In 2025, Luxembourg reached the highest score in the quality of life index in Europe, with 220 points. In second place, The Netherlands registered 211 points. On the opposite side of the spectrum, Albania and Ukraine registered the lowest quality of life across Europe with 104 and 115 points respectively. The Quality of Life Index (where a higher score indicates a higher quality of life) is an estimation of overall quality of life, calculated using an empirical formula. This formula considers various factors, including the purchasing power index, pollution index, house price-to-income ratio, cost of living index, safety index, health care index, traffic commute time index, and climate index.

In 2023, Paris was the most livable city worldwide according to the Global Power City Index (GCPI), with *** points. Furthermore, Madrid was the second most livable city with ***** points, while Tokyo was the third with ***** points.

The criteria taken into consideration include, among others, costs and ease of living, number of retail shops and restaurants, and availability of medical services.

This statistic shows a list of the best cities to live in around the world as of 2019. The rating is based on five indicators: stability, healthcare, culture and environment, education, and infrastructure. In 2019, the Austrian capital Vienna topped the ranking with **** out of 100 possible points.

Data was initially taken from Numbeo as an aggregation of user voting.

• Quality of Life Index varies from 0 (bad quality) to 190 (top good quality)

This dataset is one of the public parts of City API project data. Need more? Try our full data

A list of some key resources for comparing London with other world cities.

European Union/Eurostat, Urban Audit

Arcadis, Sustainable cities index

AT Kearney, Global Cities Index

McKinsey, Urban world: Mapping the economic power of cities

Knight Frank, Wealth report

OECD, Better Life Index

UNODC, Statistics on drugs, crime and criminal justice at the international level

Economist, Hot Spots

Economist, Global Liveability Ranking and Report August 2014

Mercer, Quality of Living Reports

PWC, Cities of opportunity

BCG, Decoding Global Talent

Forbes, World's most influential cities

Mastercard, Global Destination Cities Index

Numbeo, Database of user contributed data

Cities are in constant competition for residents, businesses, and employees, and inclusiveness is a crucial factor that attracts all three. The Municipal Equality Index (MEI) specifically measures laws and policies of municipalities to examine how inclusive cities are of LGBTQ (Lesbian, Gay, Bisexual, Transgender, and Questioning) people.Administered by the Human Rights Campaign, the MEI scorecard criteria annually evaluate a municipality on six categories, with bonus points available: Non-Discrimination Laws: This category evaluates whether discrimination based on sexual orientation and gender identity is prohibited by city, county, or state in areas of employment m housing, and public accommodations.Relationship Recognition: Marriage, civil unions, and comprehensive domestic partnerships are matters of state policy; cities and counties have only the power to create domestic partner registries.Municipality as Employer: By offering equivalent benefits and protections to LGBTQ employees, and by awarding contracts to fair-minded businesses, municipalities commit themselves to treating LGBTQ employees equally.Municipal Services: The section assesses the efforts of the city to ensure LGBTQ constituents are included in city services and programs.Law Enforcement: Fair enforcement of the law includes responsible reporting of hate crimes and engaging with the LGBTQ community in a thoughtful and respectful way.Relationship with the LGBTQ Community: This category measures the city leadership’s commitment to fully include the LGBTQ community and to advocate for full equality. Additional information available at hrc.org/meiThis page provides data for the Municipality Equality Index performance measure. The performance measure dashboard is available at 3.12 Municipal Equality Index. Additional Information Source: Contact: Wydale HolmesContact E-Mail: wydale_holmes@tempe.govData Source Type: ExcelPreparation Method: Publish Frequency: Annually, OctoberPublish Method: ManualData Dictionary

Bangalore ranked first in the Ease of Living Survey conducted among more than ********** cities across India in 2020. The southern metropolis, which was Karnataka's capital, also ranked first that year in economic abilities, and ranked ** in the quality of life category.

In 2024, Bergamo was the most livable province in Italy, according to the annual study conducted by the newspaper Il Sole 24 Ore. Trento and Bolzano followed in the ranking. Among the top-15 provinces, only Ascoli Piceno and Siena are located in central Italy, while all others are in the north, confirming the deep north-south divide between these two areas of the country. As far as macro categories are concerned, Biella, located in Piedmont, recorded the best score in wealth and consumption, while Milan for business and employment. Ascoli Piceno was the best province in terms of security and justice, and Bolzano performed very well in demography and society. Lastly, for environment and services, Brescia ranked at the top, whereas citizens could enjoy at best their leisure and cultural activities in Trieste.

