About the Dataset

This dataset contains Quality of Life indices for various countries around the globe, extracted from the Numbeo website. The data provides valuable metrics for comparing countries based on several aspects of living standards, which can assist in decisions such as choosing a place to live or analyzing global trends in quality of life.

OBS: The code to generate this dataset is presented on: https://www.kaggle.com/code/marcelobatalhah/web-scrapping-quality-of-life-index

Columns in the Dataset

1. Rank:
   The global rank of the country based on its Quality of Life Index according to Year (1 = highest quality of life).

2. Country:
   The name of the country.

3. Quality of Life Index:
   A composite index that evaluates the overall quality of life in a country by combining other indices, such as Safety, Purchasing Power, and Health Care.

4. Purchasing Power Index:
   Measures the relative purchasing power of the average consumer in a country compared to New York City (baseline = 100).

5. Safety Index:
   Indicates the safety level of a country. A higher score suggests a safer environment.

6. Health Care Index:
   Evaluates the quality and accessibility of healthcare in the country.

7. Cost of Living Index:
   Measures the relative cost of living in a country compared to New York City (baseline = 100).

8. Property Price to Income Ratio:
   Compares the affordability of real estate by dividing the average property price by the average income.

9. Traffic Commute Time Index:
   Reflects the average time spent commuting due to traffic.

10. Pollution Index:
    Rates the level of pollution in the country (air, water, etc.).

11. Climate Index:
    Rates the favorability of the climate in the country (higher = more favorable).

12. Year:
    Year when the metrics were extracted.

Key Insights from the Dataset

• The Quality of Life Index aggregates multiple indicators, making it a useful single metric to compare countries.
• Specific indices such as Safety Index or Health Care Index allow for focused analysis on areas like security or healthcare quality.
• Cost of Living Index and Purchasing Power Index can help determine the affordability of living in each country.

How the Data Was Collected

• The dataset was built using web scraping techniques in Python.
• The data was extracted from the "Quality of Life Rankings by Country" page on Numbeo.
• Libraries used:
  • requests for retrieving webpage content.
  • BeautifulSoup for parsing the HTML and extracting relevant information.
  • pandas for organizing and storing the data in a structured format.

Possible Applications

1. Relocation Decision Making:
   Use the dataset to compare countries and identify destinations with high quality of life, safety, and healthcare.

2. Global Analysis:
   Perform exploratory data analysis (EDA) to identify trends and correlations across quality of life metrics.

3. Visualization:
   Plot global maps, bar charts, or other visualizations to better understand the data.

4. Predictive Modeling:
   Use this dataset as a base for machine learning tasks, like predicting Quality of Life Index based on other metrics.

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

Geopolitical and economic shocks are destabilising living conditions globally. As instability intensifies, liveability scores for traditionally attractive cities are declining, while those for emerging cities are improving in education and infrastructure.

EIU’s Liveability Index ranks cities based on more than 30 indicators across five categories: stability, healthcare, culture and environment, education and infrastructure.

Each indicator is rated as acceptable, tolerable, uncomfortable, undesirable or intolerable. The ratings are then weighted to provide a score from 1 to 100.

The liveability rating of a city is given both as an overall score and as a score for each category. An overall position in the ranking of 173 cities is also provided.

Title: Top Cities Worldwide: Quality of Life Index 2024 Subtitle: Ranking the World's Best Cities for Living Based on Key Metrics

Source of Data: The dataset was collected from Numbeo.com, a publicly accessible database that provides data on various quality-of-life indicators across cities worldwide. Numbeo aggregates user-contributed data validated through statistical methods to ensure reliability.

Data Collection Method: Data was acquired through web scraping. Care was taken to follow ethical web scraping practices, adhering to Numbeo’s terms of service and respecting their robots.txt file.

Columns Description:

The dataset includes the following columns:

• Rank: City ranking based on the Quality of Life Index.
• City: Name of the city.
• Country: Country where the city is located.
• Quality of Life Index: Overall index measuring quality of life, calculated based on various sub-indices.
• Purchasing Power Index: Measures relative purchasing power in the city.
• Safety Index: Indicates how safe the city is based on crime rates.
• Health Care Index: Reflects the quality and accessibility of healthcare services.
• Cost of Living Index: Represents the cost of living, including housing, food, and transportation.
• Property Price to Income Ratio: A measure of housing affordability, calculated as the ratio of property prices to average incomes.
• Traffic Commute Time Index: Average time spent commuting within the city.

