Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata. DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted. REGION: Africa SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available. Pakistan data available from WorldPop here.

The population density in Pakistan increased by *** inhabitants per square kilometer (+**** percent) in 2022. Therefore, the population density in Pakistan reached a peak in 2022 with ****** inhabitants per square kilometer. Notably, the population density continuously increased over the last years.Population density refers to the number of people living in a certain country or area, given as an average per square kilometer. It is calculated by dividing the total midyear population by the total land area.Find more key insights for the population density in countries like India and Sri Lanka.

Pakistan PK: Population Density: People per Square Km data was reported at 255.573 Person/sq km in 2017. This records an increase from the previous number of 250.627 Person/sq km for 2016. Pakistan PK: Population Density: People per Square Km data is updated yearly, averaging 135.674 Person/sq km from Dec 1961 (Median) to 2017, with 57 observations. The data reached an all-time high of 255.573 Person/sq km in 2017 and a record low of 59.652 Person/sq km in 1961. Pakistan PK: 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 Pakistan – Table PK.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;

Population density (people per sq. km of land area) in Pakistan was reported at 316 sq. Km in 2022, according to the World Bank collection of development indicators, compiled from officially recognized sources. Pakistan - Population density (people per sq. km) - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

316.1 (people per sq. km) in 2022. Population density is midyear population divided by land area in square kilometers.

Pakistan PK: Population Density: Inhabitants per sq km data was reported at 316.130 Person in 2022. This records an increase from the previous number of 310.660 Person for 2021. Pakistan PK: Population Density: Inhabitants per sq km data is updated yearly, averaging 233.090 Person from Dec 1990 (Median) to 2022, with 33 observations. The data reached an all-time high of 316.130 Person in 2022 and a record low of 150.680 Person in 1990. Pakistan PK: Population Density: Inhabitants per sq km data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Pakistan – Table PK.OECD.GGI: Social: Demography: Non OECD Member: Annual.

The territories of Pakistan and India are mostly covered by the non-political blocks AS42 through AS50, going roughly from West to East. Please see the attached map of these non-political boundary blocks.

In 2022, the population density in India remained nearly unchanged at around 479.43 inhabitants per square kilometer. Still, the population density reached its highest value in the observed period in 2022. Population density refers to the number of people living in a certain country or area, given as an average per square kilometer. It is calculated by dividing the total midyear population by the total land area.Find more key insights for the population density in countries like Sri Lanka and Pakistan.

Rural population (% of total population) in Pakistan was reported at 61.64 % in 2024, according to the World Bank collection of development indicators, compiled from officially recognized sources. Pakistan - Rural population - actual values, historical data, forecasts and projections were sourced from the World Bank on July of 2025.

Comprehensive socio-economic dataset for Pakistan including population demographics, economic indicators, geographic data, and social statistics. This dataset covers key metrics such as GDP, population density, area, capital city, and regional classifications.

The population density in Nepal saw no significant changes in 2022 in comparison to the previous year 2021 and remained at around 207.29 inhabitants per square kilometer. Still, the population density reached its highest value in the observed period in 2022. Population density refers to the average number of residents per square kilometer of land across a given country or region. It is calculated by dividing the total midyear population by the total land area.Find more key insights for the population density in countries like Sri Lanka and Pakistan.

The territories of Pakistan and India are mostly covered by the non-political blocks AS42 through AS50, going roughly from West to East. Please see the attached map of these non-political boundary blocks.

Chart and table of population level and growth rate for the Lahore, Pakistan metro area from 1950 to 2025.

The data shows the distribution of population by gender, gender ratio, population density per square kilometre, decadal growth rate percentage for states and union territories according to the 2011 census.

Note: For Jammu and Kashmir: Area figures includes the area under unlawful occupation of Pakistan and China. The area includes 78,114 sq.km. under illegal occupation of Pakistan, 5,180 sq. km. illegally handed over by Pakistan to China and 37,555 sq.km. under illegal occupation of China.

