7,386 results returned
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Title: LandScan 2017 global population database
Contributors:- Not specified
- 2017
- Not owned by MIT (Owned by Stanford)
Summary: At approximately 1 km resolution (30" X 30"), LandScan is the finest resolution global population distribution data available and represents an ambient population (average over 24 hours). The LandScan algorithm uses spatial data and imagery analysis technologies and a multi-variable dasymetric modeling approach to disaggregate census counts within an administrative boundary. Since no single population distribution model can account for the differences in spatial data availability, quality, scale, and accuracy as well as the differences in cultural settlement practices, LandScan population distribution models are tailored to match the data conditions and geographical nature of each individual country and region. Title from disk surface Developed by Oak Ridge National Laboratory.
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Title: LandScan 2018 global population database
Contributors:- Not specified
- 2017
- Not owned by MIT (Owned by Stanford)
Summary: At approximately 1 km resolution (30" X 30"), LandScan is the finest resolution global population distribution data available and represents an ambient population (average over 24 hours). The LandScan algorithm uses spatial data and imagery analysis technologies and a multi-variable dasymetric modeling approach to disaggregate census counts within an administrative boundary. Since no single population distribution model can account for the differences in spatial data availability, quality, scale, and accuracy as well as the differences in cultural settlement practices, LandScan population distribution models are tailored to match the data conditions and geographical nature of each individual country and region. Title from disk surface Developed by Oak Ridge National Laboratory.
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Title: LandScan 2016 global population database
Contributors:- Not specified
- 2017
- Not owned by MIT (Owned by Stanford)
Summary: At approximately 1 km resolution (30" X 30"), LandScan is the finest resolution global population distribution data available and represents an ambient population (average over 24 hours). The LandScan algorithm uses spatial data and imagery analysis technologies and a multi-variable dasymetric modeling approach to disaggregate census counts within an administrative boundary. Since no single population distribution model can account for the differences in spatial data availability, quality, scale, and accuracy as well as the differences in cultural settlement practices, LandScan population distribution models are tailored to match the data conditions and geographical nature of each individual country and region. Title from disk surface Developed by Oak Ridge National Laboratory.
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Title: LandScan 2014 global population database
Contributors:- Not specified
- 2015
- Not owned by MIT (Owned by Stanford)
Summary: At approximately 1 km resolution (30" X 30"), LandScan is the finest resolution global population distribution data available and represents an ambient population (average over 24 hours). The LandScan algorithm uses spatial data and imagery analysis technologies and a multi-variable dasymetric modeling approach to disaggregate census counts within an administrative boundary. Since no single population distribution model can account for the differences in spatial data availability, quality, scale, and accuracy as well as the differences in cultural settlement practices, LandScan population distribution models are tailored to match the data conditions and geographical nature of each individual country and region. Title from disk surface. Developed by Oak Ridge National Laboratory. "Disk 1 of 1."
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Title: LandScan global 2013
Contributors:- Not specified
- 2014
- Not owned by MIT (Owned by Stanford)
Summary: At approximately 1 km resolution (30" X 30"), LandScan is the finest resolution global population distribution data available and represents an ambient population (average over 24 hours). The LandScan algorithm uses spatial data and imagery analysis technologies and a multi-variable dasymetric modeling approach to disaggregate census counts within an administrative boundary. Since no single population distribution model can account for the differences in spatial data availability, quality, scale, and accuracy as well as the differences in cultural settlement practices, LandScan population distribution models are tailored to match the data conditions and geographical nature of each individual country and region. Title from disk surface. Developed by Oak Ridge National Laboratory. "Disk 1 of 1."
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Title: China county boundaries, 2010
Contributors:- Polygon data
- 2010
- Not owned by MIT (Owned by Princeton)
Summary: Polygon shapefile representing county-level boundaries in China as of 2010.
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Title: China province boundaries, 2010
Contributors:- Polygon data
- 2010
- Not owned by MIT (Owned by Princeton)
Summary: Polygon shapefile representing province-level boundaries in China as of 2010.
