Mogadishu in Somalia led the ranking of cities with the highest population density in 2025, with ****** residents per square kilometer. When it comes to countries, Monaco is the most densely populated state worldwide.

Monaco led the ranking for countries with the highest population density in 2024, with nearly 26,000 residents per square kilometer. The Special Administrative Region of Macao came in second, followed by Singapore. The world’s second smallest country Monaco is the world’s second-smallest country, with an area of about two square kilometers and a population of only around 40,000. It is a constitutional monarchy located by the Mediterranean Sea, and while Monaco is not part of the European Union, it does participate in some EU policies. The country is perhaps most famous for the Monte Carlo casino and for hosting the Monaco Grand Prix, the world's most prestigious Formula One race. The global population Globally, the population density per square kilometer is about 60 inhabitants, and Asia is the most densely populated region in the world. The global population is increasing rapidly, so population density is only expected to increase. In 1950, for example, the global population stood at about 2.54 billion people, and it reached over eight billion during 2023.

Mexico City ranked as the most densely populated city in Mexico as of 2023. The capital recorded ***** inhabitants per square kilometer. Xalapa and Acapulco followed with ***** and ***** inhabitants per square kilometer, respectively.

As of 2023, the top five most densely populated cities in Latin America and the Caribbean were in Colombia. The capital, Bogotá, ranked first with over ****** inhabitants per square kilometer.

This map shows four of these densely populated areas are in California. The San Francisco-Oakland and San Jose Urban Areas rank second and third, respectively. That the New York Metropolitan area ranks fifth on this list shows that this density ranking is greatly affected by the nature of the land area designated as urban. Census Urban Areas comprise an urban core and associated suburbs. California's urban and suburban areas are more uniform in density when compared to New York's urban core and suburban periphery which have vastly different densities. Delano ranks fourth because it has a very small land area and its population is augmented by two large California State Prisons housing 10,000 inmates.

Context

This is a dataset of the most highly populated city (if applicable) in a form easy to join with the COVID19 Global Forecasting (Week 1) dataset. You can see how to use it in this kernel

Content

There are four columns. The first two correspond to the columns from the original COVID19 Global Forecasting (Week 1) dataset. The other two is the highest population density, at city level, for the given country/state. Note that some countries are very small and in those cases the population density reflects the entire country. Since the original dataset has a few cruise ships as well, I've added them there.

Acknowledgements

Thanks a lot to Kaggle for this competition that gave me the opportunity to look closely at some data and understand this problem better.

Inspiration

Summary: I believe that the square root of the population density should relate to the logistic growth factor of the SIR model. I think the SEIR model isn't applicable due to any intervention being too late for a fast-spreading virus like this, especially in places with dense populations.

After playing with the data provided in COVID19 Global Forecasting (Week 1) (and everything else online or media) a bit, one thing becomes clear. They have nothing to do with epidemiology. They reflect sociopolitical characteristics of a country/state and, more specifically, the reactivity and attitude towards testing.

The testing method used (PCR tests) means that what we measure could potentially be a proxy for the number of people infected during the last 3 weeks, i.e the growth (with lag). It's not how many people have been infected and recovered. Antibody or serology tests would measure that, and by using them, we could go back to normality faster... but those will arrive too late. Way earlier, China will have experimentally shown that it's safe to go back to normal as soon as your number of newly infected per day is close to zero.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F197482%2F429e0fdd7f1ce86eba882857ac7a735e%2Fcovid-summary.png?generation=1585072438685236&alt=media" alt="">

My view, as a person living in NYC, about this virus, is that by the time governments react to media pressure, to lockdown or even test, it's too late. In dense areas, everyone susceptible has already amble opportunities to be infected. Especially for a virus with 5-14 days lag between infections and symptoms, a period during which hosts spread it all over on subway, the conditions are hopeless. Active populations have already been exposed, mostly asymptomatic and recovered. Sensitive/older populations are more self-isolated/careful in affluent societies (maybe this isn't the case in North Italy). As the virus finishes exploring the active population, it starts penetrating the more isolated ones. At this point in time, the first fatalities happen. Then testing starts. Then the media and the lockdown. Lockdown seems overly effective because it coincides with the tail of the disease spread. It helps slow down the virus exploring the long-tail of sensitive population, and we should all contribute by doing it, but it doesn't cause the end of the disease. If it did, then as soon as people were back in the streets (see China), there would be repeated outbreaks.

