Recruitment of Researchers
The Happy City Index is built as a global, community-driven research effort. For the 2026 edition, we invited researchers from across the world to contribute to the collection and verification of city-level data, forming a diverse and geographically balanced research network.
The recruitment process was conducted entirely in English and open to participants globally. As a result, the project attracted interest from all major regions, including North America, South America, Africa, Europe, Asia, as well as Australia and Oceania. This international diversity was a key strength of the methodology, ensuring that the research process was not limited to a single regional perspective.
In total, 1,199 individuals expressed interest in collaborating on the project. Of these, 863 formally entered into cooperation by signing an agreement and committing to analyse an assigned city and provide data for the study.
Participation and Completion Criteria
A researcher was considered to have fulfilled their role only if two conditions were met: the submission of a complete dataset for validation and active participation in the validation process itself. This ensured that each participant contributed not only to data collection but also to the peer-review stage of the methodology.
Out of the participating group, 466 researchers completed all required tasks. These individuals are formally recognised as Researchers of the Happy City Index 2026. Each of them received certificates and reference letters accurately reflecting the scope and nature of their contribution.
Assignment of Cities
Indication of country preferences
Each researcher was invited to indicate between 3 and 10 countries from which they would prefer to analyse a city. This allowed participants to align their work with their interests, language skills or regional expertise.
Random allocation of cities
Cities were assigned randomly, while taking into account the declared preferences wherever possible. The principle of one city per researcher was maintained to ensure clear responsibility and accountability. In a limited number of cases, some researchers voluntarily agreed to analyse an additional city, where this supported overall data coverage.
Alternative allocation when needed
Where all cities within a researcher’s preferred countries had already been assigned, alternative cities were proposed. This ensured continuity of the process while maintaining fairness across participants.
Flexible reassignment in specific cases
In situations where a researcher wished to work only on a single country or where all preferred cities were unavailable, additional cities were assigned or the number of analysed cities within a given country was increased. This approach supported the overall objective of maximising data coverage.
Training and Onboarding
To support consistency and quality, all researchers were offered access to structured training. Participants could attend one of five live online training sessions delivered across different time zones or use a recorded training session available on demand.
After submitting their declaration of participation and signing the agreement, each researcher received access to all training materials along with information about their assigned city. Formal confirmation of participation was recorded only once the researcher accepted the assigned city for analysis.
Principles of the Recruitment Model
Global inclusiveness
Researchers joined from all continents, ensuring a geographically diverse and globally representative research community.
Community-driven approach
The model relied on the engagement of individuals motivated to contribute to sustainable urban development through data-driven analysis.
Maximum city coverage
A key priority was to include as many cities as possible by effectively using the available research capacity and adapting assignments where needed.
Clear accountability
The principle of one city per researcher ensured that responsibility for data collection remained clearly defined.
1,199 individuals expressed interest in joining the project, of whom 863 formally committed to participate.
466 researchers completed all required tasks and are officially recognised as Happy City Index Researchers 2026.
The recruitment model prioritised global participation, methodological consistency and the maximisation of city-level data coverage.
Indicators and Scoring Framework
The methodology is based on a set of 64 indicators grouped into 6 themes. Although all indicators contribute to the final assessment, they do not all function in the same way. For methodological reasons, the indicators are divided into four principal types, each of which is treated differently within the scoring model.
The purpose of this structure is to ensure that the Index remains both evidence-based and proportionate. Some indicators measure the direct situation of a city, some reflect national regulatory or socio-economic conditions that shape everyday life regardless of the specific city, and some describe wider country-level background conditions which are important in interpretation but should not be allowed to dominate the assessment of an individual urban area.
As a result, the weighting system was designed to preserve balance across the full dataset. It rewards city-level performance where city-level action is measurable, while limiting the influence of those indicators that, although highly relevant to quality of life, are primarily determined beyond the city itself.
Structure of the Indicator Set
64 indicators in total
The full methodology is based on 64 indicators forming the analytical basis of Happy City Index 2026.
6 thematic groups
These indicators are organised into 6 themes in order to reflect the multidimensional nature of urban quality of life.
4 methodological types
Indicators are divided into 4 types, with different rules for interpretation, weighting and scoring.
Weighted final result
Each indicator contributes to the final score according to the weight assigned to it in the methodology.
Type 1: Yes/No Indicators
The first type consists of binary indicators assessed on a yes/no basis. These indicators capture the existence or absence of a given policy, service, institutional solution or formal mechanism. Their role is to identify whether a city has introduced a specific measure rather than to compare intensity or scale.
In the case of this category, each individual indicator may carry a maximum weight of 1.2% of the overall Index. This ensures that binary variables remain meaningful, but do not disproportionately influence the final ranking.
Type 2: Country-Level Indicators with Direct Impact on Residents
The second type includes indicators measured at country level but treated as directly relevant to the lived experience of a city resident. These are variables whose value is generally regulated or determined nationally and which shape quality of life regardless of the particular city in which a person lives.
In methodological terms, these indicators are considered legitimate elements of the city assessment because they influence the circumstances of the resident rather than the administrative performance of the city itself. They therefore form part of the final structure even though they are not collected at municipal level.
Cost of Higher Education Compared to Average Annual Salary
This indicator reflects how accessible higher education is in practical financial terms for the average resident.
Paid Parental Leave Weeks at 50% or More of Salary
This indicator captures the level of protection and support available to parents through national or equivalent regulatory systems.
Population Covered by Medical Insurance (%)
This indicator reflects the scale of effective health coverage available to residents.
Average Weekly Working Hours
This indicator captures a structural feature of work-life balance affecting the daily quality of life of residents.
Average Number of Paid Vacation Days
This indicator reflects nationally established employment conditions with direct implications for rest and wellbeing.
An important exception was applied in the case of the United States. Because certain matters, including parental leave arrangements and other resident-relevant provisions, may be regulated or materially shaped at state level, the methodology did not always rely solely on a national United States average. Where internal state-level arrangements provided additional or more favourable solutions for residents, those state values were assessed directly and scored accordingly.
Type 3: Country-Level Indicators Describing National Context
The third type also consists of indicators measured at country level, but with a different methodological interpretation. In these cases, the data describe the broader statistical and structural condition of the country as a whole. Such indicators are highly relevant to understanding the context in which cities operate, yet they should not be used to judge a particular city too positively or too negatively where the variable is not meaningfully controlled at municipal level.
For that reason, these indicators were included with a limited methodological effect. Their maximum contribution in overall terms could not exceed 0.5 percentage points of the whole Index.
Urban Innovation Ecosystem Potential
Used as an indicator of the wider national environment for innovation and development.
Population Able to Communicate in a Foreign Language (%)
Included as a background measure of social and economic openness at country level.
Use of Electronic Banking Services (%)
Treated as a national-level indicator of financial modernisation and digital inclusion.
Population Aged 65+ Benefiting from Municipal Home Care (%)
Used as a contextual signal of support structures affecting older residents at system level.
Employment in Creative Industries (%)
Included as an indicator of wider economic structure and creative-sector presence.
GDP per Capita
Used to describe the broader economic environment of the country in which the city operates.
GDP Growth (%)
Included as a contextual indicator of national economic momentum.
Patents per 100,000 Inhabitants
Used as a country-level signal of knowledge production and innovation capacity.
Type 4: Standard Quantitative City Indicators
All remaining indicators were scored using the standard weighting model presented in the methodology. In these cases, the score for each city was normalised in relation to the full range of values observed across the complete dataset of all assessed cities.
Where a higher value was considered desirable, the following formula was used:
Score = 100 × (city value − minimum value in the full dataset) / (maximum value − minimum value)
This approach was applied, for example, to indicators where a higher result reflects better urban performance, such as the number of cultural institutions per resident.
Where a lower value was considered preferable, such as in the case of unemployment, the formula was reversed:
Score = 100 × (maximum value − city value) / (maximum value − minimum value)
This ensured that the direction of the indicator was always correctly reflected in the final score. In practical terms, cities received higher normalised results either for outperforming others on positively valued indicators or for maintaining lower values on negatively valued indicators.
Methodological Rationale
Balance between city and country effects
The framework distinguishes between what a city can shape directly and what is primarily determined at national level.
Protection against over-scoring
Country-level background variables are capped so that they do not dominate the municipal assessment.
Comparability across the full dataset
Normalisation against minimum and maximum values allows cities to be assessed within one common comparative framework.
Transparent scoring logic
Each indicator type is governed by a clear and explicit rule, making the methodology easier to interpret and review.
Happy City Index 2026 is based on 64 indicators grouped into 6 themes and organised into 4 methodological types.
Binary yes/no indicators may carry a maximum weight of 1.2% each, while selected country-level contextual indicators are capped at 0.5 percentage points of the whole Index.
All remaining quantitative indicators are scored through dataset-wide normalisation using minimum and maximum values observed across all cities.
Indicator Weights
The full list of indicators and their respective weights is presented below. This table should be read together with the methodological explanations above, as the same nominal indicator weight may operate differently depending on the indicator type and the rule applied to its scoring.
The indicators used in the Happy City Index 2026 were not created as a one-off set for a single edition. Rather, they are the result of a methodological framework developed and refined over the course of five previous editions of the Index. Their current form reflects a cumulative process of expert review, practical testing and iterative improvement led by the Institute’s team.
The qualification of indicators is carried out by the Institute on the basis of three complementary considerations. First, attention is given to research and wider evidence concerning quality of life, urban wellbeing and the conditions that shape everyday life in cities. Second, the process draws on observations gathered through earlier editions of the Index, including practical experience of how particular indicators perform in comparative international analysis. Third, the team takes into account the realistic availability of data, so that the final framework is based on variables that can be identified, verified and compared across a wide range of urban contexts.
This approach is intended to ensure that the methodology remains both ambitious and operationally credible. The indicator set is therefore shaped not only by substantive relevance, but also by methodological feasibility. In this way, the Happy City Index seeks to maintain a balance between analytical depth and the practical realities of global data availability.
Development and Qualification of Indicators
Happy City Index #2026 Indicators and Weights
Below is the full set of 64 indicators used in the Happy City Index #2026 methodology. The indicators are grouped into 6 themes and weighted according to their role within the overall scoring framework described above.
| No. | Theme | Indicator | Weight (%) |
|---|---|---|---|
| 1 | Citizens | Urban Innovation Ecosystem Potential | 0.50% |
| 2 | Citizens | School Density | 1.70% |
| 3 | Citizens | Higher Education Accessibility | 2.80% |
| 4 | Citizens | Presence of a Globally Ranked Higher Education Institution | 3.00% |
| 5 | Citizens | Cost of Higher Education Compared to Average Annual Salary | 2.00% |
| 6 | Citizens | Residents with a Master's Degree (%) | 1.76% |
| 7 | Citizens | Population Able to Communicate in a Foreign Language (%) | 0.50% |
| 8 | Citizens | Use of Electronic Banking Services (%) | 0.50% |
| 9 | Citizens | Population Aged 65+ Benefiting from Municipal Home Care (%) | 0.50% |
| 10 | Citizens | Homeless People per 10,000 Residents | 2.30% |
| 11 | Citizens | Availability of City Website | 0.30% |
| 12 | Citizens | Net Internal Migration Rate per 1,000 Population | 2.80% |
| 13 | Citizens | Pupils per School Building | 0.80% |
| 14 | Citizens | Employment in Creative Industries (%) | 0.50% |
| 15 | Citizens | Housing Affordability Ratio | 2.16% |
| 16 | Citizens | Housing Affordability Ratio (Rent) | 2.48% |
| 17 | Citizens | Cultural Institutions per 100,000 Residents | 1.12% |
| 18 | Citizens | Libraries per 10 km2 | 2.16% |
| 19 | Governance | Voter Turnout in the Last Local Elections (%) | 2.84% |
| 20 | Governance | Open Data Portal Availability | 1.20% |
| 21 | Governance | Datasets Available in Machine-Readable Formats | 1.20% |
| 22 | Governance | Fault Reporting System via Website or Mobile App | 1.20% |
| 23 | Governance | Electronic Payments for Municipal Services | 1.20% |
| 24 | Governance | Online Appointment Booking with City Hall | 1.20% |
| 25 | Governance | Up-to-Date Official Development Strategy | 1.20% |
| 26 | Governance | Key Elements Included in the Strategy | 1.04% |
| 27 | Governance | AI Mentioned in Official Strategic Documents in Relation to Residents' Needs | 1.20% |
| 28 | Environment | Annual Average PM2.5 Concentration | 3.00% |
| 29 | Environment | Green Mobility Share (%) | 1.68% |
| 30 | Environment | Waste Generated per Resident | 2.30% |
| 31 | Environment | Population Served by Sewage Treatment Facilities (%) | 1.40% |
| 32 | Environment | Recycling Rate (%) | 2.50% |
| 33 | Environment | Landfill Waste Burden | 2.28% |
| 34 | Environment | Biodiversity Protection Strategy | 1.20% |
| 35 | Economy | GDP per Capita | 0.50% |
| 36 | Economy | GDP Growth (%) | 0.50% |
| 37 | Economy | Patents per 100,000 Inhabitants | 0.50% |
| 38 | Economy | Businesses per 1,000 Residents | 2.20% |
| 39 | Economy | City Fiscal Power Index | 1.40% |
| 40 | Economy | Annual Average Unemployment Rate | 2.00% |
| 41 | Economy | Annual Average Youth Unemployment Rate | 1.76% |
| 42 | Economy | Youth Unemployment Ratio | 1.12% |
| 43 | Economy | Percentage Deviation in Earnings Relative to the National Average | 2.56% |
| 44 | Health | Paid Parental Leave Weeks at 50% or More of Salary | 1.80% |
| 45 | Health | Psychiatrists per 100,000 Inhabitants | 0.50% |
| 46 | Health | Mental Health or Well-Being Strategy | 1.20% |
| 47 | Health | Strategy to Prevent and Address Hate Speech or Cyberbullying | 1.20% |
| 48 | Health | Licensed Medical Doctors Practising in the City (FTE) | 1.44% |
| 49 | Health | Population Covered by Medical Insurance (%) | 1.80% |
| 50 | Health | Intentional Homicides per 100,000 Residents | 0.80% |
| 51 | Health | Adults Classified as Overweight or Obese (%) | 2.00% |
| 52 | Health | Parks per km2 | 1.30% |
| 53 | Health | Green Space per Capita | 3.00% |
| 54 | Health | Average Weekly Working Hours | 2.50% |
| 55 | Health | Average Number of Paid Vacation Days | 2.00% |
| 56 | Health | Life Expectancy at Birth | 3.00% |
| 57 | Mobility | Types of Electronic Payment Systems for Transport Services | 0.76% |
| 58 | Mobility | Distance to the Nearest International Airport | 0.40% |
| 59 | Mobility | Access Modes to the Nearest Airport | 1.80% |
| 60 | Mobility | Public Transport Ridership per Capita | 2.00% |
| 61 | Mobility | Accessible Public Transport Fleet (%) | 1.28% |
| 62 | Mobility | Road Traffic Accidents per 1,000 Residents | 1.76% |
| 63 | Mobility | Open Public Transport Data Availability | 1.20% |
| 64 | Mobility | Integrated Journey Planner Availability | 1.20% |
Data Validation
To ensure that the final dataset used for Happy City Index 2026 was consistent, credible and methodologically sound, all data collected for 457 cities were submitted to a structured validation process. This stage was designed not only to review the quality of individual data records, but also to assess the reliability of the work carried out by each researcher.
Validation was a formal and independent quality-control stage applied to the full set of 457 cities for which city data had been collected. Its purpose was to test whether the submitted records met the required standard of reliability, clarity and evidence-based documentation before being accepted into the final Happy City Index dataset.
The process was based on peer review. For each city dataset, 10 data records were selected at random and passed to researchers for assessment. This meant that every researcher’s work was reviewed through a sample-based validation exercise in which individual records were examined independently by 10 other researchers. In this way, the validation process assessed both the quality of the records themselves and the consistency of the wider research effort.
How the Validation Process Worked
Submission of all city datasets
All data collected for 457 cities were transferred to the validation stage. No city included in this pool bypassed the review process.
Random selection of records
For each city, 10 individual data records were selected at random. These records formed the validation sample used to test the quality of the broader dataset.
Independent peer assessment
Each sampled record was reviewed by other researchers, who classified the quality of the record using one of three grades: HIGH, ACCEPTABLE or POOR.
Point-based scoring
Each HIGH assessment was awarded 1 point, each ACCEPTABLE assessment 0.5 points, and each POOR assessment minus 2 points. This scoring model was intended to reward strong evidence, recognise records of acceptable standard, and apply a clear penalty where the quality of a record was considered weak.
Minimum threshold for passing validation
A city dataset passed validation only where at least 5 of the 10 sampled records had been assessed and the total score for that sample was above 0. Both conditions had to be met.
Validation Rules in Practice
Assessment categories
Reviewers were asked to judge each sampled record using a simple three-level scale: HIGH, ACCEPTABLE or POOR.
Weighted scoring model
The scoring framework distinguished clearly between strong, usable and weak records by assigning 1 point, 0.5 points and minus 2 points respectively.
Minimum review coverage
Validation could not be passed on the basis of only a small number of returned judgements. At least 5 of the 10 sampled records had to receive an assessment.
Positive overall result required
Even where the minimum number of reviewed records had been reached, the city passed only if the combined score remained above 0.
This method ensured that validation was not reduced to a simple box-ticking exercise. A city could not pass solely because a few records were reviewed, nor could it pass if the overall quality signal from the sample was negative. The process therefore combined coverage and quality in a single validation rule.
Final Outcome of the Validation Stage
Of the 457 cities submitted to validation, 272 met the required threshold. These were the cities whose datasets satisfied both the minimum review condition and the scoring condition, and they therefore formed the validated pool for the final Happy City Index 2026 listing.
From that validated group of 272 cities, the first 250 were published in the ranking. This means that publication in the ranking followed a two-step logic: first, a city had to pass validation; second, the published ranking presented the top 250 cities from the final validated set.
All data for 457 cities were submitted to validation.
A city passed only where at least 5 of 10 randomly selected records were assessed and the total validation score was above 0.
Under this rule, 272 cities passed validation and entered the final Happy City Index 2026 dataset, with the first 250 published in the ranking.
City Selection and Inclusion Framework
Every city in the world can be included in the Happy City Index. This openness applies to the wider Happy City Index framework, including participation through the Municipal Data Submission Stage, the On-Site Assessment Stage and community-led initiatives, while the Open Data Collection Stage is subject to methodological selection criteria. Our aim is to make the Index as globally representative as possible, regardless of size, location or resources, while ensuring that the final assessment remains evidence-based, comparable and methodologically robust.
