<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<metadata xml:lang="en">
<Esri>
<CreaDate>20250821</CreaDate>
<CreaTime>16293900</CreaTime>
<ArcGISFormat>1.0</ArcGISFormat>
<SyncOnce>TRUE</SyncOnce>
<DataProperties>
<itemProps>
<imsContentType Sync="TRUE" export="False">002</imsContentType>
</itemProps>
</DataProperties>
<scaleRange>
<minScale>50000</minScale>
<maxScale>5000</maxScale>
</scaleRange>
</Esri>
<Binary>
<Thumbnail>
<Data EsriPropertyType="PictureX">iVBORw0KGgoAAAANSUhEUgAAASwAAADICAYAAABS39xVAAAAAXNSR0IB2cksfwAAAAlwSFlzAAAO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</Data>
</Thumbnail>
</Binary>
<mdFileID>564db46c-3153-423c-ac84-90d41ec2652c</mdFileID>
<mdLang>
<languageCode value="eng">
</languageCode>
<countryCode Sync="TRUE" value="GBR">
</countryCode>
</mdLang>
<mdChar>
<CharSetCd value="004">
</CharSetCd>
</mdChar>
<mdHrLv>
<ScopeCd Sync="TRUE" value="005">
</ScopeCd>
</mdHrLv>
<mdHrLvName Sync="TRUE">dataset</mdHrLvName>
<mdContact>
<rpOrgName>Scottish Government</rpOrgName>
<rpPosName>Geospatial Team</rpPosName>
<rpCntInfo>
<cntAddress addressType="postal">
<delPoint>Victoria Quay</delPoint>
<city>City of Edinburgh</city>
<postCode>EH6 6QQ</postCode>
<country>GB</country>
<eMailAdd>GIS@gov.scot</eMailAdd>
<adminArea>Scotland</adminArea>
</cntAddress>
<cntOnlineRes>
<linkage>https://www.gov.scot/</linkage>
<protocol>WWW:LINK-1.0-http--link</protocol>
</cntOnlineRes>
</rpCntInfo>
<role>
<RoleCd value="007">
</RoleCd>
</role>
<displayName>Scottish Government</displayName>
<rpIndName>GIS Analyst</rpIndName>
</mdContact>
<mdDateSt Sync="TRUE">20250821</mdDateSt>
<mdStanName>ArcGIS Metadata</mdStanName>
<mdStanVer>1.0</mdStanVer>
<distInfo>
<distFormat>
<formatName>ESRI Shapefile</formatName>
<formatVer>1.0</formatVer>
</distFormat>
<distFormat>
<formatName>ESRI REST</formatName>
<formatVer>1.0</formatVer>
</distFormat>
<distFormat>
<formatName>WMS</formatName>
<formatVer>1.3.0</formatVer>
</distFormat>
<distFormat>
<formatName>WFS</formatName>
<formatVer>2.0.0</formatVer>
</distFormat>
<distTranOps>
<onLineSrc>
<linkage>https://maps.gov.scot/ATOM/shapefiles/SG_UrbanRural_2016.zip</linkage>
<protocol>WWW:DOWNLOAD-1.0-http--download</protocol>
<orName>Urban Rural Classification 2016</orName>
<orDesc>ESRI Shapefile Download</orDesc>
<orFunct>
<OnFunctCd value="001">
</OnFunctCd>
</orFunct>
</onLineSrc>
<onLineSrc>
<linkage>https://maps.gov.scot/server/services/ScotGov/PeopleSociety/MapServer/WMSServer?</linkage>
<protocol>OGC:WMS</protocol>
<orName>UrbanRural</orName>
<orDesc>OGC View Service</orDesc>
<orFunct>
<OnFunctCd value="002">
</OnFunctCd>
</orFunct>
</onLineSrc>
<onLineSrc>
<linkage>https://maps.gov.scot/ATOM/shapefiles/SG_UrbanRural_2013_2014.zip</linkage>
<protocol>WWW:DOWNLOAD-1.0-http--download</protocol>
<orName>Urban Rural Classification 2013-2014</orName>
<orDesc>ESRI Shapefile Download</orDesc>
<orFunct>
<OnFunctCd value="001">
</OnFunctCd>
</orFunct>
</onLineSrc>
<onLineSrc>
<linkage>https://maps.gov.scot/server/rest/services/ScotGov/PeopleSociety/MapServer/1</linkage>
<protocol>WWW:LINK-1.0-http--link</protocol>
<orName>Urban Rural Classification</orName>
