Measuring the contribution of built-settlement data to global population mapping.

Autor:	Nieves JJ; WorldPop, School of Geography and Environmental Science, University of Southampton, UK., Bondarenko M; WorldPop, School of Geography and Environmental Science, University of Southampton, UK., Kerr D; WorldPop, School of Geography and Environmental Science, University of Southampton, UK., Ves N; WorldPop, School of Geography and Environmental Science, University of Southampton, UK., Yetman G; Center for International Earth Science Information Network (CIESIN), Columbia University, Palisades, NY, USA., Sinha P; WorldPop, School of Geography and Environmental Science, University of Southampton, UK.; Department of Geography and Geosciences, University of Louisville, Kentucky, USA., Clarke DJ; WorldPop, School of Geography and Environmental Science, University of Southampton, UK., Sorichetta A; WorldPop, School of Geography and Environmental Science, University of Southampton, UK., Stevens FR; WorldPop, School of Geography and Environmental Science, University of Southampton, UK.; Department of Geography and Geosciences, University of Louisville, Kentucky, USA., Gaughan AE; WorldPop, School of Geography and Environmental Science, University of Southampton, UK.; Department of Geography and Geosciences, University of Louisville, Kentucky, USA., Tatem AJ; WorldPop, School of Geography and Environmental Science, University of Southampton, UK.
Jazyk:	angličtina
Zdroj:	Social sciences & humanities open [Soc Sci Humanit Open] 2021; Vol. 3 (1), pp. 100102.
DOI:	10.1016/j.ssaho.2020.100102
Abstrakt:	Top-down population modelling has gained applied prominence in public health, planning, and sustainability applications at the global scale. These top-down population modelling methods often rely on remote-sensing (RS) derived representation of the built-environment and settlements as key predictive covariates. While these RS-derived data, which are global in extent, have become more advanced and more available, gaps in spatial and temporal coverage remain. These gaps have prompted the interpolation of the built-environment and settlements, but the utility of such interpolated data in further population modelling applications has garnered little research. Thus, our objective was to determine the utility of modelled built-settlement extents in a top-down population modelling application. Here we take modelled global built-settlement extents between 2000 and 2012, created using a spatio-temporal disaggregation of observed settlement growth. We then demonstrate the applied utility of such annually modelled settlement data within the application of annually modelling population, using random forest informed dasymetric disaggregations, across 172 countries and a 13-year period. We demonstrate that the modelled built-settlement data are consistently the 2nd most important covariate in predicting population density, behind annual lights at night, across the globe and across the study period. Further, we demonstrate that this modelled built-settlement data often provides more information than current annually available RS-derived data and last observed built-settlement extents. Competing Interests: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of the data; in the writing of the manuscript, or in the decision to publish the results.Table 1List of countries modelled. Countries are given by their ISO standard 3-letter code.Table 1RegionCountries (ISO 3 Code)East Asia & the PacificASM AUS BRN CHN FJI FSM GUM HKG IDN JPN KHM KIR KOR LAO MMR MNG MNP MYS NCL NZL PHL PNG PRK PYF SGP SLB THA TLS TUV TWN VNM VUT WSMEuropeALB ARM AUT AZE BEL BGR BIH BLR CHE CYP CZE DEU DNK ESP EST FIN FRA FRO GBR GEO GRC HRV HUN IRL ISL ITA KOS LTU LUX LVA MDA MKD MLT NLD NOR POL PRT ROU RUS SRB SVK SVN SWE TUR UKRLatin America & the CaribbeanABW ARG BOL BRA CHL COL CRI CUB CUW DOM ECU GTM GUY HND HTI MEX MTQ NIC PAN PER PRI PRY SLV SUR URY VENSouth AsiaAFG BGD BTN IND LKA MDV NPL PAKSub-Saharan AfricaAGO BDI BEN BFA CAF CIV CMR COD ETH GAB GHA GIN GMB GNB KEN LBR LSO MDG MLI MOZ MRT MUS MWI NAM NER NGA RWA SEN SLE SOM SWZ SYC TCD TGO TZA UGA ZAF ZMB ZWEWest Asia & Northern AfricaDZA EGY IRN IRQ ISR JOR KAZ KGZ LBN MAR OMN QAT SAU SDN SSD SYR TJK TUN YEMTable 2List of countries excluded from analysis and corresponding reason for exclusionTable 2Countries ExcludedReason for ExclusionAntarcticaNot modelled at allUnited States of AmericaResource limitsAnguilla; Aland Islands; Andorra; United Arab Emirates;Antigua and Barbuda; Bonaire, Sint Eustatius, and Saba; Bahrain; Bahamas; Saint Barthelemy; Belize; Bermuda; Barbados; Botswana; Republic of Congo; Cook Islands; Comoros; Cape Verde; Cayman Islands; Djibouti; Dominica; Eritrea;Western Sahara; Falkland Islands; Guernsey; Gibraltar; Guadeloupe; Equatorial Guinea; Grenada; French Guiana;Isle of Man; Jamaica; Saint Kitts and Nevis; Kuwait; Libya;Saint Lucia; Lichtenstein; Macao; Saint Martin (French portion); Monaco; Marshall Islands; Montenegro; Montserrat; Mayotte; New Caledonia; Norfolk Island; Niue; Nauru; Pitcairn Islands; Palau; Palestine; Reunion; Saint Helena;Svalbard and Jan Mayen Islands; San Marino;Saint Pierre and Miquelon; Sao Tome and Principe;Sint Maarten (Dutch portion); Seychelles;Turks and Caicos Islands; Tokelau; Turkmenistan; Tonga; Trinidad and Tobago; Vatican City;Saint Vincent and the Grenadines; British Virgin Islands;Virgin Islands (U.S.); Wallis and FutunaRegional parameterization of BSGM and or population model (© 2020 The Author(s).)
Databáze:	MEDLINE
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