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Title: China prefecture boundaries, 2010
Contributors:- Polygon data
- 2010
- Not owned by MIT (Owned by Princeton)
Summary: Polygon shapefile representing prefecture-level boundaries in China as of 2010.
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Title: China township boundaries, 2010
Contributors:- Polygon data
- 2010
- Not owned by MIT (Owned by Princeton)
Summary: Polygon shapefile representing township-level boundaries in China as of 2010.
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Title: China national boundaries, 2010
Contributors:- Polygon data
- 2010
- Not owned by MIT (Owned by Princeton)
Summary: Polygon shapefile representing national boundaries of China as of 2010.
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Title: Peru economic data from 2007 census
Contributors:- Polygon data
- 2007
- Not owned by MIT (Owned by Princeton)
Summary: Official population and housing data for Peru from the 2007 census. This census is provided at the district administrative level (ADM3) and includes 1,834 polygons with 781 attribute variables. Attribute descriptions retain native-language names and unique identifier codes. All variable names were translated from native language (Spanish). Field name prefixes, originally written with coded titles, were renamed to create unique names across the geodatabase (e.g. Original variable of “PAREA_Urbano” was updated to D001 and given the alias “People in Area Type: Urban [PAREA_Urbano]”, the letter D was given as it relates to the Demographic theme table. D = Demographic, E = Economic, H = Housing, M = Migration and S = Social). All variables from the original data were reviewed for completeness and organized into their relevant themes of Demographic, Economic, Housing, Migration and Social. The geodatabase was built to hold these specific tables with all geographic names and codes represented as text fields and census data represented as doubles or long integers. Automated tools were run to apply aliases in the format “English translation field description[Native language field description]”. Vector data was analyzed for accuracy and compared with national boundaries.
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Title: Dominican Republic economic data from 2010 census
Contributors:- Polygon data
- 2010
- Not owned by MIT (Owned by Princeton)
Summary: Official population and housing data for the Dominican Republic from the 2010 census. This census is provided at the barrio administrative level (ADM5) and includes 12,565 polygons with 756 attribute variables. Attribute descriptions retain native-language names and unique identifier codes. All variable names were translated from native language (Spanish). Field name prefixes, originally written with coded titles, were renamed to create unique names across the geodatabase (e.g. Original variable of “H23_Si” was updated to D001 and given the alias “There is a newborn girl or boy that has not been previously mentioned: Yes[H23_Si]”, the letter D was given as it relates to the Demographic theme table. D = Demographic, E = Economic, H = Housing, M = Migration and S = Social). All variables from the original data were reviewed for completeness and organized into their relevant themes of Demographic, Economic, Housing, Migration and Social. The geodatabase was built to hold these specific tables with all geographic names and codes represented as text fields and census data represented as doubles or long integers. Automated tools were run to apply aliases in the format “English translation field description[Native language field description]”. Vector data was analyzed for accuracy and compared with national boundaries.
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Title: Nicaragua transportation data from 2005 census
Contributors:- Polygon data
- 2005
- Not owned by MIT (Owned by Princeton)
Summary: Official population and housing data for Nicaragua from the 2005 census. This census is provided at the barrio administrative level (ADM4) and includes 8,136 polygons with 352 attribute variables. Attribute descriptions retain native-language names and unique identifier codes. All variable names were translated from native language (Spanish). Field name prefixes, originally written with coded titles, were renamed to create unique names across the geodatabase (e.g. Original variable of “Miembro Del Hogar Falleció Desde El 1 De Enero De 2004: Si” was updated to D001 and given the alias “Member Of The Household Has Died Since January 1st 2004: Yes[Miembro Del Hogar Falleció Desde El 1 De Enero De 2004: Si]”, the letter D was given as it relates to the Demographic theme table. D = Demographic, E = Economic, H = Housing, M = Migration, S = Social and T = Transportation). All variables from the original data were reviewed for completeness and organized into their relevant themes of Demographic, Economic, Housing, Migration, Social and Transportation. The geodatabase was built to hold these specific tables with all geographic names and codes represented as text fields and census data represented as doubles or long integers. Automated tools were run to apply aliases in the format “English translation field description[Native language field description]”. Vector data was analyzed for accuracy and compared with national boundaries.