Limitations and Considerations:

• User-Generated Data: Since data on Numbeo is user-contributed, it may be subject to biases.
• Data Update Frequency: As Numbeo updates its data regularly, the dataset represents a snapshot in time and may require periodic updates.

Usage Note: The dataset is intended for research and analytical purposes. Users should verify the data's applicability for their specific use cases, considering the limitations mentioned above.

In 2024, Paris was the most livable city worldwide according to the Global Power City Index (GCPI), with ******points. Furthermore, Madrid was the second most livable city with ******points, while Tokyo was the third with ******points. The criteria taken into consideration include, among others, costs and ease of living, number of retail shops and restaurants, and availability of medical services.

Quality of Life Indicators by Country

Overview

This dataset provides a detailed view of quality-of-life metrics for various countries, sourced from Numbeo. It includes indicators such as purchasing power, safety, health care, climate, cost of living, property prices, traffic, pollution, and overall quality of life. The data combines both numerical scores and descriptive categories to give a comprehensive understanding of these metrics.

Dataset Content

The dataset includes the following columns:

1. country: Name of the country.
2. Purchasing Power Value: Numeric score for purchasing power.
3. Purchasing Power Category: Qualitative category for purchasing power.
4. Safety Value: Numeric safety index score.
5. Safety Category: Qualitative safety category.
6. Health Care Value: Numeric score for health care quality.
7. Health Care Category: Qualitative health care category.
8. Climate Value: Numeric score for climate quality.
9. Climate Category: Qualitative climate category.
10. Cost of Living Value: Numeric score for cost of living.
11. Cost of Living Category: Qualitative cost of living category.
12. Property Price to Income Value: Numeric ratio of property price to income.
13. Property Price to Income Category: Qualitative property price-to-income category.
14. Traffic Commute Time Value: Numeric score for commute times.
15. Traffic Commute Time Category: Qualitative traffic commute category.
16. Pollution Value: Numeric pollution index score.
17. Pollution Category: Qualitative pollution category.
18. Quality of Life Value: Numeric score for overall quality of life.
19. Quality of Life Category: Qualitative quality of life category.

Source

The data from Numbeo, a global database providing cost of living, housing indicators, health care, traffic, crime, and pollution statistics for cities and countries.

Usage

This dataset can be used for: - Comparative analysis of quality-of-life indicators across countries. - Data visualization and storytelling for social, economic, or environmental trends. - Statistical modeling or machine learning projects on global living conditions.

Acknowledgments

The data was collected from Numbeo, which aggregates user-contributed data from individuals worldwide. Proper citation and credit to Numbeo are appreciated when using this dataset.

License

This data provided under Free Data Usage License by number. """

Official ranking of 173 global cities based on stability, healthcare, culture, education, and infrastructure by the Economist Intelligence Unit

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

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

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

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

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

A list of some key resources for comparing London with other world cities. European Union/Eurostat, Urban Audit Arcadis, Sustainable cities index AT Kearney, Global Cities Index McKinsey, Urban world: Mapping the economic power of cities Knight Frank, Wealth report OECD, Better Life Index UNODC, Statistics on drugs, crime and criminal justice at the international level Economist, Hot Spots Economist, Global Liveability Ranking and Report August 2014 Mercer, Quality of Living Reports PWC, Cities of opportunity BCG, Decoding Global Talent Forbes, World's most influential cities Mastercard, Global Destination Cities Index Numbeo, Database of user contributed data

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

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

Abstract The current panorama of urban problems highlights issues related to a diminishing and deteriorating quality of life in cities. In this context, green areas are important variables for the evaluation of urban life quality and wellbeing. Hence, the purpose of this study is to evaluate the quality of life of people, based on the perception of regular users of two urban parks in the city of João Pessoa-PB, northeastern Brazil, the Solon de Lucena Park (PSL) and the ArrudaCâmaraZoobotanical Park (Pzac). For this purpose, the authors developed the Wellbeing Index in Green Areas (Ibeav), a methodology proposed and adapted from the Urban Wellbeing Index (Ibeu) with indicators developed from the following dimensions: mobility and accessibility, urban environmental conditions, urban housing conditions, urban collective services and urban infrastructure. The results obtained for the Ibeav were 0.80155 for the PSL and 0.7716 for the Pzac, indicating good and average quality of life conditions, respectively. Hence, through the methodology applied in the two urban parks, it was possible to identify the dimensions and indicators that contribute to expand and/or reduce the quality-of-life index of the evaluated spaces.

