{"data":{"id":"10.7807/pop:forecast:suf:v1","type":"dois","attributes":{"doi":"10.7807/pop:forecast:suf:v1","prefix":"10.7807","suffix":"pop:forecast:suf:v1","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"RWI – Leibniz Institute for Economic Research","nameType":"Organizational","givenName":null,"familyName":null,"affiliation":[],"nameIdentifiers":[{"schemeUri":"https://ror.org","nameIdentifier":"https://ror.org/02pse8162","nameIdentifierScheme":"ROR"}]}],"titles":[{"lang":"en","title":"Population Forecast","titleType":null},{"lang":"de","title":"Bevölkerungsprognose","titleType":null}],"publisher":"RWI – Leibniz Institute for Economic Research","container":{"type":"DataRepository","title":"RWI-GEO-GRID"},"publicationYear":2017,"subjects":[{"lang":"en","subject":"J11 Demographic Trends, Macroeconomic Effects, and Forecasts","subjectScheme":"JEL"},{"lang":"de","subject":"Bevölkerungsprognose","subjectScheme":""},{"lang":"de","subject":"kleinräumig","subjectScheme":""},{"lang":"en","subject":"population projection","subjectScheme":""},{"lang":"en","subject":"small scale","subjectScheme":""}],"contributors":[{"name":"Kaeding, Matthias","nameType":"Personal","givenName":"Matthias","familyName":"Kaeding","affiliation":["RWI – Leibniz Institute for Economic Research"],"contributorType":"ContactPerson","nameIdentifiers":[]},{"name":"Breidenbach, Philipp","nameType":"Personal","givenName":"Philipp","familyName":"Breidenbach","affiliation":["RWI – Leibniz Institute for Economic Research"],"contributorType":"ContactPerson","nameIdentifiers":[]}],"dates":[{"date":"2015","dateType":"Collected","dateInformation":null},{"date":"2017","dateType":"Issued","dateInformation":null}],"language":"de","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"schemeUri":null,"schemeType":null,"relationType":"References","relatedIdentifier":"10.7807/microm:einwgeal:v5","resourceTypeGeneral":null,"relatedIdentifierType":"DOI","relatedMetadataScheme":null,"relationTypeInformation":null}],"relatedItems":[],"sizes":["27,1 GB"],"formats":[],"version":"1","rightsList":[{"lang":"de","rights":"Keine kommerzielle Nutzung"},{"lang":"en","rights":"non-commercial use only"}],"descriptions":[{"lang":"de","description":"Datengrundlage sind Bevölkerungsdaten aus dem RWI-GEO-GRID (10.7807/microm:einwGeAl:v5); für jedes 1km2 Raster im Jahr 2015 liegen Bevölkerungszahlen für  Altersjahr (0,…,100,100+) und Geschlecht (männlich, weiblich) vor. Diese Bevökerungsdaten werden bis zum Jahr 2050 fortgeschrieben. Zur Beachtung von räumlichen Strukturen werden Hilfsvariablen auf lokaler Ebene herangespielt.","descriptionType":"Abstract"},{"lang":"en","description":"Population projections based on data of RWI-Geo-GRID (10.7807/microm:einwGeAl:v5): Population numbers for ages [0,…,100,100+] and sex [male,female] for every 1km2 grid from year 2015.  Projections are done up to the year 2050. Spatial structure is accounted for using local variables.","descriptionType":"Abstract"},{"lang":"de","description":"RWI-GEO-GRID","descriptionType":"SeriesInformation"},{"lang":"en","description":"RWI-GEO-GRID","descriptionType":"SeriesInformation"},{"lang":"en","description":"Simulation","descriptionType":"Methods"},{"lang":"de","description":"Vollerhebung aller bewohnter Raster im RWI-GEO-GRID im Jahr 2015","descriptionType":"Methods"}],"geoLocations":[{"geoLocationPlace":"Germany"}],"fundingReferences":[],"xml":"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