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.

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;

The population density in Pakistan increased by 5.5 inhabitants per square kilometer (+1.77 percent) in 2022. Therefore, the population density in Pakistan reached a peak in 2022 with 316.13 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.

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.

Historical chart and dataset showing Pakistan population density by year from 1961 to 2022.

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.

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.

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.

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

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 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 population density in Bangladesh reached its highest in 2020, amounting to approximately 1.27 thousand people per square kilometer. The South Asian country was the tenth most densely populated country in the world in 2019. Within the Asia Pacific region, Bangladesh’s population density was only exceeded by Macao, Singapore, Hong Kong, and the Maldives. Overall, Asia had the highest population density in the world in 2018.

Population growth in Bangladesh

In 1971, Bangladesh gained its independence from Pakistan. Bangladesh’s birth rate and mortality rate had declined significantly in the past years with a life expectancy of 72.59 years in 2019. In general, the population in Bangladesh had been growing at a slow pace, slightly fluctuating around an annual rate of one percent. This growth was forecasted to continue, although it was estimated to halve by 2040. As of today, Dhaka is the largest city in Bangladesh.

Population density explained

According to the source, “population density is the mid-year population divided by land area in square kilometers.” Further, “population is based on the de facto definition of population, which counts all residents.” Bangladesh’s population reached an estimated number of 164.69 million inhabitants in 2020. In 2018, the country’s land area amounted 130.2 thousand square kilometers.

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).

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 dataset contains Year and State wise Density of Population

Note: 1. The 1981 Census could not be held in Assam. Total Population for 1981 has been worked out by Interpolation. 2. Includes estimated population of Paomata, Mao Maram and Purul sub-divisions of Senapati District of Manipur for 2001. 3. For working out the density of India and Jammu & Kashmir for 1991,2001, the entire area and population of those portions of Jammu & Kashmir which are under illegal occupation of Pakistan and China have not been taken into account.

The raster dataset consists of a 500m score grid for dairy processing industry (UHT and milk powder) 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 dairy 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) 4. Poverty.

It consists of an arithmetic weighted sum of normalized grids (0 to 100): ("Crop Production" * 0.25) + ("Human Population Density" * 0.1) + (“Major Cities Accessibility” * 0.1) + (“ Accessibility to ports” * 0.1) + ( "Poverty" * 0.1) + (”Dairy Intensification” * 0.35).

Data publication: 2021-10-18

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Dariia Nesterenko

Data lineage:

Major data sources, FAO GIS platform Hand-in-Hand and OpenStreetMap (open data) including the following datasets: 1. Human Population Density 2020 – WorldPop2020 - Estimated total number of people per grid-cell 1km. 2. Mapspam Production – IFPRI's Spatial Production Allocation Model (SPAM) estimates of crop distribution within disaggregated units. 3. GLW Gridded Livestock of the World - Gridded Livestock of the World (GLW 3 and GLW 2) 4. Global Livestock Production Systems v.5 2011. 5. OpenStreetMap. 6. Poverty: VMPI source: UNDP (United Nations Development Programme).

Resource constraints:

Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO (CC BY-NC- SA 3.0 IGO)

Online resources:

Zipped raster TIF file for Dairy Processing Location Score: Cattle (Pakistan - ~ 500m)

Human population growth and the accompanying increase in anthropogenic activities pose a significant threat to forest ecosystems by reducing the natural services these ecosystems provide. Malam Jabba, located in the District Swat of Pakistan’s Hindukush-Himalayan temperate zone, is known for its ecotourism, skiing, timber-producing tree species, medicinal plants, and unique biodiversity. However, a large portion of Swat Valley’s population depends on the Malam Jabba forests for timber and fuelwood. This study investigates how deforestation rates have increased in response to the growing human population in Malam Jabba, District Swat. To monitor forest cover changes, we used remote sensing (RS) and geographic information systems (GIS) tools. Vegetation analysis was conducted using the Normalized Difference Vegetation Index (NDVI) based on multi-temporal satellite imagery from 1980, 2000, and 2020. Using a decay model, we calculated the deforestation rate from 1980 to 2020 and projected future rates using MATLAB, based on anticipated population growth. Our results show that over the last two decades, the average annual deforestation rate rose from 0.7% to 1.93%, coinciding with a population increase from 1.2 million to 2.3 million at a growth rate of 9% per year. Projections indicate that the deforestation rate will increase to 2.5% annually over the next 20 years, given the predicted 11.6% yearly population growth. Population growth in District Swat has severely endangered nearby forest ecosystems, and further increases in human activity, such as unsustainable tourism, fuel and timber collection, and urbanization, will likely exacerbate this trend. Based on our findings, we recommend: (i) the implementation of reforestation programs and sustainable forest resource use; (ii) the development of a long-term forest management plan that maintains equilibrium between forest density and population pressure; and (iii) prioritizing areas with extreme human impact for in-situ conservation efforts.