Abstract

This dataset is from the Gauteng City-Region Observatory which is a partnership between the University of Johannesburg, the University of the Witwatersrand, the Gauteng Provincial Government and several Gauteng municipalities. The GCRO has conducted previous Quality of Life Surveys in 2009 (Round 1), 2011 (Round 2), 2013-2014 (Round 3) and 2015-2016 (Round 4), and 2017-2018 (Round 5). Round 6 was conducted in 2020-2021 and is the latest round of the survey.

Geographic coverage

The survey covers the Gauteng province in South Africa.

Analysis unit

Households and individuals

Universe

The survey covers all adult residence in Gauteng province, South Africa.

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Tempe’s roadways are an important means of transportation for residents, the workforce, students, and visitors. Tempe measures the quality and condition of its roadways using a Pavement Quality Index (PQI). This measure, rated from a low of 0 to a high of 100, is used by the City to plan for maintenance and repairs, and to allocate resources in the most efficient way possible.

Data for 333 cities and urban areas on 25 indicators across the six dimensions of the City Prosperity Index (CPI): Productivity, Infrastructure Development, Quality of life, Equity and Social Inclusion, Environmental Sustainability and Governance and Legislation.

This survey monitors wellbeing among residents of the City of Guelph, located in Ontario, Canada. The survey is a joint initiative of the Canadian Index of Wellbeing in partnership with the City of Guelph. The purpose of the survey is to better understand subjective perceptions of wellbeing of residents in the survey area. The primary objectives of this survey are to (a) gather data on the wellbeing of residents which could be monitored o ver time; and, (b) to provide information on specific aspects of wellbeing that could be used to inform policy issues and community action. The survey provides information based on eight domains of wellbeing, as identified by the Canadian Index of Wellbeing: Community Vitality, Democratic Engagement, Environment, Education, Healthy Populations, Leisure and Culture, Living Standards, and Time Use. The questionnaire collected additional information about socio-economic and household characteristics, and feelings of overall satisfaction with each domain of wellbeing. A total of N=1,390 residents completed the survey.

Not many studies have documented climate and air quality changes of settlements at early stages of development. This is because high quality climate and air quality records are deficient for the periods of the early 18th century to mid 20th century when many U.S. cities were formed and grew. Dramatic landscape change induces substantial local climate change during the incipient stage of development. Rapid growth along the urban fringe in Phoenix, coupled with a fine-grained climate monitoring system, provide a unique opportunity to study the climate impacts of urban development as it unfolds. Generally, heat islands form, particularly at night, in proportion to city population size and morphological characteristics. Drier air is produced by replacement of the countryside's moist landscapes with dry, hot urbanized surfaces. Wind is increased due to turbulence induced by the built-up urban fabric and its morphology; although, depending on spatial densities of buildings on the land, wind may also decrease. Air quality conditions are worsened due to increased city emissions and surface disturbances. Depending on the diversity of microclimates in pre-existing rural landscapes and the land-use mosaic in cities, the introduction of settlements over time and space can increase or decrease the variety of microclimates within and near urban regions. These differences in microclimatic conditions can influence variations in health, ecological, architectural, economic, energy and water resources, and quality-of-life conditions in the city. Therefore, studying microclimatic conditions which change in the urban fringe over time and space is at the core of urban ecological goals as part of LTER aims. In analyzing Phoenix and Baltimore long-term rural/urban weather and climate stations, Brazel et al. (In progress) have discovered that long-term (i.e., 100 years) temperature changes do not correlate with populations changes in a linear manner, but rather in a third-order nonlinear response fashion. This nonlinear temporal change is consistent with the theories in boundary layer climatology that describe and explain the leading edge transition and energy balance theory. This pattern of urban vs. rural temperature response has been demonstrated in relation to spatial range of city sizes (using population data) for 305 rural vs. urban climate stations in the U.S. Our recent work on the two urban LTER sites has shown that a similar climate response pattern also occurs over time for climate stations that were initially located in rural locations have been overrun bu the urban fringe and subsequent urbanization (e.g., stations in Baltimore, Mesa, Phoenix, and Tempe). Lack of substantial numbers of weather and climate stations in cities has previously precluded small-scale analyses of geographic variations of urban climate, and the links to land-use change processes. With the advent of automated weather and climate station networks, remote-sensing technology, land-use history, and the focus on urban ecology, researchers can now analyze local climate responses as a function of the details of land-use change. Therefore, the basic research question of this study is: How does urban climate change over time and space at the place of maximum disturbance on the urban fringe? Hypotheses 1. Based on the leading edge theory of boundary layer climate change, largest changes should occur during the period of peak development of the land when land is being rapidly transformed from open desert and agriculture to residential, commercial, and industrial uses. 2. One would expect to observe, on average and on a temporal basis (several years), nonlinear temperature and humidity alterations across the station network at varying levels of urban development. 3. Based on past research on urban climate, one would expect to see in areas of the urban fringe, rapid changes in temperature (increases at night particularly), humidity (decreases in areas from agriculture to urban; increases from desert to urban), and wind speed (increases due to urban heating). 4. Changes of the surface climate on the urban fringe are expected to be altered as a function of various energy, moisture, and momentum control parameters, such as albedo, surface moisture, aerodynamic surface roughness, and thermal admittance. These parameters relate directly to population and land-use change (Lougeay et al. 1996).