<orDesc>ESRI REST Service</orDesc>
<orFunct>
<OnFunctCd value="001">
</OnFunctCd>
</orFunct>
</onLineSrc>
<onLineSrc>
<linkage>https://maps.gov.scot/ATOM/shapefiles/SG_UrbanRural_2011_2012.zip</linkage>
<protocol>WWW:DOWNLOAD-1.0-http--download</protocol>
<orName>Urban Rural Classification 2011-2012</orName>
<orDesc>ESRI Shapefile Download</orDesc>
<orFunct>
<OnFunctCd value="001">
</OnFunctCd>
</orFunct>
</onLineSrc>
<onLineSrc>
<linkage>https://maps.gov.scot/ATOM/shapefiles/SG_UrbanRural_2020.zip</linkage>
<protocol>WWW:LINK-1.0-http--link</protocol>
<orName>Urban Rural Classification 2020</orName>
<orDesc>ESRI Shapefile Download</orDesc>
<orFunct>
<OnFunctCd value="001">
</OnFunctCd>
</orFunct>
</onLineSrc>
<onLineSrc>
<linkage>https://maps.gov.scot/server/services/ScotGov/PeopleSociety/MapServer/WFSServer?</linkage>
<protocol>OGC:WFS</protocol>
<orName>SOC:UrbanRural</orName>
<orDesc>OGC Feature Download Service</orDesc>
<orFunct>
<OnFunctCd value="001">
</OnFunctCd>
</orFunct>
</onLineSrc>
<onLineSrc>
<linkage>https://www.gov.scot/collections/agriculture-fisheries-and-rural-statistics/#urbanruralclassification</linkage>
<protocol>WWW:LINK-1.0-http--link</protocol>
<orName>Topic Page - Scottish Government Website</orName>
<orDesc>Urban Rural Classification</orDesc>
</onLineSrc>
<transSize Sync="TRUE">32.497</transSize>
<onLineSrc>
<linkage>https://maps.gov.scot/ATOM/shapefiles/SG_UrbanRural_2022.zip</linkage>
<protocol>WWW:LINK-1.0-http--link</protocol>
<orName>Urban Rural Classification 2022</orName>
<orDesc>ESRI Shapefile Download</orDesc>
<orFunct>
<OnFunctCd value="001">
</OnFunctCd>
</orFunct>
</onLineSrc>
</distTranOps>
</distInfo>
<dataIdInfo>
<idCitation>
<resTitle>UrbanRural</resTitle>
<date>
<reviseDate>2024-12-16T00:00:00</reviseDate>
</date>
<presForm>
<PresFormCd Sync="TRUE" value="005">
</PresFormCd>
</presForm>
<citId>
<identCode>SG_UrbanRural_2022</identCode>
</citId>
</idCitation>
<idAbs>&lt;div style='text-align:Left;'&gt;&lt;div&gt;&lt;div&gt;&lt;p&gt;&lt;span&gt;The Scottish Government (SG) Urban Rural Classification provides a consistent way of defining urban and rural areas across Scotland. The classification aids policy development and the understanding of issues facing urban, rural and remote communities. It is based upon two main criteria: (i) population as defined by National Records of Scotland (NRS), and (ii) accessibility based on drive time analysis to differentiate between accessible and remote areas in Scotland. The classification can be analysed in a two, three, six or eight fold form. The two-fold classification simply distinguishes between urban and rural areas through two categories, urban and rural, while the three-fold classification splits the rural category between accessible and remote. Most commonly used is the 6-fold classification which distinguishes between urban, rural, and remote areas through six categories. The 8-fold classification further distinguishes between remote and very remote regions. The Classification is normally updated on a biennial basis, with the current dataset reflective of the year 2022. Data for previous versions are available for download in ESRI Shapefile format.&lt;/span&gt;&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;</idAbs>
