Region Approximation using Delaunay Filtration and Crowd-Sourced Data During a Natural Disaster

Authors: Brady Woods*, University of Maryland - College Park, Kathleen Stewart, University of Maryland - College Park
Topics: Geographic Information Science and Systems, Hazards, Risks, and Disasters, Spatial Analysis & Modeling
Keywords: Footprint, Delaunay Triangulation, Hazard, Region Approximation, Crowd-Source
Session Type: Paper
Day: 4/5/2019
Start / End Time: 8:00 AM / 9:40 AM
Room: Washington 6, Marriott, Exhibition Level
Presentation File: No File Uploaded

When evaluating point-based phenomenon, it can be useful examine the points at a coarser level of granularity than the points themselves. This process is known as ‘region approximation’ and seeks to delineate the region(s) occupied by a set of points. The resulting regions are commonly known as the ‘footprint’ of the point data set. These footprints can be used, for example, to evaluate the impact of a natural disaster represented by points beyond the scope of the points themselves. In late August of 2017, Hurricane Harvey brought devastating flooding to the Houston, Texas metropolitan region, overwhelming first-responders and the local emergency management system. In response, volunteers from the HarveyRELIEF organization used social media to crowd-source the locations of those trapped by floodwater and coordinate their rescue with informal rescue efforts such as the ‘Cajun Navy’. We evaluate approximately 4,000 geo-located rescue requests collected from the HarveyRELIEF organization between August 26, 2017 and September 3, 2017 as a sample of the impact Hurricane Harvey had on the Houston metropolitan region. We use a region approximation algorithm based on Delaunay triangulation to semi-automatically generate the footprint of the rescue data set. These footprints are then validated using FEMA damage assessments created after the disaster.

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