p.65.

A. Belesiotis, D. Skoutas, A. Efstathiades, V. Kaffes, and D. Pfoser, 2018. Spatio-textual user matching and clustering based on set similarity joins. The VLDB Journal—The International Journal on Very Large Data Bases, 27(3), pp.297-320.

The Center for Geospatial Intelligence is affiliated with the Department of Geography and Geoinformation Science of the College of Science

The Center is heavily involved with the following academic programs:

• The Graduate Certificate for Geospatial Intelligence, approved by the US Geospatial intelligence Foundation
• The Master of Science in Geoinformatics and Geospatial Intelligence
• The Ph.D. in Earth Systems and Geoinformation Science

Training projects administered by the Center include:

“Geoinformatics and Spatial Intelligence Training Educational Offering”. Duration: 2010-15. Sponsor: Army Geospatial Center. (a training project) PI: A. Stefanidis.