Mexico MX: Urban Land Area data was reported at 102,418.109 sq km in 2010. This stayed constant from the previous number of 102,418.109 sq km for 2000. Mexico MX: Urban Land Area data is updated yearly, averaging 102,418.109 sq km from Dec 1990 (Median) to 2010, with 3 observations. The data reached an all-time high of 102,418.109 sq km in 2010 and a record low of 102,418.109 sq km in 2010. Mexico MX: Urban Land Area data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mexico – Table MX.World Bank.WDI: Land Use, Protected Areas and National Wealth. Urban land area in square kilometers, based on a combination of population counts (persons), settlement points, and the presence of Nighttime Lights. Areas are defined as urban where contiguous lighted cells from the Nighttime Lights or approximated urban extents based on buffered settlement points for which the total population is greater than 5,000 persons.; ; Center for International Earth Science Information Network (CIESIN)/Columbia University. 2013. Urban-Rural Population and Land Area Estimates Version 2. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/lecz-urban-rural-population-land-area-estimates-v2.; Sum;

Population in urban agglomerations of more than 1 million in Mexico was reported at 55541296 in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Mexico - Population in urban agglomerations of more than 1 million - actual values, historical data, forecasts and projections were sourced from the World Bank on November of 2025.

Mexico MX: Population in Urban Agglomerations of More Than 1 Million: as % of Total Population data was reported at 39.292 % in 2017. This records a decrease from the previous number of 39.314 % for 2016. Mexico MX: Population in Urban Agglomerations of More Than 1 Million: as % of Total Population data is updated yearly, averaging 36.710 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 39.352 % in 2015 and a record low of 25.140 % in 1960. Mexico MX: Population in Urban Agglomerations of More Than 1 Million: as % of Total Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mexico – Table MX.World Bank.WDI: Population and Urbanization Statistics. Population in urban agglomerations of more than one million is the percentage of a country's population living in metropolitan areas that in 2018 had a population of more than one million people.; ; United Nations, World Urbanization Prospects.; Weighted average;

Population in urban agglomerations of more than 1 million (% of total population) in Mexico was reported at 42.44 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Mexico - Population in urban agglomerations of more than 1 million - actual values, historical data, forecasts and projections were sourced from the World Bank on November of 2025.

The Urban Place Time-Series Population of Mexico contains population counts for more than 700 urban centers every 10 years from 1921 through 1990. The urban centers include metropolitan, conurbation, and city areas with more than 5,000 inhabitants as of 1980. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).

The Urban Place GIS Coverage of Mexico is a vector based point Geographic Information System (GIS) coverage of 696 urban places in Mexico. Each Urban Place is geographically referenced down to one tenth of a minute. The attribute data include time-series population and selected census/geographic data items for Mexican urban places from from 1921 to 1990. The cartographic data include urban place point locations on a state boundary file of Mexico. This data set is produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the Instituto Nacional de Estadistica Geografia e Informatica (INEGI) and the Environmental Research Institute (ERI) of Michigan.

Mexico MX: Population in Urban Agglomerations of More Than 1 Million data was reported at 50,750,263.000 Person in 2017. This records an increase from the previous number of 50,140,649.000 Person for 2016. Mexico MX: Population in Urban Agglomerations of More Than 1 Million data is updated yearly, averaging 30,431,310.000 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 50,750,263.000 Person in 2017 and a record low of 9,597,041.000 Person in 1960. Mexico MX: Population in Urban Agglomerations of More Than 1 Million data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mexico – Table MX.World Bank.WDI: Population and Urbanization Statistics. Population in urban agglomerations of more than one million is the country's population living in metropolitan areas that in 2018 had a population of more than one million people.; ; United Nations, World Urbanization Prospects.; ;

Mexico MX: Population Living in Slums: % of Urban Population data was reported at 11.100 % in 2014. This records a decrease from the previous number of 14.400 % for 2007. Mexico MX: Population Living in Slums: % of Urban Population data is updated yearly, averaging 17.150 % from Dec 1990 (Median) to 2014, with 6 observations. The data reached an all-time high of 23.100 % in 1990 and a record low of 11.100 % in 2014. Mexico MX: Population Living in Slums: % of Urban Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mexico – Table MX.World Bank.WDI: Population and Urbanization Statistics. Population living in slums is the proportion of the urban population living in slum households. A slum household is defined as a group of individuals living under the same roof lacking one or more of the following conditions: access to improved water, access to improved sanitation, sufficient living area, and durability of housing.; ; UN HABITAT, retrieved from the United Nation's Millennium Development Goals database. Data are available at : http://mdgs.un.org/; Weighted average;

