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 stood at ****** people in 2022. In a steady upward trend, the population density rose by ****** people from 1961.

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

Actual value and historical data chart for Pakistan Population Density People Per Sq Km

View yearly updates and historical trends for Pakistan Population Density. Source: World Bank. Track economic data with YCharts analytics.

Content

The current population of Pakistan is 229,160,509 as of Wednesday, June 8, 2022, based on Worldometer elaboration of the latest United Nations data. This three datasets contain population data of Pakistan (2020 and historical), population forecast and population in major cities.

Attribute Information

• Year - Years from 2020-1955
• Population - Population in the respective year
• Yearly % Change - Percentage Yearly Change in Population
• Yearly Change - Yearly Change in Population
• Migrants (net) - Total number of migrants
• Median Age - Median age of the population
• Fertility Rate - Fertility rate
• Density (P/Km²)- Population density (population per square km)
• Urban Pop %- Percentage of urban population
• Urban Population- Urban population
• Country's Share of World Pop - Population share
• World Population - World Population in the respective year
• Pakistan Global Rank - Global Rank in Population

Source

Link : https://www.worldometers.info/world-population/pakistan-population/

Updated Covid 19 and Other Datasets

Link : https://www.kaggle.com/anandhuh/datasets

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Thank You

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.

Density of physicians of Pakistan surged by 14.47% from 0.9 number per thousand population in 2018 to 1.1 number per thousand population in 2019. Since the 1.80% upward trend in 2009, density of physicians soared by 47.68% in 2019.

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.

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 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.

A comprehensive dataset of 1,513 Pakistani cities, towns, tehsils, districts and places with latitude/longitude, administrative region, population (when available) and Wikidata IDs — ideal for mapping, geospatial analysis, enrichment, and location-based ML.

Why this dataset is valuable:

• Full geocoordinates for every entry (100% coverage) — ready for mapping and spatial joins.
• Wide geographic coverage across all 7 major regions of Pakistan (provinces / administrative regions).
• Wikidata IDs included for reliable cross-referencing and automatic enrichment from external knowledge bases.
• Useful for data scientists, GIS engineers, civic tech projects, academic research, and startups building Pakistan-focused location services.

Highlights (fetched from the data):

• Total rows: 1,513
• Unique places (city field): 1,497
• Rows with population > 0: 526 (≈34.8%)
• Coordinate coverage: 1513 / 1513 (100%) — directly usable with mapping libraries.

Column definitions (short):

• id — Internal numeric row id (unique integer).
• wikiDataId — Wikidata QID (e.g., Q####) for the place; use to fetch rich metadata.
• type — Administrative/place type (e.g., ADM1, ADM2, city, district, tehsil).
• city — Common/local city/place name (short label).
• name — Full name / official name of the place (may include “District”, “Tehsil”, etc.).
• country — Country name (Pakistan).
• countryCode — ISO country code (e.g., PK).
• region — Primary administrative region / province (e.g., Punjab, Sindh).
• regionCode — Short code for region (e.g., PB, KP depending on your encoding).
• regionWdId — Wikidata QID for the region.
• latitude — Latitude in decimal degrees (float).
• longitude — Longitude in decimal degrees (float).
• population — Integer population (0 or NA where unknown).

Typical & high-value use cases:

• Mapping & visualization: choropleth maps, point overlays, heatmaps of population or density.
• Geospatial analysis: distance calculations, nearest-neighbor queries, clustering of urban centers.
• Data enrichment: join with other datasets (OpenStreetMap, Wikidata, census data) using wikiDataId and coordinates.
• Machine learning & NLP: training geolocation models, geoparsing, toponym resolution, place name disambiguation.
• Urban planning & research: analyze distribution of population-ready places vs administrative units.
• Mobile / location-based apps: lookup & reverse geocoding fallback, seeding POI databases for Pakistan.
• Humanitarian & disaster response: baseline location lists for logistics and situational awareness.

The raster dataset consists of a 500m score grid for wheat storage location achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse location: • Supply: Wheat. • 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).

Linkage disequilibrium (LD) across the genome provides information to identify the genes and variations related to quantitative traits in genome-wide association studies (GWAS) and for the implementation of genomic selection (GS). LD can also be used to evaluate genetic diversity and population structure and reveal genomic regions affected by selection. LD structure and Ne were assessed in a set of 83 water buffaloes, comprising Azeri (AZI), Khuzestani (KHU), and Mazandarani (MAZ) breeds from Iran, Kundi (KUN) and Nili-Ravi (NIL) from Pakistan, Anatolian (ANA) buffalo from Turkey, and buffalo from Egypt (EGY). The values of corrected r2 (defined as the correlation between two loci) of adjacent SNPs for three pooled Iranian breeds (IRI), ANA, EGY, and two pooled Pakistani breeds (PAK) populations were 0.24, 0.28, 0.27, and 0.22, respectively. The corrected r2 between SNPs decreased with increasing physical distance from 100 Kb to 1 Mb. The LD values for IRI, ANA, EGY, and PAK populations were 0.16, 0.23, 0.24, and 0.21 for less than 100Kb, respectively, which reduced rapidly to 0.018, 0.042, 0.059, and 0.024, for a distance of 1 Mb. In all the populations, the decay rate was low for distances greater than 2Mb, up to the longest studied distance (15 Mb). The r2 values for adjacent SNPs in unrelated samples indicated that the Affymetrix Axiom 90 K SNP genomic array was suitable for GWAS and GS in these populations. The persistency of LD phase (PLDP) between populations was assessed, and results showed that PLPD values between the populations were more than 0.9 for distances of less than 100 Kb. The Ne in the recent generations has declined to the extent that breeding plans are urgently required to ensure that these buffalo populations are not at risk of being lost. We found that results are affected by sample size, which could be partially corrected for; however, additional data should be obtained to be confident of the results.

This dataset contains information of district wise population of Pakistan from the censes conducted in 2023 with numbers of available educational institutions. It also provide the data of numbers of household, annual growth rate, area of district, density of population and numbers of available schools for boys and girls.

The Dataset consist of distribution of population across different states. The dataset also gives information regarding the area of the state, urban-rural distribution of population, population density, sex ratio and literacy rates in different states with reference from 2011 census. The dataset helps in analysis of population distribution of India.

Note: *Disputed area of 13 km^2 between Puducherry and Andhra Pradesh is included in neither. *The shortfall of 7 km^2 area of Madhya Pradesh and 3 km^2 area of Chhattisgarh is yet to be resolved by the Survey of India. *Area figures do not include the areas claimed by India that are in Pakistani or Chinese administrative control. This includes 78,114 km^2 of area in Azad Kashmir and Gilgit-Baltistan under Pakistani administration, 5,180 km^2 of area in Shaksgam Valley ceded to China by Pakistan and 37,555 km^2 of area in Aksai Chin under Chinese administration totaling to 120,849 km^2.

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。

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.

The raster dataset consists of a 500m score grid for rice storage location achieved by processing sub-model outputs that characterize logistical factors for selected crop warehouse location: • Supply: Rice. • 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).