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The IPDM questionnaire survey data is part of the IPDM/STEPSA project's spatial planning exercise, and was aimed at supplying detailed migration history data for 8 provinces (Gauteng, Limpopo, North West, Mpumalanga, KZN, Eastern Cape, Western Cape and Free State), together with respondent data on household structure and economy, transport access and needs, and housing access and needs.

The data set contains 3114 variables and 5916 cases.

The IPDM questionnaire survey data is part of the IPDM / STEPSA project's spatial planning exercise, and was aimed at supplying detailed migration history data in the major migration corridors in South Africa, (Gauteng, northern parts of Mpumalanga and Sekhukhune district in Limpopo) together with respondent data on household structure and economy, transport access and needs, and housing access and needs.

The 2008 Phase 1a dataset contains more detailed migration and housing information.

The data set contains 1664 variables and 2865 cases.

The IPDM questionnaire survey data is part of the IPDM / STEPSA project's spatial planning exercise, and was aimed at supplying detailed migration history data for the northern parts of Limpopo, North West, Free State, KwaZulu-Natal, Eastern Cape and Western Cape, together with respondent data on household structure and economy, transport access and needs, and housing access and needs.

The 2010 Phase 1b data contains proportionately more transport information and devotes less space to migration.

The data set contains 2461 variables and 3051 cases.

This is aggregated data of individuals or households. The data originates from the South African censuses of 1991, 1996 and 2001, as well as the community survey of 2007. The geographical units were standardised to the 2005 municipal boundaries so that spatial measuring was consistent. The data therefore covers the whole country at a municipal level for different time periods. The major variables focus on employment status.

The data set consists of 32 variables and 257 cases. It contains the same socio-economic variables for different time periods, namely 1991, 1996, 2001 and 2007.

Combined ranking - municipalities were ranked for each year, i.e. 1991, 1996, 2001 and 2007, in terms of unemployment rate and assigned a rank value. There is also a combined unemployment rank value for all years and all municipalities.

Population density - this was calculated by dividing the total population of a municipality in 1991 by the area and the answer is expressed as number of people per square kilometer.

The linking of different census geographies was done by using areal interpolation to transfer data from one set of boundaries to another. The 2005 municipality boundaries were used as the common denominator and it is part of a spatial hierarchy developed by Statistics SA for the 2001 census.

This is aggregated data of individuals or households. The data originates from the South African censuses of 1991, 1996, 2001 and 2011, as well as the community survey of 2007. The geographical units were standardised to the 2005 municipal boundaries so that spatial measuring was consistent. The data therefore covers the whole country at a municipal level for different time periods. The major variables focus on employment status.

The data set consists of 156 variables and 257 cases. It contains the same socio-economic variables for different time periods, namely 1991, 1996, 2001, 2007 and 2011.

Combined ranking - municipalities were ranked for each year, i.e. 1991, 1996, 2001, 2007 and 2011, in terms of unemployment rate and assigned a rank value. The combined unemployment ranking is calculated by adding up the ranking per individual year.

Population density - this was calculated by dividing the total population of a municipality in 1991 by the area and the answer is expressed as number of people per square kilometer.

Urban - the number of urban people in an area in a specific year.

Rural - the number of rural people in an area in a specific year.

Per capita income - the per capita income in a specific area and year.

The linking of different census geographies was done by using areal interpolation to transfer data from one set of boundaries to another. The 2005 municipality boundaries were used as the common denominator and it is part of a spatial hierarchy developed by Statistics SA for the 2001 census.