The city with the lowest quality of life is Harare. Harare leads the ranking with a value of 37.5.

In 2023, the highest levels of quality of life in Denmark was found in small towns with less than 10,000 inhabitants. Quality of life levels in Denmark decreased from 2018 to 2023, which must be seen in relation with the COVID-19 pandemic and the rising inflation rates.

In 2023, Paris was the most livable city worldwide according to the Global Power City Index (GCPI), with 390 points. Furthermore, Madrid was the second most livable city with 380.9 points, while Tokyo was the third with 367.7 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 99.1 out of 100 possible points.

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

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.

According to a report on Chinese cities from 2024 that provided a ranking of their cultural vitality and quality of life, Shanghai led the list with a total composite score of 281. Beijing and Hangzhou came in second and third. The overall city ranking, which comprised ten subsets, was headed by China's capital Beijing.

Cities are in constant competition for residents, business and employees and inclusiveness is an important 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 evaluates a municipality on six categories with bonus points available: Non-Discrimination Laws: This category evaluates whether discrimination on the basis of 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 InformationSource: Contact: Wydale HolmesContact E-Mail: wydale_holmes@tempe.govData Source Type: ExcelPreparation Method: Publish Frequency: Annually, OctoberPublish Method: ManualData Dictionary

Smart cities have existed since the 1960s, evolving through the decades. These cities utilize technology and data to improve overall quality of life and ultimately increase the urban area's efficiency. As of 2024, New York City was the top ranked smart city scoring a motion index score of 100.

This statistic shows a ranking of the best U.S. federal states to live in, according to selected metrics and based on a survey among more than 530,000 Americans. The survey was conducted between January 2011 and June 2012. The findings are presented as index scores composed of the scores regarding various parameters*. According to this index, Utah is the city with the highest liveability and life quality, as it scored 7.5 points.

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.

Green development is the necessary path and fundamental way for urban development. Exploring the influence mechanism and spatial effect of green development on the urban human settlement resilience is conducive to promoting high-quality and sustainable urban development. We used the entropy method, super-efficient SBM model, spatial econometric model and threshold model to analyze the spatial spillover effect of green development efficiency on urban human settlement resilience and its nonlinear impact in the Yangtze River Delta(YRD) urban agglomeration. The results indicated that During the study period, the level of green development efficiency and urban settlement resilience was on the rise, and the spatial differences between different regions was significant. The green development efficiency of each city in the YRD urban agglomeration has a significant contribution to urban human settlement resilience in the region, but has a negative spatial effect on the level of urban human settlement resilience in the neighboring region. At different population density levels, the effect of green development efficiency on urban human settlement resilience shows a significant "V" non-linear characteristic. Furthermore, the influence of green development efficiency on urban human settlement resilience increases in a stepwise manner under different industrial structure distribution. The results of this study can help provide a reference basis for the creation of high-level, high-quality green and livable resilient cities in the YRD urban agglomeration under the concept of green development, and provide relevant experience for the construction of livable cities in other regions of China.

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.

Abstract

The Measuring Living Standards in Cities (MLSC) survey is a new instrument designed to enhance understanding of cities in Africa and support evidence based policy design. The instrument was developed under the World Bank’s Spatial Development of African Cities Program, and was piloted in Dar es Salaam (Tanzania) and Durban (South Africa) over the course of 2014/15. These geo-referenced surveys provide information on urban living standards at an unprecedented level of granularity: they can be compared across different geographic levels within the cities, and between areas of ‘regular’ and ‘irregular’ settlement patterns. They also respond to the need to increased understanding of specifically ‘urban’ dimensions of quality of living: housing attributes, access to basic services, and commuting patterns, among others.

Geographic coverage

The survey covered households in Dar es Salaam, Tanzania.

Analysis unit

• Household

• Individual

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLE FRAME

16,000 EAs generated by the Tanzania National Bureau of Statistics (NBS) for the 2012 Census.

STAGE ONE

200 EAs sorted into four strata. The central strata was divided into ‘central core, shanty’ and ‘central core, non-shanty’. Two EAs were replaced with reserve EAs as the original EAs were found to be inaccessible.