During the last census, Iztapalapa registered a total of 1.8 million inhabitants, a population higher than the eight States with the lowest population in Mexico. Gustavo A. Madero and Álvaro Obregón ranked second and third, respectively. In contrast, Milpa Alta registered only 152,685 inhabitants.

CLICK ON THE ABOVE IMAGE TO LAUNCH THE MAP - Healthcare access issues vary greatly between urban and rural areas of New Mexico. Launch the map to explore alternate ways to classify geographies as urban or rural. These classifications are often used for food access as well as healthcare access.BIBLIOGRAPHY WITH LINKS:US Census Bureau, Urban Area - Urban Cluster FAQ - https://www2.census.gov/geo/pdfs/reference/ua/2010ua_faqs.pdfAre the problems with Rural areas actually just a result of definitions that change?: "When a rural county grows, it transmutes into an urban one." - The real (surprisingly comforting) reason rural America is doomed to decline, https://www.washingtonpost.com/business/2019/05/24/real-surprisingly-comforting-reason-rural-america-is-doomed-decline/ (See also the complete study - http://programme.exordo.com/2018annualmeeting/delegates/presentation/130/ )Rural Definitions for Health Policy, Harvey Licht, a presentation for the University of New Mexico Center for Health Policy: : http://nmcdc.maps.arcgis.com/home/item.html?id=7076f283b8de4bb69bf3153bc42e0402Rural Definitions for Health Policy, update of 2019, Harvey Licht, a presentation to the NMDOH Quarterly Epidemiology Meeting, November, 2019 - http://www.arcgis.com/home/item.html?id=a60a73f4e5614eb3ab01e2f96227ce4bNew Mexico Rural-Urban Counties Comparison Tables - October 2017, Harvey Licht, A preliminary compilation for the National Conference of State Legislators Rural Health Plan Taskforce : https://nmcdc.maps.arcgis.com/home/item.html?id=d3ca56e99f8b45c58522b2f9e061999eNew Mexico Rural Health Plan - Report of the Rural Health Planning Workgroup convened by the NM Department of Health 2018-2019 - http://nmcdc.maps.arcgis.com/home/item.html?id=d4b9b66a5ca34ec9bbe90efd9562586aFrontier and Remote Areas Zip Code Map - http://nmcdc.maps.arcgis.com/home/webmap/viewer.html?webmap=56b4005256244499a58f863c17bbac8aHOUSING ISSUES, RURAL & URBAN, 2017 - http://nmcdc.maps.arcgis.com/home/webmap/viewer.html?webmap=3e3aeabc04ac4672994e25a1ec94df83FURTHER READING:What is Rural? Rural Health Information Hub: https://www.ruralhealthinfo.org/topics/what-is-ruralDefining Rural. Research and Training Center on Disability in Rural Communities: http://rtc.ruralinstitute.umt.edu/resources/defining-rural/What is Rural? USDA: https://www.ers.usda.gov/topics/rural-economy-population/rural-classifications/what-is-rural/National Center for Health Statistics Urban–Rural Classification Scheme: https://www.cdc.gov/nchs/data_access/urban_rural.htm.Health-Related Behaviors by Urban-Rural County Classification — United States, 2013, CDC: https://www.cdc.gov/mmwr/volumes/66/ss/ss6605a1.htm?s_cid=ss6605a1_wExtending Work on Rural Health Disparities, The Journal of Rural Health: http://onlinelibrary.wiley.com/doi/10.1111/jrh.12241/fullMinority Populations Driving Community Growth in the Rural West, Headwaters Economics: https://headwaterseconomics.org/economic-development/trends-performance/minority-populations-driving-county-growth/ Methodology - https://headwaterseconomics.org/wp-content/uploads/Minorities_Methods.pdfThe Role of Medicaid in Rural America, Kaiser Family Foundation: http://www.kff.org/medicaid/issue-brief/the-role-of-medicaid-in-rural-america/The Future of the Frontier: Water, Energy & Climate Change in America’s Most Remote Communities: http://frontierus.org/wp-content/uploads/2017/09/FUTURE-OF-THE-FRONTIER_Final-Version_Spring-2017.pdfRural and Urban Differences in Passenger-Vehicle–Occupant Deaths and Seat Belt Use Among Adults — United States, 2014, CDC: https://www.cdc.gov/mmwr/volumes/66/ss/ss6617a1.htm

