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

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.

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.

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).

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.

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.; ;

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.

A dataset listing New Mexico cities by population for 2024.

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

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.

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.

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;

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.

This dataset collects information on municipal expenditures, water-sewerage-and trash collection service coverage, and basic socioeconomic characteristics at municipal level, for two census waves (2000; 2010) for all municipalities of Brazil, Chile, and Mexico.

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.

Unemployment Rate in Mexico remained unchanged at 2.70 percent in June. 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.

[English description below] Estos son los datos para México de la Encuesta de Dinámicas Territoriales y Bienestar 2017-2018, parte del Programa Transformando Territorios de Rimisp, financiado por IDRC y realizado en colaboración, para su componente de investigación, con la Universidad Iberoamericana de México y la Universidad de los Andes de Colombia. La Encuesta de Dinámicas Territoriales y Bienestar (EDTB) es una encuesta de hogares aplicada en territorios rural-urbanos de Chile, Colombia y México durante el 2017 y 2018, en una muestra de cerca de 12 mil hogares entre los tres países. Es comparable entre los tres países. En el caso de Mexico, la encuesta es representativa de la población que vive en territorios funcionales rural-urbanos dentro de cada país, esto es, ciudades pequeñas y medianas (entre 15 mil y 350 mil habitantes) funcionalmente integradas con los municipios rurales colindantes. Estos territorios funcionales representan areas con una alta frecuencia de interacciones sociales y económicas, y con vínculos socio-espaciales complejos entre sus habitantes, organizaciones y firmas. El 38% de la población de México vive en estos territorios. La muestra total para el país es de 4.375 hogares, siendo una muestra representativa de los habitantes de territorios rural-urbanos. La EDTB incluye, además de los tradicionales módulos socioeconómicos y demográficos de las encuestas de estándares de vida, módulos innovadores sobre autoempleo, movilidad intergeneracional, aspiraciones, capital social, y relaciones con instituciones locales y nacionales. También incluye información detallada en la distribución espacial de las actividades de los hogares y sus medios de vida: dónde viven, dónde estudian sus hijos, adónde realizan su trabajo, venden sus productos y realizan sus compras, y en dónde acceden a bienes y servicios. La encuesta permite mapear flujos rural-urbanos recíprocos de personas, bienes y servicios, y analizar su correlación con características del hogar, características de su medio y trayectorias de crecimiento inclusivo. De manera importante, la información de la EDTB no incluye solo flujos de zonas rurales a urbanas, sino también de zonas urbanas a rurales. Este nivel de detalle permite también estudiar los resultados en bienestar en relación con la intensidad de los vínculos rural-urbanos y la participación de las personas en estos vínculos. Más aún, el uso de datos de hogares ofrece información sobre las heterogeneidades que existen entre diferences grupos de personas (como los hogares en distintos grupos socioeconómicos y miembros del hogar por género y edad), lo que permite un mejor entendimiento del impacto diferencial que las distintas intervenciones de política territoriales podrían tener para diferentes grupos de la población. La información contenida en la EDTB sobre movimientos relacionados con trabajos, educación , salud y alimentación, pueden guiar la planificación de la inversión y y la coordinacio´n entre unidades administrativas. Nuestros datos se encuentran registrados en 7 archivos diferentes, tanto en formato DTA (para uso con Stata 15) como en CSV: Base hogar ("b1_mexico_base_hogar"): Contiene todas las preguntas de los módulos básicos de la encuesta que consideran al hogar como unidad básica. Base individual ("b2_mexico_base_individual"): Contiene preguntas dirigidas a los distintos miembros del hogar como individuos, principalmente relacionadas con sus actividades laborales y sus características sociodemográficas. Base autoempleado no silvoagropecuario ("b3_mexico_base_autoempleado_no_silvoagropecuario"): Contiene información sobre las actividades autoempleadas de los miembros del hogar, en sectores distintos a la agricultura, ganadería y silvicultura. Base autoempleado silvoagropecuario ("b4_mexico_base_autoempleado_silvoagropecuario"): Contiene información sobre las actividades autoempleadas de los miembros del hogar en agricultura, ganadería y silvicultura. Módulo especial 1 ("e1_mexico_especiales_1"): Contiene información sobre los otros módulos especiales: movilidad intergeneracional, bienestar psicosocial, instituciones públicas y capital social, y percepción del entorno. 