At Happy City Index, our starting principle is simple: every city in the world can be part of the study. We do not believe that only the largest, wealthiest or best-resourced cities should be assessed. Our ambition is to include as many cities as possible worldwide and to create a framework in which urban progress can be recognised wherever it happens.
The practical limits of the study arise only from time and research capacity. For that reason, the methodology for each edition is designed to balance openness with rigour. For the 2026 edition, we introduced a city selection process intended to secure the broadest possible global representation while remaining consistent with the project’s wider values: sustainability, inclusiveness and a genuinely community-driven spirit.
Initial Global Coverage
In the first phase of the 2026 edition, we included 3,417 cities from around the world in the initial review. This was the broadest analytical stage, focused on basic city-level information and on testing whether further, deeper analysis would be feasible.
In practical terms, this stage asked a simple question: where is it realistic to identify reliable, credible and comparable data without compromising methodological quality? The purpose of this early filtering was not to exclude cities in principle, but to make deep analysis manageable and evidence-based.
How the Core Group of Cities Was Selected
Country-level data feasibility check
We first reviewed whether, during the previous edition of Happy City Index, our researchers had been able to identify reliable, credible and comparable data for at least one city within a given country. If they had, all cities from that country remained eligible for further analysis in the 2026 edition. If not, only the national capital was taken forward, based on the assumption that capitals are usually the most transparent and best-documented cities in the country.
Population threshold
In the second phase, we narrowed the selection to cities with more than 100,000 inhabitants. This allowed us to focus the deep analysis on cities with a sufficient scale of governance, services and public data availability.
National population relevance
In the third phase, we retained only those cities whose population represented at least 0.1 per cent of the total population of their country. This helped us preserve national relevance while maintaining consistency across different urban systems.
National cap by size
In the final phase, we introduced a cap of up to 30 of the largest cities in each country. This produced a core group of 924 cities selected for deep analysis in the 2026 edition.
These 924 cities formed the planned Deep Analysis stage. They were passed to our researchers for the Open Data Collection Stage, during which researchers from around the world were recruited and invited to identify reliable, robust and measurable evidence using publicly available sources.
Four Routes into the Study
To maximise inclusion, the 2026 edition did not rely on a single route of participation. Instead, the methodology was designed around four complementary pathways through which cities could enter the broader research framework.
1. Open Data Collection Stage
The core route for the 924 cities selected for deep analysis. Researchers sought reliable, comparable and measurable data from public sources.
2. Municipal Data Submission Stage
Any city could join by delegating a representative to prepare and submit the relevant data directly, without waiting to be reached only through open-source research.
3. On-Site Assessment Stage
Cities could also choose a more direct and structured route through an on-site assessment process designed to support deeper engagement.
4. Community Initiative
A new route introduced in practice through resident-led action. In two cases, local communities organised themselves, selected a lead coordinator, divided responsibilities and submitted data independently, without formal initiation by the city administration.
This final pathway was especially important. It showed that cities can enter the research process not only through municipal institutions or commissioned work, but also through the determination of residents themselves. That development strongly reflects the wider idea behind the project: a city is shaped not only by its administration, but also by its community.
For the 2026 edition, our planned deep search covered 924 cities. At the same time, additional cities entered the wider process because their residents wanted to participate or because city administrations actively chose to engage by assigning staff to provide data.
Most importantly, inclusion in Happy City Index does not require a city to bear any financial cost. In principle, every city in the world can be part of the Index.
Initial Global City Pool (3,417 Cities)
| City | Country |
|---|
Cities Selected for Deep Analysis
This table presents the cities included in the Deep Analysis stage of the Happy City Index 2026. Use the search field below to filter the list by city or country.
| City | Country |
|---|---|
| A Coruna | Spain |
| Aachen | Germany |
| Aalborg | Denmark |
| Aarhus | Denmark |
| Aberdeen | United Kingdom |
| Abha | Saudi Arabia |
| Abidjan | Ivory Coast |
| Abu Dhabi | United Arab Emirates |
| Abuja | Nigeria |
| Acapulco de Juarez | Mexico |
| Accra | Ghana |
| Adana | Turkiye |
| Addis Ababa | Ethiopia |
| Adelaide | Australia |
| Aguascalientes | Mexico |
| Ahmedabad | India |
| Aix-en-Provence | France |
| Ajman | United Arab Emirates |
| Al Ain | United Arab Emirates |
| Al Bahah | Saudi Arabia |
| Al Hufuf | Saudi Arabia |
| Al Mubarraz | Saudi Arabia |
| Al Qatif | Saudi Arabia |
| Al Rayyan | Qatar |
| Al-Khafji | Saudi Arabia |
| Al-Kharj | Saudi Arabia |
| Al-Majma’ah | Saudi Arabia |
| Alesund | Norway |
| Algiers | Algeria |
| Alicante | Spain |
| Almada | Portugal |
| Almere | Netherlands |
| Alor Setar | Malaysia |
| Amadora | Portugal |
| Amersfoort | Netherlands |
| Amiens | France |
| Amman | Jordan |
| Amsterdam | Netherlands |
| Andorra la Vella | Andorra |
| Angeles City | Philippines |
| Angers | France |
| Ankara | Turkiye |
| Ansan | Republic of Korea |
| Antalya | Turkiye |
| Antananarivo | Madagaskar |
| Antipolo | Philippines |
| Antwerp | Belgium |
| Anyang | Republic of Korea |
| Apeldoorn | Netherlands |
| Ar Rass | Saudi Arabia |
| Arar | Saudi Arabia |
| Arendal | Norway |
| Arnhem | Netherlands |
| Asan | Republic of Korea |
| Ashdod | Israel |
| Ashgabat | Turkmenistan |
| Ashkelon | Israel |
| Asmara | Eritrea |
| Astana | Kazakhstan |
| Asuncion | Paraguay |
| Athens | Greece |
| Atlanta | United States of America |
| Auckland | New Zealand |
| Augsburg | Germany |
| Austin | United States of America |
| Aydin | Turkiye |
| Bacolod | Philippines |
| Bacoor | Philippines |
| Badalona | Spain |
| Baerum | Norway |
| Bahia Blanca | Argentina |
| Baku | Azerbaijan |
| Ballarat | Australia |
| Ballerup | Denmark |
| Balti | Republic of Moldova |
| Baltimore | United States of America |
| Bamako | Mali |
| Bandar Seri Begawan | Brunei Darussalam |
| Bangkok | Thailand |
| Bangui | Central African Republic |
| Banjul | Gambia |
| Barcelona | Spain |
| Bari | Italy |
| Barnaul | Russian Federation |
| Basel | Switzerland |
| Bat Yam | Israel |
| Batman | Turkiye |
| Batu Pahat | Malaysia |
| Beersheba | Israel |
| Beijing | China |
| Beirut | Lebanon |
| Belfast | United Kingdom |
| Belgrade | Serbia |
| Belo Horizonte | Brazil |
| Bendigo | Australia |
| Bengaluru | India |
| Bergen | Norway |
| Berlin | Germany |
| Bern | Switzerland |
| Besancon | France |
| Bialystok | Poland |
| Biel | Switzerland |
| Bielefeld | Germany |
| Bielsko-Biala | Poland |
| Bilbao | Spain |
| Binan | Philippines |
| Bintulu | Malaysia |
| Birmingham | United Kingdom |
| Bishkek | Kyrgyzstan |
| Bissau | Guinbea-Bissau |
| Blackpool | United Kingdom |
| Bloemfontein | South Africa |
| Bnei Brak | Israel |
| Bochum | Germany |
| Bogota | Colombia |
| Bologna | Italy |
| Bonn | Germany |
| Bordeaux | France |
| Boston | United States of America |
| Bradford | United Kingdom |
| Braga | Portugal |
| Brampton | Canada |
| Brasilia | Brazil |
| Bratislava | Slovakia |
| Braunschweig | Germany |
| Breda | Netherlands |
| Bremen | Germany |
| Brescia | Italy |
| Brest | France |
| Bridgetown | Barbados |
| Brisbane | Australia |
| Bristol | United Kingdom |
| Brno | Czechia |
| Bruges | Belgium |
| Brussels | Belgium |
| Bucharest | Romania |
| Bucheon | Republic of Korea |
| Budapest | Hungary |
| Buenos Aires | Argentina |
| Buraidah | Saudi Arabia |
| Burgas | Bulgaria |
| Burlington | Canada |
| Burnaby | Canada |
| Bursa | Turkiye |
| Busan | Republic of Korea |
| Bydgoszcz | Poland |
| Bytom | Poland |
| Cabuyao | Philippines |
| Cagayan de Oro | Philippines |
| Cagliari | Italy |
| Cairns | Australia |
| Cairo | Egypt |
| Calamba | Philippines |
| Calgary | Canada |
| Caloocan City | Philippines |
| Cambridge | United Kingdom |
| Canakkale | Turkiye |
| Canberra | Australia |
| Cancun | Mexico |
| Cape Town | South Africa |
| Caracas | Venezuela |
| Cardiff | United Kingdom |
| Cartagena | Spain |
| Casablanca | Morocco |
| Catania | Italy |
| Cebu City | Philippines |
| Celaya | Mexico |
| Changchun | China |
| Changhua | Taiwan |
| Changwon-Si | Republic of Korea |
| Charleroi | Belgium |
| Charlotte | United States of America |
| Chelyabinsk | Russian Federation |
| Chemnitz | Germany |
| Chengdu | China |
| Chennai | India |
| Cheonan | Republic of Korea |
| Cheongju | Republic of Korea |
| Chiang Mai | Thailand |
| Chiayi | Taiwan |
| Chiba | Japan |
| Chicago | United States of America |
| Chihuahua | Mexico |
| Chisinau | Republic of Moldova |
| Chongqing | China |
| Christchurch | New Zealand |
| Clermont-Ferrand | France |
| Coffs Harbour | Australia |
| Coimbra | Portugal |
| Cologne | Germany |
| Colombo | Sri Lanka |
| Columbus | United States of America |
| Comodoro Rivadavia | Argentina |
| Conakry | Guinea |
| Copenhagen | Denmark |
| Cordoba | Argentina |
| Cordoba | Spain |
| Cork | Ireland |
| Corrientes | Argentina |
| Coventry | United Kingdom |
| Cracow | Poland |
| Crawley | United Kingdom |
| Culiacan | Mexico |
| Curitiba | Brazil |
| Czestochowa | Poland |
| Daegu | Republic of Korea |
| Daejeon | Republic of Korea |
| Dakar | Senegal |
| Dalian | China |
| Dallas | United States of America |
| Damascus | Syrian Arab Republic |
| Dammam | Saudi Arabia |
| Darwin | Australia |
| Dasmarinas | Philippines |
| Davao City | Philippines |
| Debrecen | Hungary |
| Delft | Netherlands |
| Delhi | India |
| Denizli | Turkiye |
| Denver | United States of America |
| Derby | United Kingdom |
| Detroit | United States of America |
| Dhahran | Saudi Arabia |
| Dhaka | Bangladesh |
| Dijon | France |
| Dili | East Timor |
| Diyarbakir | Turkiye |
| Djibouti City | Djibouti |
| Dodoma | United Republic of Tanzania |
| Doha | Qatar |
| Dongguan | China |
| Dordrecht | Netherlands |
| Dortmund | Germany |
| Douliu | Taiwan |
| Drammen | Norway |
| Dresden | Germany |
| Dubai | United Arab Emirates |
| Dublin | Ireland |
| Dudley | United Kingdom |
| Duisburg | Germany |
| Dunedin | New Zealand |
| Durango | Mexico |
| Durban | South Africa |
| Dusseldorf | Germany |
| Ede | Netherlands |
| Edinburgh | United Kingdom |
| Edmonton | Canada |
| Eindhoven | Netherlands |
| El Paso | United States of America |
| Elche | Spain |
| Enschede | Netherlands |
| Erzurum | Turkiye |
| Esbjerg | Denmark |
| Eskisehir | Turkiye |
| Espoo | Finland |
| Essen | Germany |
| Faisalabad | Pakistan |
| Ferrara | Italy |
| Florence | Italy |
| Foggia | Italy |
| Formosa | Argentina |
| Fort Worth | United States of America |
| Foshan | China |
| Frankfurt am Main | Germany |
| Freetown | Sierra Leone |
| Fukuoka | Japan |
| Funabashi | Japan |
| Funchal | Portugal |
| Gaborone | Botswana |
| Gatineau | Canada |
| Gaziantep | Turkiye |
| Gdansk | Poland |
| Gdynia | Poland |
| Geelong | Australia |
| Gelsenkirchen | Germany |
| General Santos | Philippines |
| General Trias | Philippines |
| Geneva | Switzerland |
| Genoa | Italy |
| George Town | Malaysia |
| Georgetown | Guyana |
| Ghaziabad | India |
| Ghent | Belgium |
| Gijon | Spain |
| Gimhae | Republic of Korea |
| Gimpo | Republic of Korea |
| Gitega | Burundi |
| Glasgow | United Kingdom |
| Gliwice | Poland |
| Godoy Cruz | Argentina |
| Gold Coast | Australia |
| Gondomar | Portugal |
| Gorzow Wielkopolski | Poland |
| Gothenburg | Sweden |
| Goyang | Republic of Korea |
| Granada | Spain |
| Graz | Austria |
| Grenoble | France |
| Groningen | Netherlands |
| Guadalajara | Mexico |
| Guangzhou | China |
| Guatemala City | Guatemala |
| Guayaquil | Ecuador |
| Guimaraes | Portugal |
| Gumi | Republic of Korea |
| Gwangju | Republic of Korea |
| Gyor | Hungary |
| Haarlem | Netherlands |
| Hachioji | Japan |
| Hadera | Israel |
| Hafar al-Batin | Saudi Arabia |
| Haifa | Israel |
| Hail | Saudi Arabia |
| Halifax | Canada |
| Hamamatsu | Japan |
| Hamburg | Germany |
| Hamilton | Canada |
| Hamilton | New Zealand |
| Hangzhou | China |
| Hannover | Germany |
| Hanoi | Viet Nam |
| Harare | Zimbabwe |
| Harbin | China |
| Havana | Cuba |
| Helsingborg | Sweden |
| Helsinki | Finland |
| Heraklion | Greece |
| Heredia | Costa Rica |
| Hermosillo | Mexico |
| Higashiosaka | Japan |
| Himeji | Japan |
| Hiroshima | Japan |
| Hobart | Australia |
| Holon | Israel |
| Holstebro | Denmark |
| Hong-Kong | China |
| Hospitalet de Llobregat | Spain |
| Houston | United States of America |
| Hsinchu | Taiwan |
| Huddinge | Sweden |
| Hwaseong | Republic of Korea |
| Hyderabad | India |
| Ichikawa | Japan |
| Iloilo City | Philippines |
| Imus | Philippines |
| Incheon | Republic of Korea |
| Indianapolis | United States of America |
| Indore | India |
| Innsbruck | Austria |
| Ioannina | Greece |
| Ipoh | Malaysia |
| Irkutsk | Russian Federation |
| Islamabad | Pakistan |
| Istanbul | Turkiye |
| Izhevsk | Russian Federation |
| Izmir | Turkiye |
| Izmit | Turkiye |
| Jacksonville | United States of America |
| Jaipur | India |
| Jakarta | Indonesia |
| Jeddah | Saudi Arabia |
| Jeonju | Republic of Korea |
| Jerez de la Frontera | Spain |
