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.

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/.

Comprehensive socio-economic dataset for Mexico including population demographics, economic indicators, geographic data, and social statistics. This dataset covers key metrics such as GDP, population density, area, capital city, and regional classifications.

A dataset listing New Mexico cities by population for 2024.

Mexico MX: Population in Largest City data was reported at 21,500,251.000 Person in 2017. This records an increase from the previous number of 21,419,976.000 Person for 2016. Mexico MX: Population in Largest City data is updated yearly, averaging 15,225,498.500 Person from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 21,500,251.000 Person in 2017 and a record low of 5,479,184.000 Person in 1960. Mexico MX: Population in Largest City 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 largest city is the urban population living in the country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; ;

CONTENT

• It is perhaps unsurprising that the majority of the most populous cities in the world are in the two most populated countries in the world, China and India. Among these are Shanghai and Beijing, with populations of 25 and 22 million respectively, Delhi (27 million), and Mumbai (over 21.5 million).

• Tokyo is the largest city in the world if the entire Tokyo metro area is included, with a total of more than 38 million residents. Another Japanese city, Osaka, also has a very large population of almost 20.5 million. There are also a number of non-Asian cities with high populations, including Mexico City (over 21 million), Cairo (almost 19.5 million), and Buenos Aires (almost 15.5 million).

• European cities, Istanbul is the most populous, with more than 14.5 million residents. This is followed by Moscow (over 12 million) and Paris (11 million including the Paris metro area). These cities are of course also culturally significant and between them welcome millions of tourists each year.

• There are quite a number of popular and culturally rich cities that have smaller populations, often making for higher living standards for their residents. Barcelona, Sydney, Berlin and Vancouver all have fewer than five million residents, but are very popular choices for city living. There are also some comparatively very small cities with big cultural, historical or political reputations, such as Sarajevo (314,000), Edinburgh (502,000), and Venice (631,000), demonstrating that small cities can be highly significant regardless of the size of their population.

Historical dataset of population level and growth rate for the Leon de los Aldamas, Mexico metro area from 1950 to 2025.

Historical dataset of population level and growth rate for the Mexico City, Mexico metro area from 1950 to 2025.

The Global Human Footprint dataset of the Last of the Wild Project, version 2, 2005 (LWPv2) is the Human Influence Index (HII) normalized by biome and realm. The HII is a global dataset of 1 km grid cells, created from nine global data layers covering human population pressure (population density), human land use and infraestructure (built-up areas, nighttime lights, land use/land cover) and human access (coastlines, roads, navigable rivers).The Human Footprint Index (HF) map, expresses as a percentage the relative human influence in each terrestrial biome. HF values from 0 to 100. A value of zero represents the least influence -the "most wild" part of the biome with value of 100 representing the most influence (least wild) part of the biome.

The below dataset shows the top 800 biggest cities in the world and their populations in the year 2024. It also tells us which country and continent each city is in, and their rank based on population size. Here are the top ten cities:

• Tokyo, Japan - in Asia, with 37,115,035 people.
• Delhi, India - in Asia, with 33,807,403 people.
• Shanghai, China - in Asia, with 29,867,918 people.
• Dhaka, Bangladesh - in Asia, with 23,935,652 people.
• Sao Paulo, Brazil - in South America, with 22,806,704 people.
• Cairo, Egypt - in Africa, with 22,623,874 people.
• Mexico City, Mexico - in North America, with 22,505,315 people.
• Beijing, China - in Asia, with 22,189,082 people.
• Mumbai, India - in Asia, with 21,673,149 people.
• Osaka, Japan - in Asia, with 18,967,459 people.

Inhabitants Per City Block
This dataset falls under the category Traffic Generating Parameters Population.
It contains the following data: Number of inhabitants per block, it's downloading is possible but very slow, the data comes from national level data, but downloading is only allowed at neighbourhood level.
This dataset was scouted on 2022-02-13 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://www.inegi.org.mx/app/mapa/espacioydatos/default.aspx?ag=19039See URL for data access and license information.

Life Expectancy at Birth: Mexico City data was reported at 76.325 Year in 2018. This records an increase from the previous number of 76.220 Year for 2017. Life Expectancy at Birth: Mexico City data is updated yearly, averaging 72.400 Year from Dec 1970 (Median) to 2018, with 49 observations. The data reached an all-time high of 76.385 Year in 2013 and a record low of 62.130 Year in 1970. Life Expectancy at Birth: Mexico City data remains active status in CEIC and is reported by National Population Council. The data is categorized under Global Database’s Mexico – Table MX.G006: Life Expectancy at Birth: by State.