6. Módulo especial 2 ("e2_mexico_especiales_2"): Contiene información sobre los módulos de migración de miembros del hogar. Módulo otros migrantes ("e3_mexico_otros_migrantes"): Contiene información sobre antiguos miembros del hogar que han migrado recientemente. Se pueden encontrar, junto con estos datos, los documentos sobre metodología de muestreo, cuestionarios y diccionarios de variables, en formato PDF. Los diccionarios se encuentran divididos por diccionarios de módulos básicos (referentes a las 4 primeras bases) y módulos especiales (referentes a las 3 últimas bases), y siguen la estructura de los cuestionarios. ****************************************************** These are the datasets of the Territorial Dynamics and Wellbeing Household Survey 2017-2018 for Mexico, part of the Transforming Territories Program from Rimisp, financed by IDRC and developed collaboratively, in its research component, with the Universidad Iberoamedicana in Mexico and the Universidad de los Andes in Colombia. The Territorial Dynamics and Wellbeign Household Survey (TDWHS) is a household survey applied in rural-urban territories in Chile, Colombia and Mexico around 2018 on a sample of aproximately 12,000 households for the three countries. Is comparable between the three countries. In the case of Mexico, the survey is representative of the population living in rural-urban functional territories in each country, that is, small and medium cities (between 15 thousand and 350 thousand inhabitants approximately) that are functionally integrated with surrounding rural municipalities. 38% of the population in Mexico lives in those territories. In addition to the typical modules of living standard surveys, the TDWHS includes innovative modules on self-employment, inter-generational mobility, aspirations,social capital, and relationship with local and national institutions. It also includes detailed information on the spatial distribution of household activities and livelihood strategies: where people live and their children go to school, where they work and sell their products, where they buy food and access public goods andservices. The TDWHS allows to map reciprocal rural-urban flows of people, goods and services, and analyze their correlation with household characteristics, place characteristics and trajectories of inclusive growth. Importantly, the information of the TDWHS does not only include flows from rural to urban, but also from urban to rural. This level of detail also allows investigating people’s welfare outcomes in relation to the intensity of local rural-urban linkages and people’s participation in them. Moreover, the use of household data offers insights on the heterogeneities that exist among different groups of people (such as households in different socioeconomic groups and household members by gender and age), which in turn provides a better understanding of the differential impact that different territorial policy interventions may have for different groups of the population. The information contained in the T&BHS on movements related to jobs, education, health, and food, can guide investments planning and coordination among administrative units. Our data is stored in 7 separated archives per country, available both in DTA (for Stata 15) and in CSV: Household data ("b1_mexico_base_hogar"): Contains all questions in our questionnaire that considered the household as the fundamental unit. Individual data ("b2_mexico_base_individual"): Contains the data asked for different members of the household as individuals, mainly data about jobs and sociodemographic characteristics. Self-employed activities outside agriculture, livestock and forestry ("b3_mexico_base_autoempledo_no_silvoagropecuario"): This dataset contain information about the self-employed activities of household members in any activity other than agrarian, forestry and livestock activities. Self-employed activities in agriculture, livestock and forestry ("b4_mexico_base_autoempledo_silvoagropecuario"): This dataset contain information about the self-employed activities of household members in agrarian, forestry and livestock activities. Special modules 1 ("e1_mexico_especiales_1"): Contains information about other special modules: inter-generational mobility, psychosocial wellbeing, political institutions and social capital, and perception of living environment. Special modules 2 ("e2_mexico_especiales_2"): This dataset contains information about migration from household members. Other migrant ("e3_mexico_otros_migrantes"): Contains information about former household members recently migrated. Included also are the dictionaries and questionnaires for the dataset (in spanish). The dictionaries follow the structure of the questionnaires and are divided in basic modules (the first 4 datasets) and special modules (the last 3 datasets).