Abstract
Abstract. 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.
Highlights
The effects of the built environment on human behavior conducive to COVID-19 transmission are not fully understood at the community level
To contribute to the state of understanding, this paper introduces a visualization mechanism using a threedimensional (3D) point cloud on which to project egress behaviors of individuals leaving a COVID-19 exposed healthcare facility in Spring 2020 in New York City (NYC), during the initial peak outbreak of COVID-19
This paper introduces an initial demonstration of a method for the web-based visualization of 2D spatio-temporal human behavior data within a 3D representation of the built environment, in the context of the COVID-19 pandemic
Summary
The effects of the built environment on human behavior conducive to COVID-19 transmission are not fully understood at the community level. To contribute to the state of understanding, this paper introduces a visualization mechanism using a threedimensional (3D) point cloud on which to project egress behaviors of individuals leaving a COVID-19 exposed healthcare facility in Spring 2020 in New York City (NYC), during the initial peak outbreak of COVID-19. The goal was to demonstrate in an aggregated manner how individuals exhibiting specific characteristics (e.g. whether they touched their body or their cell phone) physically interacted with the surrounding environment and community and how these behaviors differed by subgroup (e.g. men vs women). The tendency of cases to cluster typically requires data points to be aggregated into density plots to enable sufficient visual interpretation. Atkinson and Unwin (2002) noted that visualizing such data as density plots in 3D space could further improve their interpretability in geographic contexts The tendency of cases to cluster typically requires data points to be aggregated into density plots to enable sufficient visual interpretation. Atkinson and Unwin (2002) noted that visualizing such data as density plots in 3D space could further improve their interpretability in geographic contexts
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
