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.

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.

This dataset provides detailed information about the population of all the 300 US Cities for the years 2024 and 2020. It includes the annual population change, population density, and the area of all the US cities.

In recent decades, some cities have seen their urban centers lose population density, as residents spread farther out to suburbs and exurbs. Others have kept populous downtowns even as their environs have grown. Population density in general has economic advantages, so one might wonder whether a loss of density, which may be a symptom of negative economic shocks, could amplify those shocks. We look at four decades of census data and show that growing cities have maintained dense urban centers, while shrinking cities have not. There are reasons to think that loss of population density at the core of the city could be particularly damaging to productivity. If this is the case, there could be productivity gains from policies aimed at reversing that trend.

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.

Vietnam Population Density: SE: Ho Chi Minh city data was reported at 4,513.100 Person/sq km in 2023. This records an increase from the previous number of 4,481.000 Person/sq km for 2022. Vietnam Population Density: SE: Ho Chi Minh city data is updated yearly, averaging 4,196.400 Person/sq km from Dec 2011 (Median) to 2023, with 13 observations. The data reached an all-time high of 4,513.100 Person/sq km in 2023 and a record low of 3,633.100 Person/sq km in 2011. Vietnam Population Density: SE: Ho Chi Minh city data remains active status in CEIC and is reported by General Statistics Office. The data is categorized under Global Database’s Vietnam – Table VN.G003: Population Density: By Provinces.

CONTENT

This is a list of cities worldwide by population density. The population, population density and land area for the cities listed are based on the entire city proper, the defined boundary or border of a city or the city limits of the city. The population density of the cities listed is based on the average number of people living per square kilometer or per square mile. This list does not refer to the population, population density or land area of the greater metropolitan area or urban area, nor particular districts in any of the cities listed.

In 2024, the population density in Ho Chi Minh City reached ***** inhabitants per square kilometer, making the largest city of Vietnam also the most crowded. Ha Noi, the capital, was much less crowded, with ***** people per square kilometer. In both Da Nang and Can Tho, this figure stayed around *** inhabitants per square kilometer.

Population Numbers By New York City Neighborhood Tabulation Areas The data was collected from Census Bureaus' Decennial data dissemination (SF1). Neighborhood Tabulation Areas (NTAs), are aggregations of census tracts that are subsets of New York City's 55 Public Use Microdata Areas (PUMAs). Primarily due to these constraints, NTA boundaries and their associated names may not definitively represent neighborhoods. This report shows change in population from 2000 to 2010 for each NTA. Compiled by the Population Division – New York City Department of City Planning.

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

In 2023, on average, ***** people were living in a square kilometer in Ho Chi Minh City, Vietnam. As the most crowded city in the country, the population density of Ho Chi Minh City has been steadily increasing during the given period. In less than ten years, this figure rose by around *** inhabitants per square kilometer.

Philippines Population Density: NCR: City of Valenzuela data was reported at 13,195.000 Person/sq km in 2015. This records an increase from the previous number of 12,236.000 Person/sq km for 2010. Philippines Population Density: NCR: City of Valenzuela data is updated yearly, averaging 9,810.500 Person/sq km from Dec 1975 (Median) to 2015, with 8 observations. The data reached an all-time high of 13,195.000 Person/sq km in 2015 and a record low of 3,204.362 Person/sq km in 1975. Philippines Population Density: NCR: City of Valenzuela data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.G005: Population Density.

About this dataset

Context

This list ranks the 19,348 cities in the United States by Canadian population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Canadian Population: This column displays the rank of city in the United States by their Canadian population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• Canadian Population: The Canadian population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as Canadian. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total United States Canadian Population: This tells us how much of the entire United States Canadian population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

This statistic shows the population density in urban areas of China in 2023, by region. In 2023, cities in Heilongjiang province had the highest population density in China with around ***** people living on one square kilometer on average. However, as the administrative areas of many Chinese cities reach beyond their contiguous built-up urban areas - and this by varying degree, the statistical significance of the given figures may be limited. By comparison, the Chinese province with the highest overall population density is Jiangsu province in Eastern China reaching about 7956 people per square kilometer in 2023.