In 2025, approximately 23 million people lived in the São Paulo metropolitan area, making it the biggest in Latin America and the Caribbean and the sixth most populated in the world. The homonymous state of São Paulo was also the most populous federal entity in the country. The second place for the region was Mexico City with 22.75 million inhabitants. Brazil's cities Brazil is home to two large metropolises, only counting the population within the city limits, São Paulo had approximately 11.45 million inhabitants, and Rio de Janeiro around 6.21 million inhabitants. It also contains a number of smaller, but well known cities such as Brasília, Salvador, Belo Horizonte and many others, which report between 2 and 3 million inhabitants each. As a result, the country's population is primarily urban, with nearly 88 percent of inhabitants living in cities. Mexico City Mexico City's metropolitan area ranks sevenths in the ranking of most populated cities in the world. Founded over the Aztec city of Tenochtitlan in 1521 after the Spanish conquest as the capital of the Viceroyalty of New Spain, the city still stands as one of the most important in Latin America. Nevertheless, the preeminent economic, political, and cultural position of Mexico City has not prevented the metropolis from suffering the problems affecting the rest of the country, namely, inequality and violence. Only in 2023, the city registered a crime incidence of 52,723 reported cases for every 100,000 inhabitants and around 24 percent of the population lived under the poverty line.

This is a basemap of Mexico showing the various administrative boundaries, urban areas, municipalities, and rural areas, along with the population of these. Further it shows indigenous populations and linguitical families. Data was collected between 2007 - 2012.Source: Secretaria de Medio Ambiente y Recursos Naturales

Abstract

The World Values Survey (www.worldvaluessurvey.org) is a global network of social scientists studying changing values and their impact on social and political life, led by an international team of scholars, with the WVS association and secretariat headquartered in Stockholm, Sweden. The survey, which started in 1981, seeks to use the most rigorous, high-quality research designs in each country. The WVS consists of nationally representative surveys conducted in almost 100 countries which contain almost 90 percent of the world’s population, using a common questionnaire. The WVS is the largest non-commercial, cross-national, time series investigation of human beliefs and values ever executed, currently including interviews with almost 400,000 respondents. Moreover the WVS is the only academic study covering the full range of global variations, from very poor to very rich countries, in all of the world’s major cultural zones. The WVS seeks to help scientists and policy makers understand changes in the beliefs, values and motivations of people throughout the world. Thousands of political scientists, sociologists, social psychologists, anthropologists and economists have used these data to analyze such topics as economic development, democratization, religion, gender equality, social capital, and subjective well-being. These data have also been widely used by government officials, journalists and students, and groups at the World Bank have analyzed the linkages between cultural factors and economic development.

Geographic coverage

The survey covers Mexico.

Analysis unit

• Household
• Individual

Universe

The WVS for Mexico covers national population aged 18 years and over for both sexes.

Kind of data

Sample survey data [ssd]

Sampling procedure

The Mexico 2005 survey used a multi-stage sampling procedure. Interviewers selected an adult using a random selection method. However, we also employed control quotas according to sex and age this practice was more common in rural areas, where the male population is more difficult to find at home during interviewing hours. Interviewers made sure that respondents were at least 18 years old, that they lived in the selected household. Interviews were all conducted in-home.