| Jerusalem | Israel |
| Jinan | China |
| Jizan | Saudi Arabia |
| Johor Bahru | Malaysia |
| Jonkoping | Sweden |
| Juarez | Mexico |
| Juba | South Sudan |
| Jubail | Saudi Arabia |
| Jyvaskyla | Finland |
| Kabul | Afghanistan |
| Kagoshima | Japan |
| Kahramanmaras | Turkiye |
| Kajang | Malaysia |
| Kampala | Uganda |
| Kandy | Sri Lanka |
| Kanpur | India |
| Kaohsiung | Taiwan |
| Karachi | Pakistan |
| Karlsruhe | Germany |
| Kathmandu | Nepal |
| Katowice | Poland |
| Kaunas | Lithuania |
| Kawasaki | Japan |
| Kayseri | Turkiye |
| Kazan | Russian Federation |
| Kecskemet | Hungary |
| Keelung | Taiwan |
| Kemerovo | Russian Federation |
| Kfar Saba | Israel |
| Khabarovsk | Russian Federation |
| Khamis Mushait | Saudi Arabia |
| Khartoum | Sudan |
| Khobar | Saudi Arabia |
| Kiel | Germany |
| Kielce | Poland |
| Kigali | Rwanda |
| Kingston | Jamaica |
| Kingston upon Hull | United Kingdom |
| Kinshasa | Democratic Republic of the Congo |
| Kisumu | Kenya |
| Kitakyushu | Japan |
| Kitchener | Canada |
| Klagenfurt am Worthersee | Austria |
| Klaipeda | Lithuania |
| Kobe | Japan |
| Kolkata | India |
| Konya | Turkiye |
| Kopavogur | Iceland |
| Kota Bharu | Malaysia |
| Kota Kinabalu | Malaysia |
| Krasnodar | Russian Federation |
| Krasnoyarsk | Russian Federation |
| Kristiansand | Norway |
| Kuala Lumpur | Malaysia |
| Kuala Terengganu | Malaysia |
| Kuantan | Malaysia |
| Kuching | Malaysia |
| Kulim | Malaysia |
| Kumamoto | Japan |
| Kunming | China |
| Kuopio | Finland |
| Kuwait City | Kuwait |
| Kyiv | Ukraine |
| Kyoto | Japan |
| La Coruna | Spain |
| La Paz | Bolivia |
| La Plata | Argentina |
| Lagos | Nigeria |
| Lahore | Pakistan |
| Lahti | Finland |
| Lapu-Lapu | Philippines |
| Larissa | Greece |
| Las Heras | Argentina |
| Las Palmas de Gran Canaria | Spain |
| Las Pinas City | Philippines |
| Las Vegas | United States of America |
| Lausanne | Switzerland |
| Laval | Canada |
| Le Havre | France |
| Le Mans | France |
| Leeds | United Kingdom |
| Leeuwarden | Netherlands |
| Leicester | United Kingdom |
| Leiden | Netherlands |
| Leipzig | Germany |
| Leiria | Portugal |
| Leon | Mexico |
| Leuven | Belgium |
| Leverkusen | Germany |
| Liberec | Czechia |
| Libreville | Gabon |
| Liege | Belgium |
| Lille | France |
| Lilongwe | Malawi |
| Lima | Peru |
| Limassol | Cyprus |
| Limerick | Ireland |
| Limoges | France |
| Linkoping | Sweden |
| Linz | Austria |
| Lipa | Philippines |
| Lisbon | Portugal |
| Liverpool | United Kingdom |
| Livorno | Italy |
| Ljubljana | Slovenia |
| Lodz | Poland |
| Lome | Togo |
| London | Canada |
| London | United Kingdom |
| Longueuil | Canada |
| Los Angeles | United States of America |
| Louisville | United States of America |
| Loures | Portugal |
| Lower Hutt | New Zealand |
| Luanda | Angola |
| Lubeck | Germany |
| Lublin | Poland |
| Lucerne | Switzerland |
| Lucknow | India |
| Lugano | Switzerland |
| Lund | Sweden |
| Luoyang | China |
| Lusaka | Zambia |
| Luton | United Kingdom |
| Luxembourg | Luxembourg |
| Lyon | France |
| Maastricht | Netherlands |
| Madrid | Spain |
| Magdeburg | Germany |
| Maia | Portugal |
| Maitland | Australia |
| Makati | Philippines |
| Makhachkala | Russian Federation |
| Malabo | Equatorial Guinea |
| Malacca | Malaysia |
| Malaga | Spain |
| Malatya | Turkiye |
| Male | Maldives |
| Malmo | Sweden |
| Managua | Nicaragua |
| Manama | Bahrain |
| Manchester | United Kingdom |
| Mandaue | Philippines |
| Manila | Philippines |
| Manjung | Malaysia |
| Mannheim | Germany |
| Maputo | Mozambique |
| Mar del Plata | Argentina |
| Maribor | Slovenia |
| Markham | Canada |
| Marseille | France |
| Maseru | Lesotho |
| Mashhad | Iran |
| Matosinhos | Portugal |
| Matsudo | Japan |
| Matsuyama | Japan |
| Mazatlan | Mexico |
| Mecca | Saudi Arabia |
| Mechelen | Belgium |
| Medellin | Colombia |
| Medina | Saudi Arabia |
| Melbourne | Australia |
| Memphis | United States of America |
| Mendoza | Argentina |
| Merida | Mexico |
| Mersin | Turkiye |
| Messina | Italy |
| Metz | France |
| Mexicali | Mexico |
| Mexico City | Mexico |
| Miami | United States of America |
| Milan | Italy |
| Milton Keynes | United Kingdom |
| Milwaukee | United States of America |
| Minneapolis | United States of America |
| Minsk | Belarus |
| Mios | France |
| Miri | Malaysia |
| Miskolc | Hungary |
| Mississauga | Canada |
| Modena | Italy |
| Mogadishu | Somalia |
| Mombasa | Kenya |
| Monaco | Monaco |
| Monchengladbach | Germany |
| Monrovia | Liberia |
| Monterrey | Mexico |
| Montevideo | Uruguay |
| Montpellier | France |
| Montreal | Canada |
| Morelia | Mexico |
| Moscow | Russian Federation |
| Mostoles | Spain |
| Muar | Malaysia |
| Mugla | Turkiye |
| Mumbai | India |
| Munich | Germany |
| Munster | Germany |
| Murcia | Spain |
| Muscat | Oman |
| N'Djamena | Chad |
| Naberezhnye Chelny | Russian Federation |
| Nacka | Sweden |
| Nagoya | Japan |
| Nagpur | India |
| Nairobi | Kenya |
| Najran | Saudi Arabia |
| Namur | Belgium |
| Namyangju | Republic of Korea |
| Nanchang | China |
| Nanchong | China |
| Nancy | France |
| Nanjing | China |
| Nantes | France |
| Naples | Italy |
| Nashville | United States of America |
| Nassau | Bahamas |
| Naypyidaw | Myanmar |
| Negombo | Sri Lanka |
| Netanya | Israel |
| Neuquen | Argentina |
| New Taipei | Taiwan |
| New York | United States of America |
| Newcastle upon Tyne | United Kingdom |
| Newport | United Kingdom |
| Niamey | Niger |
| Nice | France |
| Nicosia | Cyprus |
| Niigata | Japan |
| Nijmegen | Netherlands |
| Nimes | France |
| Ningbo | China |
| Nizhny Novgorod | Russian Federation |
| Norrkoping | Sweden |
| Northampton | United Kingdom |
| Norwich | United Kingdom |
| Nottingham | United Kingdom |
| Nouakchott | Mauritania |
| Novosibirsk | Russian Federation |
| Nuremberg | Germany |
| Nyiregyhaza | Hungary |
| Oakville | Canada |
| Odense | Denmark |
| Odivelas | Portugal |
| Okayama | Japan |
| Oklahoma City | United States of America |
| Olomouc | Czechia |
| Olsztyn | Poland |
| Omsk | Russian Federation |
| Opole | Poland |
| Oran | Algeria |
| Orebro | Sweden |
| Osaka | Japan |
| Oshawa | Canada |
| Oslo | Norway |
| Osmaniye | Turkiye |
| Ostrava | Czechia |
| Ottawa | Canada |
| Ouagadougou | Burkina Faso |
| Oulu | Finland |
| Oviedo | Spain |
| Oxford | United Kingdom |
| Pachuca | Mexico |
| Padua | Italy |
| Paju-si | Republic of Korea |
| Palermo | Italy |
| Palma | Spain |
| Panama City | Panama |
| Paramaribo | Suriname |
| Parana | Argentina |
| Paris | France |
| Parma | Italy |
| Parnu | Estonia |
| Pasir Gudang | Malaysia |
| Patras | Greece |
| Pecs | Hungary |
| Peristeri | Greece |
| Perm | Russian Federation |
| Perth | Australia |
| Perugia | Italy |
| Petah Tikva | Israel |
| Peterborough | United Kingdom |
| Philadelphia | United States of America |
| Phnom Penh | Cambodia |
| Phoenix | United States of America |
| Pingtung | Taiwan |
| Plymouth | United Kingdom |
| Plzen | Czechia |
| Podgorica | Montenegro |
| Pohang | Republic of Korea |
| Port Louis | Mauritius |
| Port Moresby | Papua New Guinea |
| Port-au-Prince | Haiti |
| Portland | United States of America |
| Porto | Portugal |
| Portsmouth | United Kingdom |
| Posadas | Argentina |
| Poznan | Poland |
| Prague | Czechia |
| Praia | Cabo Verde |
| Prato | Italy |
| Preston | United Kingdom |
| Pretoria | South Africa |
| Pristina | Kosovo |
| Puebla | Mexico |
| Pula | Croatia |
| Pune | India |
| Pyongyang | Democratic People's Republic of Korea |
| Qingdao | China |
| Quebec City | Canada |
| Queretaro | Mexico |
| Quezon City | Philippines |
| Quito | Ecuador |
| Rabat | Morocco |
| Radom | Poland |
| Ramallah | State of Palestine |
| Ramat Gan | Israel |
| Randers | Denmark |
| Ravenna | Italy |
| Rawalpindi | Pakistan |
| Reading | United Kingdom |
| Recife | Brazil |
| Reggio Calabria | Italy |
| Reggio Emilia | Italy |
| Regina | Canada |
| Rehovot | Israel |
| Reims | France |
| Rennes | France |
| Resistencia | Argentina |
| Reykjavik | Iceland |
| Reynosa | Mexico |
| Richmond | Canada |
| Richmond Hill | Canada |
| Riga | Latvia |
| Rijeka | Croatia |
| Rimini | Italy |
| Rio de Janeiro | Brazil |
| Rishon LeZion | Israel |
| Riyadh | Saudi Arabia |
| Rockhampton | Australia |
| Roeselare | Belgium |
| Rome | Italy |
| Rosario | Argentina |
| Roskilde | Denmark |
| Rostov-on-Don | Russian Federation |
| Rotterdam | Netherlands |
| Ruda Slaska | Poland |
| Ruse | Bulgaria |
| Rybnik | Poland |
| Ryde | Australia |
| Rzeszow | Poland |
| Sabadell | Spain |
| Saint Petersburg | Russian Federation |
| Saint-Etienne | France |
| Saitama | Japan |
| Sakai | Japan |
| Sakaka | Saudi Arabia |
| Salerno | Italy |
| Salta | Argentina |
| Saltillo | Mexico |
| Salzburg | Austria |
| Samara | Russian Federation |
| Samsun | Turkiye |
| San Antonio | United States of America |
| San Diego | United States of America |
| San Francisco | United States of America |
| San Jose | Costa Rica |
| San Jose | United States of America |
| San Jose del Monte | Philippines |
| San Juan | Puerto Rico |
| San Luis | Argentina |
| San Luis Potosi | Mexico |
| San Miguel de Tucuman | Argentina |
| San Salvador | El Salvador |
| San Salvador de Jujuy | Argentina |
| Sana’a | Yemen |
| Sandakan | Malaysia |
| Sanliurfa | Turkiye |
| Santa Cruz de la Sierra | Bolivia |
| Santa Cruz de Tenerife | Spain |
| Santa Fe | Argentina |
| Santa Rosa | Philippines |
| Santiago | Chile |
| Santiago de Queretaro | Mexico |
| Santiago del Estero | Argentina |
| Santo Domingo | Dominican Republic |
| Sao Paulo | Brazil |
| Sapporo | Japan |
| Sarajevo | Bosnia and Herzegovina |
| Saratov | Russian Federation |
| Saskatoon | Canada |
| Seattle | United States of America |
| Seberang Perai | Malaysia |
| Seixal | Portugal |
| Sejong | Republic of Korea |
| Sendai | Japan |
| Seongnam | Republic of Korea |
| Seoul | Republic of Korea |
| Sepang | Malaysia |
| Seremban | Malaysia |
| Setubal | Portugal |
| Seville | Spain |
| Sfax | Tunisia |
| Shanghai | China |
| Sharjah | United Arab Emirates |
| Sheffield | United Kingdom |
| Shenyang | China |
| Shenzhen | China |
| Sherbrooke | Canada |
| Shizuoka | Japan |
| Siauliai | Lithuania |
| Sibu | Malaysia |
| Siheung | Republic of Korea |
| Singapore | Singapore |
| Sintra | Portugal |
| Sivas | Turkiye |
| Sofia | Bulgaria |
| Sopot | Poland |
| Sosnowiec | Poland |
| Southampton | United Kingdom |
| Southend-on-Sea | United Kingdom |
| Split | Croatia |
| Sri Jayawardenepura Kotte | Sri Lanka |
| Stara Zagora | Bulgaria |
| Stavanger | Norway |
| Stockholm | Sweden |
| Stoke-on-Trent | United Kingdom |
| Strasbourg | France |
| Stuttgart | Germany |
| Sunderland | United Kingdom |
| Sungai Petani | Malaysia |
| Surat | India |
| Surrey | Canada |
| Suva | Fiji |
| Suwon | Republic of Korea |
| Suzhou | China |
| Swansea | United Kingdom |
| Swindon | United Kingdom |
| Sydney | Australia |
| Szczecin | Poland |
| Szeged | Hungary |
| Tabuk | Saudi Arabia |
| Taichung | Taiwan |
| Taif | Saudi Arabia |
| Tainan | Taiwan |
| Taipei | Taiwan |
| Taitung | Taiwan |
| Tallinn | Estonia |
| Tampere | Finland |
| Tangier | Morocco |
| Tangshan | China |
| Taoyuan | Taiwan |
| Taranto | Italy |
| Tashkent | Uzbekistan |
| Tauranga | New Zealand |
| Tawau | Malaysia |
| Tbilisi | Georgia |
| Tegucigalpa | Honduras |
| Tehran | Iran |
| Tel Aviv-Yafo | Israel |
| Terrassa | Spain |
| The Hague | Netherlands |
| Thessaloniki | Greece |
| Thimphu | Bhutan |
| Thuqbah | Saudi Arabia |
| Tianjin | China |
| Tijuana | Mexico |
| Tilburg | Netherlands |
| Tirana | Albania |
| Tiraspol | Republic of Moldova |
| Tlaquepaque | Mexico |
| Tokat | Turkiye |
| Tokyo | Japan |
| Tolyatti | Russian Federation |
| Tomsk | Russian Federation |
| Toowoomba | Australia |
| Toronto | Canada |
| Torreon | Mexico |
| Torun | Poland |
| Toulon | France |
| Toulouse | France |
| Tours | France |
| Townsville | Australia |
| Trieste | Italy |
| Tripoli | Libya |
| Trondheim | Norway |
| Tucson | United States of America |
| Tunis | Tunisia |
| Turin | Italy |
| Turku | Finland |
| Tychy | Poland |
| Tyumen | Russian Federation |
| Ufa | Russian Federation |
| Uijeongbu | Republic of Korea |
| Ulaanbaatar | Mongolia |
| Ulsan | Republic of Korea |
| Ulyanovsk | Russian Federation |
| Uppsala | Sweden |
| Utrecht | Netherlands |
| Utsunomiya | Japan |
| Valencia | Spain |
| Valetta | Malta |
| Valladolid | Spain |
| Van | Turkiye |
| Vancouver | Canada |
| Vantaa | Finland |
| Varna | Bulgaria |
| Vasteras | Sweden |
| Vaughan | Canada |
| Venice | Italy |
| Venlo | Netherlands |
| Verona | Italy |
| Victoria | Seychelles |
| Vienna | Austria |
| Vientiane | Laos |
| Vigo | Spain |
| Vila Franca de Xira | Portugal |
| Vila Nova de Gaia | Portugal |
| Vilnius | Lithuania |
| Viseu | Portugal |
| Vitoria-Gasteiz | Spain |
| Vladivostok | Russian Federation |
| Volgograd | Russian Federation |
| Voronezh | Russian Federation |
| Warsaw | Poland |
| Washington DC | United States of America |
| Wellington | New Zealand |
| Whakatane | New Zealand |
| Wiesbaden | Germany |
| Windhoek | Namibia |
| Windsor | Canada |
| Winnipeg | Canada |
| Winterthur | Switzerland |
| Wollongong | Australia |
| Wolverhampton | United Kingdom |
| Wonju | Republic of Korea |
| Wroclaw | Poland |
| Wuhan | China |
| Wuppertal | Germany |
| Wuxi | China |
| Xian | China |
| Yanbu | Saudi Arabia |
| Yaounde | Cameroon |
| Yaroslavl | Russian Federation |
| Yekaterinburg | Russian Federation |
| Yerevan | Armenia |
| Yokohama | Japan |
| Yongin | Republic of Korea |
| Yuanlin | Taiwan |
| Zaanstad | Netherlands |
| Zabrze | Poland |
| Zagreb | Croatia |
| Zamboanga City | Philippines |
| Zaragoza | Spain |
| Zhengzhou | China |
| Zhubei | Taiwan |
| Zielona Gora | Poland |
| Zoetermeer | Netherlands |
| Zonguldak | Turkiye |
| Zurich | Switzerland |
| Zwolle | Netherlands |
Cities Submitted for Validation
Following the analysis of cities selected for Deep Analysis, this list presents the cities for which researchers submitted data for validation, considering the information gathered to be sufficiently complete, reliable and transparent to move forward in the assessment process.