This statistic shows a list of the best cities to live in in Asia-Pacific countries as of 2018. In 2018, the Australian city Melbourne topped the ranking with 98.4 out of 100 possible points, followed by the Japanese megacity Osaka with 97.7 points.

The City Prosperity Indices comprise six major components (Productivity, Infrastructure Development, Quality of Life, Equity and Social Inclusion, Environmental Sustainability, Urban Governance and Legislation) and each components has it own key ingredients and indicators which enable to calculate the city prosperity index of a city.

Urban areas have always been an attraction for the population. As a result, urbanization is constantly happening as more people migrate to cities. This is since urban areas have become a hope for improving the quality of life. This study aims to measure and analyze the Quality of Life (QoL) Index and the influence of economic and geographical factors on the urban QoL during the COVID-19 pandemic. The data used in this study is the Socio-Economic Survey (SUSENAS) in 2020 from the Indonesian Central Statistics Agency. The findings show that larger cities tend to have a good QoL index. Meanwhile, remote cities or island regions have a low QoL, as indicated by a low QoL Index. population density, per capita consumption expenditure, and urban economic growth positively influence the urban QoL.

Users can obtain descriptions, maps, profiles, and ranks of U.S. metropolitan areas pertaining to quality of life, diversity, and opportunities for racial and ethnic groups in the U.S. BackgroundThe Diversity Data project operates a website for users to explore how U.S. metropolitan areas perform on evidence-based social measures affecting quality of life, diversity and opportunity for racial and ethnic groups in the United States. These indicators capture a broad definition of quality of life and health, including opportunities for good schools, housing, jobs, wages, health and social services, and safe neighborhoods. This is a useful resource for people inter ested in advocating for policy and social change regarding neighborhood integration, residential mobility, anti-discrimination in housing, urban renewal, school quality and economic opportunities. The Diversity Data project is an ongoing project of the Harvard School of Public Health (Department of Society, Human Development and Health). User FunctionalityUsers can obtain a description, profile and rank of U.S. metropolitan areas and compare ranks across metropolitan areas. Users can also generate maps which demonstrate the distribution of these measures across the United States. Demographic information is available by race/ethnicity. Data NotesData are derived from multiple sources including: the U.S. Census Bureau; National Center for Health Statistics' Vital Statistics Natality Birth Data; Natio nal Center for Education Statistics; Union CPS Utilities Data CD; National Low Income Housing Coalition; Freddie Mac Conventional Mortgage Home Price Index; Neighborhood Change Database; Joint Center for Housing Studies of Harvard University; Federal Financial Institutions Examination Council Home Mortgage Disclosure Act (HMD); Dr. Russ Lopez, Boston University School of Public Health, Department of Environmental Health; HUD State of the Cities Data Systems; Agency for Healthcare Research and Quality; and Texas Transportation Institute. Years in which the data were collected are indicated with the measure. Information is available for metropolitan areas. The website does not indicate when the data are updated.