Smart politicians will test a lot because it will make their condition look worse. It helps them demand more resources. At the same time, they will have a low rate of fatalities due to large denominator. They can take credit for managing well a disproportionally major crisis - in contrast to people who didn't test.

We were lucky this time. We, Westerners, have woken up to the potential of a pandemic. I'm sure we will give further resources for prevention. Additionally, we will be more open-minded, helping politicians to have more direct responses. We will also require them to be more responsible in their messages and reactions.

As of July 2023, Monaco is the country with the highest population density worldwide, with an estimated population of nearly ****** per square kilometer.

Census data reveals that population density varies noticeably from area to area. Small area census data do a better job depicting where the crowded neighborhoods are. In this map, the yellow areas of highest density range from 30,000 to 150,000 persons per square kilometer. In those areas, if the people were spread out evenly across the area, there would be just 4 to 9 meters between them. Very high density areas exceed 7,000 persons per square kilometer. High density areas exceed 5,200 persons per square kilometer. The last categories break at 3,330 persons per square kilometer, and 1,500 persons per square kilometer.This dataset is comprised of multiple sources. All of the demographic data are from Michael Bauer Research with the exception of the following countries:Australia: Esri Australia and MapData ServicesCanada: Esri Canada and EnvironicsFrance: Esri FranceGermany: Esri Germany and NexigaIndia: Esri India and IndicusJapan: Esri JapanSouth Korea: Esri Korea and OPENmateSpain: Esri España and AISUnited States: Esri Demographics

United States US: Population Density: People per Square Km data was reported at 35.608 Person/sq km in 2017. This records an increase from the previous number of 35.355 Person/sq km for 2016. United States US: Population Density: People per Square Km data is updated yearly, averaging 26.948 Person/sq km from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 35.608 Person/sq km in 2017 and a record low of 20.056 Person/sq km in 1961. United States US: Population Density: People per Square Km data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Population and Urbanization Statistics. Population density is midyear population divided by land area in square kilometers. Population is based on the de facto definition of population, which counts all residents regardless of legal status or citizenship--except for refugees not permanently settled in the country of asylum, who are generally considered part of the population of their country of origin. Land area is a country's total area, excluding area under inland water bodies, national claims to continental shelf, and exclusive economic zones. In most cases the definition of inland water bodies includes major rivers and lakes.; ; Food and Agriculture Organization and World Bank population estimates.; Weighted average;

Government city The Hague was the most densely populated city in the Netherlands in 2019, with a population density of nearly ***** people per square kilometer. Perhaps surprisingly, Amsterdam is not the most densely populated city in the country, ranking fourth on the list of most populous cities in the Netherlands in 2019.

In 2023, Washington, D.C. had the highest population density in the United States, with 11,130.69 people per square mile. As a whole, there were about 94.83 residents per square mile in the U.S., and Alaska was the state with the lowest population density, with 1.29 residents per square mile. The problem of population density Simply put, population density is the population of a country divided by the area of the country. While this can be an interesting measure of how many people live in a country and how large the country is, it does not account for the degree of urbanization, or the share of people who live in urban centers. For example, Russia is the largest country in the world and has a comparatively low population, so its population density is very low. However, much of the country is uninhabited, so cities in Russia are much more densely populated than the rest of the country. Urbanization in the United States While the United States is not very densely populated compared to other countries, its population density has increased significantly over the past few decades. The degree of urbanization has also increased, and well over half of the population lives in urban centers.

The population density picture of Boston is generally a story of two Bostons: the high density central and northern neighborhoods, and the low density southern neighborhoods.The highest density areas of Boston are particularly concentrated in Brighton, Allston, and the Fenway area, areas of the city with large numbers of college students and young adults. There is also high population density in areas such as the Back Bay, the South End, Charlestown, the North End, and South Boston. These are all relatively small areas geographically, but have housing stock conducive to population density (e.g. multi-family dwelling units, row housing, large apartment buildings). The southern neighborhoods, specifically Hyde Park and West Roxbury, have significant numbers of people living in them, but lots sizes tend to be much larger. These areas of the city also tend to have more single family dwelling units. In that, there are fewer people per square mile than places north in the city. Census data reveals that population density varies noticeably from area to area. Small area census data do a better job depicting where the crowded neighborhoods are. In this map, areas of highest density exceed 30,000 persons per square kilometer. Very high density areas exceed 7,000 persons per square kilometer. High density areas exceed 5,200 persons per square kilometer. The last categories break at 3,330 persons per square kilometer, and 1,500 persons per square kilometer.How to make this map for your city

Naples is the Italian city with the highest population density. As of 2025, the largest south Italian city counts 7,780 inhabitants per square kilometer. Milan followed with around 7,500 residents per square kilometer, whereas Rome, the largest Italian city, registered a population density of only 2,135 people, 5,645 inhabitants per square kilometer less than Naples.