| City | Country |
|---|---|
| A Coruna | Spain |
| Aachen | Germany |
| Aalborg | Denmark |
| Aarhus | Denmark |
| Abidjan | Ivory Coast |
| Abuja | Nigeria |
| Acapulco de Juarez | Mexico |
| Accra | Ghana |
| Adana | Turkiye |
| Addis Ababa | Ethiopia |
| Adelaide | Australia |
| Aguascalientes | Mexico |
| Al Ain | United Arab Emirates |
| Al Hufuf | Saudi Arabia |
| Al Qatif | Saudi Arabia |
| Alesund | Norway |
| Algiers | Algeria |
| Almada | Portugal |
| Amersfoort | Netherlands |
| Amsterdam | Netherlands |
| Andorra la Vella | Andorra |
| Angers | France |
| Ankara | Turkiye |
| Ansan | Republic of Korea |
| Antwerp | Belgium |
| Anyang | Republic of Korea |
| Apeldoorn | Netherlands |
| Arnhem | Netherlands |
| Asan | Republic of Korea |
| Asmara | Eritrea |
| Athens | Greece |
| Atlanta (GA) | United States of America |
| Auckland | New Zealand |
| Augsburg | Germany |
| Austin | United States of America |
| Baerum | Norway |
| Bahia Blanca | Argentina |
| Baku | Azerbaijan |
| Ballarat | Australia |
| Ballerup | Denmark |
| Balti | Republic of Moldova |
| Baltimore | United States of America |
| Bandar Seri Begawan | Brunei Darussalam |
| Bangkok | Thailand |
| Barcelona | Spain |
| Bari | Italy |
| Basel | Switzerland |
| Beijing | China |
| Belfast | United Kingdom |
| Belo Horizonte | Brazil |
| Bendigo | Australia |
| Bengaluru | India |
| Bergen | Norway |
| Berlin | Germany |
| Bern | Switzerland |
| Bialystok | Poland |
| Bilbao | Spain |
| Birkenhead | United Kingdom |
| Birmingham | United Kingdom |
| Blackpool | United Kingdom |
| Bloemfontein | South Africa |
| Bochum | Germany |
| Bologna | Italy |
| Bonn | Germany |
| Bordeaux | France |
| Boston | United States of America |
| Bradford | United Kingdom |
| Braga | Portugal |
| Brampton | Canada |
| Brasilia | Brazil |
| Bratislava | Slovakia |
| Braunschweig | Germany |
| Breda | Netherlands |
| Bremen | Germany |
| Bristol | United Kingdom |
| Brno | Czechia |
| Bruges | Belgium |
| Brussels | Belgium |
| Bucharest | Romania |
| Bucheon | Republic of Korea |
| Budapest | Hungary |
| Buenos Aires | Argentina |
| Buraidah | Saudi Arabia |
| Burgas | Bulgaria |
| Burlington | Canada |
| Burnaby | Canada |
| Bursa | Turkiye |
| Bydgoszcz | Poland |
| Cagliari | Italy |
| Cairns | Australia |
| Calgary | Canada |
| Cambridge | United Kingdom |
| Cancun | Mexico |
| Cape Town | South Africa |
| Cartagena | Spain |
| Catania | Italy |
| Central Coast | Australia |
| Changhua | Taiwan |
| Changwon-Si | Republic of Korea |
| Charleroi | Belgium |
| Charlotte | United States of America |
| Chelyabinsk | Russian Federation |
| Chengdu | China |
| Cheongju | Republic of Korea |
| Chiang Mai | Thailand |
| Chicago | United States of America |
| Christchurch | New Zealand |
| Coimbra | Portugal |
| Colombo | Sri Lanka |
| Columbus | United States of America |
| Copenhagen | Denmark |
| Cordoba | Argentina |
| Cordoba | Spain |
| Cork | Ireland |
| Corrientes | Argentina |
| Cracow | Poland |
| Crawley | United Kingdom |
| Czestochowa | Poland |
| Daegu | Republic of Korea |
| Daejeon | Republic of Korea |
| Dalian | China |
| Dallas | United States of America |
| Damascus | Syrian Arab Republic |
| Darwin | Australia |
| Davao City | Philippines |
| Debrecen | Hungary |
| Delft | Netherlands |
| Delhi | India |
| Denver | United States of America |
| Dhaka | Bangladesh |
| Diyarbakir | Turkiye |
| Djibouti City | Djibouti |
| Dordrecht | Netherlands |
| Dortmund | Germany |
| Drammen | Norway |
| Dresden | Germany |
| Dubai | United Arab Emirates |
| Duisburg | Germany |
| Dunedin | New Zealand |
| Durban | South Africa |
| Edinburgh | United Kingdom |
| Eindhoven | Netherlands |
| El Paso | United States of America |
| Esbjerg | Denmark |
| Espoo | Finland |
| Essen | Germany |
| Faisalabad | Pakistan |
| Florence | Italy |
| Fort Worth (TX) | United States of America |
| Frankfurt am Main | Germany |
| Frederiksberg | Denmark |
| Funabashi | Japan |
| Funchal | Portugal |
| Gaborone | Botswana |
| Gatineau | Canada |
| Gaziantep | Turkiye |
| Gdansk | Poland |
| Gdynia | Poland |
| Geelong | Australia |
| Gelsenkirchen | Germany |
| Geneva | Switzerland |
| Genoa | Italy |
| Ghaziabad | India |
| Ghent | Belgium |
| Gimhae | Republic of Korea |
| Giza | Egypt |
| Gliwice | Poland |
| Gondomar | Portugal |
| Gothenburg | Sweden |
| Grenoble | France |
| Groningen | Netherlands |
| Guadalajara | Mexico |
| Guangzhou | China |
| Guimaraes | Portugal |
| Gwangju | Republic of Korea |
| Haarlem | Netherlands |
| Hachioji | Japan |
| Haifa | Israel |
| Halifax | Canada |
| Hamburg | Germany |
| Hamilton | Canada |
| Hangzhou | China |
| Hannover | Germany |
| Harbin | China |
| Helsingborg | Sweden |
| Helsinki | Finland |
| Hiroshima | Japan |
| Hobart | Australia |
| Holstebro | Denmark |
| Hong Kong | China |
| Houston | United States of America |
| Hsinchu | Taiwan |
| Huddinge | Sweden |
| Hyderabad | India |
| Indianapolis | United States of America |
| Indore | India |
| Innsbruck | Austria |
| Ioannina | Greece |
| Islington | United Kingdom |
| Istanbul | Turkiye |
| Izmir | Turkiye |
| Jacksonville | United States of America |
| Jerusalem | Israel |
| Jonkoping | Sweden |
| Jyvaskyla | Finland |
| Kampala | Uganda |
| Kaohsiung | Taiwan |
| Karachi | Pakistan |
| Kathmandu | Nepal |
| Katowice | Poland |
| Kayseri | Turkiye |
| Kazan | Russian Federation |
| Keelung | Taiwan |
| Kielce | Poland |
| Kigali | Rwanda |
| Kingston | Jamaica |
| Kinshasa | Democratic Republic of the Congo |
| Kitchener | Canada |
| Klagenfurt am Worthersee | Austria |
| Kobe | Japan |
| Kolkata | India |
| Konya | Turkiye |
| Kopavogur | Iceland |
| Kota Bharu | Malaysia |
| Kota Kinabalu | Malaysia |
| Kuala Lumpur | Malaysia |
| Kuantan | Malaysia |
| Kuching | Malaysia |
| Kumamoto | Japan |
| Kunming | China |
| Kuwait City | Kuwait |
| Kyiv | Ukraine |
| Kyoto | Japan |
| La Paz | Bolivia |
| La Plata | Argentina |
| Lahti | Finland |
| Larissa | Greece |
| Las Palmas de Gran Canaria | Spain |
| Las Vegas | United States of America |
| Lausanne | Switzerland |
| Laval | Canada |
| Le Mans | France |
| Leeds | United Kingdom |
| Leeuwarden | Netherlands |
| Leiden | Netherlands |
| Leuven | Belgium |
| Leverkusen | Germany |
| Lilongwe | Malawi |
| Lima | Peru |
| Limerick | Ireland |
| Limoges | France |
| Linkoping | Sweden |
| Linz | Austria |
| Lisbon | Portugal |
| Liverpool | United Kingdom |
| Ljubljana | Slovenia |
| Lodz | Poland |
| Lome | Togo |
| London | Canada |
| London (UK) | United Kingdom |
| Los Angeles | United States of America |
| Loures | Portugal |
| Lower Hutt | New Zealand |
| Lubeck | Germany |
| Lucerne | Switzerland |
| Lugano | Switzerland |
| Luton | United Kingdom |
| Luxembourg | Luxembourg |
| Maastricht | Netherlands |
| Madrid | Spain |
| Maia | Portugal |
| Maitland | Australia |
| Malaga | Spain |
| Malmo | Sweden |
| Manchester | United Kingdom |
| Manila | Philippines |
| Mannheim | Germany |
| Maputo | Mozambique |
| Markham | Canada |
| Marseille | France |
| Matosinhos | Portugal |
| Mecca | Saudi Arabia |
| Mechelen | Belgium |
| Medellin | Colombia |
| Melbourne | Australia |
| Memphis (TN) | United States of America |
| Merida | Mexico |
| Messina | Italy |
| Mexico City | Mexico |
| Milan | Italy |
| Milton Keynes | United Kingdom |
| Milwaukee | United States of America |
| Minneapolis (MN) | United States of America |
| Mios | France |
| Mogadishu | Somalia |
| Mombasa | Kenya |
| Monchengladbach | Germany |
| Montevideo | Uruguay |
| Montpellier | France |
| Montreal | Canada |
| Mumbai | India |
| Munich | Germany |
| Munster | Germany |
| Muscat | Oman |
| Nacka | Sweden |
| Nagoya | Japan |
| Nagpur | India |
| Nancy | France |
| Nanjing | China |
| Naples | Italy |
| Nashville | United States of America |
| Nassau | Bahamas |
| Naypyidaw | Myanmar (formerly Burma) |
| New York | United States of America |
| Nice | France |
| Nicosia | Cyprus |
| Nijmegen | Netherlands |
| Norrkoping | Sweden |
| Northampton | United Kingdom |
| Nottingham | United Kingdom |
| Nuremberg | Germany |
| Odense | Denmark |
| Odivelas | Portugal |
| Okayama | Japan |
| Oklahoma City | United States of America |
| Olomouc | Czechia |
| Oslo | Norway |
| Ostrava | Czechia |
| Ottawa | Canada |
| Oulu | Finland |
| Palma | Spain |
| Panama City | Panama |
| Paris | France |
| Parma | Italy |
| Peristeri | Greece |
| Petaling Jaya | Malaysia |
| Peterborough | United Kingdom |
| Portsmouth | United Kingdom |
| Posadas | Argentina |
| Poznan | Poland |
| Prague | Czechia |
| Prato | Italy |
| Pretoria | South Africa |
| Puebla | Mexico |
| Pune | India |
| Quebec City | Canada |
| Quito | Ecuador |
| Rabat | Morocco |
| Ramallah | State of Palestine |
| Recife | Brazil |
| Regina | Canada |
| Reykjavik | Iceland |
| Rio de Janeiro | Brazil |
| Rockhampton | Australia |
| Rome | Italy |
| Rosario | Argentina |
| Roskilde | Denmark |
| Rzeszow | Poland |
| Saint Petersburg | Russian Federation |
| Saitama | Japan |
| Sakai | Japan |
| Salerno | Italy |
| San Antonio (Texas) | United States of America |
| San Diego | United States of America |
| San Francisco | United States of America |
| San Jose | United States of America |
| San Juan | Puerto Rico |
| Santiago | Chile |
| Sao Paulo | Brazil |
| Sapporo | Japan |
| Saskatoon | Canada |
| Sejong | Republic of Korea |
| Sendai | Japan |
| Seongnam | Republic of Korea |
| Seoul | Republic of Korea |
| Shanghai | China |
| Sharjah | United Arab Emirates |
| Sheffield | United Kingdom |
| Shenzhen | China |
| Shizuoka | Japan |
| Singapore | Singapore |
| Sintra | Portugal |
| Sofia | Bulgaria |
| Sopot | Poland |
| Southampton | United Kingdom |
| Split | Croatia |
| Stavanger | Norway |
| Stockholm | Sweden |
| Stoke-on-Trent | United Kingdom |
| Strasbourg | France |
| Stuttgart | Germany |
| Sunderland | United Kingdom |
| Sunshine Coast | Australia |
| Surat | India |
| Suva | Fiji |
| Suzhou | China |
| Swansea | United Kingdom |
| Taichung | Taiwan |
| Tainan | Taiwan |
| Taipei | Taiwan |
| Tallinn | Estonia |
| Tampere | Finland |
| Tangier | Morocco |
| Taoyuan | Taiwan |
| Tashkent | Uzbekistan |
| Tauranga | New Zealand |
| Tbilisi | Georgia |
| Tehran | Iran |
| The Hague | Netherlands |
| Thessaloniki | Greece |
| Thimphu | Bhutan |
| Tianjin | China |
| Tirana | Albania |
| Tokyo | Japan |
| Toowoomba | Australia |
| Toronto | Canada |
| Toulouse | France |
| Trondheim | Norway |
| Tucson (Arizona) | United States of America |
| Tunis | Tunisia |
| Turin | Italy |
| Turku | Finland |
| Uppsala | Sweden |
| Valencia | Spain |
| Vancouver | Canada |
| Varna | Bulgaria |
| Vasteras | Sweden |
| Vaughan | Canada |
| Venice | Italy |
| Venlo | Netherlands |
| Verona | Italy |
| Victoria | Seychelles |
| Vienna | Austria |
| Vientiane | Laos |
| Vila Franca de Xira | Portugal |
| Vila Nova de Gaia | Portugal |
| Vilnius | Lithuania |
| Warsaw | Poland |
| Wellington | New Zealand |
| Whakatane | New Zealand |
| Wiesbaden | Germany |
| Windsor | Canada |
| Winnipeg | Canada |
| Winterthur | Switzerland |
| Wollongong | Australia |
| Wolverhampton | United Kingdom |
| Wroclaw | Poland |
| Wuhan | China |
| Wuppertal | Germany |
| Xian | China |
| Yekaterinburg | Russian Federation |
| Yokohama | Japan |
| Zaanstad | Netherlands |
| Zagreb | Croatia |
| Zaragoza | Spain |
| Zoetermeer | Netherlands |
| Zurich | Switzerland |
Validation Stage and Data Availability
The absence of some cities from the validation set does not mean that they were excluded as a matter of principle. In most cases, it reflects the information provided by researchers that they were unable to identify reliable, credible and publicly accessible data for the cities assigned to them.
During the Validation Stage, researchers were asked to review their assigned cities and indicate whether the evidence required for further assessment could be confirmed through open and verifiable sources. Where a city was not indicated for validation, the main reason was not a negative judgement about the city itself, but the practical lack of sufficiently reliable, robust and generally accessible data.
In other words, the omission of a city from the validation list usually resulted from a research finding that the available material did not meet the methodological threshold required for inclusion at that stage. The standard applied here was consistent across the process: data had to be trustworthy, evidence-based and available through sources that could be independently reviewed.
Why Some Cities Were Not Carried Forward
Researcher feedback on data gaps
A substantial share of cities not included in the validation set were marked by researchers as cases where they could not find reliable, credible and publicly available city-level data for the indicators required by the methodology.
Methodological threshold for evidence
Cities could move forward only where the data base was strong enough to support a comparable and evidence-based assessment. Where this threshold was not met, the city was not advanced at that stage.
Cases with no researcher feedback
In some instances, researchers did not submit any information regarding their assigned cities. In such cases, the absence of a city from the working set could not be treated as a final finding and required an additional internal review.
Rapid internal re-check
Where no researcher conclusion was available, a quick supplementary check was undertaken to assess whether city-level information could still be identified through open data systems and other publicly accessible sources.
Additional Review and Limited Updates
The project team did not treat missing researcher input as an automatic basis for exclusion. Instead, when no clear feedback had been provided, an additional rapid analysis of data availability was conducted. This review focused on whether key information could be located through open data environments, official public databases and other transparent systems that satisfied minimum comparability standards.
As a result of that process, some records were updated and, in selected cases, cities were added or reinstated where sufficient evidence was in fact available. These updates were not based on discretion, but on the practical verification that relevant data could be identified in open-access systems and used in line with the methodological requirements of the study.
How These Situations Can Be Interpreted
Not a judgement on city performance
A city’s absence from the validation list should not be read as a negative evaluation. In most cases, it reflects only the current availability or traceability of public data.
Primarily a data availability issue
The decisive factor was whether reliable and open evidence could be found, not whether a city was considered important, relevant or eligible in a broader sense.
Research process with safeguards
When researchers reported the lack of usable data, that information was taken seriously. When they reported nothing, an additional verification step was used to reduce the risk of omission caused by incomplete reporting.
Open data could still change outcomes
If credible information could later be identified in open data systems, city records could be updated and the city could be brought back into the broader analytical process.
The key reason why some cities do not appear in the validation set is that researchers reported they were unable to identify reliable, credible and publicly accessible data for the cities assigned to them.
Where researchers did not provide any information, a rapid additional check was undertaken and, in some cases, datasets were updated and cities were added where the required evidence was available through open data systems.
Data Sources and Evidence Base
The Happy City Index 2026 is based exclusively on secondary data collected from publicly available and verifiable sources. The methodology prioritises transparency, comparability and reproducibility by relying on information that can be independently accessed, reviewed and confirmed.
All data used in the Index are derived from secondary sources. Researchers were instructed to identify and document evidence that is publicly accessible, traceable and based on credible institutional or analytical work. The use of open and verifiable data is a fundamental methodological principle, ensuring that each data point can be independently checked.
Types of Data Sources Used
Open Data and Public Databases
Primary preference is given to official open data portals at municipal, regional and national levels, as well as international open datasets.
Statistical Yearbooks and Official Statistics
National statistical offices, city statistical reports and official yearbooks constitute a key source of structured and comparable data.
Government and Institutional Documents
Official documents, strategies, policy papers and administrative publications issued by public authorities are used where relevant.
Reports and Analytical Studies
Reports prepared by recognised institutions, international organisations, research bodies and think tanks are included where they meet credibility standards.
Scientific and Academic Publications
Peer-reviewed articles and academic studies are used in cases where they provide reliable and clearly defined data relevant to the indicators.
Access to Public Information
In selected cases, data may be obtained through formal requests for access to public information where such procedures are available.
Exceptional Use of Additional Sources
In very limited and clearly justified cases, high-quality journalistic materials from well-established and reputable media outlets may be used. Such sources are considered only where they provide verifiable, evidence-based information that cannot be obtained through standard official or institutional channels.
Principles of Data Selection
All data included in the methodology must meet three core criteria: accessibility, credibility and verifiability. Researchers are required to prioritise sources that are publicly available online and that allow independent validation. Data must be clearly attributable to a specific source and presented in a way that enables external review.
Reference Period of Data
The methodology prioritises the most recent data available at the time of analysis. Researchers were instructed to use data for 2025 where available, and, where necessary, 2024. In some justified cases, older data were also used, with the relevant year clearly indicated, but only where no more recent data existed and where the indicator could still reasonably be considered valid, such as voter turnout in the most recent local elections, even if those elections took place four years earlier. This approach ensures that the Index reflects the most up-to-date conditions possible while maintaining sufficient data coverage across all cities.
The use of secondary, publicly accessible data ensures that the Happy City Index remains transparent, reproducible and open to verification. Every data point included in the assessment is expected to be traceable to a clearly identified source.
Missing Data Handling
The methodology ensures that gaps in data availability do not distort results while maintaining strict comparability and methodological consistency across all assessed cities.
In cases where individual data points could not be identified during the Open Data Collection Stage, a dedicated secondary review was conducted after the validation phase. This additional analytical step ensured that missing values were addressed through a structured and consistent approach rather than being left unresolved.
Where data for a specific indicator was unavailable for a given city, values were derived using averaged results from comparable cities within the same country. This process was applied with strict methodological discipline, ensuring that only repeatable and internally consistent data patterns were used.
In rare situations where only a single city from a given country or context was available, and no reliable national comparison could be established, a proxy value was derived from a group of countries within the same geographical region. Such cases were exceptional and applied only where necessary to preserve analytical continuity.
At no stage were missing indicators assigned a value of zero, nor were cities penalised solely due to gaps in data availability. Every effort was made to ensure that each city received a complete and fair evaluation based on the best available and methodologically consistent evidence.
No city included in the final assessment was left without a score for any indicator due to missing data. All gaps were addressed through controlled, transparent and repeatable estimation procedures, applied only where necessary and always within a strict methodological framework.
Financial Data and Currency Conversion
All financial data used in the Happy City Index methodology were collected in local currencies and subsequently standardised to ensure comparability across cities and countries.
Financial indicators were initially gathered in the official national currencies of each analysed city. To enable cross-country comparison, all values were converted into British pounds (GBP) using prevailing mid-market exchange rates as of 3 March 2026.