Abstract

The objective of the survey was to produce baselines for 15 large urban centers in Kenya. The urban centers covered Nairobi, Mombasa, Naivasha, Nakuru, Malindi, Eldoret, Garissa, Embu, Kitui, Kericho, Thika, Kakamega, Kisumu, Machakos, and Nyeri. The survey covered the following issues: (a) household characteristics; (b) household economic profile; (c) housing, tenure, and rents; and (d) infrastructure services. The survey was undertaken to deepen understanding of the cities’ growth dynamics, and to identify specific challenges to quality of life for residents. The survey pays special attention to living conditions for residents of formal versus informal settlements, poor versus non-poor, and male and female headed households.

Analysis unit

Household Urban center

Kind of data

Sample survey data [ssd]

Sampling procedure

The Kenya State of the Cities Baseline Survey is aimed to produce reliable estimates of key indicators related to demographic profile, infrastructure access and economic profile for each of the 15 towns and cities based on representative samples, including representative samples of households (HHs) residing in slum and non-slum areas. For this baseline household survey, NORC used a two- or three-stage stratified cluster sampling design within each of the 15 urban centers. Our first-stage sampling frame was based on the 2009 census frame of enumeration areas. For each of the 15 towns and cities, NORC received the sampling frame of EAs from the Kenya National Bureau of Statistics (KNBS). In the first stage, NORC selected a sample of enumeration areas (PSUs). The second stage involved a random selection of households (SSUs) from each selected EA. In order to manage the field interviewing efficiently, we drew a fixed number of HHs from each selected EA, irrespective of EA size. The third stage arose in instances of very large EAs (EAs containing more than 200 households) in which EAs were divided into 2, 3 or 4 segments, from which one segment was selected randomly for household selection.

Stratification of Enumeration Areas: A few stratification factors were available for stratifying the EAs to help to achieve the survey objectives. As mentioned earlier, for this baseline survey we wanted to draw representative samples from slum and non-slum areas and also to include poor/non-poor households (HHs). For the 2009 census, depending on the location, KNBS divided the EAs into three categories: rural, urban, and peri-urban.

Although there is a clear distinction of EAs into slum and non-slum areas, it is hard to classify EAs into poor and non-poor categories. To guarantee enough representation of HHs living in slum and non-slum areas (also referred to as formal and informal areas) as well as HHs living below and above the poverty line, NORC stratified the first-stage sampling units (EAs) into strata, based on EA type (3 types) and settlement type (2 types). Given the resources available, we believe this stratification would serve our purpose as HHs living in slum and in rural areas tend to be poor. Table 1 in Appendix C of final Overview Report (provided under the Related Materials tab) presents the allocation of sampled EAs across the strata for each of the 15 cities in the baseline survey.

Sampling households is not as straightforward as the first-stage sampling of EAs, since the 2009 census frame of HHs does not exist. In the absence of a household sampling frame, NORC carried out a listing of HHs within each EA selected in the first stage. Trained listers, accompanied by local cluster guides (local residents with some form of authority in the EA), systematically listed all households in each selected EA, gathering the address, names of head of household and spouse, household description, latitude and longitude. To ensure completeness of listing data, avoid duplication and improve ease of locating households that were eventually selected for interview, listers enumerated households by chalking household identification number above the household doorway (an accepted practice for national surveys). The sampling frame of HHs produced from the listing activity was, therefore, up-to-date and included new formal and informal settlements that appeared after the 2009 census.

For adequate representativeness and to manage the interviewing task efficiently, NORC planned seven completed household interviews per EA. The final recommended sample size for the Kenya State of the Cities baseline survey is found in Table 2 in Appendix C of the final Overview Report.

Because the expected response rate was unknown prior to the start of the field period, the sampling team randomly selected ten households per enumeration area and distributed them to the interviewers working within the EA. Interviewing teams were instructed to complete at least seven interviews per EA from among the ten selected households. Interviewers were instructed to attempt at least three contacts with each selected household, approaching potential respondents on different days of the week and different times of day. Table 2 presents the final number of EAs listed per city and the final number of completed interviews per city. The table also presents the percent of planned EAs and interviews that were completed vs. planned. Please note that in several cities more interviews were completed than planned. As part of NORC's data quality plan, data collection teams were instructed to overshoot slightly the target of seven interviews per EA, if feasible, to mitigate any potential loss of cases due to poor quality or uncooperative respondents. Few cases were lost due to poor quality, therefore the target number of interviews remains over 100 percent in ten of the fifteen cities.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was developed by World Bank staff with input from stakeholders in the Kenya Municipal Program and NORC researchers and survey methodologists. The base questionnaire for the project was a 2004 World Bank survey of Nairobi slums. However, an extended iterative review process led to many changes in the questionnaire. The final version that was used for programming provided under the Related Materials tab, and in Volume II of the Overview.

