Metrics for the comparative analysis of geospatial datasets with applications to high-resolution grid-based population data |
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Authors: | Aarthy Sabesan Kathleen Abercrombie Auroop R Ganguly Budhendra Bhaduri Eddie A Bright Phillip R Coleman |
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Institution: | (1) Computational Sciences and Engineering, Oak Ridge National Laboratory, 1 Bethel Valley Road, MS 6085, Oak Ridge, TN 37831, USA;(2) Computational Sciences and Engineering, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN 37831, USA |
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Abstract: | Geospatial data sciences have emerged as critical requirements for high-priority application solutions in diverse areas, including,
but not limited to, the mitigation of natural and man-made disasters. Three sets of metrics, adopted or customized from geo-statistics,
applied meteorology and signal processing, are tested in terms of their ability to evaluate geospatial datasets, specifically
two population databases commonly used for disaster preparedness and consequence management. The two high-resolution, grid-based
population datasets are the following: The LandScan dataset available from the Geographic Information Science and Technology
(GIST) group at the Oak Ridge National Laboratory (ORNL), and the Gridded Population of the World (GPW) dataset available
from the Center for International Earth Science Information Network (CIESIN) group at Columbia University. Case studies evaluate
population data across the globe, specifically, the metropolitan areas of Washington DC, USA, Los-Angeles, USA, and Houston,
USA, and London, UK, as well as the country of Iran. The geospatial metrics confirm that the two population datasets have
significant differences, especially in the context of their utility for disaster readiness and mitigation. While this paper
primarily focuses on grid based population datasets and disaster management applications, the sets of metrics developed here
can be generalized to other geospatial datasets and applications. Future research needs to develop metrics for geospatial
and temporal risks and associated uncertainties in the context of disaster management.
The U. S. Government’s right to retain a non-exclusive, royalty-free license in and to any copyright is acknowledged. |
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Keywords: | Geospatial data Population Statistical evaluation Disaster management |
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