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.

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

Georeferenced Population Datasets of Mexico (GEO-MEX): Urban Place Time-Series Population of Mexico contains population counts for more than 700 urban centers every 10 years from 1921 through 1990. The urban centers include metropolitan, conurbation, and city areas with more than 5,000 inhabitants as of 1980. This dataset is produced by the Columbia University Center for International Earth Science Information Network (CIESIN).

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.

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.

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.

Reducing energy use is a key way in which we can help to reduce carbon emissions in the UK. Communal environments, such as shared offices, consume a large amount of energy. It is therefore important to examine people's perceptions and motivations to use and save energy. This study examines motivations to save energy at work and at home and the likely reactions to different cooperative scenarios around energy use. Data comprises: demographics, including whether participants have managerial responsibitilites, size and sector of organisation worked for; behavioural intentions for energy use at home and at work; motivations to save energy at work and at home; concern about climate change and energy security; experience of black outs, power cuts and air pollution.This project will investigate innovative ways of dividing up and representing energy use in shared buildings so as to motivate occupants to save energy. Smart meters (energy monitors that feed information back to suppliers) are currently being introduced in Britain and around the world; the government aims to have one in every home and business in Britain by 2019. One reason for this is to provide people with better information about their energy use to help them to save energy. Providing energy feedback can be problematic in shared buildings, and here we focus on workplaces, where many different people interact and share utilities and equipment within that building. It is often difficult to highlight who is responsible for energy used and difficult therefore to divide up related costs and motivate changes in energy usage. We propose to focus on these challenges and consider the opportunities that exist in engaging whole communities of people in reducing energy use. This project is multidisciplinary, drawing primarily on computer science skills of joining up data from different sources and in examining user interactions with technology, design skills of developing innovative and fun ways of representing data, and social science skills (sociology and psychology) in ensuring that displays are engaging, can motivate particular actions, and fit appropriately within the building environment and constraints. We will use a variety of methods making use of field deployments, user studies, ethnography, and small-scale surveys so as to evaluate ideas at every step. We have divided the project into three key work packages: 'Taking Ownership' which will focus on responsibility for energy usage, 'Putting it Together' where we will put energy usage in context, and 'People Power' where we will focus on creating collective behaviour change. In more detail, 'Taking Ownership' will explore how to identify who is using energy within a building, how best to assign responsibility and how to feed that back to the occupants. We know that simplicity of design is key here, as well as issues of fairness and ethics, and indeed privacy (might people be able to monitor your coffee drinking habits from this data?). 'Putting it Together' will consider different ways of combining energy data, e.g. joining this up across user groups or spaces, and combining energy data with other commonly available information, e.g. weather or diary data, so as to put it in context. We will also spend time considering the particular building context, the routines that currently exist for occupants, and the motivations that people have for using and saving energy within the building, in understanding how best to present energy information to the occupants. Our third theme, 'People Power' will focus on changing building user's behaviour collectively. We will examine how people interact around different energy goals, considering in particular cooperation and regulation, in finding out what works best in different contexts. The project then brings all aspects of research together in the use of themed challenge days where we promote specific energy actions for everyone in a building (e.g. switching off equipment after use) and demonstrate the impact that collective behaviour change can have. Beyond simply observing what works in this context through objective measures of energy usage, we will analyse when and where behaviour changes occurred and speak to the users themselves to find out what was engaging. These activities will combine to inform technical, design and policy recommendations for energy monitoring in workplaces as well as conclusions for other multi-occupancy buildings. Moreover, we will develop a tool kit to pass on to other companies and buildings so that others can use the findings and experience gained here. We will also explore theoretical implications of our results and communicate our academic findings to the range of disciplines involved

A dataset listing New Mexico cities by population for 2024.

Gentrification in Mexico City has became a prominent issue specially in Roma-Condesa area, this problem affects local economy and local business while there is no regulation. The purpose of this dataset is to raise awareness of the problem that is gentrification.

I took inspiration from this dataset and the source of the data is here

There are 2 zip files: 'listings' and 'reviews', each folder has quarterly data from 2023 up to December 26, 2023 with files named respectively.

Español

La gentrificación en la Ciudad de México se ha convertido en un problema, especialmente en el área de Roma-Condesa entre otras, este problema afecta la economía local y los negocios locales mientras no exista una regulación por parte del gobierno. El propósito de este conjunto de datos es crear conciencia sobre el problema de la gentrificación.

Me inspiré de este conjunto de datos y las fuentes son de aquí

Hay 2 archivos zip llamados 'listings' y 'reviews', cada directorio tiene datos trimestrales hasta diciembre del 2023 con archivos con su nombre respectivo con nomenclatura 'q_n'

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

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.

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.

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.

Unemployment Rate in Mexico increased to 2.80 percent in July from 2.70 percent in June 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.

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.

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.

Note: ncr: no cases reported.Hypertension and diabetes: prevalence and control in the study population.

Wages in Mexico decreased to 614.28 MXN/Day in July from 628.81 MXN/Day in June of 2025. This dataset provides - Mexico Average Daily Wages - 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, 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.

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.