Constrained estimates, total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.

More information can be found in the Release Statement

The difference between constrained and unconstrained is explained on this page: https://www.worldpop.org/methods/top_down_constrained_vs_unconstrained

Estimates, total number of people per grid-cell. The dataset is available to download in Geotiff format at a resolution of 3 arc (approximately 100m at the equator). The projection is Geographic Coordinate System, WGS84. The units are number of people per pixel. The mapping approach is Random Forest-based dasymetric redistribution.

More information can be found in the Release Statement

Please note that these data represent 2025 Alpha release versions, constructed in September 2025

Spatial coverage index compiled by East View Geospatial of set "Pakistan District Maps". Source data from SPAK (publisher). Type: Thematic - Political and Administrative. Scale: Varies. Region: Asia, Middle East.

The first comprehensive mangrove cover dataset from 1990 to 2020, at five-year intervals, across all five mangrove areas in Pakistan, i.e. Indus Delta, Sandspit, Sonmiani, Kalmat Khor, and Jiwani. Using the Google Earth Engine (GEE) geospatial cloud computing platform, Random Forest (RF) classifier was applied on Landsat 30 m spatial resolution satellite images to classify three major land cover classes: ‘mangrove’, ‘water’ and ‘other’. High temporal and spectral resolutions of Landsat images, with a low saturation level of spectral bands with the integration of indices, are the main factors that ensured >90% overall accuracy of land cover maps. Overall, the findings of this paper revealed that, at the national scale, an estimated 477.22 sq. km was covered with mangrove in 1990, which increased to 1463.59 sq. km in 2020, a 3.74% annual rate of change. Mangrove fragmentation mapping results have also showed enhancement in mangrove tree canopy density.

The mangrove cover dataset of Pakistan on 5-year interval (1990-2020) at 30m spatial resolution data is available here:

Citation:

Use of these data requires citation of this dataset:

Gilani, Hammad, Naz, Hafiza Iqra, Arshad, Masood, Nazim, Kanwal, Akram, Usman, Abrar, Aneeqa, & Asif, Muhammad. (2024). Mangrove cover dataset of Pakistan on 5-year interval (1990-2020) at 30m spatial resolution (1.0) [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.10732690

Original research article:

Gilani, H., Naz, H.I., Arshad, M., Nazim, K., Akram, U., Abrar, A., & Asif, M. (2021). Evaluating mangrove conservation and sustainability through
spatiotemporal (1990–2020) mangrove cover change analysis in Pakistan. Estuarine, Coastal and Shelf Science, 249: 107128. doi.org/10.1016/j.ecss.2020.107128

Urbanization-led land cover change impacts terrestrial carbon storage capacity: A high-resolution remote sensing-based nation-wide assessment in Pakistan (1990–2020) -

This dataset provides high-resolution, nationwide land use/land cover (LULC) and terrestrial carbon stock maps of Pakistan for four epochs: 1990, 2000, 2010, and 2020. Developed using multi-sensor satellite imagery and advanced classification techniques in Google Earth Engine (GEE), the dataset presents a comprehensive analysis of land cover changes driven by urbanization and their impacts on carbon storage capacity over 30 years.

The LULC data includes nine distinct classes, covering key land cover types such as forest cover, agriculture, rangeland, wetlands, barren lands, water bodies, built-up areas, and snow/ice. Classification was performed using a hybrid machine learning approach, and the accuracy of the land cover maps was validated using a stratified random sampling approach.

The carbon stock maps were derived using the InVEST model, which estimated carbon storage in four major carbon pools (above-ground biomass, below-ground biomass, soil organic carbon, and dead organic matter) based on the LULC maps. The results showed a significant decline in carbon storage due to rapid urban expansion, particularly in major cities like Karachi and Lahore, where substantial forest and agricultural lands were converted into urban areas. The study estimates that Pakistan lost approximately -5% of its carbon storage capacity over this period, with urban areas growing by over ~1040%.

