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

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.

In 2024, Hungary's capital, Budapest, was the most livable city in the country. It was followed by Veszprém and Pécs.

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

Live city rankings with ignore politics political preference weighting applied. Showing 1-50 of 386 cities.

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.

Real-time city rankings with political preference weighting for Ignore Politics

Access to facilities, services and socio-economic opportunities plays a critical role in the growth and decline of cities and human settlements. Previous attempts to explain changes in socio-economic indicators by differences in accessibility have not been convincing as countries with highly developed transport infrastructure have only seen marginal benefits of infrastructure improvements. Australia offers an ideal case for investigating the effects of accessibility on development since it is seen as home to some of the most liveable cities in the world while, at the same time, it also has some of the most isolated settlements. We investigate herein the connectivity and accessibility of all 1814 human settlements (population centers exceeding 200 persons) in Australia, and how they relate to the socio-economic characteristics of, and opportunities in, each population center. Assuming population as a proxy indicator of available opportunities, we present a simple ranking metric for a settlement using the number of population and the distance required to access all other settlements (and the corresponding opportunities therein). We find a strikingly unequal distribution of access to opportunities in Australia, with a marked prominence of opportunities in capital cities in four of the eight states. The two largest cities of Sydney and Melbourne have a dominant position across all socio-economic indicators, compared to all the other cities. In general, we observe across all the settlements that a decrease in access to opportunities is associated with relatively greater socio-economic disadvantage including increased median age and unemployment rate and decreased median household income. Our methodology can be used to better understand the potential benefits of improved accessibility based on infrastructure development, especially for remote areas and for cities and towns with many socio-economically disadvantaged population.

Context

This dataset contains some data to try to answer the question, "What's the best city to live and work in as a Data Scientist?" I include data from the U.S. News & World Report Best Places to Live and Best States Rankings; city scores from Nomad List; rent indices from Zillow; and the number of jobs openings on Indeed.com. All data is publicly available online and manually compiled by myself.

Data Sources

• U.S. News Best Places to Live in the U.S.
• U.S. News Best States Rankings
• Nomad List
• Zillow Research Housing Data
• Wikipedia List of metropolitan statistical areas
• Indeed.com

The U.S. News Best Places and Best States Rankings are updated annually. They were last updated in Dec 2019, so I assume the next update will come in Dec 2020.

Dataset Info

Data from Best Places

For data points from the U.S. News Best Places, drill down into the page for each metro area. I have to manually collect these data points, and not all of them are fully populated. If there's interest, I'll upload a new version with more data filled in.

About Nomad Scores

Nomad List publishes scores for each city that update in real time every 10 mins. These scores are affected by the current weather in each city. Therefore, the scores vary quite a bit seasonally as well as during the day. Learn more here. I've sampled the scores at different times of year and different times of day. Timestamps are in ISO 8601 format. Nomad Scores are not available for all metros in the dataset.

Inspiration

1. Can you combine the data for each metro into an aggregate score (or multiple scores) to determine the best places to live?
2. Should climate be factored into your scores? If so, how?
3. Can you aggregate violent and property crime rates into an aggregate crime rate?
4. Can you combine a quantitative score with a ranking measure to obtain a total score?
5. Can you aggregate all the sampled Nomad Scores to obtain an overall Nomad Score for the metro? How should you weight each score?
6. Should you include the number of Data Scientist job postings in your total score for the metro? If so, how?

Cities with the Best Work-Life Balance 2022 Three aspects of work-life balance 1. Work Intensity 2. Society and Institutions 3. City Liveability

cover image credit: https://www.pexels.com/photo/people-working-in-the-office-4623092/

Where should we live in the next 10 years? Where should we settle down without relying on public transport? Which city should we move to without fearing losing our homes?

As weather patterns become more unpredictable with aggressive changes in temperatures, I collected some data below to see if there would be a city that could help assess our answers to the prior questions. I am curious to see if cities that typically have great infrastructure for walking, biking or public transit will be better prepared than those that are more typically car centric. Whichever you prefer, we can have a sense on where you might be migrating, and to which areas.