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Title: Chile migration data from 2002 census
Contributors:- Polygon data
- 2002
- Not owned by MIT (Owned by Princeton)
Summary: Official population and housing data for Chile from the 2002 census. This census is provided at the manzana administrative level (ADM6) and includes 150,930 polygons with 875 attribute variables. Attribute descriptions retain native-language names and unique identifier codes. All variable names were translated from native language (Spanish). Field name prefixes, originally written with coded titles, were renamed to create unique names across the geodatabase (e.g. Original variable of “P18_1” was updated to D001 and given the alias “Sex: Male[P18_1]”, the letter D was given as it relates to the Demographic theme table. D = Demographic, E = Economic, H = Housing, M = Migration, S = Social and T = Transportation). All variables from the original data were reviewed for completeness and organized into their relevant themes of Demographic, Economic, Housing, Migration, Social and Transportation. The geodatabase was built to hold these specific tables with all geographic names and codes represented as text fields and census data represented as doubles or long integers. Automated tools were run to apply aliases in the format “English translation field description[Native language field description]”. Vector data was analyzed for accuracy and compared with national boundaries.
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Title: Nicaragua housing data from 2005 census
Contributors:- Polygon data
- 2005
- Not owned by MIT (Owned by Princeton)
Summary: Official population and housing data for Nicaragua from the 2005 census. This census is provided at the barrio administrative level (ADM4) and includes 8,136 polygons with 352 attribute variables. Attribute descriptions retain native-language names and unique identifier codes. All variable names were translated from native language (Spanish). Field name prefixes, originally written with coded titles, were renamed to create unique names across the geodatabase (e.g. Original variable of “Miembro Del Hogar Falleció Desde El 1 De Enero De 2004: Si” was updated to D001 and given the alias “Member Of The Household Has Died Since January 1st 2004: Yes[Miembro Del Hogar Falleció Desde El 1 De Enero De 2004: Si]”, the letter D was given as it relates to the Demographic theme table. D = Demographic, E = Economic, H = Housing, M = Migration, S = Social and T = Transportation). All variables from the original data were reviewed for completeness and organized into their relevant themes of Demographic, Economic, Housing, Migration, Social and Transportation. The geodatabase was built to hold these specific tables with all geographic names and codes represented as text fields and census data represented as doubles or long integers. Automated tools were run to apply aliases in the format “English translation field description[Native language field description]”. Vector data was analyzed for accuracy and compared with national boundaries.
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Title: Uruguay migration data from 2011 census
Contributors:- Polygon data
- 2011
- Not owned by MIT (Owned by Princeton)
Summary: Official population and housing data for Uruguay from the 2011 census. This census is provided at the census tract administrative level (ADM3) and includes 4,313 polygons with 730 attribute variables. Attribute descriptions retain native-language names and unique identifier codes. All variable names were translated from native language (Spanish). Field name prefixes, originally written with coded titles, were renamed to create unique names across the geodatabase (e.g. Original variable of “Sexo: Hombre” was updated to D001 and given the alias “Sex: Man[Sexo: Hombre]”, the letter D was given as it relates to the Demographic theme table. D = Demographic, E = Economic, H = Housing, M = Migration, S = Social and T = Transportation). All variables from the original data were reviewed for completeness and organized into their relevant themes of Demographic, Economic, Housing, Migration, Social and Transportation. The geodatabase was built to hold these specific tables with all geographic names and codes represented as text fields and census data represented as doubles or long integers. Automated tools were run to apply aliases in the format “English translation field description[Native language field description]”. Vector data was analyzed for accuracy and compared with national boundaries.