📌 Overview

"Living in India 2025" is a synthetic yet realistic dataset that explores the cost of living and quality of life across 200 Indian cities. It combines key indicators such as average rent, food cost, internet speed, healthcare rating, safety score, and happiness index to help analysts, students, and data enthusiasts perform in-depth comparisons and uncover meaningful insights. 📊 What’s Inside

The dataset contains 200 rows (one per city) and the following columns:

City – Name of the Indian city.

Average Rent (₹) – Estimated monthly rent for a standard apartment.

Food Cost (₹) – Average monthly food expenses per person.

Internet Speed (Mbps) – Typical broadband download speed.

Healthcare Rating (1-10) – Quality and accessibility of healthcare services.

Safety Score (1-10) – Perceived safety level in the city.

Happiness Index (1-10) – Overall life satisfaction rating.

💡 Potential Insights You Can Explore

Which Indian cities provide the best happiness for the least money?

How safety and happiness correlate across regions.

Which cities are most digital-nomad-friendly based on internet speed and cost.

Regional patterns in healthcare quality vs cost of living.

🛠 Ideal For

Exploratory Data Analysis (EDA)

Data Visualization Projects

Regression & Correlation Studies

Geospatial Mapping

Urban Economics & Policy Research

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

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

In 2024, Bangladesh's capital Dhaka had a pollution index score of ****, the highest among megacities in the Asia-Pacific region. In contrast, Japan's capital Tokyo had a pollution index score of **** that year. Megacities on course for growth The United Nations defines megacities as cities with over ten million inhabitants. The population living in megacities has doubled in size in the last twenty years and is expected to rise even more until 2035. Today, the Asia-Pacific region is home to the highest number of megacities, with China and India alone accounting for around half of all megacities worldwide. At the same time, only half of the population in Asia is living in cities. This figure is also expected to rise exponentially over the next years, especially with much of the younger population migrating to larger cities. The growth of megacities and their higher population densities bring along several environmental problems. Exposure to pollution in India The most populated cities in APAC are located in Japan, China and India. As seen above, India's capital also falls among the top three most polluted megacities in the region and ranks second among the most polluted capital cities worldwide with an average PM2.5 concentration. As one of the fastest emerging economies in the world, India's rapid urbanization and industrialization have led to high pollution rates in different areas. The volume of emissions from coal-fired power plants has led to electricity and heat accounting for the largest share of greenhouse gas emissions in India. The country is also among the nations with the highest population share exposed to hazardous concentrations of air pollution worldwide.

Abstract

The Gauteng City-Region Observatory (GCRO) was established in 2008 as a partnership between the University of Johannesburg (UJ), the University of the Witwatersrand (Wits) and the Gauteng Provincial Government (GPG), with municipalities in Gauteng also represented. The objective of the GCRO is to inform policy in various spheres of the Gauteng government with data on the quality of the lives of people who live in Guateng. The Quality of Life Survey IV was conducted in 2015 and 2016.

Geographic coverage

The Quality of Life Survey covers the whole of Gauteng and also areas with GCR 'footprints' in the four neighbouring provinces of Free State, North West, Limpopo and Mpumalanga. The lowest level of geographic aggregation of the data is municipality.

Analysis unit

Household and individual

Universe

The Gauteng City-Region Observatory's Quality of Life Survey covers all household residents of Gauteng and selected areas of the four neighbouring provinces of Free State, North West, Limpopo and Mpumalanga.

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey instrument (questionnaire) which was used was provided by the GCRO. The instrument was similar to the questionnaire used for the initial Quality of Life survey, with new questions being added only where questions from the previous survey were removed. This was done with the intention of keeping the duration of the survey the same as the initial one. The survey instrument was a 20 page questionnaire, broken up into 12 sections. The bulk of the possible answers were pre-defined, such that most of the survey could be answered using a combination of tick-boxes or by writing down a number from a predefined set. There are therefore not many open-ended questions in the survey.

Quality of Life Indicators: Body mass index by autonomous community. Annual. Autonomous Communities and Cities.