The USACE SACS Population and Infrastructure Exposure Index combines the normalized score of each Infrastructure and Population Index, and then re-normalizes these values across the study area.The SACS Infrastructure Exposure Index was created by spatially joining 52 infrastructure datasets from DHS’s HIFLD database and Military Installation Ranges and Training Areas from the DOD OSD to census tracts across the study area. The infrastructure elements were assigned a weighting value consistent with the NACCS Tier 1 Methodology (URL). The weighted infrastructure element values were then aggregated by count within each census tract, and then divided by the census tract area to generate an infrastructure density value. This infrastructure density value was then enumerated and ranked across all census tracts, and normalized to 0 to 1 using these rankings. The census tract feature dataset was then converted to a grid using these normalized values for aggregation with other SACS datasets.The SACS Population Exposure Index depicts the 2015 Census – American Community Survey data as population density. This population density is calculated as persons per square mile, per census tract. The census tracts depicted are within the USACE South Atlantic Coastal Study boundaries with an inland extent of NOAA’s Category 5 Maximum of Maximum Storm Surge Hazard Layer. The index ranks all census tracts within the SACS study area on a percentile index by population density. These data are represented on a normalized scale of 0 to 1, with 1 being the most densely populated census tract in the study area, and zero being the least densely populated census tract. The census tract feature dataset was then converted to a grid using these normalized values for aggregation with other SACS datasets.References:DHS HIFLDhttps://gii.dhs.gov/hifld/DOD OSDhttps://www.acq.osd.mil/dodsc/fast41_gisdatasets.htmlUS Censushttps://www.census.gov/NACCS (appendix C, page 107)https://www.nad.usace.army.mil/Portals/40/docs/NACCS/NACCS_Appendix_C.pdfThis Tier 1 dataset is available for download here:Tier 1 Risk Assessment Download

A dataset listing Florida cities by population for 2024.

Global Urban Area Indicators

Population Density, Rent & Real Estate Prices, Transport Times & Land Use

By [source]

About this dataset

This dataset contains comprehensive information on population densities, rental and real estate prices, transport times and land uses from around the world. It provides an in-depth range of cities, allowing for a comprehensive snapshot of worldwide urban development. Use this data to uncover how regional differences in population, infrastructure and regional designations can affect mobility patterns as well as economic and environmental issues linked to city life. Gridded key indicators including public transport, private cars and much more are included for analysis purposes within a fully reproducible workflow system. This data is an invaluable asset for understanding the complexities of global urban areas from both social and ecological perspectives

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

This dataset provides a comprehensive comparison of population density, rent and real estate prices, transport times and land use across 192 different cities around the world. As such, it offers a valuable resource for studying the effects of urban area development on aspects such as mobility and living patterns around the world. In this guide we'll provide an overview of how to use this data set to best gain insight.

• Get familiar with the structure of the data: The dataset contains more than 200 columns divided among four main categories: population density, rent/real estate prices, transport time & information and land use information from government sources and survey reports. All columns are clearly labeled meaning that it's easy to quickly identify which column contains what kind of information
• Identify important variables for your particular study topic: Depending upon your particular goal or research question you may want to focus on certain columns or categories more than others in order to reveal patterns between areas or locations within cities or regions
• Analyze existing correlations between variables & locations: Once you're familiar with all available data then you can start analyzing existing correlations - either visualizing them as maps or charts in multiple software packages like Tableau or R - by joining above mentioned data set with location coordinates (latitude/longitude) provided in the global urban indicators dataset

Research Ideas

• Analyzing the correlation between real estate prices, transport times and land use in urban areas to make decisions about how to improve city infrastructure.
• Examining the impact of different external factors on population densities, such as transportation links and natural preservation policies.
• Comparing urban development indicators across different cities around the world to better understand global trends in urbanization

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: TransportData.csv | Column name | Description | |:--------------------|:---------------------------------------------------------------| | X | X coordinate of the city. (Numeric) | | Y | Y coordinate of the city. (Numeric) | | Area | Area of the city. (Numeric) | | City | Name of the city. (String) | | Country | Country of the city. (String) | | Continent | Continent of the city. (String) | | dCenter | Distance to the city center. (Numeric) | | TransportSource | Source of the transport data. (String) | | RushHour | Whether the transport data is from rush hour or not. (Boolean) | | TransportYear | Year of the transport data. (Numeric) | | DistanceDriving | Driving distance. (Numeric) ...