<idPoC>
<rpOrgName>Scottish Government</rpOrgName>
<rpPosName>Geospatial Team</rpPosName>
<rpCntInfo>
<cntAddress addressType="postal">
<delPoint>Victoria Quay</delPoint>
<city>City of Edinburgh</city>
<postCode>EH6 6QQ</postCode>
<country>GB</country>
<eMailAdd>GIS@gov.scot</eMailAdd>
<adminArea>Scotland</adminArea>
</cntAddress>
<cntOnlineRes>
<linkage>https://www.gov.scot/</linkage>
<protocol>WWW:LINK-1.0-http--link</protocol>
</cntOnlineRes>
</rpCntInfo>
<rpIndName>GIS Analyst</rpIndName>
<role>
<RoleCd value="010">
</RoleCd>
</role>
</idPoC>
<idPoC>
<rpOrgName>Scottish Government</rpOrgName>
<rpPosName>Rural and Environment Science and Analytical Services</rpPosName>
<rpCntInfo>
<cntAddress addressType="postal">
<delPoint>Victoria Quay</delPoint>
<city>City of Edinburgh</city>
<postCode>EH6 6QQ</postCode>
<country>GB</country>
<eMailAdd>RuralStatistics@gov.scot</eMailAdd>
<adminArea>Scotland</adminArea>
</cntAddress>
<cntOnlineRes>
<linkage>https://www.gov.scot/</linkage>
<protocol>WWW:LINK-1.0-http--link</protocol>
</cntOnlineRes>
</rpCntInfo>
<rpIndName>Statistician</rpIndName>
<role>
<RoleCd value="007">
</RoleCd>
</role>
</idPoC>
<themeKeys>
<thesaName>
<resTitle>GEMET - Concepts, version 4.2.3</resTitle>
<date>
<pubDate>2021-12-06T00:00:00</pubDate>
</date>
<citId>
<identCode>geonetwork.thesaurus.external.theme.gemet</identCode>
</citId>
<citOnlineRes>
<linkage>https://www.eionet.europa.eu/gemet/en/themes/</linkage>
<orFunct>
<OnFunctCd value="002">
</OnFunctCd>
</orFunct>
</citOnlineRes>
</thesaName>
<keyword>urban area</keyword>
<keyword>rural area</keyword>
</themeKeys>
<themeKeys>
<thesaName>
<resTitle>IPSV Subjects List</resTitle>
<date>
<pubDate>2020-11-18T00:00:00</pubDate>
</date>
<citId>
<identCode>geonetwork.thesaurus.external.theme.subjects</identCode>
</citId>
<citOnlineRes>
<linkage>http://id.esd.org.uk/list/subjects</linkage>
<orFunct>
<OnFunctCd value="002">
</OnFunctCd>
</orFunct>
</citOnlineRes>
</thesaName>
<keyword>Rural communities</keyword>
<keyword>Urban communities</keyword>
</themeKeys>
<themeKeys>
<thesaName>
<resTitle>GEMET - INSPIRE themes, version 1.0</resTitle>
<date>
<pubDate>2008-06-01T00:00:00</pubDate>
</date>
<citId>
<identCode>geonetwork.thesaurus.external.theme.httpinspireeceuropaeutheme-theme</identCode>
</citId>
<citOnlineRes>
<linkage>http://www.eionet.europa.eu/gemet/inspire_themes</linkage>
<orFunct>
<OnFunctCd value="002">
</OnFunctCd>
</orFunct>
</citOnlineRes>
</thesaName>
<keyword>Population distribution — demography</keyword>
</themeKeys>
<searchKeys>
<keyword>Rural communities</keyword>
<keyword>Urban communities</keyword>
<keyword>urban area</keyword>
<keyword>rural area</keyword>
<keyword>Population distribution — demography</keyword>
</searchKeys>
<resConst>
<LegConsts>
<othConsts>The following attribution statement must be used to acknowledge the source of the information: Copyright Scottish Government, contains Ordnance Survey data © Crown copyright and database right [insert year].</othConsts>