A single reference date was applied consistently across the entire dataset to eliminate temporal distortions related to currency volatility and ensure methodological coherence.
| Currency | Code | GBP Conversion Rate |
|---|---|---|
| 🇦🇪UAE Dirham | AED | 0.20463 |
| 🇦🇷Argentine Peso | ARS | 0.00054 |
| 🇦🇺Australian Dollar | AUD | 0.53039 |
| 🇧🇬Bulgarian Lev | BGN | 0.44663 |
| 🇧🇷Brazilian Real | BRL | 0.14492 |
| 🇨🇦Canadian Dollar | CAD | 0.54880 |
| 🇨🇭Swiss Franc | CHF | 0.95773 |
| 🇨🇳Chinese Yuan | CNY | 0.10894 |
| 🇨🇴Colombian Peso | COP | 0.00020 |
| 🇨🇿Czech Koruna | CZK | 0.03592 |
| 🇩🇰Danish Krone | DKK | 0.11690 |
| 🇪🇺Euro | EUR | 0.87332 |
| 🇬🇧British Pound | GBP | 1.00000 |
| 🇭🇰Hong Kong Dollar | HKD | 0.09618 |
| 🇭🇺Hungarian Forint | HUF | 0.00227 |
| 🇮🇱Israeli Shekel | ILS | 0.24234 |
| 🇮🇳Indian Rupee | INR | 0.00816 |
| 🇮🇸Icelandic Krona | ISK | 0.00608 |
| 🇯🇵Japanese Yen | JPY | 0.00477 |
| 🇰🇷South Korean Won | KRW | 0.00051 |
| 🇰🇼Kuwaiti Dinar | KWD | 2.45411 |
| 🇲🇦Moroccan Dirham | MAD | 0.08094 |
| 🇲🇽Mexican Peso | MXN | 0.04308 |
| 🇲🇾Malaysian Ringgit | MYR | 0.19067 |
| 🇳🇴Norwegian Krone | NOK | 0.07812 |
| 🇳🇿New Zealand Dollar | NZD | 0.44329 |
| 🇵🇱Polish Zloty | PLN | 0.20509 |
| 🇷🇴Romanian Leu | RON | 0.17130 |
| 🇷🇺Russian Ruble | RUB | 0.00970 |
| 🇸🇦Saudi Riyal | SAR | 0.20058 |
| 🇸🇪Swedish Krona | SEK | 0.08163 |
| 🇸🇬Singapore Dollar | SGD | 0.58864 |
| 🇹🇭Taiwan Dollar | TWD | 0.02374 |
| 🇹🇷Turkish Lira | TRY | 0.01710 |
| 🇺🇦Ukrainian Hryvnia | UAH | 0.01732 |
| 🇺🇸US Dollar | USD | 0.75208 |
| 🇺🇾Uruguayan Peso | UYU | 0.01950 |
| 🇺🇿Uzbekistani Som | UZS | 0.00006 |
Indicator Framework Explanation
The Happy City Index is built on a structured and policy-oriented indicator framework designed to explain how cities create the conditions for well-being, inclusion, resilience and long-term urban quality.
The methodology does not rely solely on perception or reputation. Instead, it uses a carefully selected set of indicators that can be verified, compared across cities and interpreted in relation to public policy, institutional capacity and everyday urban experience. This ensures that the Index reflects not only how a city performs at a given moment, but also the systems that shape its long-term development path.
Each indicator included in the framework has been selected for a clear methodological reason. First, it must capture a relevant aspect of urban life that contributes to happiness understood in a broad civic sense, including access, opportunity, safety, health, participation and environmental quality. Second, it must be capable of being assessed in a reasonably consistent way across a diverse international sample of cities. Third, it must carry practical value for interpretation, allowing the final results to support public reflection, benchmarking and policy learning.
The framework is organised into six themes: Citizens, Governance, Environment, Economy, Health and Mobility. Together, these themes represent the principal dimensions through which cities influence the daily lives of residents. Some indicators measure outcomes, such as life expectancy, air quality or unemployment. Others capture enabling conditions, such as institutional accessibility, service availability, strategic planning or connectivity. This combination is deliberate. A city should be assessed not only for what it currently achieves, but also for the structures it has in place to improve future outcomes.
The weighting system reflects the relative significance of each indicator within the wider model. Higher weights are assigned to indicators judged to have stronger relevance to urban well-being, systemic importance or broader social consequences. Lower-weighted indicators remain important, but usually serve as complementary measures that deepen interpretation rather than define the overall picture on their own. In this way, the Index balances breadth with analytical discipline.
The following section explains the indicators one by one. For each indicator, the rationale focuses on why it matters, what aspect of city performance it helps reveal, and why it belongs within its respective thematic area. This approach is intended to make the methodology transparent, intelligible and open to informed scrutiny.
The indicator framework is therefore not a loose collection of urban statistics, but a coherent measurement architecture. Each indicator has a defined role within the overall logic of the Index and contributes to a broader understanding of what makes a city supportive of human flourishing.
Urban Innovation Ecosystem Potential
This indicator reflects the extent to which a city operates within a broader innovation environment and has the financial capacity, on a per-resident basis, to translate that environment into local development potential.
Urban Innovation Ecosystem Potential measures whether a city is positioned within a wider setting that supports the emergence, circulation and practical application of new ideas. In methodological terms, it combines the national share of expenditure on research and development with the city budget calculated per resident, thereby linking systemic innovation conditions with the scale of local public capacity.
Although one part of this indicator is derived from country-level data, its inclusion provides important context for understanding how innovation potential may be expressed at urban level. National investment in research and development signals the strength of the surrounding knowledge economy, while city budget per capita helps indicate whether local institutions may have the means to absorb, support or translate that broader potential into municipal action.
The relationship with quality of life is indirect but meaningful. Cities located in stronger national innovation systems, and at the same time operating with higher public resources per resident, are generally better placed to expand services, support entrepreneurship, invest in modern infrastructure and respond to emerging social or technological needs.
This contributes to a broader sense of opportunity, resilience and long-term confidence among residents. While the indicator does not capture immediate well-being outcomes, it helps identify whether a city is situated in conditions that may support future-oriented development and more adaptive forms of local governance.
As a contextual indicator, its influence on the final score is deliberately limited. However, it plays a critical interpretative role by showing that innovation potential depends not only on national research intensity, but also on whether the city’s financial capacity per resident suggests an ability to convert that wider context into practical local opportunities.
School Density
This indicator measures the spatial availability of educational infrastructure by assessing how many schools are distributed across the urban area in relation to its total surface.
School Density captures the physical accessibility of education within a city by relating the number of educational institutions to the total urban area. In methodological terms, it reflects how intensively schools are distributed per square kilometre, providing a spatial measure of service availability rather than purely population-based coverage.
A higher value of the indicator suggests that schools are more densely embedded within the urban fabric, which typically translates into shorter travel distances and easier access for residents. This also indicates a more even spatial distribution of educational infrastructure, reducing the risk of territorial disparities between different neighbourhoods.
The relationship with quality of life is both direct and structural. Higher school density supports daily convenience for families, improves accessibility for children and contributes to more balanced urban development. It also reflects planning approaches that integrate essential services within residential areas.
At the same time, the indicator highlights the importance of spatial efficiency. Cities with similar numbers of schools may perform differently depending on their size, making density a more informative measure of how effectively educational infrastructure is distributed across the territory.
As a standard quantitative indicator, School Density is normalised across all cities in the dataset. This ensures that results reflect relative performance in distributing educational infrastructure in spatial terms, allowing meaningful comparison between cities of different sizes.
Higher Education Institution in or within 10 km of City Limits
This indicator assesses whether residents have access to higher education institutions located within the city or in its immediate functional urban area.
This indicator captures the presence of universities or other higher education institutions within the city or within a 10-kilometre radius of its administrative boundaries. It reflects whether residents can realistically access advanced education without the need for relocation, making it a proximity-based measure rather than a density or volume metric.
The existence of nearby higher education institutions is a key structural component of an urban knowledge ecosystem. Universities contribute not only to education but also to research activity, innovation, cultural life and the overall intellectual environment of a city. Their presence often correlates with greater economic dynamism and talent attraction.
From a quality of life perspective, access to higher education expands life opportunities for residents, particularly young people. It reduces barriers to skill development, supports career pathways and strengthens long-term socio-economic mobility. Cities without such access may face outmigration of young populations seeking education elsewhere.
The indicator also reflects broader urban resilience. Cities with nearby higher education institutions tend to benefit from stronger networks between academia, business and public institutions, which can support problem-solving, policy innovation and adaptive capacity in times of change.
This is a threshold-based indicator. It does not measure the number or size of institutions, but rather the existence of accessible higher education within a defined proximity, ensuring comparability across cities of different scales.
Best Rank of University in Global University Rankings
This indicator evaluates the global standing of the highest-ranked university associated with a city, based on its best position achieved across selected international rankings.
This indicator measures the best position achieved by a university located in or strongly associated with the city within three widely recognised global rankings: QS World University Rankings, Academic Ranking of World Universities, and Times Higher Education (THE) World University Rankings. For each city, the highest position obtained in any of these rankings is taken into account.
The approach does not prioritise or promote any single ranking methodology. Instead, it recognises that different systems apply varying criteria and perspectives on academic performance. By considering multiple sources and selecting the best observed result, the indicator aims to capture a more balanced and robust signal of academic excellence.
A strong global ranking signals the presence of high-quality research, teaching standards and international recognition. Universities that perform well globally tend to attract top students, researchers and partnerships, contributing to a more dynamic and competitive urban environment.
In terms of quality of life, the presence of a highly ranked university enhances access to advanced knowledge, cultural resources and professional opportunities. It can influence local labour markets, support innovation ecosystems and raise the overall intellectual profile of the city.
This indicator focuses on peak academic performance within a city. By taking the best position across multiple global rankings, it highlights whether at least one institution achieves internationally competitive excellence, without favouring a specific ranking system.
Cost of Higher Education Compared to Average Annual Salary
This indicator evaluates the affordability of higher education by comparing the average annual tuition cost for domestic students at country level with the median net annual income of residents in the city.
This indicator measures how financially accessible higher education is for city residents by relating the average annual cost of studying per person for domestic students, based solely on university tuition fees across the entire country, to the median net annual income earned in the city. In methodological terms, it captures the relative burden that tuition costs may place on local households.
The use of a ratio makes it possible to assess affordability in a way that is sensitive to local purchasing capacity rather than nominal prices alone. Even when tuition levels are nationally uniform or broadly similar, their practical impact may differ substantially depending on the income conditions of residents in a given city.
In terms of quality of life, more affordable higher education expands access to personal development, qualifications and long-term career advancement. Where the financial burden of tuition is lower in relation to local income, higher education is more likely to remain within reach for a broader share of the population.
The indicator also highlights the relationship between national education costs and local socio-economic realities. It therefore reflects not only the formal availability of higher education, but also the extent to which participation may be realistically attainable for residents without disproportionate financial strain.
The indicator focuses on relative affordability rather than absolute tuition levels. By comparing national tuition costs with median net annual income at city level, it provides a more meaningful view of how accessible higher education may be in everyday economic terms.
Residents with a Master's Degree (%)
This indicator measures the share of residents who have completed a master's degree, reflecting the advanced educational profile of the city's population.
This indicator captures the proportion of the population holding a master's degree or equivalent qualification. It reflects the level of advanced education within the city and provides insight into the depth of human capital available in the local labour market.
A higher share of residents with advanced degrees is often associated with greater innovation capacity, higher productivity and stronger knowledge-based economic activity. It also suggests the presence of an environment that supports continued education and professional development.
From a quality of life perspective, higher educational attainment is linked to improved employment opportunities, higher incomes and greater job stability. It also correlates with broader social outcomes such as civic participation, health awareness and long-term well-being.
The indicator additionally reflects the attractiveness of the city to skilled individuals. Cities with a strong concentration of highly educated residents tend to offer richer professional networks, more diverse opportunities and a more dynamic social and cultural environment.
This is a structural indicator of human capital. It does not measure access to education directly, but rather the outcome of educational systems and migration patterns over time.
Population Able to Communicate in a Foreign Language (%)
This indicator measures the share of residents who can communicate in at least one foreign language, reflecting the city's openness and communicative capacity.
This indicator captures the proportion of residents who are able to communicate in a language other than their native one. It reflects both formal education outcomes and practical linguistic skills acquired through mobility, work or cultural exposure.
A higher level of foreign language proficiency enhances a city's ability to connect with global networks. It facilitates international business, tourism, academic exchange and cultural interaction, contributing to a more outward-looking and adaptable urban environment.
From a quality of life perspective, language skills expand individual opportunities and reduce barriers in accessing education, employment and services. They also support social inclusion, particularly in diverse or internationalised cities, by enabling communication across different groups.
The indicator also reflects everyday urban experience. Cities where more residents can communicate across languages tend to offer a more welcoming and accessible environment for visitors, newcomers and international talent.
Although relatively low in weight, this indicator signals an important dimension of urban openness and social connectivity, complementing more structural measures of education and human capital.
Use of Electronic Banking Services (%)
This indicator measures the share of residents who use electronic banking services, reflecting digital financial inclusion and everyday access to modern financial tools.
This indicator captures the proportion of residents who actively use electronic banking services, including online banking platforms, mobile banking applications and digital payment systems. It reflects both access to financial infrastructure and the digital capabilities of the population.
A high level of electronic banking usage suggests that residents can efficiently manage financial transactions, access services remotely and participate in a modern, digitally enabled economy. It is also closely linked to trust in financial institutions and the reliability of digital systems.
From a quality of life perspective, electronic banking improves convenience, reduces time costs and expands access to financial services. It enables residents to perform everyday tasks such as payments, transfers and account management quickly and securely, without physical barriers.
The indicator also reflects broader financial inclusion. Cities with higher digital banking usage tend to have more inclusive access to financial tools, supporting economic participation across different population groups and reducing exclusion from essential services.
Although relatively low in weight, this indicator provides insight into the everyday functioning of urban life, highlighting the extent to which digital infrastructure translates into practical benefits for residents.
Population Aged 65+ Benefiting from Municipal Home Care (%)
This indicator measures the share of older people receiving home care support, reflecting the accessibility and responsiveness of care systems for ageing populations, while recognising that comparable data are available only at national level.
This indicator captures the proportion of the population aged 65 and above who benefit from home care services. Although the concept is directly relevant to the local experience of ageing, internationally comparable data for this measure are generally available only at country level. For methodological consistency, the indicator is therefore measured using national-level values and applied as contextual information in the city assessment.
The availability of home care services is a key component of an inclusive and age-friendly society. It enables older people to maintain independence, remain in their homes for longer and avoid unnecessary institutionalisation, which is often associated with higher costs and lower quality of life.
From a quality of life perspective, access to home care significantly improves well-being among older residents. It supports dignity, safety and continuity of daily life, while also reducing isolation and supporting mental health. Even when measured at country level, the indicator provides important insight into the broader care environment within which cities operate.
The indicator also reflects the systemic capacity to respond to demographic ageing. Countries with higher coverage rates are generally better prepared to manage increasing care needs and to ensure broader access to essential support services for older populations, which in turn shapes the context of urban ageing.
Although modest in weight, this indicator highlights a critical dimension of social infrastructure. Its interpretation should take into account that the available data are measured at national rather than municipal level, and therefore describe the wider care framework surrounding the city rather than a strictly local service outcome.
Homeless People per 10,000 Residents
This indicator measures the scale of homelessness in the form of rough sleeping relative to the population, reflecting structural inequalities and the effectiveness of social support systems within the city.
This indicator captures the number of people experiencing homelessness per 10,000 residents, with a specific focus on so-called rough sleepers, meaning individuals sleeping in public or outdoor spaces without access to formal shelter. This provides a consistent and observable measure that allows comparison across cities of different sizes.
The level of rough sleeping is a direct expression of housing affordability, labour market conditions and the strength of social protection systems. Higher rates typically indicate structural challenges such as insufficient housing supply, income inequality or gaps in welfare and support services.
From a quality of life perspective, visible homelessness in the form of rough sleeping is a critical negative outcome. It affects not only those directly impacted but also the broader social fabric, influencing perceptions of safety, inclusion and fairness within the city. Lower levels are generally associated with stronger social cohesion and more effective public policy.
The indicator also reflects the capacity of local authorities to prevent and respond to acute forms of social exclusion. Cities with lower rates of rough sleeping often have more comprehensive housing policies, early intervention mechanisms and coordinated outreach and support systems.
This is a high-sensitivity indicator of extreme urban inequality. By focusing on rough sleepers, it highlights the most visible and severe form of housing exclusion and whether a city is able to protect its most vulnerable residents.
City Website Available in Other Languages
This indicator assesses whether official city websites are accessible in languages other than the primary local language, taking into account the number of additional languages available.
This indicator captures whether the official digital interface of the city, typically its main website, is available in one or more additional languages. The assessment distinguishes between availability in one, two, three, or three and more languages, providing a simple gradient of multilingual accessibility.
Multilingual accessibility is a key component of inclusive governance. It ensures that essential information about public services, regulations and city life can be understood by a wider audience, including international residents, visitors and businesses.
From a quality of life perspective, the availability of information in multiple languages improves everyday usability of the city, particularly for newcomers. It supports integration, reduces uncertainty and enables more effective interaction with public institutions.
The indicator also reflects the openness and global orientation of a city. Cities that invest in multilingual communication tend to be more accessible and welcoming, although this dimension represents a supporting rather than a core structural factor in overall quality of life.
Given its relatively low weight, this indicator should be interpreted as a complementary signal of institutional accessibility. It captures an important but limited aspect of inclusiveness, distinguishing between different levels of multilingual provision without significantly influencing the overall ranking.
Net Internal Migration Rate per 1,000 Population
People make real-life choices about where to live, effectively “voting with their feet.” This indicator measures the balance of people moving into and out of a city from within the same country, reflecting its attractiveness and demographic dynamics.
This indicator captures the net internal migration rate, calculated as the difference between people moving into and out of a city from other parts of the country, expressed per 1,000 residents. A positive value indicates net inflow, while a negative value signals population outflow.
Internal migration is a strong signal of perceived attractiveness. Cities that attract residents tend to offer better employment opportunities, access to services, educational options and overall living conditions. Conversely, sustained outflows may indicate structural challenges such as limited opportunities or declining quality of life.
From a quality of life perspective, this indicator reflects revealed preferences rather than stated perceptions. People “vote with their feet,” choosing to relocate to places that they consider more liveable, dynamic or supportive of their needs and aspirations.
The indicator also highlights broader urban trends, including growth, decline and spatial inequality within a country. It provides insight into whether a city is gaining or losing population relative to national dynamics, which has implications for planning, infrastructure and long-term sustainability.
This is a high-impact indicator of urban attractiveness. It synthesises multiple dimensions of quality of life into a single behavioural outcome, reflecting real-world decisions made by residents.
Pupils per School Building
This indicator measures the average number of pupils assigned to each school building, reflecting infrastructure capacity and the risk of overcrowding in the education system.
Pupils per School Building captures how educational infrastructure aligns with the number of students in a city. In methodological terms, a lower result is treated as more favourable, as it suggests less pressure on school buildings and a greater likelihood of more manageable learning conditions.
It is important to note that this interpretation is specific to cities. In rural areas, very low pupil numbers may create different challenges, including weaker service viability, reduced peer environments and lower attractiveness of the workplace for teachers. In urban contexts, however, the more typical and policy-relevant problem is the opposite one: too many pupils concentrated in too few school buildings.
