Jesse Piburn Research Scientist in Geographic Data Sciences Contact PIBURNJO@ORNL.GOV All Publications Inferring building height from footprint morphology data Temporal Dynamics of Place and Mobility Near real time monitoring and forecasting for COVID-19 situational awareness DOE COVID-19 Data Curation Effort: Overview of Initial Data Collection Coverage (March - June 2020) COVID-19 Data Curation Effort: An Initial Analysis of the Data... A Taxonomic Classification Approach for Global Spatio-temporal Data INSTALLATION ASSIGNMENT DURATIONS AND PATTERNS OF ARMY PERSONNEL Active Learning Approach to Record Linking in Large Geodatasets Methodologies for Evaluation of Corrosion Protection for Ductile Iron Pipe Need A Boost? A Comparison of Traditional Human Commuting Models with the XGBoost Model for Predicting Commuting Flows Need A Boost? A Comparison of Traditional Commuting Models with the XGBoost Model for Predicting Commuting Flows Utilizing Geo-located Sensors and Social Media for Studying Population Dynamics and Land Classification A Simulation Approach for Modeling High-Resolution Daytime Commuter Travel Flows and Distributions of Worker Subpopulations... An Approximate Entropy Based Approach for Quantifying Stability in Spatio-Temporal Data with Limited Temporal Observations Towards a Virtual Reality Elicitation of Building Occupancy A Dasymetric-Based Monte Carlo Simulation Approach to the Probabilistic Analysis of Spatial Variables A 3D Virtual Environment for Spatio-Temporal Analysis: Theoretical Approach, Proof of Concept, and User Study ... A Hybrid dasymetric and machine learning approach to high-resolution residential electricity consumption modeling Can Social Media Play a Role in the Development of Building Occupancy Curves? A High-Resolution Spatially Explicit Monte-Carlo Simulation Approach to Commercial and Residential Electricity and Water Demand Modeling A Bayesian Machine Learning Model for Estimating Building Occupancy from Open Source Data... PlanetSense: A Real-time Streaming and Spatio-temporal Analytics Platform for Gathering Geo-spatial Intelligence from Open Source Data Estimates of Glacier Mass Loss and Contribution to Streamflow in the Wind River Range in Wyoming: Case Study... World Spatiotemporal Analytics and Mapping Project (WSTAMP): Discovering, Exploring, and Mapping Spatiotemporal Patterns across the World’s Largest Open Source Geographic Data Sets A Hybrid Dasymetric and Machine Learning Approach to High-Resolution Residential Electricity Consumption Modeling... Pagination Current page 1 Page 2 Next page ›â¶Äº Last page Last » Key Links Organizations National Security Sciences Directorate Geospatial Science and Human Security Division Geographic Data Science Section
