IPUMS provides two kinds of geography variables: harmonized and year-specific.
- Harmonized variables provide consistent geographic units for a country across sample years, to facilitate comparisons over time. Some geographic detail is typically lost in the construction of the harmonized units.
- Year-specific variables retain all of the original detail from each sample, but they are usually not fully consistent over time.
Both harmonized and year-specific variables are regionalized, meaning units with less than 20,000 people are combined with neighboring units for confidentiality purposes.
Click HERE for a list of geography variables across all IPUMS-International samples.
IPUMS geographic harmonization is performed on first- and second-level administrative units, which are provided by most countries. GIS boundary files are provided for harmonized and year-specific variables at both administrative levels. Some samples also have additional geography variables. The geographic harmonization process is described below.
The basic set of geography variables for any IPUMS country includes the following (using Mozambique as an example):
|Variable||Geographic level||Type of geography|
|GEO1_MZ||First level: provinces||Spatially harmonized, 1997-2007 [GIS]|
|GEO1_MZ1997||First level: provinces||1997 provinces [GIS]|
|GEO1_MZ2007||First level: provinces||2007 provinces [GIS]|
|GEO2_MZ||Second level: districts||Spatially harmonized, 1997-2007 [GIS]|
|GEO2_MZ1997||Second level: districts||1997 districts [GIS]|
|GEO2_MZ2007||Second level: districts||2007 districts [GIS]|
Geographic Harmonization Process
Creation of spatially consistent geography and year-specific geography involves a series of processes:
- Historical GIS files and images are acquired and digitized for each countries censuses. For older samples, images from historical maps or census volumes are digitized and converted into digital files.
- Where the borders of the modern units do not align with historical units, because of boundary changes, larger aggregated units are created that remain stable over time; we refer to this process as harmonization of geographic boundaries.
- Regionalization (combining) techniques are applied to create spatio-temporally harmonized units that meet the 20,000 population threshold required for public dissemination of the data.
- The base units are then disaggregated to smaller year-specific units in order not to disadvantage users interested in only a specific sample year.
A more detailed description of the geographic harmonization process is provided in our working paper.
Displaying IPUMS-I Geography
Spatially harmonized geography showing household access to electricity for Bangladesh 1991-2011
These maps show the percentage of households in Bangladesh with access to electricity in three census years: 1991, 2001 and 2011. The IPUMS integrated household variable "ELECTRIC" is summarized for second-level geography, zillas (GEOLEV2). The base map uses the IPUMS harmonized boundary file that provides consistent geographic units for cross-year comparison of census microdata. Nationally, there was a 40.2 percent increase in household access to electricity between 1991 and 2011. However, disaggregation by zillas reveals significant regional disparities. The greatest increase occurred in major urban centers, particularly zillas surrounding the capital city Dhaka (central part of Bangladesh) and the port city Chittagong (south-east corner). Between 1991 and 2011, Munshiganj (south of Dhaka), Gazipur (north of the Dhaka) and Fendi (north of Chittagong) experienced an increase of 76.5 percent, 60.6 percent and 60.7 percent, respectively. However, Lalmonirhat and Panchagar zillas (both located in the far northwestern tip) saw only an average increase of 12.5 percent. Less than 25 percent of households in the two zillas had access to electricity in 2011.
Year-specific geography demonstrating Sustainable Development Goal (SDG) indicator 8.6.1 for Mozambique 2007
These maps show the proportion of youth (age 15-24 years) who were not in education, employment, or training (NEET) in Mozambique 2007. NEET is disaggregated by gender and by first and second levels of geography using IPUMS integrated variables school attendance "SCHOOL" and employment status "EMPSTAT". In Mozambique in 2007, 19.6 percent of youth age 15-24 were not in education, employment, or training. Disaggregation by gender and administrative unit identifies enclaves of high NEET, particularly among females. Gender disparities exist throughout the country, but are particularly pronounced in the capital region of Maputo (southern tip of Mozambique) and in the urban center of Tete in the northwest. NEET among young men is concentrated in the province of Gaza, where male employment levels are the lowest in the country. Among young women, high levels of NEET are evident throughout the country. There is parity in labor force participation at the national level, but much lower levels of school attendance for young women (27.6 percent) compared to young men (45.3 percent).
Year-specific second level geography depicting children aged 12 to 17 attending school in Latin America 2000-2007
This map shows the proportion of secondary school-age children (age 12-17 years) who were currently attending school in Latin America from censuses 2000 to 2007, calculated at the second administrative level of geography. The map only shows countries that have year-specific second level geography information in the IPUMS-International database. This map includes censuses from Argentina 2001, Bolivia 2001, Brazil 2000, Colombia 2005, Costa Rica 2000, Ecuador 2001, El Salvador 2007, Haiti 2003, Mexico 2000, Panama 2000, Peru 2007, and Venezuela 2001. School attendance is stratified by year-specific second level geography using IPUMS integrated variables school attendance "SCHOOL" and age "AGE". The map can be interpreted as a measure of Latin American progress on U.N. Sustainable Development Goal 4 - "quality education" and ensuring all children complete primary and secondary education. Mean country-wide percentages of secondary school-aged children attending school ranged from 48.8% in Haiti to 85.0% in Argentina. Second-level administrative units within urban centers in Mexico City and Merida (Mexico), Panama City (Panama), Rio de Janeiro (Brazil), Lima (Peru), and Caracas (Venezuela) have school attendance rates exceeding 90%. Conversely, some rural areas have attendance rates below 60%.