Remarks about sampling: The first stage was the selection of polling points based on the list of electoral sections defined by the Federal Elections Institute. The sections were previously stratified as urban (70 percent), and rural and mixed (30 percent). Each section is relatively homogeneous in size, with about 1,092 registered voters in 63,810 sections that cover all the countrys adult population. Respondents included, of course, also adults nonregistered as voters. We selected 130 electoral sections in a systematically random fashion in each stratum, based on the list arranged proportionally to size of population. In the second stage we selected the household with a systematic random selection, based on a standard strategy of walking around the housing districts selected in the sample. In the third stage, interviewers selected an adult respondent in each household. We used control quotas based on sex and age in districts where random selection of interviewers was disproportionately leaning towards a specific group. Each polling point represents 12 interviews, and quota control established that 6 were male respondents and 6 women respondents, to ensure an appropriate distribution, especially in areas where some specific group is difficult to reach during the hours of interviewing (i.e. rural towns and communities). The Mexican countryside presents problems, for example, to reach male populations during the day in their households. In terms of age, the following quotas were employed where needed: 4 out of 12 were 18 to 29 years old; 5 out of 12 were 30 to 49 years old, and 3 out of 12 were 50 years old or older. We substituted four of the originally selected addresses; three in rural areas and one in an urban area. In the rural cases, the interviewers were not able to get to them because of the absence of roads and transportation. In the urban case, the polling point was substituted because the neighborhood represented serious safety problems at the time of the survey. All the polling points were substituted with addresses with the same socioeconomic level, in the same region, state and electoral district. Substitution of households and respondents were also employed, in the cases where either one of them was registered as a no contact or a refusal and remained under those categories after call backs or returns. Interviewers kept record of non response items (no contact, refusals, suspension) at every time.

The sample size for Mexico is N=1560 and includes the national population aged 18 years and over for both sexes.

Mode of data collection

Face-to-face [f2f]

Research instrument

English and Spanish Questionnaires. The Mexico 2005 questionnaire includes these additional questions:

• Main television news broadcast R watches: v229a (placed between v229 Rs information sources and v230 how often R uses a personal computer).
• Voting intentions for President: v233b to v233c (placed between v233 party R would never vote for and v234 party R voted for federal deputy in 2003). In this question, interviewers used a secret-ballot method with the names of the candidates and the party logos.
• Party identification: v233a (placed between v222 party R would never vote for and v223 party R voted for federal deputy in 2003)
• Items on Mexicos economic relationships: v234a to v234d (placed between v234 party R voted for federal deputy in 2003 and v235 gender.
• Items on the relationship between Mexico and United States: v234a to v234d (placed between v234 party R voted for federal deputy in 2003 and v235 gender) -Items on underground economy: v247a to v247c (placed between v247 Does R supervise people in his job and v248 Is R the chief wage earner) v248 Is R the chief wage earner)

Response rate

Total number of starting names/addresses (electoral sections) 130 No contact at selected address (households) 1759 No contact with selected person 1084 Refusal at selected address 667 Personal refusal by selected respondent 824 Full productive interview 1560 Break Off 52 No elegible respondent 357 Quota filled 999

Remarks about non-response: Electoral sections are a reliable sampling unit in Mexico. Between 95 and 97 percent of all adult population is reachable using the electoral sections as sampling frame. The sample distribution in Mexico does not appear to have any known limitations. Non response rate is 70%, including no contacts and refusals.

The concentration of people living in small areas has increased in the last decade, with more than half of the world's population living in cities. This is particularly true for Latin America, a region with no particular high contribution to the world total population, but hosts several large cities. The increase in urbanization causes several threats to wildlife that face the loss of their habitat and novel environmental pressures. As the number of wildlife entering cities seems to have increased in the last year, we characterize the temporal and geographical events of a widely distributed carnivore, the puma, Puma concolor. We performed an exhaustive search for media news regarding the sighting, capture, and/or killing of pumas within human settlement areas, and tried to relate them with potential explanatory variables. We found a total of 162 events in Latin America in a period of the last 10 years, particularly concentrated in the year 2020. Most records came from Brazil, followed by Argentina, Chile, and Mexico. Of the total, 41% were only sightings, 58% were captures, and a minor percentage were considered as mascotism. Almost the same number of records came from highly populated areas (cities) than from low populated areas (rural) but with important differences between countries. The countries with more records in urban areas (Brazil and Mexico) showed a larger surface occupied by cities. The countries with most records in rural areas (Argentina and Chile) present the opposite pattern of occupied surface. This might indicate that different percentages of areas dedicated to cities or urban spaces might explain the differences among countries. The most important variable related to puma events in the populated areas was sky brightness, while human density and cattle density explained minor parts. The “anthropause” due to the COVID-19 pandemic might explain the larger number of records from 2020, while the absence of high-quality habitats due to fragmentation and high cattle density, might force the pumas to enter populated areas searching for food. Minor values of night lights could be related to a facilitation of efficiency of foraging behavior. Although some bias might exist in the data, the results should be taken into account as general statements for all analyzed countries.