This dataset is a valuable resource for researchers, policymakers, and environmental managers, providing crucial insights into the long-term impacts of urbanization on land cover and carbon sequestration. It is expected to support future land management strategies, urban planning, and climate change mitigation efforts. The high temporal and spatial resolution of the dataset makes it ideal for monitoring land cover dynamics and assessing ecosystem services over time.

This dataset is aslo available as Google Earth Engine application. For more details check:

> Github Project repository: https://github.com/waleedgeo/lulc_pk
> Paper DOI: https://doi.org/10.1016/j.eiar.2023.107396
> Paper PDF: https://waleedgeo.com/papers/waleed2024_paklulc.pdf

If you find this work useful, please consider citing it as

Waleed, M., Sajjad, M., & Shazil, M. S. (2024). Urbanization-led land cover change impacts terrestrial carbon storage capacity: A high-resolution remote sensing-based nation-wide assessment in Pakistan (1990–2020). Environmental Impact Assessment Review, 105, 107396.

Contributors:
Mirza Waleed (email) (Linkedin)
Muhammad Sajjad (email) (Linkedin)
Muhammad Shareef Shazil

To check other work, please check:
My Webpage & Google Scholar

Studies investigating the interconnection of health poverty and climatic variability are rare in spatial perspectives. Given the importance of sustainable development goals 3, goal 10, and goal 13, we explored whether the geographic regions with diverse climate structure has a spatial association with health poverty; whether spatial disparities exist across districts of Pakistan. We implied the A-F methodology to estimate the MHP index using the PSLM survey, 2019–20. The climate variables were extracted from the online NASA website. We applied the spatial techniques of Moran’s I, univariate and bivariate LISA, to address the research questions. The findings revealed that the magnitude of MHP differs across districts. Punjab was found to be the better-ff whereas Baluchistan was the highest health poverty-stricken province. The spatial results indicated positive associations of MHP and climate indicators with their values in the neighbors, whereas a negative spatial association was found between the MHP and climate indicators. Also, spatial clusters and outliers of higher MHP were significant in Baluchistan and KP provinces. Government intervention and policymaker’s prioritization are needed towards health and health-related social indicators, mainly in the high poverty-stricken districts, with high temperature and low humidity and precipitation rates, especially in Baluchistan.

The Army Map Service was a cartographic agency that focused on the compilation, publication, and distribution of military topographic maps. This collection contains georeferenced historical maps of India and Pakistan collected from 1955-1963 from the U502 series. The maps are provided as TIFF files that include spatial references that can be read by GIS software. These maps are organized by segments which are then divided into square tiles. The corners of each of these tiles contain an anchor point with corresponding coordinates alongside additional anchor points like a: coastal region, legend, glossary, scale, and a location diagram.

Spatial coverage index compiled by East View Geospatial of set "EVGmap 50 Pakistan Vector Data". Source data from EVG (publisher). Type: Topographic. Scale: 1:50,000. Region: Asia, Middle East.

This dataset represents the first geospatial mapping of brick kiln sites in the IGP region of Pakistan, providing an invaluable resource for understanding the spatial distribution of these sites. Each data point captures a brick kiln's precise location, including coordinates, state, and other important information, standardized in Coordinate Reference System (CRS) EPSG:4326 (WGS 84). This dataset, to the best of our knowledge, is the first of its kind to consolidate and geolocate brick kiln operations across this region, where air pollution impacts from kiln emissions are a significant environmental and public health concern.

In addition to the primary geolocation data, the dataset also includes an initial, secondary estimation of emissions (PM10, PM2.5, NOx, and SOx) from these sites. This supplementary information supports preliminary risk assessments, emphasizing proximity-based exposure for populations and sensitive areas (e.g., schools, hospitals) within a 1 km radius of each kiln site.

