Population: Mexico City data was reported at 9,045.719 Person th in 2018. This records an increase from the previous number of 5,436.946 Person th for 2017. Population: Mexico City data is updated yearly, averaging 8,616.773 Person th from Dec 1970 (Median) to 2018, with 49 observations. The data reached an all-time high of 9,062.102 Person th in 2014 and a record low of 5,264.681 Person th in 2015. Population: 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.G002: Population: by State.

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.

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.; ;

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

This dataset is about cities in Mexico. It has 1,687 rows. It features 7 columns including country, population, latitude, and longitude.

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;

A dataset listing New Mexico cities by population for 2024.

This dataset is about countries per year in Mexico. It has 1 row and is filtered where the date is 2023. It features 4 columns: country, capital city, and female population.

Mexico cities with populations greater than 10,000 within the Rio Grande River Basin. The map contains the geographical identification of the 192 245 inhabited localities, as a result of the 2010 Population and Housing Census. Each registry includes the values of length, latitude, altitude and total population.This shapefile was created by selecting all the localities with populations greater than 10,000 within the boundary of the RGB watershed, as delinated in the UC-Davis geodatabase (located hereL:\Interns_stuff\Kristin\GIS\UC-Davis_geodatabases\Complete_database\170311_RGB_geodatabase\Hydrology_and_Climate\RGB_watershed), from the UC-Davis's MX_cities shapefile. This shapefile was created by Kristin Davis (Geospatial Centroid Intern) on November 27, 2018 for the Rio Grande Basin Environmental Justice project.

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.

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 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.

Mexico cities with populations greater than 5,000 within the Rio Grande River Basin. The map contains the geographical identification of the 192 245 inhabited localities, as a result of the 2010 Population and Housing Census. Each registry includes the values of length, latitude, altitude and total population.This shapefile was created by selecting all the localities with populations greater than 5,000 within the boundary of the RGB watershed, as delinated in the UC-Davis geodatabase (located here: L:\Interns_stuff\Kristin\GIS\From_Pablo\170311_RGB_geodatabase\Hydrology_and_Climate\RGB_watershed). This shapefile was created by Kristin Davis, CSU Geospatial Centroid intern, on November 27, 2018 for the Rio Grande Basin Environmental Justice project.

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/

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.

This is one of five general categories that contain the water related elements of the Rio Grande/Bravo basin. This category includes boundaries of the United States and Mexico as well as the States, Counties, and Municipalities that overlap with the basin boundary. This category includes also the extent and location of the cities within the basin and the current and historic population of such cities.

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).