<accessConsts>
<RestrictCd value="008">
</RestrictCd>
</accessConsts>
<useConsts>
<RestrictCd value="008">
</RestrictCd>
</useConsts>
</LegConsts>
</resConst>
<dataScale>
<equScale>
<rfDenom>10000</rfDenom>
</equScale>
</dataScale>
<dataLang>
<languageCode value="English">
</languageCode>
<countryCode Sync="TRUE" value="GBR">
</countryCode>
</dataLang>
<dataExt>
<geoEle>
<GeoBndBox>
<westBL>-9.229868</westBL>
<eastBL>-0.705303</eastBL>
<southBL>54.513381</southBL>
<northBL>60.866111</northBL>
</GeoBndBox>
</geoEle>
<geoEle>
<GeoDesc>
<geoId>
<identCode>http://statistics.gov.scot/id/statistical-geography/S92000003</identCode>
</geoId>
<exTypeCode>0</exTypeCode>
</GeoDesc>
</geoEle>
<tempEle>
<TempExtent>
<exTemp>
<TM_Period>
<tmBegin>2022-03-20T00:00:00</tmBegin>
<tmEnd>2022-03-20T00:00:00</tmEnd>
</TM_Period>
</exTemp>
</TempExtent>
</tempEle>
<geoEle>
<GeoDesc>
<exTypeCode>0</exTypeCode>
<geoId>
<identCode>GB-SCT</identCode>
<identAuth>
<resTitle>ISO 3166 — Codes for the representation of names of countries and their subdivisions</resTitle>
<date>
<pubDate>2022-11-29T00:00:00</pubDate>
</date>
<citOnlineRes>
<linkage>https://www.iso.org/obp/ui/#iso:code:3166:GB</linkage>
</citOnlineRes>
</identAuth>
</geoId>
</GeoDesc>
</geoEle>
</dataExt>
<suppInfo>http://www.gov.scot/urbanrural</suppInfo>
<envirDesc Sync="FALSE">Esri ArcGIS 13.5.2.57366</envirDesc>
<spatRpType>
<SpatRepTypCd Sync="TRUE" value="001">
</SpatRepTypCd>
</spatRpType>
<dataChar>
<CharSetCd value="004">
</CharSetCd>
</dataChar>
<idPurp>The Scottish Government (SG) Urban Rural Classification provides a consistent way of defining urban and rural areas across Scotland.</idPurp>
<idCredit>© Scottish Government, contains Ordnance Survey data © Crown copyright and database right 2024.</idCredit>
<resConst>
<LegConsts>
<inspireAccessUseConditions>
<ConditionsAccUseCd value="001">
</ConditionsAccUseCd>
</inspireAccessUseConditions>
</LegConsts>
</resConst>
<resConst>
<LegConsts>
<inspirePublicAccessLimits>
<PublicAccessCd value="001">
</PublicAccessCd>
</inspirePublicAccessLimits>
</LegConsts>
</resConst>
<resConst>
<Consts>
<useLimit>&lt;div style='text-align:Left;'&gt;&lt;div&gt;&lt;div&gt;&lt;p&gt;&lt;span&gt;Available under the terms of the &lt;/span&gt;&lt;a href='https://www.nationalarchives.gov.uk/doc/open-government-licence' style='text-decoration:underline;'&gt;&lt;span&gt;Open Government Licence&lt;/span&gt;&lt;/a&gt;&lt;span&gt;. The following attribution statement must be used to acknowledge the source of the information: Copyright Scottish Government, contains Ordnance Survey data © Crown copyright and database right (insert year).&lt;/span&gt;&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt;</useLimit>
</Consts>
</resConst>
<resMaint>
<maintFreq>
<MaintFreqCd value="009">
</MaintFreqCd>
</maintFreq>
</resMaint>
<tpCat>
<TopicCatCd value="016">
</TopicCatCd>
</tpCat>
</dataIdInfo>
<mdMaint>
<maintFreq>
<MaintFreqCd value="009">
</MaintFreqCd>
</maintFreq>
</mdMaint>
<dqInfo>
<report dimension="" type="DQDomConsis">
<measResult>
<ConResult>
<conSpec>