The dataset is made available in multiple formats to facilitate wide usage across spatial analysis platforms:

• Geojson (.geojson)
• Shapefile (.shp)
• Comma-Separated Values (.csv)

Metadata:

• Geographic Coverage: IGP - Pakistan
• CRS: EPSG:4326 (WGS 84)

Data Structure:

"Main" files have Basic ID & Location information, while the "Emission" files have Basic ID & Location + Production & Emission information.

Basic ID & Location

• id: Unique identifier for the kiln.
• lat: Latitude of the kiln location.
• lon: Longitude of the kiln location.
• state: State where the kiln is located.
• type: Type of kiln (e.g., FCBK).

Production Data

• avg_bricks: Average number of bricks produced daily. Refer to the GitHub repository for detailed methodology.
• seasonprod(bricks): Seasonal production of bricks in kilograms (excluding monsoon and smog days, considering 215 operational days).

Daily Emissions

• pm2.5d(kg): Daily PM2.5 emissions in kilograms.
• pm10d(kg): Daily PM10 emissions in kilograms.
• noxd(kg): Daily NOx emissions in kilograms.
• soxd(kg): Daily SOx emissions in kilograms.

Seasonal Emissions

• pm2.5s(kg): Seasonal PM2.5 emissions in kilograms.
• pm10s(kg): Seasonal PM10 emissions in kilograms.
• noxs(kg): Seasonal NOx emissions in kilograms.
• soxs(kg): Seasonal SOx emissions in kilograms.

Emission Factors

• emf_coal(pm2.5): Emission factor for PM2.5 using coal as fuel (kg).
• emf_coal(pm10): Emission factor for PM10 using coal as fuel (kg).
• emf_coal(nox): Emission factor for NOx using coal as fuel (kg).
• emf_coal(so2): Emission factor for SOx using coal as fuel (kg).
• emf_coal(pm2.5): Emission factor for PM2.5 using biomass as fuel (kg).
• emf_coal(pm10): Emission factor for PM10 using biomass as fuel (kg).
• emf_coal(nox): Emission factor for NOx using biomass as fuel (kg).
• emf_coal(so2): Emission factor for SOx using biomass as fuel (kg).

Seasonal Emissions by Fuel Type

• pm2.5s_c(kg): Seasonal PM2.5 emissions in kilograms using coal as a fuel.
• pm10s_c(kg): Seasonal PM10 emissions in kilograms using coal as a fuel.
• noxs_c(kg): Seasonal NOx emissions in kilograms using coal as a fuel.
• so2s_c(kg): Seasonal SO2 emissions in kilograms using coal as a fuel.
• pm2.5s_b(kg): Seasonal PM2.5 emissions in kilograms using biomass as a fuel.
• pm10s_b(kg): Seasonal PM10 emissions in kilograms using biomass as a fuel.
• noxs_b(kg): Seasonal NOx emissions in kilograms using biomass as a fuel.
• so2s_b(kg): Seasonal SO2 emissions in kilograms using biomass as a fuel.

Funding Sources: This research and data collection were funded by Amazon Web Services (AWS) and Smith School of Enterprise and The Environment (University of Oxford).

Spatial coverage index compiled by East View Geospatial of set "Pakistan 1:253,440 Scale Topographic Maps". Source data from SPAK (publisher). Type: Topographic. Scale: 1:253,440. Region: Asia, Middle East.

Spatial coverage index compiled by East View Geospatial of set "Pakistan 1:50,000 Scale Topographic Maps". Source data from SPAK (publisher). Type: Topographic. Scale: 1:50,000. Region: Asia, Middle East.

Our field_685e8fb67dd91 zip code Database offers comprehensive postal code data for spatial analysis, including postal and administrative areas. This dataset contains accurate and up-to-date information on all administrative divisions, cities, and zip codes, making it an invaluable resource for various applications such as address capture and validation, map and visualization, reporting and business intelligence (BI), master data management, logistics and supply chain management, and sales and marketing. Our location data packages are available in various formats, including CSV, optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more. Product features include fully and accurately geocoded data, multi-language support with address names in local and foreign languages, comprehensive city definitions, and the option to combine map data with UNLOCODE and IATA codes, time zones, and daylight saving times. Companies choose our location databases for their enterprise-grade service, reduction in integration time and cost by 30%, and weekly updates to ensure the highest quality.

