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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.; ;
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License information was derived automatically
All cities with a population > 1000 or seats of adm div (ca 80.000)Sources and ContributionsSources : GeoNames is aggregating over hundred different data sources. Ambassadors : GeoNames Ambassadors help in many countries. Wiki : A wiki allows to view the data and quickly fix error and add missing places. Donations and Sponsoring : Costs for running GeoNames are covered by donations and sponsoring.Enrichment:add country name
https://www.newmexico-demographics.com/terms_and_conditionshttps://www.newmexico-demographics.com/terms_and_conditions
A dataset listing New Mexico cities by population for 2024.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Mexico MX: Population in Largest City: as % of Urban Population data was reported at 20.842 % in 2017. This records a decrease from the previous number of 21.105 % for 2016. Mexico MX: Population in Largest City: as % of Urban Population data is updated yearly, averaging 25.978 % from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 28.774 % in 1969 and a record low of 20.842 % in 2017. Mexico MX: Population in Largest City: as % 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 in largest city is the percentage of a country's urban population living in that country's largest metropolitan area.; ; United Nations, World Urbanization Prospects.; Weighted average;
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Mexico Beach: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..
Key observations
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Income brackets:
Variables / Data Columns
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.
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 is a part of the main dataset for Mexico Beach median household income by age. You can refer the same here
Cities ranking and mega citiesTokyo is the world’s largest city with an agglomeration of 37 million inhabitants, followed by New Delhi with 29 million, Shanghai with 26 million, and Mexico City and São Paulo, each with around 22 million inhabitants. Today, Cairo, Mumbai, Beijing and Dhaka all have close to 20 million inhabitants. By 2020, Tokyo’s population is projected to begin to decline, while Delhi is projected to continue growing and to become the most populous city in the world around 2028.By 2030, the world is projected to have 43 megacities with more than 10 million inhabitants, most of them in developing regions. However, some of the fastest-growing urban agglomerations are cities with fewer than 1 million inhabitants, many of them located in Asia and Africa. While one in eight people live in 33 megacities worldwide, close to half of the world’s urban dwellers reside in much smaller settlements with fewer than 500,000 inhabitants.About the dataThe 2018 Revision of the World Urbanization Prospects is published by the Population Division of the United Nations Department of Economic and Social Affairs (UN DESA). It has been issued regularly since 1988 with revised estimates and projections of the urban and rural populations for all countries of the world, and of their major urban agglomerations. The data set and related materials are available at: https://esa.un.org/unpd/wup/
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
This dataset contains Twitter data collected during the 2018 Mexican presidential election campaign, focusing on mentions and tweets related to the main presidential candidates (@JoseAMeadeK, @RicardoAnayaC, @lopezobrador_, and @JaimeRdzNL). It represents a sample of 10,000 tweets from a larger dataset gathered as part of the research project "In-Context Learning for Misinformation Detection: Detecting Political Propaganda on Twitter Mexico using Large Language Model Meta AI".
The dataset includes the following fields: tweet_id: Unique identifier for each tweet followers_count: Number of followers of the user who posted the tweet created_at: Original timestamp of tweet creation (UTC) local_time: Timestamp converted to Mexico City time zone tweet: Text content of the tweet source: Platform or application used to post the tweet
This sample dataset was collected using the Twitter's streaming API in 2018. The script filtered the global Twitter stream for mentions of Mexico's presidential candidates.
Several fields present in the original data collection have been removed from this sample to comply with Twitter's terms of service and to protect user privacy:
Username (screen_name) Tweet URL Geographical coordinates User location information
Only publicly accessible tweets (those without privacy restrictions set by users) were collected in the original dataset.
This dataset serves as a sample to provide insights into the larger research project focusing on misinformation detection and political propaganda analysis in Mexican social media during the 2018 presidential campaign. The research applies large language models to detect patterns of misinformation in political discourse.
Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
Context
The dataset presents the the household distribution across 16 income brackets among four distinct age groups in Mexico: Under 25 years, 25-44 years, 45-64 years, and over 65 years. The dataset highlights the variation in household income, offering valuable insights into economic trends and disparities within different age categories, aiding in data analysis and decision-making..
Key observations
When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.
Income brackets:
Variables / Data Columns
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.