Where excessive numbers of pupils are assigned to a limited school base, cities may face overcrowded schools, overstretched facilities, reduced comfort and less flexibility in organising teaching. For that reason, the indicator favours lower numbers of pupils per building in urban settings, assuming that they are more likely to correspond with less crowded schools and smaller class groups.
From a quality of life perspective, this matters not only for educational performance but also for everyday wellbeing. Less crowded school environments can support better concentration, more individual attention, smoother daily routines and stronger confidence among families in the capacity of local public services.
The indicator also reflects broader urban planning effectiveness, including whether cities are able to anticipate demographic change and expand social infrastructure in step with population growth. It therefore helps show whether education capacity is keeping pace with residential development.
While moderate in weight, this indicator is intentionally interpreted in favour of lower pupil concentration in cities, because overcrowded schools are a common urban problem and lower ratios are assumed to support less numerous classes and better learning conditions.
Employment in Creative Industries (%)
This indicator measures the share of the workforce employed in creative sectors such as arts, culture, media, design and knowledge-based creative services.
Employment in Creative Industries reflects the extent to which a city supports and sustains creative professions and cultural production. It captures not only artistic activity but also broader sectors where creativity, innovation and intellectual capital play a central role.
A higher share of employment in these industries is often associated with more dynamic and diverse urban environments. Creative sectors tend to foster innovation, attract talent and contribute to the development of vibrant public spaces and cultural life.
From a quality of life perspective, creative industries enhance everyday urban experience by enriching access to culture, entertainment and meaningful work opportunities. They also contribute to identity, place-making and social cohesion within cities.
The presence of a strong creative workforce can signal openness, adaptability and a forward-looking economy. It also supports cross-sector innovation, influencing areas such as technology, education and urban development.
Despite its lower weight, this indicator provides insight into the cultural and innovative character of a city, highlighting how creativity contributes to both economic resilience and the lived experience of residents.
Housing Affordability Ratio for 50 m² Dwelling
This indicator measures the relationship between the cost of a standard 50 m² dwelling and the average annual income, reflecting the accessibility of housing in a city.
Housing Affordability Ratio captures how financially accessible housing is for residents by comparing property prices with income levels. It provides a direct insight into whether people can realistically secure adequate housing without excessive financial strain.
Methodologically, the indicator is calculated as the ratio of the median residential property price for a standard 50 m² dwelling in the city to the median annual household income in the same city. This allows for consistent comparison of housing affordability across different urban contexts.
This indicator is strongly linked to quality of life, as housing is one of the most fundamental human needs. When affordability is low, households may face overcrowding, long commuting distances or financial stress, all of which negatively affect well-being.
Affordable housing supports stability, social inclusion and the ability to plan for the future. It also influences demographic patterns, as cities with poor affordability often struggle to retain younger populations and key workers.
From an urban perspective, this indicator reflects the balance between economic growth, real estate markets and public policy. It highlights whether cities are able to translate prosperity into accessible living conditions.
With a relatively high weight, this indicator plays a critical role in assessing everyday living standards, as housing affordability directly shapes financial security, social equity and long-term urban sustainability.
Housing Affordability Ratio (Rent)
This indicator measures the relationship between rental costs and household income, reflecting how accessible rented housing is for residents.
Housing Affordability Ratio (Rent) captures the financial burden of renting by comparing typical rental prices with household income. It reflects the day-to-day reality of a large share of urban residents, particularly younger populations and mobile workers.
Methodologically, the indicator is calculated as the ratio of the median monthly rental cost of housing in the city to the median monthly household income in the same city, ensuring that both components are expressed on a monthly basis. This makes it possible to directly assess what share of monthly income is required to cover housing costs.
This indicator has a direct and immediate impact on quality of life. High rental costs can reduce disposable income, limit access to well-located housing and increase financial stress, while more affordable rental markets support stability and flexibility.
Rental affordability is especially important in dynamic cities where home ownership is less accessible or less common. It shapes access to jobs, education and services by influencing where people can afford to live.
The indicator also reflects broader housing market dynamics, including supply constraints, demand pressures and regulatory frameworks. It highlights whether cities provide inclusive access to housing across different income groups.
With one of the higher weights in the Citizens theme, this indicator plays a key role in understanding real living costs and financial pressure on residents, making it central to assessing everyday urban well-being.
Cultural Institutions per 100,000 Residents
This indicator measures the availability of cultural institutions such as museums, theatres, galleries and cultural centres relative to the size of the population.
Cultural Institutions per 100,000 Residents reflects how accessible cultural infrastructure is within a city. It provides a standardised way to compare the density of cultural venues across cities of different sizes.
Access to culture plays an important role in shaping quality of life. Cultural institutions offer opportunities for learning, social interaction and creative expression, contributing to both individual well-being and community cohesion.
Cities with a higher density of cultural institutions tend to offer richer everyday experiences, supporting diversity, identity and a sense of belonging. These spaces also act as anchors for public life and urban vitality.
The indicator also reflects broader investment in the social and cultural fabric of the city, highlighting whether development extends beyond economic and infrastructural priorities.
With a moderate weight, this indicator captures an essential but often under-measured dimension of urban life, showing how cultural access contributes to happiness, inclusion and the overall attractiveness of cities.
Libraries per 10 km²
This indicator measures the spatial density of public libraries, reflecting how easily residents can access knowledge, learning resources and community spaces within the urban area.
Libraries per 10 km² captures how evenly distributed library infrastructure is across a city. Unlike population-based measures, it focuses on spatial accessibility, highlighting whether residents can reach library services within a reasonable distance.
Libraries play a key role in quality of life by providing free access to knowledge, digital resources and quiet study environments. They also function as inclusive public spaces that support education, social interaction and lifelong learning.
A higher density of libraries generally indicates better accessibility, reducing barriers related to distance, mobility or income. This is particularly important for vulnerable groups, including students, elderly residents and low-income households.
The indicator also reflects how cities prioritise distributed public infrastructure. Well-spread libraries suggest a more equitable urban structure, where services are not concentrated only in central areas.
With a relatively high weight, this indicator highlights the importance of everyday access to knowledge and community spaces, making it a meaningful contributor to educational inclusion and overall urban well-being.
Voter Turnout in the Last Local Elections (%)
This indicator measures the share of eligible voters who participated in the most recent local elections, reflecting civic engagement and trust in local governance.
Voter Turnout in the Last Local Elections captures the extent to which residents actively participate in shaping local decision-making. It reflects not only electoral participation but also broader levels of civic awareness and engagement.
High voter turnout is often associated with stronger democratic legitimacy and a greater sense of ownership over local policies. It indicates that residents feel their voice matters and that institutions are responsive and credible.
From a quality of life perspective, active civic participation contributes to more inclusive and representative governance. Cities with engaged populations are better positioned to address local needs and adapt policies to residents’ expectations.
The indicator also signals levels of social cohesion and trust. Low turnout may point to disengagement, inequality or barriers to participation, while high turnout suggests stronger connections between citizens and local institutions.
With a relatively high weight, this indicator plays a key role in understanding how governance functions in practice, highlighting the importance of civic participation as a foundation for responsive, accountable and people-centred cities.
Open Data Portal Availability
This indicator assesses whether a city provides a publicly accessible open data portal, enabling residents, researchers and businesses to access and reuse municipal data.
Open Data Portal Availability reflects the extent to which a city makes its data openly accessible in structured, reusable formats. It is a key signal of transparency, accountability and modern governance practices.
The presence of an open data portal enables citizens to better understand how their city functions, from budgets and transport systems to environmental conditions and public services. It supports informed decision-making and civic oversight.
From a quality of life perspective, open data fosters innovation and improves service delivery. It allows developers, researchers and civic actors to create new tools, applications and insights that can enhance everyday urban experiences.
This indicator also reflects institutional openness and trust. Cities that proactively share data tend to build stronger relationships with residents and encourage collaborative problem-solving.
With a meaningful weight, this indicator highlights the role of data transparency as a foundation for smart, responsive and participatory urban governance.
Datasets Available in Machine-Readable Formats
This indicator evaluates whether publicly available datasets are provided in machine-readable formats, enabling automated processing, reuse and analysis.
Datasets Available in Machine-Readable Formats assesses whether a city provides at least a basic level of structured, machine-readable data (such as CSV, JSON or XML) through its official platforms. It focuses on the presence of such data rather than its volume or share.
As a binary indicator, it captures whether this condition is met (Yes) or not (No). This approach reflects the foundational nature of machine-readable data as a prerequisite for more advanced open data ecosystems.
From a quality of life perspective, machine-readable data enables the development of digital services, transparency tools and evidence-based decision-making. It supports innovation in areas such as mobility, environment, housing and public services.
The indicator also reflects the maturity of digital governance systems. Cities that ensure even a basic level of structured data availability are better positioned to support smart city solutions and collaboration with external stakeholders.
This indicator plays a foundational role by distinguishing cities that have crossed the minimum threshold of usable open data from those where data remains inaccessible or difficult to process.
Fault Reporting System via Website or Mobile App
This indicator evaluates whether residents can report urban issues through accessible digital tools such as websites or mobile applications.
Fault Reporting System via Website or Mobile App assesses whether a city provides at least one official digital channel that allows residents to report issues such as broken infrastructure, waste problems, lighting failures or public safety concerns.
As a binary indicator, it captures whether such a system exists and is accessible (Yes) or not (No). This reflects the foundational role of digital service availability in modern urban governance.
Time is one of the most valuable resources for residents. By enabling fast, simple and remote reporting of problems, these e-services significantly reduce the time required to notify authorities compared to traditional methods such as in-person visits or phone calls.
From a quality of life perspective, this translates into greater convenience, higher engagement and quicker issue escalation. It lowers participation barriers and allows residents to contribute to city maintenance with minimal effort.
The presence of such tools also signals a shift towards more responsive and user-oriented governance, where administrative processes are designed around efficiency and real-life needs of citizens.
This indicator captures a key transition from traditional administration to digitally enabled governance, where saving residents’ time becomes a central measure of service quality.
Electronic Payments for Municipal Services
This indicator evaluates whether residents can pay for municipal services through digital channels such as online platforms or mobile applications.
Electronic Payments for Municipal Services assesses whether a city provides at least one official digital payment channel enabling residents to pay for services such as utilities, taxes, transport or administrative fees.
As a binary indicator, it captures whether such functionality exists and is accessible (Yes) or not (No), without evaluating the range or frequency of its use.
The availability of digital payments significantly reduces the need for in-person visits and administrative procedures. It streamlines everyday interactions with the municipality and increases convenience for residents.
From a quality of life perspective, this translates into time savings, easier access to services and reduced administrative burden, particularly for working individuals and digitally active populations.
The presence of such systems also reflects broader digital transformation in governance, indicating that the city is adapting its services to modern user expectations and operational efficiency standards.
This indicator highlights a fundamental shift towards frictionless public services, where the ability to complete essential transactions quickly and remotely becomes a key component of urban liveability.
Online Appointment Booking with City Hall
This indicator evaluates whether residents can schedule appointments with municipal offices through digital platforms such as websites or mobile applications.
Online Appointment Booking with City Hall assesses whether a city provides an official digital system that allows residents to book appointments with municipal offices in advance.
As a binary indicator, it captures whether such functionality exists and is accessible (Yes) or not (No), without evaluating the scale or sophistication of the system.
The availability of online booking significantly improves the organisation of public services by reducing queues, distributing demand more evenly and enabling better time management for both residents and administration.
From a quality of life perspective, this translates directly into time savings and reduced uncertainty. Residents can plan their visits efficiently and avoid long waiting times, which are a common source of frustration in traditional administrative systems.
The presence of such tools also reflects a broader shift towards user-centred governance, where accessibility, predictability and respect for residents’ time become key priorities.
This indicator highlights the growing importance of time as a critical resource in urban life, showing how digital solutions can transform everyday interactions with public administration.
Up-to-Date Official Development Strategy
This indicator measures whether a city maintains a current and publicly available strategic development document guiding its long-term growth and policy priorities.
Up-to-Date Official Development Strategy evaluates whether a municipality has an officially adopted, recent and accessible strategic document that outlines its vision, priorities and planned actions for future development. This may include economic, social, environmental and spatial planning dimensions.
The indicator reflects the capacity of local government to think and act in a structured, forward-looking manner. Cities with updated strategies are more likely to coordinate policies effectively and align investments with clearly defined objectives.
From a quality of life perspective, a well-defined and current strategy supports more coherent urban development. It helps ensure that infrastructure, services and public investments respond to long-term needs rather than short-term decisions.
The existence of such a strategy also enhances transparency and accountability. Residents and stakeholders can better understand the direction of the city and assess whether commitments are being implemented.
Although moderate in weight, this indicator captures a fundamental dimension of governance: the ability of a city to define, communicate and consistently pursue a long-term development vision.
Key Elements Included in the Strategy
This indicator evaluates whether a city's development strategy includes a set of substantive governance components that make the document operational, resident-oriented and aligned with long-term implementation logic.
Key Elements Included in the Strategy assesses the internal completeness of a city’s development strategy by verifying whether the document contains specific components considered essential for coherent strategic governance. In methodological terms, the review focuses on the presence of a clearly articulated vision, a description of the methods used to attract talent to the city, prioritisation of development responding to residents’ needs, support for innovation, and verification of the effectiveness and outcomes of implemented solutions in relation to the stated strategic objectives.
The indicator therefore does not measure the formal existence of a strategy alone. Instead, it considers whether the strategy demonstrates a structured logic linking long-term ambition with implementation priorities and review mechanisms. A strategy that includes these elements is more likely to function as a practical governance instrument rather than as a purely declarative policy document.
From a quality of life perspective, the inclusion of these components matters because it shows whether the city is planning development in a way that is both future-oriented and responsive to local conditions. A strategic vision establishes direction, talent-attraction methods indicate economic and human capital planning, resident-focused priorities reflect social responsiveness, innovation support signals adaptability, and outcome verification strengthens institutional learning.
The presence of these key elements also improves accountability and interpretability. It allows observers to understand whether the city’s strategic framework contains the minimum substantive building blocks needed to guide implementation, evaluate progress and assess whether the adopted solutions are producing results consistent with declared objectives.
In practice, this indicator helps distinguish between strategies that are comprehensive and actionable and those that remain too general, fragmented or weakly connected to implementation and evaluation.
AI Mentioned in Official Strategic Documents in Relation to Residents' Needs
This indicator assesses whether a city explicitly refers to artificial intelligence in its official strategic documents in ways that are linked to the needs, wellbeing or everyday experience of residents.
AI Mentioned in Official Strategic Documents in Relation to Residents' Needs captures whether a city has formally recognised artificial intelligence as a relevant tool or policy area within its strategic planning framework. The key issue is not the technical sophistication of the language used, but whether AI is connected to tangible public purposes such as improving services, responding to social needs, supporting accessibility, strengthening public management or enhancing quality of life.
The indicator is designed to distinguish between general references to innovation and a more deliberate policy orientation in which AI is understood as part of a city’s future response to residents’ needs. This may include references to smarter service delivery, better access to information, predictive maintenance, health and care support, mobility management, education or other public-interest applications.
From a governance perspective, the inclusion of AI in official strategy documents signals institutional awareness that emerging technologies are becoming part of the urban policy environment. Where this is framed in relation to residents rather than only administration or competitiveness, it suggests a more people-centred understanding of technological change.
The indicator does not attempt to measure implementation, scale or effectiveness. Its purpose is narrower and more foundational. It tests whether the city has already entered the stage of strategic recognition, which is often a necessary first step before budgets, programmes, standards and safeguards can be developed.
This indicator is scored as a binary variable. A value of “Yes” is assigned if explicit references to AI linked to residents’ needs are present in official strategic documents; otherwise, the value is “No”.
Annual Average PM2.5 Concentration
This indicator measures the annual average concentration of fine particulate matter (PM2.5) in the air, reflecting the level of long-term exposure to air pollution experienced by residents.
Annual Average PM2.5 Concentration is one of the most critical environmental indicators in the assessment of urban quality of life. PM2.5 refers to fine particulate matter with a diameter of less than 2.5 micrometres, which can penetrate deep into the respiratory system and enter the bloodstream, posing significant health risks.
This indicator captures the long-term exposure of residents to air pollution rather than short-term fluctuations. By focusing on annual averages, it reflects structural environmental conditions shaped by factors such as transport systems, industrial activity, energy sources, urban planning and regulatory frameworks.
Lower PM2.5 levels are associated with better public health outcomes, including reduced incidence of respiratory and cardiovascular diseases. In contrast, higher concentrations are linked to increased mortality, chronic illness and reduced life expectancy, making air quality a fundamental determinant of wellbeing.
From a policy perspective, this indicator reflects both local and wider influences. While cities can act through measures such as low-emission zones, public transport improvements and urban greening, air quality is also affected by regional and national factors, including energy systems and cross-boundary pollution.
With one of the highest weights in the Index, this indicator plays a decisive role in capturing environmental conditions that directly and continuously affect residents’ health and everyday life.
Green Mobility Share (%)
This indicator measures the proportion of trips made using environmentally sustainable transport modes such as public transport, cycling and walking.
Green Mobility Share evaluates how residents move within the city and to what extent daily travel patterns rely on low-emission or zero-emission modes of transport. It reflects the share of trips undertaken by public transport, cycling and walking in relation to total urban mobility.
This indicator captures both infrastructure and behaviour. High values typically indicate the presence of well-developed public transport systems, safe and accessible cycling networks, pedestrian-friendly urban design and policies that discourage excessive reliance on private cars.
From a quality of life perspective, a higher share of green mobility contributes to reduced air pollution, lower greenhouse gas emissions, decreased traffic congestion and improved public health through increased physical activity. It also supports more equitable access to mobility, particularly for residents who do not own a car.
The indicator also reflects long-term urban planning choices. Cities that invest in compact development, integrated transport systems and multimodal accessibility tend to achieve higher green mobility shares, while car-dependent urban structures often limit progress in this area.
This indicator provides a strong signal of how effectively a city aligns its mobility system with environmental sustainability and everyday accessibility for residents.
Waste Generated per Resident
This indicator measures the average amount of municipal waste produced per resident, reflecting consumption patterns and efficiency of resource use within the city.
Waste Generated per Resident evaluates the total volume of municipal waste produced in a city relative to its population, typically expressed in kilograms per person per year. It provides a direct insight into consumption habits, production systems and the effectiveness of waste prevention policies.
Lower levels of waste per resident generally indicate more sustainable consumption patterns, higher levels of reuse and repair, and effective public awareness. Conversely, higher values may reflect overconsumption, inefficient resource management or limited waste reduction strategies.