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Title: Costa Rica demographic data from 2011 census
Contributors:- Polygon data
- 2011
- Not owned by MIT (Owned by Princeton)
Summary: Official population and housing data for Costa Rica from the 2011 census. This census is provided at the district administrative level (ADM3) and includes 472 polygons with 719 attribute variables. Attribute descriptions retain native-language names and unique identifier codes. All variable names were translated from native language (Spanish). Field name prefixes, originally written with coded titles, were renamed to create unique names across the geodatabase (e.g. Original variable of “Estado Civil: Unión Libre o Juntadoa” was updated to D001 and given the alias “Marital Status: Free Union[Estado Civil: Unión Libre o Juntadoa]”, the letter D was given as it relates to the Demographic theme table. D = Demographic, E = Economic, H = Housing, M = Migration and S = Social). All variables from the original data were reviewed for completeness and organized into their relevant themes of Demographic, Economic, Housing, Migration and Social. The geodatabase was built to hold these specific tables with all geographic names and codes represented as text fields and census data represented as doubles or long integers. Automated tools were run to apply aliases in the format “English translation field description[Native language field description]”. Vector data was analyzed for accuracy and compared with national boundaries.
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Title: Argentina economic data from 2010 census
Contributors:- Polygon data
- 2010
- Not owned by MIT (Owned by Princeton)
Summary: Official population housing data for Argentina down to township, including 52408 vector polygons with native-language names and unique identifier codes and 17 fields. All variable names were translated from native (Spanish) to English using Google Translate. Field name prefixes, originally written with nativ elanguage titles, were renamed to create unique names across the geodatabase (e.g. Original variable of “Conyuge o pareja” was updated to D002 and given the alias “Relationship to the head of household: Spouse or partner[Relación o parentesco con el jefe(a) del hogar: Conyuge o pareja]”, the letter D was given as it relates to the Demographic theme table. D = Demographic, E = Economic, H = Housing, M = Migration and S = Social). All variables from the original data was reviewed for completeness and organized into their relevant themes of Demographic, Economic, Housing, Migration and Social. The GDB was built to hold these specific tables with all geographic names and codes represented as text fields and census data represented as doubles. Automated tools were run to apply aliases in the format “English translation field description[Native language field description]”. Vector data was analyzed for accuracy and compared with national boundaries.
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Title: El Salvador housing data from 2007 Census
Contributors:- Polygon data
- 2007
- Not owned by MIT (Owned by Princeton)
Summary: Official population and housing data for El Salvador from the 2007 census. This census is provided at the municipal administrative level (ADM2) and includes 273 polygons with 1162 attribute variables. Attribute descriptions retain native-language names and unique identifier codes. All variable names were translated from native language (Spanish). Field name prefixes, originally written with coded titles, were renamed to create unique names across the geodatabase (e.g. Original variable of “P02_Hombre” was updated to D001 and given the alias “Sex: Man[P02_Hombre]”, the letter D was given as it relates to the Demographic theme table. D = Demographic, E = Economic, H = Housing, M = Migration, S = Social and T = Transportation). All variables from the original data were reviewed for completeness and organized into their relevant themes of Demographic, Economic, Housing, Migration, Social and Transportation. The geodatabase was built to hold these specific tables with all geographic names and codes represented as text fields and census data represented as doubles or long integers. Automated tools were run to apply aliases in the format “English translation field description[Native language field description]”. Vector data was analyzed for accuracy and compared with national boundaries.
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Title: Chile demographic data from 2002 census
Contributors:- Polygon data
- 2002
- Not owned by MIT (Owned by Princeton)
Summary: Official population and housing data for Chile from the 2002 census. This census is provided at the manzana administrative level (ADM6) and includes 150,930 polygons with 875 attribute variables. Attribute descriptions retain native-language names and unique identifier codes. All variable names were translated from native language (Spanish). Field name prefixes, originally written with coded titles, were renamed to create unique names across the geodatabase (e.g. Original variable of “P18_1” was updated to D001 and given the alias “Sex: Male[P18_1]”, the letter D was given as it relates to the Demographic theme table. D = Demographic, E = Economic, H = Housing, M = Migration, S = Social and T = Transportation). All variables from the original data were reviewed for completeness and organized into their relevant themes of Demographic, Economic, Housing, Migration, Social and Transportation. The geodatabase was built to hold these specific tables with all geographic names and codes represented as text fields and census data represented as doubles or long integers. Automated tools were run to apply aliases in the format “English translation field description[Native language field description]”. Vector data was analyzed for accuracy and compared with national boundaries.