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

Unemployment Rate in Mexico increased to 2.70 percent in May from 2.50 percent in April 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.

A dataset listing New Mexico cities by population for 2024.

Contains data from the World Bank's data portal. There is also a consolidated country dataset on HDX.

Cities can be tremendously efficient. It is easier to provide water and sanitation to people living closer together, while access to health, education, and other social and cultural services is also much more readily available. However, as cities grow, the cost of meeting basic needs increases, as does the strain on the environment and natural resources. Data on urbanization, traffic and congestion, and air pollution are from the United Nations Population Division, World Health Organization, International Road Federation, World Resources Institute, and other sources.

Wages in Mexico decreased to 278.93 MXN/Day in May from 621.89 MXN/Day in April of 2025. This dataset provides - Mexico Average Daily Wages - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Employment Rate in Mexico decreased to 97.25 percent in May from 97.46 percent in April of 2025. This dataset provides - Mexico Employment Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The TIGER/Line shapefiles include both incorporated places (legal entities) and census designated places or CDPs (statistical entities). An incorporated place is established to provide governmental functions for a concentration of people as opposed to a minor civil division (MCD), which generally is created to provide services or administer an area without regard, necessarily, to population. Places always nest within a state, but may extend across county and county subdivision boundaries. An incorporated place usually is a city, town, village, or borough, but can have other legal descriptions. CDPs are delineated for the decennial census as the statistical counterparts of incorporated places. CDPs are delineated to provide data for settled concentrations of population that are identifiable by name, but are not legally incorporated under the laws of the state in which they are located. The boundaries for CDPs often are defined in partnership with state, local, and/or tribal officials and usually coincide with visible features or the boundary of an adjacent incorporated place or another legal entity. CDP boundaries often change from one decennial census to the next with changes in the settlement pattern and development; a CDP with the same name as in an earlier census does not necessarily have the same boundary. The only population/housing size requirement for CDPs is that they must contain some housing and population. The boundaries of most incorporated places in this shapefile are as of January 1, 2015, as reported through the Census Bureau's Boundary and Annexation Survey (BAS). The boundaries of all CDPs were delineated as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2010 Census.

Context

This data set contains accidents registered by the C4, a Mexican system that registers all traffic incidents.

Content

The data set has the following columns:

1. folio: a unique ID for each register
2. fecha_creacion: creation date
3. hora_creacion: creation time
4. dia_semana: day of the week when incident happens
5. codigo_cierre: internal classification. The column could contain the following codes.
6. A: Affirmative, if the incident is confirmed by emergencies team.
7. N: Negative, if the emergencies team doesn't confirm the incident at the location point.
8. I: Informative, in case attention teams want to add extra information.
9. F: False, if initial report doesn't match with the real events
10. D: Duplicated, records with closing code affirmative, negative or false but operators identify them
11. fecha_cierre: close date, the date when the incident was resolved
12. año_cierre: close year
13. mes_cierre: close month
14. hora_cierre: close time
15. delegacion_inicio: entity inside Mexico City where the incident was registered
16. incidente_c4: a brief explanation about the incident.
17. latitud: accident latitude
18. longitud: accident longitude
19. clas_con_f_alarma: code identifying the situation's severity
20. tipo_entrada: how the incident was reported
21. delegacion_cierre: entity inside Mexico City where the incident was closed
22. geopoint: latitude and longitude columns combined
23. mes: month when the incident was reported

Additional Note: To properly use and interpret the information, must consider those registers with closing codes Affirmative and Informative, these are real incidents.

Acknowledgements

All files were downloaded from here The Mexico City web page containing open data about traffic incidents.

Abstract

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.

Geographic coverage

Mexico City

Analysis unit

-Individuals

Universe

Participants in a financial education evaluation

Kind of data

Sample survey data [ssd]

Sampling procedure

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.

Mode of data collection

Face-to-face [f2f]

Research instrument

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.

Response rate

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).

How do moral beliefs influence favorability to collective violence? In this article, I argue that, first, moral beliefs are influential depending on their salience, as harm avoidance is a common moral concern. The more accessible moral beliefs in decision-making, the more they restrain harmful behavior. Second, moral beliefs are influential depending on their content. Group-oriented moral beliefs can overturn the harm avoidance principle and motivate individuals to favor collective violence. Analysis is based on a representative survey in Mexico City and focuses on a proximate form of collective violence, locally called lynching. Findings support both logics of moral influence. Experimentally induced moral salience reduces favorability to lynching, and group-oriented moral beliefs are related to more favorability. Against existing theories that downplay the relevance of morality and present it as cheap talk, these findings demonstrate how moral beliefs can both restrain and motivate collective violence.

Context

With a population just short of 3 million people, the city of Toronto is the largest in Canada, and one of the largest in North America (behind only Mexico City, New York and Los Angeles). Toronto is also one of the most multicultural cities in the world, making life in Toronto a wonderful multicultural experience for all. More than 140 languages and dialects are spoken in the city, and almost half the population Toronto were born outside Canada.It is a place where people can try the best of each culture, either while they work or just passing through. Toronto is well known for its great food.

Content

This dataset was created by doing webscraping of Toronto wikipedia page . The dataset contains the latitude and longitude of all the neighborhoods and boroughs with postal code of Toronto City,Canada.