The statistic depicts the total population of Mexico from 2020 to 2024, with projections up until 2030. In 2020, Mexico's total population amounted to about 128.21 million people. Total population of Mexico The total population of Mexico was expected to reach 116.02 million people by the end of 2013. Despite being the source of one of the largest migration flows in the world, Mexico has managed to maintain around a 1.25 percent population growth rate for the last several years, roughly the same growth rate as India. Among the largest cities in Mexico, Mexico City is leading with more than 8.5 million inhabitants. A slowly declining fertility rate still holds above the replacement rate, and life expectancy is growing, expanding the population from both ends of the age spectrum. With the rising life expectancy, the median age of Mexican residents has also increased, and an increasing stream of immigrants from the financially-troubled Spain has also boosted population numbers. The majority of the Mexican population is Roman Catholic, owing to its colonial Spanish background. Spanish is the predominant language, with several regional and local dialects spoken, but a number of indigenous languages, such as Nahuatl, survive and are also spoken around Mexico. One worrying and relatively recent trend in Mexico is the growing share of the population becoming overweight or obese. It is not entirely clear what sort of effect the obesity epidemic is going to have on Mexican population numbers in the long run, but is starting to manifest itself not just in physical appearance, but in the increased rates of heart disease, hypertension, and diabetes. In fact, diabetes was one of the top causes of deaths for Mexicans in recent years.

This paper focuses on the interactions between peripheralization, vulnerabilities of agricultural livelihoods, and local collective agency in the creation of new capabilities in intermediate cities. It discusses the theoretical implications of a study conducted in the municipality of Tarímbaro, part of the intermediate city of Morelia, Mexico; it expands on results already published in preliminary form. The unit of analysis was the ejido, since this type of social land tenure, granted to landless peasants in 1917 after the Mexican Revolution, is one of the most important forms of social organization in rural Mexico. About one-half of the Mexican territory comprises >30,000 community-based land tenures (mainly ejidos), and a high proportion of the land now occupied by urban centers was ejido land. This paper uses the example of 15 ejidos, notably affected by the expansion of Morelia city, to illustrate how local (rural) organizations can foster collective agency to reduce differential vulnerabilities in peri-urban agricultural livelihoods in intermediate cities. In 2015, a semi-structured interview was undertaken with the president of each ejido, followed by a survey of 61 individuals from 11 of the 15 ejidos. The peripheralization of Morelia has produced inequalities in the adjacent municipality of Tarímbaro. Differential vulnerabilities in peri-urban agricultural livelihoods were found in the participant ejidos. Not all the ejidos have been successful in addressing vulnerabilities associated with urbanization of agricultural land, but those who have achieved some success have certain characteristics that reinforce common values and motivations to establish common goals to sustain local livelihoods. This paper highlights the importance of functional (rural) organizations in regulating access to, and distribution of, resources in the peripheries of intermediate cities.

About this dataset

Context

This list ranks the 74 cities in the New Mexico by Mexican population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Mexican Population: This column displays the rank of city in the New Mexico by their Mexican population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• Mexican Population: The Mexican population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as Mexican. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total New Mexico Mexican Population: This tells us how much of the entire New Mexico Mexican population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

This dataset incorporates Mexico City related essential data files associated with Beth Tellman's dissertation: Mapping and Modeling Illicit and Clandestine Drivers of Land Use Change: Urban Expansion in Mexico City and Deforestation in Central America. It contains spatio-temporal datasets covering three domains; i) urban expansion from 1992-2015, ii) district and section electoral records for 6 elections from 2000-2015, iii) land titling (regularization) data for informal settlements from 1997-2012 on private and ejido land. The urban expansion data includes 30m resolution urban land cover for 1992 and 2013 (methods published in Goldblatt et al 2018), and a shapefile of digitized urban informal expansion in conservation land from 2000-2015 using the Worldview-2 satellite. The electoral records include shapefiles with the geospatial boundaries of electoral districts and sections for each election, and .csv files of the number of votes per party for mayoral, delegate, and legislature candidates. The private land titling data includes the approximate (in coordinates) location and date of titles given by the city government (DGRT) extracted from public records (Diario Oficial) from 1997-2012. The titling data on ejido land includes a shapefile of georeferenced polygons taken from photos in the CORETT office or ejido land that has been expropriated by the government, and including an accompany .csv from the National Agrarian Registry detailing the date and reason for expropriation from 1987-2007. Further details are provided in the dissertation and subsequent article publication (Tellman et al 2021).