Dataset 1 (AXA collisions 2015–2019) was curated and used to evaluate the effect of two road traffic regulations implemented in Mexico City in 2015 and 2019 on collisions using an interrupted time series analysis. Collisions data came from insurance collision claims (January 2015 to December 2019). The dataset contains 8 variables: year (anio_n), week (semana), count of total collisions per week (c_total), count of collisions resulting in injury per week (c_p_lesion), binary variable to identify the 2015 intervention (limit), binary variable to identify the 2019 intervention (limit1), the number of weeks from baseline (time), an estimate of the number of insured vehicles per week (veh_a_cdmx). Dataset 2 (Road traffic deaths 2013–2019) was curated and used to evaluate the effect of two road traffic regulations implemented in Mexico City in 2015 and 2019 on mortality using an interrupted time series analysis. Mortality data came from vital registries collated by the Mexican Institute for Geography and Statistics, INEGI, (January 2013 to December 2019). The dataset contains 7 variables: year (anio_ocur), week (semana), count of traffic-related deaths per week (def_trans), binary variable to identify the 2015 intervention (limit), binary variable to identify the 2019 intervention (limit1), the number of weeks from baseline (time) and an estimate of the Mexico City population per week (pob_tot_p). Methods Dataset 1 arises from publicly available data on insurance-reported collisions published on the website of the International Institute for Data Science (see reference below). The data were collected by claims adjusters from the company AXA at the site of the collision using an electronic device. These data were available for public use from January 2015 to December 2019 and include information on individual collisions and their characteristics: date the collision occurred, location (coordinates and adjuster reported location), type of vehicle involved and whether there were injuries or deaths. Data were processed and cleaned, mapping collisions, and keeping only those georeferenced within Mexico City boundaries as well as coded to Mexico City in the reported location variable. We then summed the number of collisions per week and merged it with data on an estimate of the number of insured registered vehicles per week (using information from registered vehicles and proportion of insured vehicles from the Mexican Association of Insurance companies). Two more variables were created, one that identifies the week when the intervention came into effect and another variable to number the weeks since baseline. This dataset contains all the necessary information to conduct the interrupted time series analysis for total collisions and collisions resulting in injuries. Dataset 2: mortality data were validated and reported by INEGI (see reference below) from death certificates filed mainly by the Health Sector, using the International Classification of Disease, 10th Revision (ICD-10) for diagnosis codes. We used data from January 2013 to December 2019 and included deaths with the following ICD-10 codes: V02-V04 (.1-.9), V09, V092, V09.3, V09.9, V12-V14 (.3-.9), V19.4-V19.6, V19.9, V20-V28 (.3-.9), V29, V30-V39, V40-V79 (.4-.9), V80.3-V80.5, V81.1, V82.1, V82.1, V83-V86 (.0-.3), V87-V89.2 and V89.9. We summed the number of traffic-related deaths per week and merged it with data on an estimate of the total population in Mexico City per week (see refs below). Two more variables were created, one that identifies the week when the intervention came into effect and another variable to number the weeks since baseline. This dataset contains all the necessary information to conduct the interrupted time series analysis for road traffic deaths. References to original data:

Instituto Internacional de Ciencia de Datos. Datos AXA de Percances Viales [Internet]. 2020 [July 2021]. Available from: https://i2ds.org/datos-abiertos/. Instituto Nacional de Geografía y Estadística. Parque Vehicular [Internet]. 2019 [July 2021]. Available from: https://www.inegi.org.mx/temas/vehiculos/default.html#Tabulados. Dirección Ejecutiva de Líneas de Negocio área de Automóviles. Sistema Estadístico del Sector Asegurador del ramo Automóviles SESA 2018. Mexico City: Asociación Mexicana de Instituciones de Seguro, 2020. Instituto Nacional de Geografía y Estadística. Mortalidad [Internet]. 2020 [July 2021]. Available from: https://www.inegi.org.mx/programas/mortalidad/default.html#Datos_abiertos.

World Health Organisation. ICD-10 Version:2010 [Internet]. 2010 [July 2021]. Available from: https://icd.who.int/browse10/2010/en. Consejo Nacional de Población. Proyecciones de la Población de México y de las Entidades Federativas, 2016-2050 [Internet]. 2018 [July 2021]. Available from: https://datos.gob.mx/busca/dataset/proyecciones-de-la-poblacion-de-mexico-y-de-las-entidades-federativas-2016-2050.