Context

Pakistan districts profile data set with key seven attributes.

Content

This dataset contains district profile of 134 districts of Pakistan including Islamabad. The dataset contains following information about each district of Pakistan. • Name and introduction

• Background Information ('Area (Sq. km)', 'Forest Area (acres)', 'Total Housing Units', 'No. of Tehsils', 'No. of Union Councils')

• Population ('Population', 'Population Density per Sq. Km', 'Ratio Male per hundred females', 'Urban Population', 'Rural Population', 'Male', 'Female', 'Transgender')

• Economic Profile ('Labou force', 'Number of Printing Presses', 'Number of Television Sets', 'Operational Bank Branches', 'Sale of fertilizers (tonnes)', 'Number of Animals', 'Number of Cattle', 'Value produced by manufacturing industries', 'Average daily persons engaged in industry (No)', 'Employment cost', 'Wages & Salaries', 'Multi Dimensional Poverty Index')

• Transport and Communication ('Road Kilometrage', 'Railway Route Kilometrage', 'Motor Vehicles Registered', 'Number of Exchanges', 'Telephone Connections', 'Public Call offices', 'Number of Post Offices')

• Health and Sanitation Profile ('Govt. Health Institutions', 'Bed Strength', 'Number of Doctors', 'Registered Private Medical Practitioners', 'Number of patients treated', 'Percentage of children fully immunized Urban', 'Immunized rural', 'Percentage of households with tap water Urban', 'Rural', 'Percentage of households with toilet facility', , 'Private Health Institutions')

• Education Profile ('Number of primary schools', 'Number of middle schools', 'Number of high schools', 'Number of higher secondary schools', 'Learning Score Percentage', 'Edu Inst Availability of Electricity', 'Edu Inst Availability of Water', 'Edu Inst Availability of Toilet')

Acknowledgements

The data was extracted from public dataset available on https://opendata.com.pk/dataset/district-profiles-all-districts-of-pakistan

Inspiration

I’d like to call the attention of my fellow Kagglers to use Machine Learning and Data Sciences to utilize dataset for useful insights and learning.

• The current US Census Bureau world population estimate in June 2019 shows that the current global population is 7,577,130,400 people on Earth, which far exceeds the world population of 7.2 billion in 2015. Our estimate based on UN data shows the world's population surpassing 7.7 billion.
• China is the most populous country in the world with a population exceeding 1.4 billion. It is one of just two countries with a population of more than 1 billion, with India being the second. As of 2018, India has a population of over 1.355 billion people, and its population growth is expected to continue through at least 2050. By the year 2030, India is expected to become the most populous country in the world. This is because India’s population will grow, while China is projected to see a loss in population.
• The following 11 countries that are the most populous in the world each have populations exceeding 100 million. These include the United States, Indonesia, Brazil, Pakistan, Nigeria, Bangladesh, Russia, Mexico, Japan, Ethiopia, and the Philippines. Of these nations, all are expected to continue to grow except Russia and Japan, which will see their populations drop by 2030 before falling again significantly by 2050.
• Many other nations have populations of at least one million, while there are also countries that have just thousands. The smallest population in the world can be found in Vatican City, where only 801 people reside.
• In 2018, the world’s population growth rate was 1.12%. Every five years since the 1970s, the population growth rate has continued to fall. The world’s population is expected to continue to grow larger but at a much slower pace. By 2030, the population will exceed 8 billion. In 2040, this number will grow to more than 9 billion. In 2055, the number will rise to over 10 billion, and another billion people won’t be added until near the end of the century. The current annual population growth estimates from the United Nations are in the millions - estimating that over 80 million new lives are added yearly.
• This population growth will be significantly impacted by nine specific countries which are situated to contribute to the population growth more quickly than other nations. These nations include the Democratic Republic of the Congo, Ethiopia, India, Indonesia, Nigeria, Pakistan, Uganda, the United Republic of Tanzania, and the United States of America. Particularly of interest, India is on track to overtake China's position as the most populous country by 2030. Additionally, multiple nations within Africa are expected to double their populations before fertility rates begin to slow entirely.