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 is a part of the main dataset for Mexico median household income by age. You can refer the same here
Attribution-NonCommercial-NoDerivs 4.0 (CC BY-NC-ND 4.0)https://creativecommons.org/licenses/by-nc-nd/4.0/
License information was derived automatically
We explore two approaches to social inequality — social class and spatial inequality — in relation to the distribution of neighbourhood plazas at ancient Teotihuacan, a large premodern urban centre in the basin of Mexico. We use the digitized map from the Teotihuacan Mapping Project to identify publicly accessible plazas and three-temple complexes across the city, and we measure spatial access to these facilities for elite and non-elite residents. Our findings are twofold. First, Teotihuacan had a large amount of assembly space, and all Teotihuacanos have good access to it, irrespective of class. This finding is consistent with a collective model of Teotihuacan government. Second, there is significant inequality in spatial access to service facilities between elites and non-elites, with elites living closer to facilities. This pattern of accessibility suggests both relatively high quality of life for the residents of Teotihuacan as well as the maintenance of hierarchical inequality within the city. Alexandra L. Norwood, Anne Sherfield, and Michael E. Smith, 'Plazas, Social Class, and Spatial Inequality at Ancient Teotihuacan, Mexico', Journal of Urban Archaeology, 10 (Turnhout: Brepols, 2024), 35–54
To educate consumers about responsible use of financial products, many governments, non-profit organizations and financial institutions have started to provide financial literacy courses. However, participation rates for non-compulsory financial education programs are typically extremely low.
Researchers from the World Bank conducted randomized experiments around a large-scale financial literacy course in Mexico City to understand the reasons for low take-up among a general population, and to measure the impact of this financial education course. The free, 4-hour financial literacy course was offered by a major financial institution and covered savings, retirement, and credit use. Motivated by different theoretical and logistics reasons why individuals may not attend training, researchers randomized the treatment group into different subgroups, which received incentives designed to provide evidence on some key barriers to take-up. These incentives included monetary payments for attendance equivalent to $36 or $72 USD, a one-month deferred payment of $36 USD, free cost transportation to the training location, and a video CD with positive testimonials about the training.
A follow-up survey conducted on clients of financial institutions six months after the course was used to measure the impacts of the training on financial knowledge, behaviors and outcomes, all relating to topics covered in the course.
The baseline dataset documented here is administrative data received from a screener that was used to get people to enroll in the financial course. The follow-up dataset contains data from the follow-up questionnaire.
Mexico City
-Individuals
Participants in a financial education evaluation
Sample survey data [ssd]
Researchers used three different approaches to obtain a sample for the experiment.
The first one was to send 40,000 invitation letters from a collaborating financial institution asking about interest in participating. However, only 42 clients (0.1 percent) expressed interest.
The second approach was to advertise through Facebook, with an ad displayed 16 million times to individuals residing in Mexico City, receiving 119 responses.
The third approach was to conduct screener surveys on streets in Mexico City and outside branches of the partner institution. Together this yielded a total sample of 3,503 people. Researchers divided this sample into a control group of 1,752 individuals, and a treatment group of 1,751 individuals, using stratified randomization. A key variable used in stratification was whether or not individuals were financial institution clients. The analysis of treatment impacts is based on the sample of 2,178 individuals who were financial institution clients.
The treatment group received an invitation to participate in the financial education course and the control group did not receive this invitation. Those who were selected for treatment were given a reminder call the day before their training session, which was at a day and time of their choosing.
Face-to-face [f2f]
The follow-up survey was conducted between February and July 2012 to measure post-training financial knowledge, behavior and outcomes. The questionnaire was relatively short (about 15 minutes) to encourage participation.
Interviewers first attempted to conduct the follow-up survey over the phone. If the person did not respond to the survey during the first attempt, researchers offered one a 500 pesos (US$36) Walmart gift card for completing the survey during the second attempt. If the person was still unavailable for the phone interview, a surveyor visited his/her house to conduct a face-to-face interview. If the participant was not at home, the surveyor delivered a letter with information about the study and instructions for how to participate in the survey and to receive the Walmart gift card. Surveyors made two more attempts (three attempts in total) to conduct a face-to-face interview if a respondent was not at home.
72.8 percent of the sample was interviewed in the follow-up survey. The attrition rate was slightly higher in the treatment group (29 percent) than in the control group (25.3 percent).
No description is available. Visit https://dataone.org/datasets/eb9c0b7869c0622aafaf7f8c695a9af3 for complete metadata about this dataset.
First signed 11/17/2010 Last signed 11/24/2010 Entry into force (supplemented by last signed) 11/24/2010 stamped 2011-012 C06544776 cover memo
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Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
License information was derived automatically
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.; ;