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      Social media usage reveals recovery of small businesses after natural hazard events

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          Abstract

          The challenge of nowcasting the effect of natural hazard events (e.g., earthquakes, floods, hurricanes) on assets, people and society is of primary importance for assessing the ability of such systems to recover from extreme events. Traditional recovery estimates, such as surveys and interviews, are usually costly, time consuming and do not scale. Here we present a methodology to indirectly estimate the post-emergency recovery status (downtime) of small businesses in urban areas looking at their online posting activity on social media. Analysing the time series of posts before and after an event, we quantify the downtime of small businesses for three natural hazard events occurred in Nepal, Puerto Rico and Mexico. A convenient and reliable method for nowcasting the post-emergency recovery status of economic activities could help local governments and decision makers to better target their interventions and distribute the available resources more effectively.

          Abstract

          Natural hazards can have huge impacts on individuals and societies, however, monitoring the economic recovery in the aftermath of extreme events remains a challenge. Here, the authors find that Facebook posting activity of small businesses can be used to monitor post-disaster economic recovery, and can allow local governments to better target distribution of resources.

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          Dynamic population mapping using mobile phone data.

          During the past few decades, technologies such as remote sensing, geographical information systems, and global positioning systems have transformed the way the distribution of human population is studied and modeled in space and time. However, the mapping of populations remains constrained by the logistics of censuses and surveys. Consequently, spatially detailed changes across scales of days, weeks, or months, or even year to year, are difficult to assess and limit the application of human population maps in situations in which timely information is required, such as disasters, conflicts, or epidemics. Mobile phones (MPs) now have an extremely high penetration rate across the globe, and analyzing the spatiotemporal distribution of MP calls geolocated to the tower level may overcome many limitations of census-based approaches, provided that the use of MP data is properly assessed and calibrated. Using datasets of more than 1 billion MP call records from Portugal and France, we show how spatially and temporarily explicit estimations of population densities can be produced at national scales, and how these estimates compare with outputs produced using alternative human population mapping methods. We also demonstrate how maps of human population changes can be produced over multiple timescales while preserving the anonymity of MP users. With similar data being collected every day by MP network providers across the world, the prospect of being able to map contemporary and changing human population distributions over relatively short intervals exists, paving the way for new applications and a near real-time understanding of patterns and processes in human geography.
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            OSMnx: New methods for acquiring, constructing, analyzing, and visualizing complex street networks

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              Rapid assessment of disaster damage using social media activity

              Researchers show a correlation between per-capita social media activity and disaster damage, facilitating its rapid assessment.
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                Author and article information

                Contributors
                flavia.deluca@bristol.ac.uk
                f.simini@bristol.ac.uk
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                2 April 2020
                2 April 2020
                2020
                : 11
                : 1629
                Affiliations
                [1 ]ISNI 0000 0004 1936 7603, GRID grid.5337.2, University of Bristol, Department of Engineering Mathematics, ; Bristol, BS8 1UB UK
                [2 ]ISNI 0000 0004 1936 7603, GRID grid.5337.2, University of Bristol, Department of Civil Engineering, ; Bristol, BS8 1TR UK
                [3 ]ISNI 0000 0004 5903 3632, GRID grid.499548.d, The Alan Turing Institute, ; London, UK
                Author information
                http://orcid.org/0000-0001-9496-5249
                http://orcid.org/0000-0003-2387-8580
                http://orcid.org/0000-0001-8675-3529
                Article
                15405
                10.1038/s41467-020-15405-7
                7118130
                32242023
                34dc677f-cc01-4e60-81e0-2f94c2c091cc
                © The Author(s) 2020

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 30 July 2019
                : 5 March 2020
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100000266, RCUK | Engineering and Physical Sciences Research Council (EPSRC);
                Award ID: EP/N509619/1
                Award ID: EP/P510920/1
                Award ID: EP/P012906/1
                Award Recipient :
                Funded by: FundRef https://doi.org/10.13039/501100000275, Leverhulme Trust;
                Award ID: RPG2017-006
                Award Recipient :
                Categories
                Article
                Custom metadata
                © The Author(s) 2020

                Uncategorized
                natural hazards,applied mathematics,statistics,decision making,economics
                Uncategorized
                natural hazards, applied mathematics, statistics, decision making, economics

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