STAGE TWO

12 households randomly selected by systematic equal-probability from updated listing of each EA.

LISTING METHODOLOGY

The listing exercise took place between the first and the second stage of sampling. The household listing operations were implemented with computer assisted paperless interviewing (CAPI) techniques, which generates electronic files directly. Enumerators collected basic information about household: the name of the household head name, phone number and total number of household members living in the dwelling. Enumerators also recorded the GPS location of all structures,18 defined the type of structure, and aimed to provide measurement of structure size.

Listing was preceded by community sensitisation in both cities. In Dar es Salaam, enumerators visited the local chief (Mjumbe) of their assigned EA two days in advance of listing and on the day of listing.

Enumerators were equipped with maps created on Google My Maps to display shapefiles for the listing exercise. Hardcopies of their respective EA maps were also provided to be use in case of network failure. In Dar es Salaam, enumerators conducted a listing of all households in each of the selected EAs.

The listing exercise was conducted by 30 enumerators, each of which was assigned between 3 and 9 EAs for listing (enumerators were selected on the basis of performance from a group of 35 that were trained for listing). Enumerators were allocated EAs based on: (i) distance from enumerators’ homes in order to minimize transport time and cost; (ii) distance between the EAs; and (iii) safety and response rate considerations.

SURVEY IMPLEMENTATION

The surveys were fielded over the course of several months. The Dar es Salaam survey was implemented between November 2014 and January 2015.

Cases were assigned to interviewers using Survey Solutions. Interviewers were provided with both an electronic and hardcopy map, as well as a printed completion form, and could contact the listing manager through email, WhatsApp, or google hangouts if they were unable to find the assigned house.

Completing the survey often required repeat visits. This is because the survey required input from up to three separate respondents: the main respondent, who could be any present household member, and answered questions on household composition, basic information on members, assets, remittances, grants, housing, properties and consumption; the household head, who answered questions on residential history, satisfaction, employment, time use and commuting; and a random respondent, who was randomly selected from household members over the age of 12 (not including the head), who responded questions on satisfaction, employment, time use and commuting. Enumerators visited each house at least twice before a component could be marked as unavailable - in many cases, however, more than two visits were conducted.

Quality assurance procedures included: (i) In-interview feedback from CAPI, which provided a check that modules or questions were not missing, and alerted interviewers to mistakes and inconsistencies in given answers, so that these could be addressed while the interviewer was still with the respondent; (ii) Aggregate checks conducted using the Survey Solutions Supervisor application, which allows supervisors to identify common mistakes (applied to all initial interviews, and then through spot checks); interviewer performance and completion monitoring conducted by the implementing firm, through interviewer and EA level summaries of response rates, interview completion, and progress; (iii) weekly summaries of key indictors provided by the World Bank team (following each data delivery); (iv) direct observation of fieldwork; and (v) back check interviews. A key lesson learned is that the portion of back check interviews should be agreed in advance with the implementing firm: in Dar es Salaam back checks were conducted on 5% of the sample.

Mode of data collection

Computer Assisted Personal Interview [capi]

Response rate

Non-response rate: 13%

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.

The Russian capital Moscow had the highest quality of life in the country in 2022, according to the ranking, in which the city received 83 points. The second best score was achieved at less than one point lower by Saint Petersburg.

Evaluation index system of urban human settlement resilience.

As per the Global Liveability Index of 2024, five Indian cities figured on the list comprising 173 across the world. Indian megacities Delhi and Mumbai tied for 141st place with a score of 60.2 out of 100. They were followed by Chennai (59.9), Ahmedabad (58.9), and Bengaluru (58.7). What are indicators for livability The list was topped by Vienna for yet another year. The index measures cities on five broad indicators such as stability, healthcare, culture and environment, education, and infrastructure. As per the Economic Intelligence Unit’s suggestions, if a city’s livability score is between 50 to 60 then “livability is substantially constrained”. Less than 50 means most aspects of living are severely restricted. Least Liveable cities on the index The least liveable cities were in Sub-Saharan Africa and the Middle East and North Africa regions. Damascus and Tripoli ranked the lowest. Tel Aviv also witnessed significant drop due to war with Hamas.

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 regression results of the endogeneity and robustness tests.