The "Major Cities" layer is derived from the "World Cities" dataset provided by ArcGIS Data and Maps group as part of the global data layers made available for public use. "Major cities" layer specifically contains National and Provincial capitals that have the highest population within their respective country. Cities were filtered based on the STATUS (“National capital”, “National and provincial capital”, “Provincial capital”, “National capital and provincial capital enclave”, and “Other”). Majority of these cities within larger countries have been filtered at the highest levels of POP_CLASS (“5,000,000 and greater” and “1,000,000 to 4,999,999”). However, China for example, was filtered with cities over 11 million people due to many highly populated cities. Population approximations are sourced from US Census and UN Data. Credits: ESRI, CIA World Factbook, GMI, NIMA, UN Data, UN Habitat, US Census Bureau Disclaimer: The designations employed and the presentation of material at this site do not imply the expression of any opinion whatsoever on the part of the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.

Population density (people per sq. km of land area) in Brazil was reported at 25.26 sq. Km in 2023, according to the World Bank collection of development indicators, compiled from officially recognized sources. Brazil - Population density (people per sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on November of 2025.

**🌍 World Countries Dataset This World Countries Dataset contains detailed information about countries across the globe, offering insights into their geographic, demographic, and economic characteristics.

It includes various features such as population, area, GDP, languages, and regional classifications. This dataset is ideal for projects related to data visualization, statistical analysis, geographical studies, or machine learning applications such as clustering or classification of countries.

This dataset was manually compiled/collected from reliable open data sources (e.g., Wikipedia, World Bank, or other governmental datasets).

**🔍 Sample Questions Explored Using Python: - Q. 1) Which countries have the highest and lowest population? - Q. 2) What is the average area (in sq. km) of countries in each region? - Q. 3) Which countries have more than 100 million population and GDP above $1 trillion? - Q. 4) Which languages are most commonly spoken across countries? - Q. 5) Show a bar graph comparing GDPs of G7 nations. - Q. 6) How many countries are there in each continent or region? - Q. 7) Which countries have both a high population density and low GDP per capita? - Q. 8) Create a world map visualization of population or GDP distribution. - Q. 9) What are the top 10 most densely populated countries? - Q. 10) How many landlocked countries are there in the world?

**🧾 Features / Columns in the Dataset: - Country: The name of the country (e.g., "Pakistan", "France").

• Capital: The capital city of the country.

• Region: Broad geographical region (e.g., "Asia", "Europe").

• Subregion: More specific geographical grouping (e.g., "Southern Asia").

• Population: Total population of the country.

• Area (sq. km): Total land area in square kilometers.

• Population Density: Number of people per square kilometer.

• GDP (USD): Gross Domestic Product (in U.S. dollars).

• GDP per Capita: GDP divided by the population.

• Official Languages: Officially recognized language(s) spoken.

• Currency: Name of the currency used.

• Timezones: Timezones in which the country falls.

• Borders: List of bordering countries (if any).

• Landlocked: Whether the country is landlocked (Yes/No).

• Latitude / Longitude: Coordinates for geographical plotting.

The SACS Population Index depicts the 2015 Census – American Community Survey data as population density. This population density is calculated as persons per square mile, per census tract. The census tracts depicted are within the USACE South Atlantic Coastal Study boundaries with an inland extent of NOAA’s Category 5 Maximum of Maximum Storm Surge Hazard Layer. The index ranks all census tracts within the SACS study area on a percentile index by population density. These data are represented on a normalized scale of 0 to 1, with 1 being the most densely populated census tract in the study area, and zero being the least densely populated census tract. The census tract feature dataset is converted to a 30m-by-30m raster grid for aggregation with other SACS datasets.This Tier 1 dataset is available for download here:Tier 1 Risk Assessment Download