Context

This dataset was collected using our App EC Taximeter.

An easy to use tool developed to compare fees, giving the user an accurate fee based on GPS to calculate a cost of the taxi ride. Due to the ability to verify that you are charged fairly, our App is very popular in several cities. We encourage our users to send us URLs with the taxi/transportation fees in their cities to keep growing our database.

★ Our App gets the available fares for your location based on your GPS, perfect when traveling and not getting scammed.

★ Users can start a taximeter in their own phone and check they are charged fairly

★ Several useful information is displayed to the user during the ride: Speed, Wait time, Distance, GPS update, GPS precision, Range of error.

★ Each fare has information available for reference like: Schedule, Minimum fee, Source, Last update.

★ It’s possible to surf through several cities and countries which fares are available for use. If a fare is not in the app, now it’s easier than ever to let us know thanks to Questbee Apps.

We invite users to contribute to our project and expect this data set to be useful, please don't hesitate to contact us to info@ashkadata.com to add your city or to contribute with this project.

Content

The data is collected from June 2016 until July 20th 2017. The data is not completely clean, many users forget to turn off the taximeter when done with the route. Hence, we encourage data scientist to explore it and trim the data a little bit

Acknowledgements

We have to acknowledge the valuable help of our users, who have contributed to generate this dataset and have push our growth by mouth to mouth recommendation.

Inspiration

Our first inspiration for the App was after being scammed in our home city Quito. We started it as a tool for people to be fairly charged when riding a taxi. Currently with other transportation options available, we also help user to compare fares in their cities or the cities which they are visiting.

File descriptions

mex_clean.csv - the dataset contains information of routes in Mexico City
uio_clean.csv - the dataset contains information of routes in Quito Ecuador
bog_clean.csv - the dataset contains information of routes in Bogota
all-data_clean.csv - the dataset contains information of routes in different cities

This dataset contains all crime in Mexico by state and city from January 2015 to July 2022. The data was translated to English using Power Query to extract the entire set and a custom function that access the Google Translate API.

A dataset listing New Mexico cities by population for 2024.

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.

Mexico City Complaint Records (Through March 2023)

Dataset with 20+ columns about complaints in Mexico City. Including categories, dates, types of complaints and so on.

Perfect for clean data exercise since it has tons of dirty data.

This data is from the Attorney General's Office of Mexico City.

Tips

If you want to make a cleaning process, there are several techniques that are quite perfect for this dataset:

1. Categories-based imputation
2. KNN imputation
3. Divide you dataset in two. For instance, by 'TipoPersona' feature

Considerations

Some columns are not good for interpretation. For example, the 'Edad' column could be from the person who commited the crime or from the person who complaint about the crime. Explore the data and think about the best context for each column.

Context

This dataset consists of 300k+ records of tortilla prices from Mexico's national System of Information and Market Integration, which surveys 53 cities, 384 mom-and-pop stores, and 120 retail stores that sell "tortillas" throughout Mexico.

Mexico's Bureau of Economic Affairs publishes the information on this site based on a survey made across the whole country. Still, it is not very user-friendly, so the information since 2007 was downloaded and stored in a single, easy-to-use CSV file.

The price on each record consists of the mean prices for all observations made on that day, in that city, and in that state. The price shown in the file is for 1 (one) kilogram of tortillas in Mexican pesos ($MXN).

If you don't know what a "tortilla" is, the article in Wikipedia is a good start to get you up and running.

Inspiration

Tortilla is one of Mexico's most important foods. It is made almost entirely of milled corn and water, which forms a dough that is cooked for some minutes before being stored and ready to sell. It is similar to Naan bread, commonly known for its use in Indian cuisine, but made out of corn instead of wheat. Tortillas are sold in packages of 1 kilogram, which, depending on their size, can have around 40 to 50 tortillas per kilogram. Mom-and-pop stores can sell tortillas in fractions of kilograms.

This dataset contains information from both mom-and-pop stores (small stores located near residential areas dedicated solely to selling fresh tortillas) and from big retailers (such as Walmart, which sells tortillas in Mexico in almost all of its stores).

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2718659%2Ff918235c86a0d2183807b041a024f118%2Fmom-and-pop-store.png?generation=1709524219666519&alt=media" alt="mom-and-pop-store">

Example of a typical mom-and-pop store (aka "Tortillería") in Mexico

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2718659%2F6b16090673e26a509aaebc4e89b207b2%2FWalmart-Tortilleria.jpg?generation=1709524326553327&alt=media" alt="">

Example of a stand selling tortillas in Walmart

Several interesting facts can be made regarding the price of tortillas in these two types of stores... surprisingly, retail stores sell tortillas way below the prices of mom-and-pop stores, while at the same time, mom-and-pop stores usually sell tortillas to people with less income than those who buy them in a retail store.

The price difference between retailers and mom-and-pop stores has increased since the COVID-19 pandemic, as illustrated in the following figure.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F2718659%2F88cba385cef8b043ef662859e66a7b71%2Flineplot_by_type_2007-2024.png?generation=1709523281176095&alt=media" alt="">

The purpose of publishing this dataset is to raise awareness of the importance of food price monitoring and the impact those prices can have on people's lives.

Thumbnail photo by Louis Hansel on Unsplash

Employment Rate in Mexico decreased to 97.02 percent in September from 97.07 percent in August of 2025. This dataset provides - Mexico Employment Rate- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Context

This dataset was obtained from the https://datos.gob.mx/busca/dataset/registro-civil

This is the Mexican government official dataset for the number of divorces in the city of Xalapa, Mexico

Content

The dataset contains records of approximately 4,900+ divorces for the 15 years period (2000-2015) in the city of Xalapa, Mexico. The special good thing about this dataset is that it contains divorcees birth dates which are usually considered as being sensitive information and usually not included in the public datasets.

The dataset is originally in Spanish and I did translate all the column headers into English. Files are as follows:

divorces_2000-2015_original.csv - original dataset (in Spanish) descriptions_for_ column.csv - descriptions for each column (in Spanish) divorces_2000-2015_translated.csv - the version with the English translated column headers (please note that only column headers were translated) comp_matrix.csv - the table of the zodiac signs compatibility rates that were used in my notebook (https://www.kaggle.com/aagghh/testing-the-astrology-and-zodiac-claims). Note: ignore it if you do not need it

Major features are:

Date of divorce

Birth dates for both partners (man/woman)

Nationality for both partners (man/woman)

Place of birth and residence for both partners (man/woman)

Monthly income for both partners (man/woman)

Occupation for both partners (man/woman)

Date of marriage (man/woman)

Level of education for both partners (man/woman)

Employment status for both partners (man/woman)

Number of children and their custody

Other features - please refer to the file & columns descriptions below

Inspiration

A potential use-case for this data could be a practice in classification/clustering problems in an attempt to predict a divorce.

Some other data analysis can be applied to this dataset. For instance, I am gonna use this data for the horoscope/zodiac/astrology claims validation/testing.

Please refer to my notebook on it here: https://www.kaggle.com/aagghh/is-astrology-right-testing-the-zodiac-claims

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.

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.

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).

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.

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.

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

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, 2021, 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 2020 Census.

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.

Updates data on the size, composition and territorial distribution of the population, households and existing housing in the country. Data 2020.

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.

About this dataset

Context

This list ranks the 102 cities in the New Mexico by Black or African American population, as estimated by the United States Census Bureau. It also highlights population changes in each cities 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
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

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

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