The questionnaire’s topical coverage is indicated by the titles of its nine modules: 1. Demographics and household composition 2. Security of housing, land and tenure 3. Housing and settlement profile 4. Economic profile 5. Infrastructure services 6. Health 7. Household enterprises7 8. Civil participation and respondent tracking

Response rate

The completion rate is reported as the number of households that successfully completed an interview over the total number of households selected for the EA. These are shown by city in Table 5 in Appendix C of the final Overview Report, and have an average rate of 68.66 percent, with variation from 66 to 74 percent (aside from Nairobi at 61.47 percent and Machakos at 56 percent). As described earlier, ten households were selected per EA if the EA contained more than 10 households. For EAs where fewer than ten households were selected for interviews, all households were selected. In some EAs, more than ten households were selected due to a central office error.

Quality of life related to health in children, by sex and Autonomous Community. Average and standard deviation. Population aged 8 to 15 years old. Autonomous Community.

How did the City create the Equity IndexWorking with Ohio State University's Kirwan Institute of Race and Social Justice, the City complied the Equity/Opportunity Index to help facilitate data-driven decision-making processes and enable leaders to distribute resources better and plan to fund programs and services, minimize inequities and maximize opportunities.The indicators displayed in the Equity/Opportunity Index have been shown to have a direct correlation to equity. For more information, please reference the additional document on the evidence-based research determinant categories. The data is measured granularly by census block group.The list below comprise the Indicators per index: Accessibility Parks & Open SpaceVoter ParticipationHealthy Food Access IndexAverage Road QualityHome Internet AccessTransit Options & AccessVehicle AccessLivabilityTacoma Crime IndexESRI Crime IndexCost-Burdened HouseholdsAverage Life ExpectancyUrban Tree CanopyTacoma Nuisance IndexMedian Home ValueEducationAverage Student Test RateAverage Student Mobility4-Year High School Graduation RatePercent of 25+-Year-Olds with Bachelor's Degree or MoreEconomyPierce County Jobs IndexMedian Household Income200% of the Poverty line or LessUnemployment RateEnvironmental HealthEnvironmental ExposuresNOx- Diesel Emissions (Annual Tons/Km2)Ozone ConcentrationPM2.5 ConcentrationPopulations Near Heavy Traffic RoadwaysToxic Releases from Facilities (RSEI Model)Environmental EffectsLead Risk from Housing (%)Proximity to Hazardous Waste Treatment Storage and Disposal Facilities (TSDFs)Proximity to National Priorities List Facilities (Superfund Sites)Proximity to Risk Management Plan (RMP) FacilitiesWastewater DischargeWhat does Very High or Very Low Equity/Opportunity mean?Very High Equity/Opportunity represents locations that have access to better opportunities to succeed and excel in life. The data indicators would include high-performing schools, a safe environment, access to adequate transportation, safe neighborhoods, and sustainable employment. In contrast, Low Equity/Opportunty areas have more obstacles and barriers within the area. These communities have limited access to institutional or societal investments with limits their quality of life.Why is the North and West End labeled Red?When looking at data related to equity and social justice, we want to be mindful not to reinforce historical representations of low-income or communities of color as bad or negative. To help visualize the areas of high opportunity and call out the need for more equity, we chose to use red. We flipped the gradient to highlight disparities within the community. Besides, we refrained from using green or positive colors with referring to dominant communities (white communities).Can I add more data and indicators to the Equity Index?Yes, by downloading the file and uploading it to ArcGIS, you can add data and indicators to the Index, and you can import the shapefiles into your database. The indicators and standard deviations are available on ArcGIS online.Can I see additional or multiple map layers?Within the left navigation panel, you can aggregate the index layers by determinate social categories; Accessibility, Education, Economy, Livability