The Mexico City portion of these data were generated via a National Science Foundation sponsored project (No. 1657773, DDRI: Mapping and Modeling Clandestine Drivers of Urban Expansion in Mexico City). The project P.I. is Beth Tellman with collaborators at ASU (B.L Turner II and Hallie Eakin). Other collaborators include the National Autonomous University of Mexico (UNAM), at the Institute of Geography via Dr. Armando Peralta Higuera, who provided support for two students, Juan Alberto Guerra Moreno and Kimberly Mendez Gomez for validating the Landsat urbanization algorithm. Fidel Serrano-Candela, at the UNAM Laboratory of the National Laboratory for Sustainability Sciences (LANCIS) also provided support for urbanization algorithm development and validation, and Rodrigo Garcia Herrera, who provided support for hosting data at LANCIS (at: http://patung.lancis.ecologia.unam.mx/tellman/). Additional collaborators include Enrique Castelán, who provided support for the informal urbanization data from SEDEMA (Ministry of the Environmental for Mexico City). Electoral, land titling, and land zoning data were digitized with support from Juana Martinez, Natalia Hernandez, Alexia Macario Sanchez, Enrique Ruiz Durazo, in collaboration with Felipe de Alba, at CESOP (Center of Social Studies and Public Opinion, at the Mexican Legislative Assembly). The data include geospatial time series data regarding changes in urban land cover, digitized electoral results, land titling, land zoning, and public housing. Additional funding for this work was provided by NSF under Grant No. 1414052, CNH: The Dynamics of Multiscalar Adaptation in Megacities (PI H. Eakin), and the NSF-CONACYT GROW fellowship NSF No. 026257-001 and CONACYT number 291303 (PI Bojórquez).

References:

Tellman, B., Eakin, H., Janssen, M.A., Alba, F. De, Ii, B.L.T., 2021. The Role of Institutional Entrepreneurs and Informal Land Transactions in Mexico City’s Urban Expansion. World Dev. 140, 1–44. https://doi.org/10.1016/j.worlddev.2020.105374

Goldblatt, R., Stuhlmacher, M.F., Tellman, B., Clinton, N., Hanson, G., Georgescu, M., Wang, C., Serrano-Candela, F., Khandelwal, A.K., Cheng, W.-H., Balling, R.C., 2018. Using Landsat and nighttime lights for supervised pixel-based image classification of urban land cover. Remote Sens. Environ. 205, 253–275. https://doi.org/10.1016/j.rse.2017.11.026

Dataset

This dataset was created by Allan Kirwa

Released under Apache 2.0

Contents

Abstract

In order to develop various methods of comparable data collection on health and health system responsiveness WHO started a scientific survey study in 2000-2001. This study has used a common survey instrument in nationally representative populations with modular structure for assessing health of indviduals in various domains, health system responsiveness, household health care expenditures, and additional modules in other areas such as adult mortality and health state valuations.

The health module of the survey instrument was based on selected domains of the International Classification of Functioning, Disability and Health (ICF) and was developed after a rigorous scientific review of various existing assessment instruments. The responsiveness module has been the result of ongoing work over the last 2 years that has involved international consultations with experts and key informants and has been informed by the scientific literature and pilot studies.

Questions on household expenditure and proportionate expenditure on health have been borrowed from existing surveys. The survey instrument has been developed in multiple languages using cognitive interviews and cultural applicability tests, stringent psychometric tests for reliability (i.e. test-retest reliability to demonstrate the stability of application) and most importantly, utilizing novel psychometric techniques for cross-population comparability.