Here's how the data was collected:

1. Rhodium & ProPublica's combined work on counties risk factors against climate change across continental US (excludes Hawaii, Alaska, Puerto Rico and Guam. Washington D.C, is excluded as it does not have a county.)
2. The available Walk Score of major cities that have a population above 100,000 represented. Cities like Delaware's Wilmington or Maine's Portland are not considered as it falls under a small-city definition
3. Maximum temperatures (for select cities): This dataset is collected from the UC Davis Department of Agricultural and Resource by Prof. Aaron Smith. I selected a monthly temporal unit and county spatial unit ranging from 2019 - 2024 July. This dataset is extracted based on the average of highest temperatures in each selected counties. I did not use the overall daily average as it can easily shadow the extremities of temperature fluctuations.

The columns have different rating systems. The counties have all major climate risks expected in the future, while corresponding cities in each county have walking, transit and biking scores to assess livability without cars.

Understanding County Climate Risks The counties were were represented on a 1- 10 scale, based on RCP 8.5 levels. Here are the following explanations (0 = lowest, 10 = highest)

1) Heat: Heat is one of the largest drivers changing the niche of human habitability. Rhodium Group researchers estimate that, between 2040 and 2060 extreme temperatures, many counties will face extremely high temperatures for half a year. The measure shows how many weeks per year will we anticipate temperatures to soar above 95 degrees. (0 = 0 weeks, 10 = 26 weeks).

2) Wet Bulb: Wet bulb temperatures occur when heat meets excessive humidity. This is commonplace across cities that have a urban island heat effects (dense concentration of pavements, less nature, higher chances of absorbing heat). That combination creates wet bulb temperatures, where 82 degrees can feel like southern Alabama on its hottest day, making it dangerous to work outdoors and for children to play school sports. As wet bulb temperatures increase even higher, so will the risk of heat stroke — and even death. The measure shows how many days will a county experience high wet bulb temperatures yearly, from 2040 to 2060. (0 = 0 days, 10 = 70 days)

3) Farm Crop Yield: With rising temperatures, it will become more difficult to grow food. Corn and soy are the most prevalent crops in the U.S. and the basis for livestock feed and other staple foods, and they have critical economic significance. Because of their broad regional spread, they offer the best proxy for predicting how farming will be affected by rising temperatures and changing water supplies. As corn and soy production gets more sensitive to heat than drought, the US will see a huge continental divide between cooler counties now having more ability to produce, while current warmer counties loosing all abilities to produce basic crops. The expected measure shows the percent decline yields from 2040 to 2060 (0 = -20.5% decline, 10 = 92% decline).

4) Sea Level Rise: As sea levels rise, the share of property submerged by high tides increases dramatically, affecting a small sliver of the nation's land but a disproportionate share of its population. The rating measures how much of property in the county will go below high tide from 2040 to 2060 (0 = 0%, 10 = 25%).

5) Very Large Fires: With heat and evermore prevalent drought, the likelihood that very large wildfires (ones that burn over 12,000 acres) will affect U.S. regions increases substantially, particularly in the West, Northwest and the Rocky Mountains. The rating calculates how many average number of large fires will we expect to see per year (0 = N/A, 10 = 2.45) from 2040 to 2071.

6) Economic Damages: Rising energy costs, lower labor productivity, poor crop yields and increasing cr...

The City of Austin’s vision is to make Austin the most livable city in the country. The City’s mission is to be the best managed city in the country. Additionally, a set of values are shared across the organization.

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.

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.

22 factors ranking cities worldwide based on their recognition of, and initiatives to prepare for this new, up-and-coming generation.

Ever wondered which city in Greater Boston reigns supreme? 🏆 Look no further! This dataset ranks 141 cities and towns in the Greater Boston area based on a variety of factors, including:

🏡 Housing – Where can you actually afford to live? (Trick question, it’s Boston.)

🚔 Safety – Which towns are as secure as Fort Knox?

🚆 Mobility – How easy is it to get around without losing your mind?

🏥 Health – Where are the healthiest, happiest Bostonians?

🎭 Entertainment – Because life is more than just Dunkin' runs.

🌍 Diversity – The melting pot of cultures in each town.

🎓 Education – Where future Einsteins are born.

