Quantifying Remoteness for Risk and Resilience Assessment Using Nighttime Satellite Imagery
Publication: Journal of Computing in Civil Engineering
Volume 34, Issue 5
Abstract
Remoteness has a crucial role in risk assessments of megaprojects, resilience assessments of communities and infrastructure, and a wide range of public policymaking. The existing measures of remoteness require an extensive amount of population census and of road and infrastructure network data, and often are limited to narrow scopes. This paper presents a methodology to quantify remoteness using nighttime satellite imagery. The light clusters of nighttime satellite imagery are direct yet unintended consequences of human settled populations and urbanization; therefore, the absence of illuminated clusters is considered as evidence of remoteness. The proposed nighttime remoteness index (NIRI) conceptualizes the remoteness based on the distribution of nighttime lights within radii of up to 1,000 km. A predictive model was created using machine learning techniques such as multivariate adaptive regression splines and support vector machines regressions to establish a reliable and accurate link between nighttime lights and the Accessibility/Remoteness Index of Australia (ARIA). The model was used to establish NIRI for the United States and Canada, and in different years. The index was compared with the Canadian remoteness indexes published by Statistics Canada.
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Data Availability Statement
Some or all data, models, or code generated or used during the study are available in a repository online in accordance with funder data retention policies.
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The Global Radiance Calibrated Nighttime Lights for 2011 are available at: https://www.ngdc.noaa.gov/eog/dmsp/download_radcal.html
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The VIIRS Day/Night Band Nighttime Lights for 2015 are available at: https://www.ngdc.noaa.gov/eog/viirs/download_dnb_composites.html
Acknowledgments
We benefited from comments of Murray Pearson and Nick Mason in formulizing the index to best suit practical risk assessment procedures of large resource projects. We thank Statistics Canada for sharing the raw data behind the Canadian remoteness indexes with this research. We used the publicly available data of the nighttime lights satellite imagery provided by National Centers for Environmental Information (NCEI) and Earth Observation Group (EOG), and the Accessibility/Remoteness Index of Australia (ARIA) for 2011 provided by the Hugo Centre for Migration and Population Research. The project is funded by Hatch and the Natural Sciences and Engineering Research Council (NSERC) of Canada grant CRDPH 491877-15.
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Journal of Computing in Civil Engineering
Volume 34 • Issue 5 • September 2020
Copyright
©2020 American Society of Civil Engineers.
History
Received: Sep 9, 2019
Accepted: Feb 25, 2020
Published online: May 23, 2020
Published in print: Sep 1, 2020
Discussion open until: Oct 23, 2020
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