From a quality of life perspective, reducing waste generation contributes to cleaner urban environments, lower pressure on landfill and incineration systems, and reduced environmental and health risks. It is also closely linked to circular economy practices, which aim to minimise waste and maximise resource efficiency.
The indicator should be interpreted alongside recycling and recovery rates, as low waste generation combined with effective waste management systems represents the most sustainable outcome. Cities that actively promote waste reduction, separate collection and material recovery tend to perform better across multiple environmental dimensions.
This indicator serves as a key proxy for the transition towards a circular urban economy, where resource use is minimised and waste is systematically reduced.
Population Served by Sewage Treatment Facilities (%)
This indicator measures the share of the population connected to sewage treatment systems, reflecting the accessibility and effectiveness of urban sanitation infrastructure.
Population Served by Sewage Treatment Facilities assesses the proportion of residents whose wastewater is collected and treated through formal systems. It is a fundamental indicator of urban infrastructure quality and environmental protection.
High coverage indicates that a city has developed comprehensive sanitation systems capable of safely managing wastewater, reducing pollution of rivers, soil and groundwater. Low coverage may signal gaps in infrastructure, particularly in peripheral or rapidly growing areas.
From a quality of life perspective, effective sewage treatment is directly linked to public health. It reduces the risk of waterborne diseases, improves hygiene conditions and contributes to a cleaner urban environment.
This indicator also reflects long-term investment capacity and governance effectiveness in basic urban services. Cities with high coverage typically demonstrate stronger planning, financing and maintenance of critical infrastructure systems.
Although often overlooked, universal access to sewage treatment is one of the most essential foundations of a healthy and sustainable city.
Recycling Rate (%)
This indicator measures the proportion of municipal waste that is recycled, reflecting the effectiveness of waste management systems and circular economy practices in the city.
Recycling Rate evaluates the share of total municipal waste that is diverted from landfill or incineration and processed for reuse as secondary raw materials. It is one of the key indicators of how effectively a city manages its material flows.
Higher recycling rates indicate well-functioning collection systems, strong public participation and supportive policy frameworks. They also reflect the availability of infrastructure for sorting, processing and reintroducing materials into the economy.
From a quality of life perspective, efficient recycling reduces environmental pressure, lowers greenhouse gas emissions and contributes to cleaner urban spaces. It also supports local economic activity linked to material recovery and reuse.
This indicator should be considered together with total waste generation. A high recycling rate is most impactful when combined with efforts to reduce overall waste, reinforcing a transition towards sustainable consumption patterns.
With a relatively high weight, this indicator plays a central role in assessing a city's progress towards a circular economy, where materials are continuously reused rather than discarded.
Landfill Waste Burden
This indicator measures the actual residual waste burden generated by residents by verifying how much municipal waste is produced per capita, how much of that waste is recycled, and how much remains as waste effectively burdening the environment through landfill disposal.
Landfill Waste Burden evaluates the real end result of municipal waste management at resident level. Rather than describing only the total amount of waste generated or only the proportion recycled, it focuses on the remaining waste stream that is not successfully diverted and therefore continues to burden the urban environment through landfill disposal.
Methodologically, the indicator is measured by verifying three linked dimensions: how much municipal waste is produced per resident, how much of that waste is recycled, and how much waste therefore remains as the effective landfill burden attributable to the average resident. This makes the indicator outcome-oriented and more directly connected to the actual environmental result.
In analytical terms, this measure complements the two related indicators covering overall waste generation and recycling performance. Total waste generation shows the scale of waste produced, while recycling indicates how much is diverted from disposal. Landfill Waste Burden adds the missing interpretative layer by showing the real residual outcome, namely how much waste the resident effectively leaves unrecovered.
High values may reflect excessive waste generation, weak recycling systems, low participation in sorting, or a combination of these factors. Lower values indicate that the city is not only managing waste more efficiently but is also limiting the amount of waste per resident that ultimately enters the least sustainable disposal pathway.
From a quality of life perspective, this residual measure is especially important because landfill dependence is associated with land use pressure, methane emissions, long-term soil and groundwater risks, and broader evidence of an incomplete transition towards circular resource management.
This indicator supplements waste generation and recycling metrics by identifying the actual final result: how much waste per resident remains unrecycled and ends up as a real environmental burden.
Biodiversity Protection Strategy
This indicator evaluates whether a city has a formal strategy or policy framework dedicated to the protection and enhancement of biodiversity within its territory.
Biodiversity Protection Strategy assesses whether a city has formally adopted policies aimed at preserving ecosystems, habitats and species within the urban environment. The focus is on the existence of an explicit strategic or policy framework that integrates biodiversity considerations into planning and development processes.
A comprehensive strategy may include measures such as habitat conservation, development of green corridors, protection of native species and the use of nature-based solutions in urban design. It may also address climate adaptation and ecosystem resilience, although the indicator itself does not evaluate the scope or quality of these measures.
From a quality of life perspective, biodiversity contributes to healthier and more attractive urban environments. Access to natural areas supports well-being, recreation and ecological stability, while also enhancing resilience to environmental pressures.
The indicator captures a foundational aspect of environmental governance. It reflects whether the city has formally recognised biodiversity as a strategic policy area, regardless of the scale, effectiveness or implementation stage of the measures included.
This is a binary (yes/no) indicator that records whether a biodiversity protection strategy exists, without assessing its scope or effectiveness.
GDP per Capita
This indicator measures economic output per resident based on national-level data, providing a proxy for the overall level of economic prosperity affecting cities within a given country.
GDP per Capita represents the total economic value generated within a country divided by its population. In this framework, the indicator is applied at the national level and serves as a proxy for the broader economic context in which cities operate.
While it does not measure city-level economic output directly, it reflects the macroeconomic conditions that influence urban development, including income levels, labour productivity, investment capacity and access to public and private services.
Higher national GDP per capita is typically associated with stronger economic environments, which can support better infrastructure, more advanced public services and higher standards of living in cities. However, this relationship is not uniform, and significant regional disparities may exist within countries.
The indicator does not capture income distribution or inequalities within or between cities. Therefore, it should be interpreted alongside more localised indicators that reflect urban-level economic and social conditions.
This indicator provides a macroeconomic baseline for interpreting urban performance, reflecting the national economic context rather than the specific output of an individual city.
GDP Growth (%)
This indicator reflects the rate of economic growth measured for the entire country, providing context on the broader economic dynamics that influence urban development and residents’ quality of life.
GDP Growth (%) measures the annual change in a country’s gross domestic product, indicating the overall pace of economic expansion or contraction. In this framework, the indicator is measured for the whole country rather than for the city itself, and is used to describe the national economic environment in which urban areas operate.
Higher national economic growth is generally associated with increased investment, job creation and rising incomes, all of which can positively influence urban living conditions. Conversely, low or negative growth may signal economic stagnation, reduced opportunities and fiscal constraints affecting both public services and private sector activity across cities within the country.
Within the Happy City Index framework, this indicator is treated as contextual rather than directly attributable to city governance. Cities benefit from or are constrained by country-wide economic performance, but they do not fully control it, which is why the measure is interpreted as a national background condition.
For this reason, GDP Growth contributes to the interpretation of results rather than strongly shaping the final ranking. It helps explain differences between cities located in faster-growing and slower-growing national economies, while preserving the primacy of city-level performance indicators.
This indicator is measured at the level of the entire country and serves as macroeconomic context, ensuring that national growth trends inform interpretation without overshadowing city-level performance and policy effectiveness.
Patents per 100,000 Inhabitants
This indicator measures innovation intensity at the national level, reflecting the capacity of an entire country to generate new knowledge, technologies and intellectual property.
Patents per 100,000 Inhabitants captures the number of registered patents relative to population size, providing a standardised measure of innovation output. Within this framework, the indicator is measured for the whole country rather than for an individual city, and therefore reflects the broader national innovation environment in which cities operate.
Higher patent intensity is typically associated with strong knowledge ecosystems, advanced industries and active collaboration between academia, business and research institutions. These conditions often translate into more dynamic labour markets, higher productivity and greater economic resilience across the country, with indirect effects on urban development.
Within cities, innovation capacity influences the availability of high-skilled jobs, the presence of cutting-edge industries and the potential for long-term economic growth. However, because patent activity is measured at country level and shaped by national research systems, legal frameworks and innovation policy, it is treated as contextual rather than directly attributable to city performance.
As part of the methodology, this indicator helps interpret the broader innovation context surrounding each city. It complements city-level measures without determining outcomes on its own and ensures that national structural conditions are recognised in a controlled and proportionate way.
This indicator is measured at the level of the entire country and is used as contextual background, allowing national innovation capacity to inform interpretation without disproportionately affecting city rankings.
Businesses per 1,000 Residents
This indicator measures the density of business entities within a city, reflecting the level of economic activity, entrepreneurship and local market dynamism.
Businesses per 1,000 Residents captures the number of registered economic entities relative to the population size. It provides insight into how active and diversified the local economy is, as well as the accessibility of entrepreneurship within the city.
A higher density of businesses typically indicates a vibrant economic environment with a wide range of services, employment opportunities and market interactions. It suggests that the city supports business creation and offers favourable conditions for economic participation.
From a quality of life perspective, a strong business ecosystem contributes to job availability, income generation and access to goods and services. It also reflects the resilience of the local economy, as diversified business structures are generally better able to withstand economic shocks.
The indicator is fully attributable to city-level dynamics, including regulatory environment, local policies, infrastructure and the broader attractiveness of the city for entrepreneurs and investors.
With a relatively high weight, this indicator plays an important role in assessing how effectively a city fosters economic activity and supports a thriving local business environment.
City Fiscal Power Index
This indicator evaluates how far financial decision-making is shifted towards the city level by comparing state budget resources per capita with municipal budget resources per capita.
City Fiscal Power Index measures the relationship between public resources controlled at national level and those controlled at city level on a per capita basis. In methodological terms, the indicator is constructed through the ratio between state budget expenditure per resident and municipal budget expenditure per resident, allowing comparison of how much fiscal capacity is effectively placed closer to residents through local government.
The purpose of the indicator is not simply to describe the size of a city budget, but to assess the degree to which financial agency is decentralised. Where a relatively greater share of public spending power is exercised through municipal budgets, cities typically have more room to decide how money is allocated in response to local priorities, service needs and development goals.
From a governance perspective, this ratio helps capture whether financial decision-making is concentrated at state level or transferred with greater weight to the city. A stronger city position in this relationship suggests that spending decisions can be taken closer to residents, which may improve responsiveness, policy fit and democratic accountability.
From a quality of life perspective, local fiscal decision-making matters because municipalities are often responsible for the services that most directly shape everyday urban life, including transport, public space, education, social support, environmental management and local infrastructure. When financial capacity is more strongly embedded at city level, the potential for tailoring expenditure to residents’ real needs is generally higher.
The indicator therefore serves as a proxy for practical fiscal subsidiarity. It helps show to what extent the burden of public financial action has been transferred towards the city, and whether the power to decide how money is spent is institutionally located closer to the community affected by those decisions.
This indicator is designed to show whether fiscal decisiveness is situated nearer to residents by comparing per capita state budget resources with per capita city budget resources.
Annual Average Unemployment Rate
This indicator measures the proportion of the labour force that is unemployed over a given year, reflecting the overall health and inclusiveness of the local labour market.
Annual Average Unemployment Rate captures the share of economically active residents who are without work but actively seeking employment. It provides a fundamental measure of labour market efficiency and economic stability within the city.
Lower unemployment levels generally indicate a dynamic economy with sufficient job creation and strong demand for labour. Conversely, higher unemployment may signal structural economic challenges, skills mismatches or limited access to employment opportunities.
From a quality of life perspective, employment is closely linked to income security, social inclusion and overall well-being. Persistent unemployment can lead to increased inequality, social exclusion and pressure on public services.
The indicator also reflects the resilience of the local economy. Cities with stable employment levels are better positioned to withstand economic shocks and maintain social cohesion during periods of uncertainty.
With a relatively high weight, this indicator highlights the central role of employment in shaping economic performance and social stability in urban environments.
Annual Average Youth Unemployment Rate
This indicator measures youth unemployment in relation to overall unemployment, showing how strongly young people face labour market difficulties compared with the general situation.
Annual Average Youth Unemployment Rate captures the relative labour market position of young people, typically aged 15–24, by comparing youth unemployment to overall unemployment. In methodological terms, the indicator is constructed as the result of dividing the youth unemployment rate by the total unemployment rate, which allows assessment of how much more severely young people are affected than the population as a whole.
This ratio-based approach provides a more interpretative measure than youth unemployment alone. Rather than only showing how many young people are unemployed, it reveals whether the difficulties faced by young people are proportionate to the general labour market situation or significantly worse. Higher values indicate that youth experience stronger exclusion from employment relative to overall conditions.
High relative youth unemployment often signals structural barriers such as skills mismatches, limited entry-level positions, weak school-to-work transitions or insufficient alignment between education systems and labour market demand. It may also suggest that economic recovery or job creation is not reaching younger cohorts to the same extent as other groups.
From a quality of life perspective, disproportionate youth unemployment can have long-term consequences, including reduced lifetime earnings, delayed independence, weakened social mobility and increased risk of exclusion. By comparing youth conditions with the general unemployment situation, the indicator helps identify whether young residents are facing a specifically intensified disadvantage.
The indicator also reflects the future resilience of a city’s economy. Cities in which young people encounter labour market conditions markedly worse than the general population may face longer-term challenges in retaining talent, sustaining innovation and ensuring demographic and economic continuity.
This indicator shows how serious youth labour market problems are relative to the overall unemployment situation by expressing youth unemployment as a ratio of total unemployment.
Annual Average Youth Unemployment Rate
This indicator measures youth unemployment in relation to overall unemployment, showing how strongly young people face labour market difficulties compared with the general situation.
Annual Average Youth Unemployment Rate captures the relative labour market position of young people, typically aged 15–24, by comparing youth unemployment to overall unemployment. In methodological terms, the indicator is constructed as the result of dividing the youth unemployment rate by the total unemployment rate, which allows assessment of how much more severely young people are affected than the population as a whole.
This ratio-based approach provides a more interpretative measure than youth unemployment alone. Rather than only showing how many young people are unemployed, it reveals whether the difficulties faced by young people are proportionate to the general labour market situation or significantly worse. Higher values indicate that youth experience stronger exclusion from employment relative to overall conditions.
High relative youth unemployment often signals structural barriers such as skills mismatches, limited entry-level positions, weak school-to-work transitions or insufficient alignment between education systems and labour market demand. It may also suggest that economic recovery or job creation is not reaching younger cohorts to the same extent as other groups.
From a quality of life perspective, disproportionate youth unemployment can have long-term consequences, including reduced lifetime earnings, delayed independence, weakened social mobility and increased risk of exclusion. By comparing youth conditions with the general unemployment situation, the indicator helps identify whether young residents are facing a specifically intensified disadvantage.
The indicator also reflects the future resilience of a city’s economy. Cities in which young people encounter labour market conditions markedly worse than the general population may face longer-term challenges in retaining talent, sustaining innovation and ensuring demographic and economic continuity.
This indicator shows how serious youth labour market problems are relative to the overall unemployment situation by expressing youth unemployment as a ratio of total unemployment.
Percentage Deviation in Earnings Relative to the National Average
This indicator measures how average earnings in a city differ from the national average, reflecting the city’s relative economic attractiveness and income positioning within the country.
Percentage Deviation in Earnings Relative to the National Average captures the extent to which wages in a city are higher or lower compared to the national benchmark. Positive deviation indicates above-average earning potential, while negative values suggest relatively weaker income levels.
Higher earnings relative to the national average often reflect strong economic performance, higher productivity, concentration of high-value industries and greater demand for skilled labour. Conversely, lower earnings may indicate structural economic challenges or lower-value economic activity.
From a quality of life perspective, income levels directly influence living standards, consumption capacity and financial security. However, this indicator must also be interpreted alongside cost of living, as higher earnings do not automatically translate into higher real purchasing power.
The indicator also highlights spatial economic disparities within a country, illustrating how opportunities and income distribution vary between cities and regions.
With one of the higher weights in the economic dimension, this indicator underscores the importance of relative income positioning in shaping both individual well-being and urban competitiveness.
Paid Parental Leave Weeks at 50% or More of Salary
This indicator measures the duration of paid parental leave available at a minimum of 50% salary replacement, reflecting the level of direct support that residents receive during early parenthood.
Paid Parental Leave Weeks at 50% or More of Salary captures how long parents can take leave from work following the birth or adoption of a child while receiving at least half of their regular income. Although the indicator is measured at national level, it relates directly to the lived experience of individual residents, because the entitlement applies in a concrete and personal way to families during a critical stage of life.
Longer periods of adequately paid leave support early child development, maternal health and family stability. They also contribute to better health outcomes by allowing parents to dedicate time to caregiving during critical early stages of life. Unlike more general national indicators, this measure has a direct and immediate effect on household conditions and day-to-day well-being.
From a broader socio-economic perspective, parental leave policies influence labour market participation, particularly among women, and play a role in reducing gender inequalities. Well-designed systems can support both workforce continuity and family well-being, while also affecting residents’ financial security at an especially sensitive moment in the life course.
The indicator also reflects the inclusiveness and resilience of social protection systems. Cities operating within frameworks that provide sufficient paid leave are better positioned to support demographic sustainability and long-term population health. For this reason, even though the measure is derived from country-level rules, its practical relevance is closer to the resident than in many other national indicators measured for the whole population in aggregate.
This indicator carries a higher weight than broad country-level measures because, although measured nationally, it directly affects individual residents and shows the level of concrete family support available in everyday life.
Psychiatrists per 100,000 Inhabitants
This indicator measures the availability of specialised mental health professionals relative to the population, using a country-level value as a proxy for access to psychiatric care.
Psychiatrists per 100,000 Inhabitants captures the density of medical professionals specialising in mental health, including diagnosis, treatment and management of psychiatric conditions. Ideally, this indicator would be measured directly at city level, as this would provide a more precise picture of access to psychiatric care within the local urban context.
In practice, however, this type of data is often difficult to obtain in a consistent and comparable form for individual cities. For that reason, the methodology uses the national-level result and applies the weighting approach reserved for country-level indicators. This ensures comparability across cities while acknowledging the limitations of subnational health workforce data availability.
A higher number of psychiatrists generally indicates better access to specialised care, shorter waiting times and greater capacity to address a wide range of mental health needs. Conversely, low availability may lead to unmet demand, delayed treatment and increased pressure on general healthcare systems. Even when measured nationally, the indicator remains highly relevant as a proxy for the broader mental health service environment in which city residents live.