<resTitle>Commission Regulation (EU) No 1089/2010 of 23 November 2010 implementing Directive 2007/2/EC of the European Parliament and of the Council as regards interoperability of spatial data sets and services</resTitle>
<date>
<pubDate>2010-12-08T00:00:00</pubDate>
</date>
</conSpec>
<conPass>false</conPass>
<conExpl>Not conformant</conExpl>
</ConResult>
</measResult>
</report>
<dataLineage>
<statement>The Scottish Government Urban Rural Classification was created by combining population and accessibility information to distinguish between urban and rural areas across Scotland. Population information is sourced from the Settlements dataset provided by National Records of Scotland (NRS), and accessibility information is obtained by calculating drive times from the centres of Settlements with a population of 10,000 or more (i.e. urban areas). Datasets used to create the classification are as follows: • Scottish Settlements, centroids and population estimates (NRS) • English Settlement centroids for populations of 10,000 or more (i.e. Berwick-upon-Tweed and Carlisle, Ordnance Survey) • Transport network for Scotland and major routes for Northern England (Ordnance Survey) • Scottish Ferry Routes (Scottish Government) • High and Low Water Mark Coastline (Ordnance Survey) The first stage in creating the classification was to categorise the Settlements dataset using the population thresholds of 125,000, 10,000 and 3,000 to identify those settlements from which drive times will be calculated. Settlements were grouped into the following categories: (1) Large Urban Areas - populations of 125,000 or more (2) Other Urban Areas - populations of 10,000 to 124,999 (3) Small Towns - populations of 3,000 to 9,999 (4) Rural Areas - populations less than 3,000 To distinguish between accessible and remote areas, drive time isochrones were created based on a 30 minute drive time from population weighted centroids of Settlements with a population of 10,000 or more (i.e. Large and Other Urban Areas, and including the two settlements in northern England of Berwick-upon-Tweed and Carlisle). For the 8-fold Classification, an additional 60 minute drive time was also calculated. Thus, the following definitions of remoteness were defined: (1) Accessible - Areas within a 30 minute drive time from Settlements of 10,000 or more (2) Remote - Areas that are more than a 30 minute drive time (6-fold classification), or areas that have a drive time between 30 and 60 minutes (8-fold classification) from a Settlement of 10,000 or more (3) Very Remote - Areas that are more than a 60 minute drive time from a Settlement of 10,000 or more (8-fold only) Combining both the population and accessibility measures, a Scotland-wide Urban Rural Classification can be defined. The 6-fold classification distinguishes between urban, rural and remote areas by the following categories: (1) Large Urban Areas - Settlements of 125,000 people or more (2) Other Urban Areas - Settlements of 10,000 to 124,999 people (3) Accessible Small Towns - Settlements of 3,000 to 9,999 people, and within a 30 minute drive time of a Settlement of 10,000 or more (4) Remote Small Towns - Settlements of 3,000 to 9,999 people, and