Unemployment Rate in Mexico decreased to 2.60 percent in October from 3 percent in September of 2025. This dataset provides the latest reported value for - Mexico Unemployment Rate - plus previous releases, historical high and low, short-term forecast and long-term prediction, economic calendar, survey consensus and news.

The magnitude 8.1 earthquake occurred off the Pacific coast of Mexico. The damage was concentrated in a 25 square km area of Mexico City, 350 km from the epicenter. The underlying geology and geologic history of Mexico City contributed to this unusual concentration of damage at a distance from the epicenter. Of a population of 18 million, an estimated 10,000 people were killed, and 50,000 were injured.

This dataset is part of the article:

Paragraph-level Rationale Extraction through Regularization: A case study on European Court of Human Rights Cases. Ilias Chalkidis, Manos Fergadiotis, Dimitris Tsarapatsanis, Nikolaos Aletras, Ion Androutsopoulos and Prodromos Malakasiotis. In the Proceedings of the Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL 2021). June 6–11, 2021. Mexico City, Mexico.

The court (ECtHR) hears allegations regarding breaches in human rights provisions of the European Convention of Human Rights (ECHR) by European states. The Convention is available at https://www.echr.coe.int/Documents/Convention_ENG.pdf. The court rules on a subset of all ECHR articles, which are predefined (alleged) by the applicants (plaintiffs). Our dataset comprises 11k ECtHR cases and can be viewed as an enriched version of the ECtHR dataset of Chalkidis et al. (2019), which did not provide ground truth for alleged article violations (articles discussed) and rationales. Addeddate 2021-03-19 09:28:47 Identifier ECtHR-NAACL2021 Identifier-ark ark:/13960/t1gj9vs5d Scanner Internet Archive HTML5 Uploader 1.6.4

BackgroundSince its appearance, COVID-19 has immensely impacted our society. Public health measures, from the initial lockdowns to vaccination campaigns, have mitigated the crisis. However, SARS-CoV-2’s persistence and evolving variants continue to pose global threats, increasing the risk of reinfections. Despite vaccination progress, understanding reinfections remains crucial for informed public health responses.MethodsWe collected available data on clinical and genomic information for SARS-CoV-2 samples from patients treated in Mexico City from 2020 epidemiological week 10 to 2023 epidemiological week 06 encompassing the whole public health emergency’s period. To identify clinical data we utilized the SISVER (Respiratory Disease Epidemiological Surveillance System) database for SARS-CoV-2 patients who received medical attention in Mexico City. For genomic surveillance we analyzed genomic data previously uploaded to GISAID generated by Mexican institutions. We used these data sources to generate descriptors of case number, hospitalization, death and reinfection rates, and viral variant prevalence throughout the pandemic period.FindingsThe fraction of reinfected individuals in the COVID-19 infected population steadily increased as the pandemic progressed in Mexico City. Most reinfections occurred during the fifth wave (40%). This wave was characterized by the coexistence of multiple variants exceeding 80% prevalence; whereas all other waves showed a unique characteristic dominant variant (prevalence >95%). Shifts in symptom patient care type and severity were observed, 2.53% transitioned from hospitalized to ambulatory care type during reinfection and 0.597% showed the opposite behavior; also 7.23% showed a reduction in severity of symptoms and 6.05% displayed an increase in severity. Unvaccinated individuals accounted for the highest percentage of reinfections (41.6%), followed by vaccinated individuals (31.9%). Most reinfections occurred after the fourth wave, dominated by the Omicron variant; and after the vaccination campaign was already underway.InterpretationOur analysis suggests reduced infection severity in reinfections, evident through shifts in symptom severity and care patterns. Unvaccinated individuals accounted for most reinfections. While our study centers on Mexico City, its findings may hold implications for broader regions, contributing insights into reinfection dynamics.

Note: ncr: no cases reported. na: not applicable.* In general population: HBV core Ab +.** Insufficient sample size.Prevalence of transmissible infections by sex and age.

Note:* Out-off valid cases (excluding missing or prefer not to answer). ncr: no cases reported.Reported drug use by type, and use before and during imprisonment.

Note: na: not applicable.Prevalence of physical and sexual violence and sexual risk behavior before and during imprisonment.