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: Asia 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: Gaughan AE, Stevens FR, Linard C, Jia P and Tatem AJ, 2013, High resolution population distribution maps for Southeast Asia in 2010 and 2015, PLoS ONE, 8(2): e55882 FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - VNM_popmap10adj_v2.tif = Vietnam (VNM) population count map for 2010 (popmap10) adjusted to match UN national estimates (adj), version 2 (v2). DATE OF PRODUCTION: January 2013

This dataset documents a vegetation survey conducted across eight locations in Punjab, Pakistan, over two seasons. It offers a rich and well-structured record of plant species occurrence and abundance at the plot level, serving as a valuable resource for ecological, biodiversity, and biogeographical studies. A total of 76,826 records were collected from the eight locations, each surveyed multiple times. Every site was surveyed in at least two seasons, enabling the analysis of seasonal vegetation dynamics. To ensure spatial representativeness and replication, each location was subdivided into multiple sampling plots. Within these plots, vegetation was sampled using 1x1 meter quadrates, allowing for high-resolution data collection at a fine spatial scale. A wide range of plant species was recorded, with many species occurring repeatedly across different plots and seasons. For each species within each quadrate, quantitative counts were documented. These counts facilitate the calculation of key biodiversity metrics, including species abundance, frequency, richness, and diversity indices.

Spatial coverage index compiled by East View Geospatial of set "Pakistan 1:63,360 Scale Topographic Maps". Source data from SPAK (publisher). Type: Topographic. Scale: 1:63,360. Region: Asia, Middle East.

Land cover data of Hindu Kush Himalayan region of Pakistan for 1990. This dataset is created using the LandSat 30 meter spatial resolution satellite image of 1990.

Passing through the Pamirs and Karakoram Mountain System, the China-Pakistan Economic Corridor has widely developed various types of geological disasters caused by freeze-thaw cycles in permafrost at altitudes above 4,000 meters. The study on distribution and mapping of permafrost is the basis for solving the practical engineering problems in the Corridor, and it is of great importance to the water resources utilization, ecological security and border defence construction. The spatial scope of the study is approximately in 23°47′ N ~ 40°55′ N, 60°20′ E ~ 80°16′ E, including Kashgar in Xinjiang, Kizilsu Kirghiz Autonomous Prefecture and Pakistan area. The data of the permafrost distribution in the Corridor （format: Tiff, spatial resolution: 1 km） is acquired on the basis of TTOP Model, which is conducted with the data on surface temperature for MODIS in 2016, glacial cataloging data for the Pamirs of China in 2009, glacier cataloging for Pakistan in 2003-2004 and World Soil Database for 2008 （HWSD v1.2）. Coefficient of determination as a statistical method are used to analyze and evaluate the quality of the data and existed literature are used to verify the data result. This dataset can be served as a fundamental survey material of the permafrost changes in the Corridor, providing basic data support for the research on frost heaving and thaw in the construction of the region. Besides, the dataset could be analyzed with climate, hydrology, and other data to reveal the quantitative relation in hydrology-soil-atmosphere-ecology in the Corridor. With the climate change in this region, the dataset is expected to intensify the scientific understanding of the ecological environment and sustainable development in the region.

Land Use / Land Cover (LULC) information product over Karachi (Pakistan) contains spatial explicit information about the different land covers / uses for current (2017) and past (2005) dates. The level of detail for the classification scheme mainly relies on the input data sources. LULC dataset provides detailed information (level-3) over core urban areas covered by very high resolution satellite imagery, and level-1 information over peri-urban areas covered by lower resolution satellite imagery.

Additional file 2.