{"data":{"id":"10.6084/m9.figshare.1008322.v3","type":"dois","attributes":{"doi":"10.6084/m9.figshare.1008322.v3","prefix":"10.6084","suffix":"m9.figshare.1008322.v3","identifiers":[],"alternateIdentifiers":[],"creators":[{"name":"Vorel, Jakub","nameType":"Personal","givenName":"Jakub","familyName":"Vorel","affiliation":[],"nameIdentifiers":[]},{"name":"Grill, Stanislav","nameType":"Personal","givenName":"Stanislav","familyName":"Grill","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Land use change propensity maps"}],"publisher":"Taylor \u0026 Francis","container":{},"publicationYear":2015,"subjects":[{"subject":"Science Policy"},{"subject":"Inorganic Chemistry"},{"subject":"FOS: Chemical sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Chemical sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Cancer"},{"subject":"Biological Sciences"},{"subject":"Sociology"},{"subject":"FOS: Sociology","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Sociology","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Astronomy, Astrophysics, Space Science"},{"subject":"Ecology"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"FOS: Biological sciences","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Chemistry"},{"subject":"Earth and Environmental Sciences"},{"subject":"Neuroscience"},{"subject":"Microbiology"}],"contributors":[],"dates":[{"date":"2015-11-16","dateType":"Created"},{"date":"2016-01-18","dateType":"Updated"},{"date":"2015","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsSupplementTo","relatedIdentifier":"10.1080/17445647.2014.911709","relatedIdentifierType":"DOI"},{"relationType":"IsPreviousVersionOf","relatedIdentifier":"10.6084/m9.figshare.1008322","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["22183642 Bytes"],"formats":[],"version":null,"rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Land-use control and planning instruments face new challenges amid growing pressure for urbanization and annexation of valuable agricultural land and natural areas. This paper presents the land-use change propensity map, which shows the local potential for specific land-use changes. Propensity is derived empirically on the basis of historical land-use changes, with an explicit evaluation of characteristics that contributed to land-use change.Each step in creating a propensity map is described: selecting data that best represents land-use changes, identifying potential drivers of land-use change and the statistical inference of their impact on land-use change on the basis of observed historical land-use changes. The resulting propensity for land-use change is represented in the form of a binary logit model that evaluates the probability of specific land-use changes.A series of propensity maps for the territory of the Tábor microregion in the Czech Republic was created to demonstrate the method. The scale of the propensity maps is 1:310,000, and they cover an area of 1002 km\u003csup\u003e2\u003c/sup\u003e. Each propensity map represents the specific propensity for conversion from non-urban uses to family, multi-family and individual recreation houses. The evaluated propensity can be further compared to existing or proposed land-use regulations.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"xml":"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","url":"https://tandf.figshare.com/articles/dataset/Land_use_change_propensity_maps/1008322/3","contentUrl":null,"metadataVersion":7,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"viewsOverTime":[],"downloadCount":0,"downloadsOverTime":[],"referenceCount":0,"citationCount":0,"citationsOverTime":[],"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2016-01-18T23:19:54.000Z","registered":"2016-01-18T23:19:55.000Z","published":"2015","updated":"2020-09-04T23:36:19.000Z"},"relationships":{"client":{"data":{"id":"figshare.ars","type":"clients"}},"provider":{"data":{"id":"otjm","type":"providers"}},"media":{"data":{"id":"10.6084/m9.figshare.1008322.v3","type":"media"}},"references":{"data":[]},"citations":{"data":[]},"parts":{"data":[]},"partOf":{"data":[]},"versions":{"data":[]},"versionOf":{"data":[]}}}}