with a drive time of over 30 minutes to a Settlement of 10,000 or more (5) Accessible Rural Areas – Areas with a population of less than 3,000 people, and within a 30 minute drive time of a Settlement of 10,000 or more (6) Remote Rural Areas - Areas with a population of less than 3,000 people, and with a drive time of over 30 minutes to a Settlement of 10,000 or more An 8-fold classification is also defined, which distinguishes between Remote and Very Remote areas by the addition of a 60 minute drive time threshold. The categories for the 8-fold classification are as follows: (1) Large Urban Areas - Settlements of 125,000 people and over (2) Other Urban Areas - Settlements of 10,000 to 124,999 people (3) Accessible Small Towns - Settlements of 3,000 to 9,999 people, and within a 30 minute drive time of a Settlement of 10,000 or more (4) Remote Small Towns - Settlements of 3,000 to 9,999 people, and with a drive time of over 30 minutes but less than or equal to 60 minutes to a Settlement of 10,000 or more (5) Very Remote Small Towns - Settlements of 3,000 to 9,999 people, and with a drive time of over 60 minutes to a Settlement of 10,000 or more (6) Accessible Rural Areas - Areas with a population of less than 3,000 people, and within a drive time of 30 minutes to a Settlement of 10,000 or more (7) Remote Rural Areas - Areas with a population of less than 3,000 people, and with a drive time of over 30 minutes but less than or equal to 60 minutes to a Settlement of 10,000 or more (8) Very Remote Rural Areas - Areas with a population of less than 3,000 people, and with a drive time of over 60 minutes to a Settlement of 10,000 or more For further details on the methodology, please see publication report accessible from the main Urban Rural Classification topic page on the Scottish Government’s website.</statement>
</dataLineage>
<dqScope>
<scpLvl>
<ScopeCd value="005">
</ScopeCd>
</scpLvl>
</dqScope>
</dqInfo>
<spatRepInfo>
<VectSpatRep>
<geometObjs Name="gisprod.data.view_sg_urbanrural">
<geoObjTyp>
<GeoObjTypCd Sync="TRUE" value="002">
</GeoObjTypCd>
</geoObjTyp>
<geoObjCnt Sync="TRUE">8</geoObjCnt>
</geometObjs>
<topLvl>
<TopoLevCd Sync="TRUE" value="001">
</TopoLevCd>
</topLvl>
</VectSpatRep>
</spatRepInfo>
<refSysInfo>
<RefSystem dimension="">
<refSysID>
<identCode code="OSGB 1936 / British National Grid (EPSG:27700)">
</identCode>
<idCodeSpace Sync="TRUE">EPSG</idCodeSpace>
<idVersion Sync="FALSE">7.4</idVersion>
</refSysID>
</RefSystem>
</refSysInfo>
<eainfo>
<detailed Name="gisprod.data.view_sg_urbanrural">
<enttyp>
<enttypl Sync="FALSE">Urban Rural Classification - Scotland</enttypl>
<enttypt Sync="TRUE">Feature Class</enttypt>
<enttypc Sync="TRUE">8</enttypc>
<enttypd>Scottish Urban Rural Classification</enttypd>
<enttypds>Scottish Government</enttypds>
</enttyp>
<attr>
<attrlabl Sync="TRUE">OBJECTID</attrlabl>
<attalias Sync="TRUE">objectid</attalias>
<attrtype Sync="TRUE">OID</attrtype>
<attwidth Sync="TRUE">4</attwidth>
<atprecis Sync="TRUE">10</atprecis>
<attscale Sync="TRUE">0</attscale>
<attrdef Sync="TRUE">Internal feature number.</attrdef>
<attrdefs Sync="TRUE">Esri</attrdefs>
