Jesse Piburn

The built environment continues to expand due to increasing human populations with some projections indicating additional growth of 2.5 billion persons by 2050. Buildings are a key component of the built environment and information on building height and urban morphology in general can help to better understand and address a host of development and urban planning issues including energy consumption, urbanization, population dynamics, urban heat islands, environmental degradation, and others. To date, several approaches have been developed to address the need for mapping building height at scale. These approaches can be grouped into two main categories: 1) directly measure height from remote sensing products or 2) indirectly estimate height from footprint morphology features at the building-by-building level. Due to a number of these global height datasets now being available for consumption and use across domains, there is a critical need to understand the differences displayed between these products and how the use of these may influence the outcome when applied. In this analysis, we compare and evaluate 5 global building height datasets: Global Human Settlement, World Settlement Footprint 3D, Global Building Morphology dataset, three-dimensional Global Building Footprints, and an approach developed at Oak Ridge National Laboratory (ORNL). Our analysis shows that, across 5 cities at a global scale with high-quality building height reference data, the approach developed by ORNL of inferring height at the building-by-building level is more accurate and consistent. This was evidenced by the ORNL product improving upon the baseline in all 5 cities, the only product to do so. These findings will aid researchers in choosing a global building height product that best meets their needs and provides global availability, consistency, and accuracy.

The U.S. Department of Energy's Office of Scientific and Technical Information

The U.S. Department of Energy's Office of Scientific and Technical Information

The U.S. Department of Energy's Office of Scientific and Technical Information

The U.S. Department of Energy's Office of Scientific and Technical Information

We present a 3D virtual environment leveraging 3D game dynamics for statistical analysis on spatio-temporal (ST) data. We present a theoretical construct, a proof-of-concept implementation (STWorld), and preliminary results from a human-computer interaction (HCI) study. Results include a novel integration of the Unity game engine with open source tools PostGreSQL, R, and the D3.js graphics library. Preliminary results suggest that STWorld may be judged more enjoyable and incur better procedural memory retention than traditional dashboard interfaces. We conclude with findings on gender bias and the gap between gamers and non-gamers.