The study was carried out in 61 countries completing 71 surveys because two different modes were intentionally used for comparison purposes in 10 countries. Surveys were conducted in different modes of in- person household 90 minute interviews in 14 countries; brief face-to-face interviews in 27 countries and computerized telephone interviews in 2 countries; and postal surveys in 28 countries. All samples were selected from nationally representative sampling frames with a known probability so as to make estimates based on general population parameters.

The survey study tested novel techniques to control the reporting bias between different groups of people in different cultures or demographic groups ( i.e. differential item functioning) so as to produce comparable estimates across cultures and groups. To achieve comparability, the selfreports of individuals of their own health were calibrated against well-known performance tests (i.e. self-report vision was measured against standard Snellen's visual acuity test) or against short descriptions in vignettes that marked known anchor points of difficulty (e.g. people with different levels of mobility such as a paraplegic person or an athlete who runs 4 km each day) so as to adjust the responses for comparability . The same method was also used for self-reports of individuals assessing responsiveness of their health systems where vignettes on different responsiveness domains describing different levels of responsiveness were used to calibrate the individual responses.

This data are useful in their own right to standardize indicators for different domains of health (such as cognition, mobility, self care, affect, usual activities, pain, social participation, etc.) but also provide a better measurement basis for assessing health of the populations in a comparable manner. The data from the surveys can be fed into composite measures such as "Healthy Life Expectancy" and improve the empirical data input for health information systems in different regions of the world. Data from the surveys were also useful to improve the measurement of the responsiveness of different health systems to the legitimate expectations of the population.

Geographic coverage

15 federal states: Distrito Federal, Guanajuato, Jalisco, Estado de México, Michoacán, Qurétaro, Guerrero, Oaxaca, Puebla, Veracruz, Yucatán, Chihuahua, Nuevo León, San Luis Potosí, Sonora

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample used was a probabilistic, multistage, stratified and clustered sample and represented urban and rural strata.

Mexico has 32 Federal States, which were divided, into 3 regions: Centre, South and North. Out of these regions, 15 were selected as follows: Centre: Distrito Federal, Guanajuato, Jalisco, Estado de México, Michoacán, Qurétaro South: Guerrero, Oaxaca, Puebla, Veracruz, Yucatán North: Chihuahua, Nuevo León, San Luis Potosí, Sonora

Mode of data collection

Face-to-face [f2f]

Cleaning operations

Data Coding At each site the data was coded by investigators to indicate the respondent status and the selection of the modules for each respondent within the survey design. After the interview was edited by the supervisor and considered adequate it was entered locally.

Data Entry Program A data entry program was developed in WHO specifically for the survey study and provided to the sites. It was developed using a database program called the I-Shell (short for Interview Shell), a tool designed for easy development of computerized questionnaires and data entry (34). This program allows for easy data cleaning and processing.

The data entry program checked for inconsistencies and validated the entries in each field by checking for valid response categories and range checks. For example, the program didn’t accept an age greater than 120. For almost all of the variables there existed a range or a list of possible values that the program checked for.

In addition, the data was entered twice to capture other data entry errors. The data entry program was able to warn the user whenever a value that did not match the first entry was entered at the second data entry. In this case the program asked the user to resolve the conflict by choosing either the 1st or the 2nd data entry value to be able to continue. After the second data entry was completed successfully, the data entry program placed a mark in the database in order to enable the checking of whether this process had been completed for each and every case.

Data Transfer The data entry program was capable of exporting the data that was entered into one compressed database file which could be easily sent to WHO using email attachments or a file transfer program onto a secure server no matter how many cases were in the file. The sites were allowed the use of as many computers and as many data entry personnel as they wanted. Each computer used for this purpose produced one file and they were merged once they were delivered to WHO with the help of other programs that were built for automating the process. The sites sent the data periodically as they collected it enabling the checking procedures and preliminary analyses in the early stages of the data collection.

Data quality checks Once the data was received it was analyzed for missing information, invalid responses and representativeness. Inconsistencies were also noted and reported back to sites.

Data Cleaning and Feedback After receipt of cleaned data from sites, another program was run to check for missing information, incorrect information (e.g. wrong use of center codes), duplicated data, etc. The output of this program was fed back to sites regularly. Mainly, this consisted of cases with duplicate IDs, duplicate cases (where the data for two respondents with different IDs were identical), wrong country codes, missing age, sex, education and some other important variables.