<attrdomv>
<udom Sync="TRUE">Sequential unique whole numbers that are automatically generated.</udom>
</attrdomv>
</attr>
<attr>
<attrlabl Sync="TRUE">Shape</attrlabl>
<attalias Sync="TRUE">shape</attalias>
<attrtype Sync="TRUE">Geometry</attrtype>
<attwidth Sync="TRUE">8</attwidth>
<atprecis Sync="TRUE">0</atprecis>
<attscale Sync="TRUE">0</attscale>
<attrdef Sync="TRUE">Feature geometry.</attrdef>
<attrdefs Sync="TRUE">Esri</attrdefs>
<attrdomv>
<udom Sync="TRUE">Coordinates defining the features.</udom>
</attrdomv>
</attr>
<attr>
<attrlabl Sync="TRUE">UR8Class</attrlabl>
<attalias Sync="TRUE">ur8class</attalias>
<attrtype Sync="TRUE">Integer</attrtype>
<attwidth Sync="TRUE">4</attwidth>
<atprecis Sync="TRUE">10</atprecis>
<attscale Sync="TRUE">0</attscale>
<attrdef>8-fold urban rural class code</attrdef>
<attrdefs>Scottish Government</attrdefs>
<attrdomv>
<rdom>
<rdommin>1</rdommin>
<rdommax>8</rdommax>
</rdom>
</attrdomv>
</attr>
<attr>
<attrlabl Sync="TRUE">UR8Name</attrlabl>
<attalias Sync="TRUE">ur8name</attalias>
<attrtype Sync="TRUE">String</attrtype>
<attwidth Sync="TRUE">50</attwidth>
<atprecis Sync="TRUE">0</atprecis>
<attscale Sync="TRUE">0</attscale>
<attrdef>8-fold urban rural class name</attrdef>
<attrdefs>Scottish Government</attrdefs>
<attrdomv>
<udom>8-fold Urban Rural Class Name</udom>
</attrdomv>
</attr>
<attr>
<attrlabl Sync="TRUE">UR8Desc</attrlabl>
<attalias Sync="TRUE">ur8desc</attalias>
<attrtype Sync="TRUE">String</attrtype>
<attwidth Sync="TRUE">254</attwidth>
<atprecis Sync="TRUE">0</atprecis>
<attscale Sync="TRUE">0</attscale>
<attrdef>8-fold urban rural class description</attrdef>
<attrdefs>Scottish Government</attrdefs>
<attrdomv>
<udom>8-fold urban rural class description</udom>
</attrdomv>
</attr>
<attr>
<attrlabl Sync="TRUE">UR6Class</attrlabl>
<attalias Sync="TRUE">ur6class</attalias>
<attrtype Sync="TRUE">Integer</attrtype>
<attwidth Sync="TRUE">4</attwidth>
<atprecis Sync="TRUE">10</atprecis>
<attscale Sync="TRUE">0</attscale>
<attrdef>6-fold urban rural class code</attrdef>
<attrdefs>Scottish Government</attrdefs>
<attrdomv>
<rdom>
<rdommin>1</rdommin>
<rdommax>6</rdommax>
</rdom>
</attrdomv>
</attr>
<attr>
<attrlabl Sync="TRUE">UR6Name</attrlabl>
<attalias Sync="TRUE">ur6name</attalias>
<attrtype Sync="TRUE">String</attrtype>
<attwidth Sync="TRUE">50</attwidth>
<atprecis Sync="TRUE">0</atprecis>
<attscale Sync="TRUE">0</attscale>
<attrdef>6-fold urban rural class name</attrdef>
<attrdefs>Scottish Government</attrdefs>
<attrdomv>
<udom>6-fold urban rural class name</udom>
</attrdomv>
</attr>
<attr>
<attrlabl Sync="TRUE">UR6Desc</attrlabl>
<attalias Sync="TRUE">ur6desc</attalias>
<attrtype Sync="TRUE">String</attrtype>
<attwidth Sync="TRUE">254</attwidth>
<atprecis Sync="TRUE">0</atprecis>
<attscale Sync="TRUE">0</attscale>
<attrdef>6-fold urban rural class description</attrdef>
<attrdefs>Scottish Government</attrdefs>
<attrdomv>
<udom>6-fold urban rural class description</udom>
</attrdomv>
</attr>
<attr>
<attrlabl Sync="TRUE">UR3Class</attrlabl>
<attalias Sync="TRUE">ur3class</attalias>
<attrtype Sync="TRUE">Integer</attrtype>
<attwidth Sync="TRUE">4</attwidth>