From a quality of life perspective, access to mental health services is critical for overall well-being, productivity and social stability. Mental health conditions, if untreated, can significantly affect individuals’ ability to participate in economic and social life. The indicator therefore remains substantively important, even though the underlying result is available only at country level.
The indicator also reflects the prioritisation of mental health within the broader healthcare system. Countries with stronger psychiatric capacity generally provide a more supportive context for cities, even if local variation may still exist and cannot always be captured directly in internationally comparable city-level datasets.
Ideally this measure would be calculated for each city, but because comparable city-level data are often unavailable, the methodology applies the national result and assigns it the weight used for country-level indicators.
Mental Health or Well-Being Strategy
This indicator evaluates whether a city has a formal strategy or policy framework dedicated to mental health promotion and overall well-being of its residents.
Mental Health or Well-Being Strategy assesses the presence and scope of policies aimed at improving psychological well-being, preventing mental health conditions and ensuring access to support services. It reflects whether cities take a structured and proactive approach to mental health.
Comprehensive strategies often include prevention programmes, community-based services, early intervention, awareness campaigns and integration of mental health into broader public health and social policies. They may also address vulnerable groups and reduce stigma associated with mental illness.
From a quality of life perspective, mental well-being is a fundamental component of overall health. Cities that actively promote mental health create environments that support social cohesion, resilience and individual life satisfaction.
The indicator also reflects governance capacity and cross-sector coordination, as effective mental health strategies typically involve collaboration between healthcare providers, social services, education systems and local communities.
This is a binary (yes/no) indicator that records whether a formal mental health or well-being strategy exists, without assessing its scope or effectiveness.
Strategy to Prevent and Address Hate Speech or Cyberbullying
This indicator evaluates whether a city has a formal strategy or coordinated framework to prevent, monitor and respond to hate speech and cyberbullying, particularly in digital and community environments.
Strategy to Prevent and Address Hate Speech or Cyberbullying assesses whether cities recognise and actively address harmful online and offline behaviours that negatively affect mental health, social cohesion and individual safety. It reflects the presence of structured policies or programmes targeting these issues.
Comprehensive strategies typically include awareness campaigns, educational programmes in schools, reporting mechanisms, victim support services and collaboration with digital platforms or law enforcement. They may also incorporate monitoring systems and community engagement initiatives.
From a quality of life perspective, exposure to hate speech and cyberbullying can significantly impact mental well-being, particularly among young people and vulnerable groups. Effective prevention and response mechanisms contribute to safer, more inclusive urban environments.
The indicator also reflects the city’s capacity to address emerging challenges linked to digitalisation and social interaction. It highlights whether local authorities are adapting governance frameworks to protect residents in both physical and virtual spaces.
This is a binary (yes/no) indicator that records whether a formal strategy or coordinated framework exists, without assessing its scope or effectiveness.
Licensed Medical Doctors Practising in the City (FTE)
This indicator measures the number of licensed medical doctors actively practising in the city, expressed in full-time equivalents (FTE), reflecting the overall capacity of the healthcare system.
Licensed Medical Doctors Practising in the City (FTE) captures the availability of physicians across all specialisations, adjusted to full-time equivalents to ensure comparability. It provides a comprehensive measure of the healthcare workforce serving the urban population.
A higher number of doctors per population generally indicates better access to medical services, shorter waiting times and greater capacity to address both routine and specialised healthcare needs. Lower availability may result in system strain, limited access and reduced quality of care.
From a quality of life perspective, access to qualified medical professionals is fundamental to maintaining public health, managing chronic conditions and ensuring timely treatment. It directly influences life expectancy, well-being and overall health outcomes.
The indicator also reflects the resilience and organisation of the healthcare system. Cities with sufficient medical workforce capacity are better prepared to respond to public health crises, demographic changes and increasing demand for healthcare services.
This indicator provides a core measure of healthcare system strength, as the availability of medical professionals underpins all aspects of service delivery and patient care.
Population Covered by Medical Insurance (%)
This indicator measures the proportion of the population covered by medical insurance, using country-level data to reflect the accessibility and inclusiveness of the healthcare system affecting city residents.
Population Covered by Medical Insurance (%) evaluates the extent to which residents have financial protection against healthcare costs. In this framework, the indicator is measured at the national level and applied to cities as a proxy for the healthcare coverage environment in which their residents live.
It includes coverage through public health systems, social insurance schemes and private insurance plans. While the data are not always available at a consistent city level, national coverage rates provide a reliable and comparable measure of access to healthcare financing across countries.
Higher coverage levels indicate a more inclusive healthcare system, where individuals can access medical services without facing significant financial barriers. Lower coverage may lead to inequalities in access, delayed treatment and increased health risks for vulnerable populations.
From a quality of life perspective, medical insurance coverage is a key determinant of health security. It ensures that individuals can seek preventive care, diagnostics and treatment when needed, supporting better long-term health outcomes and social stability.
The indicator also reflects the effectiveness of national health policy and social protection systems. Although measured at country level, it directly influences the conditions experienced by residents in cities and therefore remains highly relevant for urban well-being.
This indicator is measured at the country level and applied uniformly to cities, providing a consistent proxy for healthcare access conditions affecting all residents within the national system.
Intentional Homicides per 100,000 Residents
This indicator measures the number of intentional homicides per 100,000 residents, reflecting the level of serious violent crime and overall public safety within the city.
Intentional Homicides per 100,000 Residents captures the most severe form of violence and serves as a widely used benchmark for assessing urban safety. It reflects not only criminal activity but also broader social, economic and institutional conditions.
Lower homicide rates typically indicate effective law enforcement, strong social cohesion and stable governance structures. Higher rates may signal systemic challenges such as inequality, social exclusion, organised crime or weak institutional capacity.
From a quality of life perspective, safety is a fundamental prerequisite for well-being. High levels of violent crime can undermine trust, restrict mobility and reduce residents’ sense of security in public and private spaces.
The indicator also highlights the intersection between public health and safety policy. Preventing violent crime often requires integrated approaches, including education, social services, urban design and targeted interventions in high-risk communities.
Even at a relatively modest weight, homicide rates provide a critical signal of urban safety and are closely linked to residents’ perception of security and overall quality of life.
Adults Classified as Overweight or Obese (%)
This indicator measures the proportion of the adult population classified as overweight or obese, reflecting key lifestyle, health and environmental conditions within the city.
Adults Classified as Overweight or Obese (%) captures the prevalence of excess body weight in the population, typically based on body mass index (BMI) thresholds. It is a key indicator of lifestyle-related health risks and broader urban health patterns.
Higher rates of overweight and obesity are associated with increased risks of chronic diseases, including cardiovascular conditions, diabetes and certain cancers. These outcomes place pressure on healthcare systems and reduce overall life expectancy and quality of life.
The indicator also reflects the urban environment. Factors such as access to healthy food, availability of public spaces for physical activity, transport systems and socio-economic conditions all influence population health behaviours.
Cities with lower prevalence rates often demonstrate effective public health policies, including preventive programmes, health education and urban planning that promotes active lifestyles and balanced nutrition.
With a relatively high weight, this indicator highlights the importance of preventive health and the role of cities in shaping healthier living conditions.
Parks per km²
This indicator measures the density of parks within the city, expressed as the number of parks per square kilometre, reflecting access to green spaces in the urban environment.
Parks per km² captures how evenly and widely green spaces are distributed across the city. It focuses not only on total green area but on spatial accessibility, indicating whether residents can easily reach parks within their daily environment.
A higher density of parks typically suggests better integration of green spaces into the urban fabric. This supports everyday physical activity, recreation and social interaction, contributing to both physical and mental well-being.
The indicator also reflects planning quality and land-use priorities. Cities that maintain a dense network of parks demonstrate a commitment to balanced urban development, ensuring that green infrastructure is available across different neighbourhoods.
From a public health perspective, access to nearby parks is associated with reduced stress, improved air quality exposure and increased levels of physical activity, making it a key component of healthy city design.
This indicator emphasises proximity and distribution of green spaces, recognising that frequent, local access to parks is as important as total green area.
Green Space per Capita
This indicator measures the amount of accessible green space available per resident, reflecting the balance between urban density and access to natural environments.
Green Space per Capita evaluates the total area of parks, gardens and other publicly accessible green areas in relation to the number of residents. It provides a direct measure of how much natural space is available to individuals within the city.
Higher values indicate that residents have greater access to open, natural environments, which are associated with improved physical activity, mental health and overall well-being. Lower values may signal high urban density without sufficient green infrastructure.
The indicator reflects long-term planning priorities. Cities that maintain high levels of green space per capita typically integrate environmental considerations into development strategies, balancing growth with quality of life.
From a health perspective, access to green space is linked to reduced stress, lower rates of chronic illness and improved air quality exposure. It also supports social interaction and community cohesion.
As one of the higher-weighted indicators, green space per capita is a key determinant of urban liveability, directly influencing both physical and mental health outcomes.
Average Weekly Working Hours
This indicator measures the average number of hours worked per week, reflecting work-life balance, labour conditions and their impact on health and well-being.
Average Weekly Working Hours captures the typical workload of employed residents. It provides insight into how time is distributed between work, rest and personal life, making it a key determinant of overall well-being.
Longer working hours are often associated with increased stress, fatigue and higher risks of physical and mental health issues. Conversely, moderate working hours tend to support better productivity, improved life satisfaction and healthier lifestyles.
The indicator also reflects broader labour market structures and economic pressures. Cities with excessive working hours may signal demanding work cultures or limited labour protections, while more balanced patterns often indicate stronger social policies and employee rights.
From a quality of life perspective, work-life balance is essential. Time available for family, leisure, physical activity and rest directly influences both mental health and social cohesion within the city.
With a relatively high weight, this indicator highlights the importance of balancing economic productivity with human well-being and sustainable working conditions.
Average Number of Paid Vacation Days
This indicator measures the average number of paid vacation days available to employees, reflecting work-life balance, labour standards and opportunities for rest and recovery.
Average Number of Paid Vacation Days captures the extent to which employees are entitled to time off while maintaining income. It is a key dimension of labour quality and reflects how cities support rest, recovery and long-term well-being.
Higher numbers of paid vacation days are associated with reduced stress, improved mental health and increased productivity over time. Insufficient time off may lead to burnout, lower job satisfaction and negative health outcomes.
The indicator also reflects regulatory frameworks and employment practices. Cities with more generous vacation entitlements typically operate within stronger labour protection systems and place greater emphasis on work-life balance.
From a broader perspective, time off enables social interaction, family life and participation in cultural and recreational activities, contributing to overall quality of life and community cohesion.
This indicator highlights that adequate rest is not only a labour right but a critical component of sustainable productivity and long-term health.
Life Expectancy at Birth
This indicator measures the average number of years a newborn is expected to live, reflecting overall health conditions, healthcare quality and socio-economic factors within the city.
Life Expectancy at Birth is one of the most comprehensive measures of population health. It aggregates the effects of healthcare access, living conditions, environmental quality, nutrition and lifestyle factors into a single outcome metric.
Higher life expectancy typically indicates effective healthcare systems, strong preventive care and favourable socio-economic conditions. Lower values may point to systemic challenges such as inequality, limited access to services or environmental risks.
The indicator reflects long-term development patterns rather than short-term fluctuations. It captures the cumulative impact of policies and conditions across multiple sectors, including health, environment, economy and social protection.
From a quality of life perspective, longer life expectancy is associated not only with longevity but also with healthier life years, enabling individuals to remain active, productive and engaged in society.
As one of the highest-weighted indicators, life expectancy provides a fundamental benchmark of how well a city supports the long-term health and well-being of its population.
Types of Electronic Payment Systems for Transport Services
This indicator evaluates the availability and diversity of electronic payment systems used in public transport, reflecting convenience, accessibility and digital integration within urban mobility.
Types of Electronic Payment Systems for Transport Services measures how many and what kinds of digital payment options are available to passengers. These may include contactless cards, mobile apps, QR codes, online ticketing and integrated mobility platforms.
A greater variety of payment methods improves accessibility for different user groups, including residents, tourists and occasional users. It reduces barriers to entry and simplifies the use of public transport systems.
The indicator also reflects the level of technological advancement and system integration within urban mobility. Cities offering seamless, interoperable payment solutions typically demonstrate higher levels of innovation and user-centred service design.
From a quality of life perspective, easy and flexible payment systems enhance the overall transport experience, saving time and reducing friction in everyday mobility.
While relatively low in weight, this indicator signals how well a city adapts to digital expectations and supports inclusive, user-friendly transport systems.
Distance to the Nearest International Airport
This indicator measures the distance between the city and the nearest international airport, reflecting external connectivity and access to global transport networks.
Distance to the Nearest International Airport captures how physically close a city is to major air transport infrastructure. It is a key factor influencing international accessibility for both residents and businesses.
Shorter distances generally indicate stronger integration into global networks, facilitating travel, tourism, trade and investment. Longer distances may create barriers to connectivity, particularly for time-sensitive travel and economic activities.
The indicator also reflects broader territorial and infrastructure conditions. Cities located near major airports or within metropolitan regions tend to benefit from better accessibility, while more remote locations may depend on additional transport connections.
From a quality of life perspective, proximity to an international airport enhances mobility options, reduces travel time and increases access to global opportunities in business, education and leisure.
Although low in weight, this indicator signals how well a city is connected beyond its national context, influencing its attractiveness and accessibility on a global scale.
Access Modes to the Nearest Airport
This indicator evaluates the diversity of transport modes available for accessing the nearest airport, reflecting the accessibility, inclusiveness and efficiency of airport connectivity.
Access Modes to the Nearest Airport measures the range of available transport options connecting the city with its closest international airport. These may include rail services, metro or tram systems, buses or coaches, taxis or ride-hailing services, and private car access.
A higher number of available modes indicates greater flexibility and resilience in the transport system. It allows residents and visitors to choose routes based on cost, time, convenience or environmental considerations.
The indicator also reflects inclusiveness in mobility. Cities that provide multiple access options ensure that airport connectivity is available to different social groups, including those without private vehicles.
From an economic perspective, strong multimodal connections to airports enhance a city's attractiveness for tourism, business travel and international investment. They also reduce congestion and dependence on single transport modes.
This indicator captures not just proximity, but the quality and diversity of connections, highlighting whether airport access is efficient, inclusive and adaptable.
Public Transport Ridership per Capita
This indicator measures the average number of public transport trips taken per resident, reflecting the usage intensity, efficiency and attractiveness of the public transport system.
Public Transport Ridership per Capita captures how frequently residents use public transport services such as buses, trams, metro systems or suburban rail. It provides insight into the real-world performance of urban mobility systems beyond infrastructure availability.
High ridership levels typically indicate that public transport is reliable, accessible and well-integrated into daily life. It suggests that residents perceive it as a viable alternative to private car use.
The indicator is also closely linked to environmental outcomes. Greater use of public transport contributes to reduced congestion, lower emissions and improved air quality in urban areas.
From a social perspective, strong ridership reflects inclusiveness and affordability, ensuring that mobility options are widely used across different population groups.
This indicator reflects not only system capacity, but actual behavioural patterns, showing whether public transport plays a central role in everyday urban mobility.
Accessible Public Transport Fleet (%)
This indicator measures the share of public transport vehicles that are accessible to persons with reduced mobility, reflecting inclusiveness and universal design in urban mobility systems.
Accessible Public Transport Fleet (%) evaluates the proportion of buses, trams, metro cars or other public transport vehicles that are designed or adapted to accommodate passengers with disabilities or reduced mobility.
Accessibility features may include low-floor entry, ramps, priority seating, audio and visual information systems, and space for wheelchairs or strollers. A higher share indicates a more inclusive and equitable transport system.
The indicator is particularly important for ensuring mobility for elderly residents, people with disabilities, and caregivers with young children. It reflects whether public transport is usable by the entire population, not only by fully able-bodied users.
From a policy perspective, a high level of accessibility demonstrates compliance with universal design principles and social inclusion objectives, contributing to equal access to employment, education and services.
This indicator highlights whether mobility systems are designed for all users, making accessibility a core component of urban transport quality.
Road Traffic Accidents per 1,000 Residents
This indicator measures the frequency of road traffic accidents relative to the population, reflecting overall road safety and the effectiveness of urban transport management.
Road Traffic Accidents per 1,000 Residents captures the incidence of traffic-related accidents within the city, standardised by population size. It provides a comparable measure of safety across different urban contexts.
A lower value indicates safer road conditions, effective traffic regulation, and well-designed infrastructure. High accident rates may signal issues such as congestion, inadequate enforcement, poor road design or risky driver behaviour.
The indicator reflects not only transport system performance but also broader urban planning decisions, including street layout, pedestrian infrastructure, cycling safety and traffic calming measures.
From a quality of life perspective, road safety is a fundamental aspect of urban well-being. Safer streets contribute to greater public confidence, encourage active mobility and reduce the social and economic costs associated with accidents.
This indicator highlights the human impact of mobility systems, showing whether cities prioritise safety alongside efficiency and accessibility.
Open Public Transport Data Availability
This indicator evaluates whether a city provides open and accessible data on its public transport system, supporting transparency, innovation and improved user experience.
Open Public Transport Data Availability assesses whether transport-related datasets are made publicly accessible in a structured and reusable format. This may include timetables, routes, real-time vehicle positions, service disruptions and ticketing information.
Cities that provide open transport data enable the development of third-party applications, journey planners and mobility services, improving the overall usability and efficiency of the transport system.
The indicator also reflects a city’s commitment to transparency and digital innovation. Open data supports evidence-based decision-making, fosters collaboration with the private sector and enhances accountability in public service delivery.
From a user perspective, access to real-time and reliable information improves travel planning, reduces uncertainty and enhances the attractiveness of public transport compared to private alternatives.
This indicator highlights whether cities treat transport data as a public asset, enabling smarter, more integrated and user-centric mobility ecosystems.
Integrated Journey Planner Availability
This indicator evaluates whether a city provides an integrated journey planning tool that allows users to plan trips across multiple transport modes within a single platform.
Integrated Journey Planner Availability assesses whether residents and visitors can access a unified tool that combines different modes of transport, such as public transit, walking, cycling, and shared mobility services, into a single trip-planning interface.
Such planners typically provide route optimisation, real-time updates, travel time estimates and connections across different operators. Their presence reflects a high level of coordination within the urban mobility ecosystem.
The indicator highlights user-centric design in transport systems. By simplifying navigation and reducing information fragmentation, integrated planners make it easier to choose efficient and sustainable travel options.
From a strategic perspective, these tools support modal shift away from private cars by making multimodal travel more accessible and intuitive, contributing to reduced congestion and environmental impact.
This indicator reflects the digital maturity of urban mobility systems, showing whether cities enable seamless, door-to-door travel planning across modes.
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