Mexico has one of the largest overweight and obesity epidemics in the world and as a response, several actions aiming to reduce the obesity epidemic have been already set in place. Some of these actions include a specific action program for schools looking to turn the scholar environments into supportive environments for the infants to make healthier food choices. The influence of the environment (the so-called “choice architecture”) on people’s perceptions and decisions is studied by economists with the aim of supporting individuals’ to make healthier decisions, using tools known as “nudges”. However, "nudges" are not commonly integrated into anti-obesity strategies. We designed an intervention trying to find out whether such a small, liberty-preserving intervention could increase the effectiveness of a water-promotion campaign, when compared to the common approach of an educative talk. The intervention was developed in three schools in Mexico City and the State of Mexico. The body mass index, standardized by Z-scores, was used as the indicator of campaign success. Although – mainly due to problems within the sample and a yet too-short follow-up – our results do not show considerable differences between the approaches, they provide insights suggesting that including “nudges” into a health promoting campaign may indeed have a positive impact.

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.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Mexico Beach. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2021

Based on our analysis ACS 2017-2021 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Mexico Beach, the median income for all workers aged 15 years and older, regardless of work hours, was $48,334 for males and $33,478 for females.

These income figures highlight a substantial gender-based income gap in Mexico Beach. Women, regardless of work hours, earn 69 cents for each dollar earned by men. This significant gender pay gap, approximately 31%, underscores concerning gender-based income inequality in the city of Mexico Beach.

- Full-time workers, aged 15 years and older: In Mexico Beach, among full-time, year-round workers aged 15 years and older, males earned a median income of $66,318, while females earned $49,587, leading to a 25% gender pay gap among full-time workers. This illustrates that women earn 75 cents for each dollar earned by men in full-time roles. This analysis indicates a widening gender pay gap, showing a substantial income disparity where women, despite working full-time, face a more significant wage discrepancy compared to men in the same roles.

Surprisingly, the gender pay gap percentage was higher across all roles, including non-full-time employment, for women compared to men. This suggests that full-time employment offers a more equitable income scenario for women compared to other employment patterns in Mexico Beach.

https://i.neilsberg.com/ch/mexico-beach-fl-income-by-gender.jpeg" alt="Mexico Beach, FL gender based income disparity">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2022
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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/.

Recommended for further research

This dataset is a part of the main dataset for Mexico Beach median household income by gender. You can refer the same here

The dataset contains data from children from COIPIS cohort of sex, age, percent of body fat and body mass index. COIPIS cohort started in 2012 in response to the need for establishing a prospective childhood obesity epidemiologic study. The sample consisted of 1061 girls and 1121 boys, from 3 to 17 years old. Participants were invited among ISSSTE right-holder teachers who were also members of the union, and patients treated at ISSSTE clinics distributed in Mexico City. Participants were examined every 6 months, since they were recruited until they turned 17 years old.

THERE IS A NEWER VERSION (1.3) THAT INCORPORATES THE UNANNOTATED TEST AND BACKGROUND FILES Gold Standard annotations for SMM4H-Spanish shared task. SMM4H 2021 accepted at NAACL (scheduled in Mexico City in June) https://2021.naacl.org/. Introduction: The entire corpus contains 10,000 annotated tweets. It has been split into training, validation and test (60-20-20). The current version contains the training and development set of the shared task with Gold Standard annotations. In future versions of the dataset, test and background sets will be released. For the subtask-1 (classification), annotations are distributed in a tab-separated file (TSV). The TSV format follows the format employed in SMM4H 2019 Task 2: tweet_id class For the subtask-2 (Named Entity Recognition, profession detection), annotations are distributed in 2 formats: Brat standoff and TSV. See Brat webpage for more information about Brat standoff format (https://brat.nlplab.org/standoff.html). The TSV format follows the format employed in SMM4H 2019 Task 2: tweet_id begin end type extraction In addition, we provide a tokenized version of the dataset, for participant's convenience. It follows the BIO format (similar to CONLL). The files were generated with the brat_to_conll.py script (included), which employs the es_core_news_sm-2.3.1 Spacy model for tokenization. Zip structure: txt-files: folder with text files. One text file per tweet. One sub-directory per corpus split (train and valid). txt-files-english: folder with text files Machine Translated to English. subtask-1: One file per corpus split (train.tsv and valid.tsv). subtask-2: brat: folder with annotations in Brat format. One sub-directory per corpus split (train and valid). tsv: folder with annotations in TSV. One file per corpus split (train and valid). BIO: folder with corpus in BIO tagging. One file per corpus split (train and valid). Annotation quality: We have performed a consistency analysis of the corpus. 10% of the documents have been annotated by an internal annotator as well as by the linguist experts following the same annotation guideliens. The preliminary Inter-Annotator Agreement (pairwise agreement) is 0.919. Important shared task information: SYSTEM PREDICTIONS MUST FOLLOW THE TSV FORMAT. And systems will only be evaluated for the PROFESION and SITUACION_LABORAL predictions (despite the Gold Standard contains 2 extra entity classes). For more information about the evaluation scenario, see the Codalab link, or the evaluation webpage. For further information, please visit https://temu.bsc.es/smm4h-spanish/ or email us at encargo-pln-life@bsc.es Do not share the data with other individuals/teams without permission from the task organizer. Tweets IDs are the primary source of information. Tweet texts are provided as support material. By downloading this resource, you agree to the Twitter Terms of Service, Privacy Policy, Developer Agreement, and Developer Policy. Resources: Web Annotation guidelines (in Spanish) Annotation guidelines (in English) FastText COVID-19 Twitter embeddings Occupations gazetteer Funded by the Plan de Impulso de las Tecnologías del Lenguaje (Plan TL).