<atprecis Sync="TRUE">10</atprecis>
<attscale Sync="TRUE">0</attscale>
<attrdef>3-fold urban rural class code</attrdef>
<attrdefs>Scottish Government</attrdefs>
<attrdomv>
<rdom>
<rdommin>1</rdommin>
<rdommax>3</rdommax>
</rdom>
</attrdomv>
</attr>
<attr>
<attrlabl Sync="TRUE">UR3Name</attrlabl>
<attalias Sync="TRUE">ur3name</attalias>
<attrtype Sync="TRUE">String</attrtype>
<attwidth Sync="TRUE">50</attwidth>
<atprecis Sync="TRUE">0</atprecis>
<attscale Sync="TRUE">0</attscale>
<attrdef>3-fold urban rural class name</attrdef>
<attrdefs>Scottish Government</attrdefs>
<attrdomv>
<udom>3-fold urban rural class name</udom>
</attrdomv>
</attr>
<attr>
<attrlabl Sync="TRUE">UR3Desc</attrlabl>
<attalias Sync="TRUE">ur3desc</attalias>
<attrtype Sync="TRUE">String</attrtype>
<attwidth Sync="TRUE">254</attwidth>
<atprecis Sync="TRUE">0</atprecis>
<attscale Sync="TRUE">0</attscale>
<attrdef>3-fold urban rural class description</attrdef>
<attrdefs>Scottish Government</attrdefs>
<attrdomv>
<udom>3-fold urban rural class description</udom>
</attrdomv>
</attr>
<attr>
<attrlabl Sync="TRUE">UR2Class</attrlabl>
<attalias Sync="TRUE">ur2class</attalias>
<attrtype Sync="TRUE">Integer</attrtype>
<attwidth Sync="TRUE">4</attwidth>
<atprecis Sync="TRUE">10</atprecis>
<attscale Sync="TRUE">0</attscale>
<attrdef>2-fold urban rural class code</attrdef>
<attrdefs>Scottish Government</attrdefs>
<attrdomv>
<rdom>
<rdommin>1</rdommin>
<rdommax>2</rdommax>
</rdom>
</attrdomv>
</attr>
<attr>
<attrlabl Sync="TRUE">UR2Name</attrlabl>
<attalias Sync="TRUE">ur2name</attalias>
<attrtype Sync="TRUE">String</attrtype>
<attwidth Sync="TRUE">50</attwidth>
<atprecis Sync="TRUE">0</atprecis>
<attscale Sync="TRUE">0</attscale>
<attrdef>2-fold urban rural class name</attrdef>
<attrdefs>Scottish Government</attrdefs>
<attrdomv>
<udom>2-fold urban rural class name</udom>
</attrdomv>
</attr>
<attr>
<attrlabl Sync="TRUE">UR2Desc</attrlabl>
<attalias Sync="TRUE">ur2desc</attalias>
<attrtype Sync="TRUE">String</attrtype>
<attwidth Sync="TRUE">254</attwidth>
<atprecis Sync="TRUE">0</atprecis>
<attscale Sync="TRUE">0</attscale>
<attrdef>2-fold urban rural class description</attrdef>
<attrdefs>Scottish Government</attrdefs>
<attrdomv>
<udom>2-fold urban rural class description</udom>
</attrdomv>
</attr>
<attr>
<attrlabl Sync="TRUE">st_area(shape)</attrlabl>
<attalias Sync="TRUE">st_area(shape)</attalias>
<attrtype Sync="TRUE">Double</attrtype>
<attwidth Sync="TRUE">0</attwidth>
<atprecis Sync="TRUE">0</atprecis>
<attscale Sync="TRUE">0</attscale>
<attrdef>Area measurement</attrdef>
<attrdefs>ESRI</attrdefs>
<attrdomv>
<udom>System calculated area of the feature.</udom>
</attrdomv>
</attr>
<attr>
<attrlabl Sync="TRUE">st_perimeter(shape)</attrlabl>
<attalias Sync="TRUE">st_perimeter(shape)</attalias>
<attrtype Sync="TRUE">Double</attrtype>
<attwidth Sync="TRUE">0</attwidth>
<atprecis Sync="TRUE">0</atprecis>
<attscale Sync="TRUE">0</attscale>
<attrdef>Perimeter measurement</attrdef>
<attrdefs>ESRI</attrdefs>
<attrdomv>
<udom>System calculated perimeter of the feature.</udom>
</attrdomv>
</attr>
</detailed>
</eainfo>
</metadata>
