The recent COVID outbreak has demonstrated that understanding the risk and the resilience of Critical Infrastructure (CI) is extremely challenging and to be prepared for future shocks, requires more research into known and unknown hazards. In addition, impacts like climate change, land use change, and population growth will continue to compound the risk and resiliency of our CI. As such, production of high-quality CI which can be freely shared and used to perform analytics to address risk and resiliency, reveal vulnerable populations, and better understand climate change impacts is necessary. This type of research supports national security, emergency response and development of sustainability initiatives, and policy by local and national governments and communities.
In this session we intend to focus on two broad areas of 1.) creation of critical infrastructure to address vulnerability, risk and resiliency and 2.) usage of such datasets for addressing risk and resiliency from pandemic outbreak (Covid19), climate change and extreme weather impacts, and sea-level rise.
We expect papers to address (but not limited to) some of the following issues:
a. Understand spread, impact, and control of pandemics (Covid19).
b. Understand impact of sea-level rise on critical infrastructure, land use, and its impact on economy and population distribution and dynamics
c. Estimate climate and weather impacts on population and critical infrastructure finer resolution.
d. Use critical infrastructure data to understand socio-economic vulnerability at higher spatial resolutions (e.g. sub-county).
e. Any application of critical infrastructure data.
f. Data fusion techniques to ingest data from widely disparate sources in widely disparate formats.
g. Methodology and challenges to ensure that geolocations from source data is on entity.
h. Applications showcasing spatially enabled critical infrastructure data derived from open source Database management and challenges associated with creation and maintenance of open source derived spatial data
i. Examples of geospatial datasets created from open source data.
|Presenter||Kieran Maynard*, , Neil Oculi, School for Field Studies, Mark A. Boyer, The University of Connecticut, Comparative Analysis of Small Island Developing States (SIDS) to Sea Level Rise Scenarios||15||8:00 AM|
|Presenter||Wanyun Shao*, University of Alabama, Hamed Moftakhari, University of Alabama, Hamid Moradkhani, University of Alabama, Comparing public perceptions of sea level rise with scientific projections across five states of the U.S. Gulf Coast region||15||8:15 AM|
|Presenter||Benjamin Lewis, Harvard University, Devika Kakkar, Harvard University, Weihe Guan*, Harvard University, Ryan Enos, Harvard University, Jacob R Brown, Harvard University, An Approach to Interactive Model Development on Big Data||15||8:30 AM|
|Presenter||Joseph Bentley, Oak Ridge National Laboratory, Gautam Thakur*, Oak Ridge National Laboratory, Kelly Sims, Oak Ridge National Laboratory, Jamie Wray, Oak Ridge National Laboratory, Chantelle Fortier, Oak Ridge National Laboratory, Kevin Sparks, Oak Ridge National Laboratory, David Sheldon, Oak Ridge National Laboratory, Spatio-Semantic Comparison Used in POI Deduplication and Dataset Enhancement||15||8:45 AM|
|Presenter||Lola Gulyamova*, Tashkent State Technical University, Open Source Resources: Challenges and Prospects to Manage Land Use in Midsized Cities in Uzbekistan||15||9:00 AM|
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