TY - JOUR
T1 - NEW POTREE SHADER CAPABILITIES for 3D VISUALIZATION of BEHAVIORS near COVID-19 RICH HEALTHCARE FACILITIES
AU - Carey, C.
AU - Romero, J.
AU - Laefer, D. F.
N1 - Funding Information:
This work was supported by the National Science Foundation (award #2027293), the Data Science and Software Services (DS3) which is funded by the Moore and Sloane foundations through the NYU Moore Sloane Data Science Environment, and Bluefield GIS.
Publisher Copyright:
© 2021 C. Carey et al.
PY - 2021/10/7
Y1 - 2021/10/7
N2 - While data on human behavior in COVID-19 rich environments have been captured and publicly released, spatial components of such data are recorded in two-dimensions. Thus, the complete roles of the built and natural environment cannot be readily ascertained. This paper introduces a mechanism for the three-dimensional (3D) visualization of egress behaviors of individuals leaving a COVID-19 exposed healthcare facility in Spring 2020 in New York City. Behavioral data were extracted and projected onto a 3D aerial laser scanning point cloud of the surrounding area rendered with Potree, a readily available open-source Web Graphics Library (WebGL) point cloud viewer. The outcomes were 3D heatmap visualizations of the built environment that indicated the event locations of individuals exhibiting specific characteristics (e.g., men vs. women; public transit users vs. private vehicle users). These visualizations enabled interactive navigation through the space accessible through any modern web browser supporting WebGL. Visualizing egress behavior in this manner may highlight patterns indicative of correlations between the environment, human behavior, and transmissible diseases. Findings using such tools have the potential to identify high-exposure areas and surfaces such as doors, railings, and other physical features. Providing flexible visualization capabilities with 3D spatial context can enable analysts to quickly advise and communicate vital information across a broad range of use cases. This paper presents such an application to extract the public health information necessary to form localized responses to reduce COVID-19 infection and transmission rates in urban areas.
AB - While data on human behavior in COVID-19 rich environments have been captured and publicly released, spatial components of such data are recorded in two-dimensions. Thus, the complete roles of the built and natural environment cannot be readily ascertained. This paper introduces a mechanism for the three-dimensional (3D) visualization of egress behaviors of individuals leaving a COVID-19 exposed healthcare facility in Spring 2020 in New York City. Behavioral data were extracted and projected onto a 3D aerial laser scanning point cloud of the surrounding area rendered with Potree, a readily available open-source Web Graphics Library (WebGL) point cloud viewer. The outcomes were 3D heatmap visualizations of the built environment that indicated the event locations of individuals exhibiting specific characteristics (e.g., men vs. women; public transit users vs. private vehicle users). These visualizations enabled interactive navigation through the space accessible through any modern web browser supporting WebGL. Visualizing egress behavior in this manner may highlight patterns indicative of correlations between the environment, human behavior, and transmissible diseases. Findings using such tools have the potential to identify high-exposure areas and surfaces such as doors, railings, and other physical features. Providing flexible visualization capabilities with 3D spatial context can enable analysts to quickly advise and communicate vital information across a broad range of use cases. This paper presents such an application to extract the public health information necessary to form localized responses to reduce COVID-19 infection and transmission rates in urban areas.
KW - COVID-19
KW - Geoscientific information
KW - Healthcare facility
KW - Point cloud
KW - Three-dimensional epidemiology
KW - WebGL
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U2 - 10.5194/isprs-archives-XLVI-4-W4-2021-61-2021
DO - 10.5194/isprs-archives-XLVI-4-W4-2021-61-2021
M3 - Conference article
AN - SCOPUS:85118292133
SN - 1682-1750
VL - 46
SP - 61
EP - 66
JO - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
JF - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
IS - 4/W4-2021
T2 - 16th 3D GeoInfo Conference 2021
Y2 - 11 October 2021 through 14 October 2021
ER -