TY - JOUR
T1 - Three-Dimensional Enablement of Place-Based, Pandemic Behaviors
AU - Bagul, S.
AU - Laefer, D.
N1 - Funding Information:
This work was supported by Bluefield GIS, the National Science Foundation (award #2027293), and NYU’s Data Science and Software Services (DS3), which is funded by the Moore and Sloane foundations through the NYU Moore Sloane Data Science Environment.
Publisher Copyright:
© 2022 S. Bagul.
PY - 2022/10/14
Y1 - 2022/10/14
N2 - Harvesting usable and meaningful disaster-related, spatio-temporal data at a highly granular level poses major challenges in its cleaning and aggregation. This paper presents a strategy related to those challenges with respect to individual behavior near COVID-19 laden healthcare facilities. This is done to enable the visualizing of egress behavior data as interactive, three-dimensional (3D) scenes to investigate human behavior patterns regarding touch-based, disease transmission. Therefore, the aim is to demonstrate how this concept of 3D epidemiology may provide new mechanisms to understand the relative risk and exposure prevalence for data analysis. This paper demonstrates 3D enablement of disaster-related field data through use of first-hand observations of 1,936 individuals egressing New York City healthcare facilities during the onset of COVID-19 in the Spring of 2020. The observations capture egress behavior in terms of where people go (e.g. coffee shop, Subway) and how they physically interact with the surroundings (i.e. what they touch and how long they remain). This paper introduces a mechanism for automated extraction and 3D visualization of such data in Potree, an open-source Web Graphics Library (WebGL) point cloud viewer. Distinctive vertex shaders are used to distinguish specific destination selection and behavioral patterns (e.g. personal protective equipment usage). Two-dimensional heatmaps are paired with 3D scenes to demonstrate the potential of using 3D visualization of spatio-temporal patterns for visualizing disease transmission potential.
AB - Harvesting usable and meaningful disaster-related, spatio-temporal data at a highly granular level poses major challenges in its cleaning and aggregation. This paper presents a strategy related to those challenges with respect to individual behavior near COVID-19 laden healthcare facilities. This is done to enable the visualizing of egress behavior data as interactive, three-dimensional (3D) scenes to investigate human behavior patterns regarding touch-based, disease transmission. Therefore, the aim is to demonstrate how this concept of 3D epidemiology may provide new mechanisms to understand the relative risk and exposure prevalence for data analysis. This paper demonstrates 3D enablement of disaster-related field data through use of first-hand observations of 1,936 individuals egressing New York City healthcare facilities during the onset of COVID-19 in the Spring of 2020. The observations capture egress behavior in terms of where people go (e.g. coffee shop, Subway) and how they physically interact with the surroundings (i.e. what they touch and how long they remain). This paper introduces a mechanism for automated extraction and 3D visualization of such data in Potree, an open-source Web Graphics Library (WebGL) point cloud viewer. Distinctive vertex shaders are used to distinguish specific destination selection and behavioral patterns (e.g. personal protective equipment usage). Two-dimensional heatmaps are paired with 3D scenes to demonstrate the potential of using 3D visualization of spatio-temporal patterns for visualizing disease transmission potential.
KW - COVID-19
KW - Citizen Science
KW - Egress Behavior
KW - Geospatial
KW - LiDAR
KW - Potree
KW - three-dimensional epidemiology
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U2 - 10.5194/isprs-annals-X-4-W2-2022-21-2022
DO - 10.5194/isprs-annals-X-4-W2-2022-21-2022
M3 - Conference article
AN - SCOPUS:85141053866
SN - 2194-9042
VL - 10
SP - 21
EP - 28
JO - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
JF - ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
IS - 4/W2-2022
T2 - 17th 3D GeoInfo Conference, 3DGeoInfo 2022
Y2 - 19 October 2022 through 21 October 2022
ER -