Philippines Population Density: NCR: Quezon City data was reported at 17,099.000 Person/sq km in 2015. This records an increase from the previous number of 16,084.000 Person/sq km for 2010. Philippines Population Density: NCR: Quezon City data is updated yearly, averaging 12,123.000 Person/sq km from Dec 1975 (Median) to 2015, with 8 observations. The data reached an all-time high of 17,099.000 Person/sq km in 2015 and a record low of 5,572.883 Person/sq km in 1975. Philippines Population Density: NCR: Quezon City data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.G005: Population Density.

China is a vast and diverse country and population density in different regions varies greatly. In 2024, the estimated population density of the administrative area of Shanghai municipality reached about 3,911 inhabitants per square kilometer, whereas statistically only around three people were living on one square kilometer in Tibet. Population distribution in China China's population is unevenly distributed across the country: while most people are living in the southeastern half of the country, the northwestern half – which includes the provinces and autonomous regions of Tibet, Xinjiang, Qinghai, Gansu, and Inner Mongolia – is only sparsely populated. Even the inhabitants of a single province might be unequally distributed within its borders. This is significantly influenced by the geography of each region, and is especially the case in the Guangdong, Fujian, or Sichuan provinces due to their mountain ranges. The Chinese provinces with the largest absolute population size are Guangdong in the south, Shandong in the east and Henan in Central China. Urbanization and city population Urbanization is one of the main factors which have been reshaping China over the last four decades. However, when comparing the size of cities and urban population density, one has to bear in mind that data often refers to the administrative area of cities or urban units, which might be much larger than the contiguous built-up area of that city. The administrative area of Beijing municipality, for example, includes large rural districts, where only around 200 inhabitants are living per square kilometer on average, while roughly 20,000 residents per square kilometer are living in the two central city districts. This is the main reason for the huge difference in population density between the four Chinese municipalities Beijing, Tianjin, Shanghai, and Chongqing shown in many population statistics.

Philippines Population Density: NCR: City of Manila data was reported at 71,263.000 Person/sq km in 2015. This records an increase from the previous number of 66,140.000 Person/sq km for 2010. Philippines Population Density: NCR: City of Manila data is updated yearly, averaging 65,706.000 Person/sq km from Dec 1975 (Median) to 2015, with 8 observations. The data reached an all-time high of 71,263.000 Person/sq km in 2015 and a record low of 59,164.640 Person/sq km in 1975. Philippines Population Density: NCR: City of Manila data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.G005: Population Density.

Philippines Population Density: NCR: City of Pasig data was reported at 15,586.000 Person/sq km in 2015. This records an increase from the previous number of 13,821.000 Person/sq km for 2010. Philippines Population Density: NCR: City of Pasig data is updated yearly, averaging 10,071.500 Person/sq km from Dec 1975 (Median) to 2015, with 8 observations. The data reached an all-time high of 15,586.000 Person/sq km in 2015 and a record low of 4,328.144 Person/sq km in 1975. Philippines Population Density: NCR: City of Pasig data remains active status in CEIC and is reported by Philippine Statistics Authority. The data is categorized under Global Database’s Philippines – Table PH.G005: Population Density.

Demographic indicators. Population density (inhabitants / ha) of the city of Barcelona

About this dataset

Context

This list ranks the 333 cities in the Massachusetts by Indian population, as estimated by the United States Census Bureau. It also highlights population changes in each city over the past five years.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 5-Year Estimates, including:

• 2019-2023 American Community Survey 5-Year Estimates
• 2014-2018 American Community Survey 5-Year Estimates
• 2009-2013 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by Indian Population: This column displays the rank of city in the Massachusetts by their Indian population, using the most recent ACS data available.
• City: The City for which the rank is shown in the previous column.
• Indian Population: The Indian population of the city is shown in this column.
• % of Total City Population: This shows what percentage of the total city population identifies as Indian. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total Massachusetts Indian Population: This tells us how much of the entire Massachusetts Indian population lives in that city. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: This column displays the rank trend across the last 5 years.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.