Content

• In this Dataset, we have Historical Population data for every Country/Territory in the world by different parameters like Area Size of the Country/Territory, Name of the Continent, Name of the Capital, Density, Population Growth Rate, Ranking based on Population, World Population Percentage, etc. >Dataset Glossary (Column-Wise):
• Rank: Rank by Population.
• CCA3: 3 Digit Country/Territories Code.
• Country/Territories: Name of the Country/Territories.
• Capital: Name of the Capital.
• Continent: Name of the Continent.
• 2022 Population: Population of the Country/Territories in the year 2022.
• 2020 Population: Population of the Country/Territories in the year 2020.
• 2015 Population: Population of the Country/Territories in the year 2015.
• 2010 Population: Population of the Country/Territories in the year 2010.
• 2000 Population: Population of the Country/Territories in the year 2000.
• 1990 Population: Population of the Country/Territories in the year 1990.
• 1980 Population: Population of the Country/Territories in the year 1980.
• 1970 Population: Population of the Country/Territories in the year 1970.
• Area (km²): Area size of the Country/Territories in square kilometers.
• Density (per km²): Population Density per square kilometer.
• Growth Rate: Population Growth Rate by Country/Territories.
• World Population Percentage: The population percentage by each Country/Territories.

The raster dataset consists of a 500m score grid for slaughterhouse industry facilities siting, produced under the scope of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis for value chain infrastructure location. The analysis is based on cattle production intensification potential defined using crop production, livestock production systems, and cattle distribution. The score is achieved by processing sub-model outputs that characterize logistical factors: 1. Supply - Feed, livestock production systems, cattle distribution. 2. Demand - Human population density, large cities, urban areas. 3. Infrastructure - Transportation network (accessibility). It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.2) + ("Human Population Density" * 0.2) + (“Major Cities Accessibility” * 0.2) + (”Cattle intensification” * 0.3) + (“Poverty” * 0.1).

The raster dataset consists of a 500m score grid for temperate fruits storage location achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse location: • Supply: Temperate fruits. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + ("Major Cities Accessibility" * 0.1) + (“Poverty” * 0.1) + ("Major Ports Accessibility" * 0.1)+("Major Regional Cities Accessibility" * 0.1). This 500m resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location).

PK：人口密度：每平方公里人口在12-01-2017达255.573Person/sq km，相较于12-01-2016的250.627Person/sq km有所增长。PK：人口密度：每平方公里人口数据按年更新，12-01-1961至12-01-2017期间平均值为135.674Person/sq km，共57份观测结果。该数据的历史最高值出现于12-01-2017，达255.573Person/sq km，而历史最低值则出现于12-01-1961，为59.652Person/sq km。CEIC提供的PK：人口密度：每平方公里人口数据处于定期更新的状态，数据来源于World Bank，数据归类于全球数据库的巴基斯坦 – 表 PK.世行.WDI：人口和城市化进程统计。

The raster dataset consists of a 500m score grid for cotton storage location achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse location: • Supply: Cotton. • Demand: Human population density, Major cities population (national and bordering countries). • Infrastructure/accessibility: main transportation infrastructure. It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.4) + ("Human Population Density" * 0.2) + ("Major Cities Accessibility" * 0.1) + (“Poverty” * 0.1) + ("Major Ports Accessibility" * 0.1)+("Major Regional Cities Accessibility" * 0.1). This 500m resolution raster dataset is part of FAO’s Hand-in-Hand Initiative, Geographical Information Systems - Multicriteria Decision Analysis (GIS-MCDA) aimed at the identification of value chain infrastructure sites (optimal location).