💼 Employment – Who's hiring and where are the best career opportunities?

This dataset is perfect for data exploration, visualizations, and even some lighthearted city rivalry. Whether you're a data analyst, a real estate enthusiast, or just looking to settle the debate with your friends on which Boston suburb is the best—this dataset has you covered!

🔍 Insights Await! Can you uncover hidden trends, build an interactive ranking map, or find the best place for your next move? Let's find out!

Example notebook: https://www.kaggle.com/code/michaeldelamaza/boston-city-rankings-linear-regression

Context

This data was scraped from Movinga (https://www.movinga.com/), the global moving company and determines the top cities around the world that offer the best conditions to raise a family for 2019. The dataset was extended to add geographical location in terms of the latitude and longitude of the city. The original data can be found here. From their website:

"We began the study by selecting 150 international cities that have a reputation as attractive locations for raising a family. We then split the study into numerous factors across three categories which indicate how family-friendly a location is. This included essentials that affect city livability like housing, education, employment rates, and general affordability, as well as family legislation such as the amount of paid parental leave and whether a city is inclusive for same-sex parents. ‍ It was important to include the opinions of the families who experience these cities themselves, so we commissioned two surveys of parents in each location to gain a measurement of public sentiment towards them. The first survey asked parents to indicate how they felt about their children’s safety in the community, and the second if they believed that their city was a good place for families in general. Last but not least, we looked at the attractiveness and breadth of a city’s family-oriented leisure activities. ‍ The final index combines all of these factors to determine the top cities around the world that offer the best conditions to raise a family."

Content

This data was scraped from Movinga (https://www.movinga.com/), the global moving company. The original data can be found here. The data includes 18 columns: 1. City 2. Country 3. Housing Affordability 4. Living Costs by Income 5. Unemployment (%) 6. Education 7. Safety 8. Mobility 9. Air Quality (µg/m3) 10. Healthcare 11. Kids' Activities 12. Paid Parental Leave (Days) 13. Family Inclusivity 14. Neighbourhood Safety 15. Family-Friendliness 16. Total 17. Latitude 18. Longitude

Note that all factors are scored out of 100 (unless noted otherwise above) where the higher the score, the better. Further, the score for these factors was calculated by applying a mix-max normalisation to the underlying indicator.

For further details of the scoring and methodology of the study, please refer to https://www.movinga.de/en/cities-of-opportunity-for-families.

Acknowledgements

I'd like to thank Movinga for providing the original data on their website.

A best fit lookup between 2001 Lower layer Super Output Areas (LSOA) and Major Towns and Cities (TCITY) as at December 2015 in England and Wales. The TCITY statistical geography provides a precise definition of the major towns and cities in England and Wales The geography has been developed specifically for the production and analysis of statistics, and is based on the Built-Up Areas geography that was created for the release of 2011 Census data (File Size 1.5MB). For more information and an overview of best-fitting follow this link - https://geoportal.statistics.gov.uk/datasets/f0aac7ccbfd04cda9eb03e353c613faa/about REST URL of Feature Access Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/LSOA01_TCITY15_EW_LU_3ce58949f68241f4892dc8d1d23a1bfd/FeatureServer

As of 2024, Seattle was rated the best prepared city in the United States for a smart city future, with an index score of ****. Miami and Austin followed, with **** and ****, respectively. All the top three cities ranked best in their connectivity and infrastructure preparedness.

A best fit lookup between 2011 Output Areas (OA) and Major Towns and Cities (TCITY) as at December 2015 in England and Wales. The TCITY statistical geography provides a precise definition of the major towns and cities in England and Wales. The geography has been developed specifically for the production and analysis of statistics, and is based on the Built-Up Areas geography that was created for the release of 2011 Census data. (File Size - 4 MB). REST URL of Feature Access Service – https://services1.arcgis.com/ESMARspQHYMw9BZ9/arcgis/rest/services/OA11_TCITY15_EW_LU_65267a69bf06490d81a4ee1458747f48/FeatureServer For more information and an overview of best-fitting follow this link - https://geoportal.statistics.gov.uk/datasets/f0aac7ccbfd04cda9eb03e353c613faa/about