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.

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.

Spatial coverage index compiled by East View Geospatial of set "Pakistan 1:250,000 Scale Topographic Maps". Source data from SPAK (publisher). Type: Topographic. Scale: 1:250,000. Region: Asia, Middle East.

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

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)

1. Dataset - Main Version

• Columns:
  • id: Unique identifier for each brick kiln site.
  • lat: Latitude of the brick kiln in decimal degrees.
  • lon: Longitude of the brick kiln in decimal degrees.
  • state: Administrative state or region of the kiln.
  • type: Classification of brick kiln (FCBK or ZigZag).
  • schools1km: Number of schools within a 1 km radius.
  • hosp1km: Number of hospitals within a 1 km radius.
  • pop1km: Estimated population within a 1 km radius.

2. Dataset - Estimates Version

• Columns:
  • Basic ID & Location: id, lat, lon, state, type (to allow for cross-referencing with the Main Version).
  • Production Data:
    • avg_bricks: Average number of kilns in operation per day.
    • dailyprod(kg): Estimated daily production of bricks in kilograms.
    • seasonprod(kg): Seasonal production in kilograms (Excluding the Monsoon and Smog Days)
  • Emission Estimates:
    • 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.

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).

This collection consists of geospatial data layers and summary data at the country and country sub-division levels that are part of USAID's Demographic Health Survey Spatial Data Repository. This collection includes geographically-linked health and demographic data from the DHS Program and the U.S. Census Bureau for mapping in a geographic information system (GIS). The data includes indicators related to: fertility, family planning, maternal and child health, gender, HIV/AIDS, literacy, malaria, nutrition, and sanitation. Each set of files is associated with a specific health survey for a given year for over 90 different countries that were part of the following surveys:Demographic Health Survey (DHS)Malaria Indicator Survey (MIS)Service Provisions Assessment (SPA)Other qualitative surveys (OTH)Individual files are named with identifiers that indicate: country, survey year, survey, and in some cases the name of a variable or indicator. A list of the two-letter country codes is included in a CSV file.Datasets are subdivided into the following folders:Survey boundaries: polygon shapefiles of administrative subdivision boundaries for countries used in specific surveys. Indicator data: polygon shapefiles and geodatabases of countries and subdivisions with 25 of the most common health indicators collected in the DHS. Estimates generated from survey data.Modeled surfaces: geospatial raster files that represent gridded population and health indicators generated from survey data, for several countries.Geospatial covariates: CSV files that link survey cluster locations to ancillary data (known as covariates) that contain data on topics including population, climate, and environmental factors.Population estimates: spreadsheets and polygon shapefiles for countries and subdivisions with 5-year age/sex group population estimates and projections for 2000-2020 from the US Census Bureau, for designated countries in the PEPFAR program.Workshop materials: a tutorial with sample data for learning how to map health data using DHS SDR datasets with QGIS. Documentation that is specific to each dataset is included in the subfolders, and a methodological summary for all of the datasets is included in the root folder as an HTML file. File-level metadata is available for most files. Countries for which data included in the repository include: Afghanistan, Albania, Angola, Armenia, Azerbaijan, Bangladesh, Benin, Bolivia, Botswana, Brazil, Burkina Faso, Burundi, Cape Verde, Cambodia, Cameroon, Central African Republic, Chad, Colombia, Comoros, Congo, Congo (Democratic Republic of the), Cote d'Ivoire, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Eswatini (Swaziland), Ethiopia, Gabon, Gambia, Ghana, Guatemala, Guinea, Guyana, Haiti, Honduras, India, Indonesia, Jordan, Kazakhstan, Kenya, Kyrgyzstan, Lesotho, Liberia, Madagascar, Malawi, Maldives, Mali, Mauritania, Mexico, Moldova, Morocco, Mozambique, Myanmar, Namibia, Nepal, Nicaragua, Niger, Nigeria, Pakistan, Papua New Guinea, Paraguay, Peru, Philippines, Russia, Rwanda, Samoa, Sao Tome and Principe, Senegal, Sierra Leone, South Africa, Sri Lanka, Sudan, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Uganda, Ukraine, Uzbekistan, Viet Nam, Yemen, Zambia, Zimbabwe

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

The products over Karachi (Pakistan) contain spatial explicit information about the transport network and nodes within the transport network and their typology as identified from Open Street Map and updated by interpretation of VHR satellite imagery. The level of detail for the classification scheme mainly relies on the input data sources.

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.

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.

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.

DATASET: Alpha version 2010, 2012, 2015, 2020, 2025, 2030, and 2035 estimates of numbers of live births per grid square, with national totals adjusted to match UN national estimates on numbers of live births (http://esa.un.org/wpp/). REGION: Asia SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated births per grid square MAPPING APPROACH: Tatem AJ, Campbell J, Guerra-Arias M, de Bernis L, Moran A, Matthews Z, 2014, Mapping for maternal and newborn health: the distributions of women of childbearing age, pregnancies and births, International Journal of Health Geographics, 13:2 FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AZE2010adjustedBirths.tif = Azerbaijan (AZE) births count map for 2010 adjusted to match UN national estimates on numbers of live births. DATE OF PRODUCTION: May 2014

Yearly citation counts for the publication titled "Spatial Distribution of Sulfate Concentration in Groundwater of South-Punjab, Pakistan".

Additional file 1.

This dataset is the geological structure map of the China-Pakistan Economic Corridor and the Tianshan Mountains. The obtained geological map is a 1:2.5 million geological map, covering the China-Pakistan Economic Corridor and the Tianshan Mountains. Geological structural maps can provide a digital space platform for the informatization of the national economy, and provide information services for national and provincial departments for regional planning, geological disaster monitoring, geological surveys, prospecting and exploration, and macro decision-making. The obtained geological map data source is obtained by first scanning the paper version of the map, then performing georeferencing on the ArcGIS 10.5 platform, and then vectorizing it. The storage format is vector data, and the spatial granularity is divided into regions.

The lack of information regarding biodiversity states hampers designing and implementation conservation strategies and future targets. Northern Pakistan consists of a unique ecoregion mosaic which supports a myriad of environmental niches for anuran diversity to flourish in comparison to the deserts and xeric shrublands throughout the rest of the country. In order to study the niche suitability, overlap and distribution patterns in Pakistan, we collected observational data for nine amphibian species across several distinct ecoregions by surveying 87 randomly selected locations from 2016 to 2018 in District Rawalpindi and Islamabad Capital Territory. Our model showed that the precipitation of the warmest and coldest quarter, distance to rivers and vegetation were the greatest drivers of anuran distribution, expectedly indicating that the presence of humid forests and proximity to waterways greatly influences the habitable range of anurans in Pakistan. Sympatric overlap between...

This data uses a large number of MODIS remote sensing images to analyze and calculate the surface vegetation coverage of the Qinghai Tibet Plateau from 2000 to 2018 based on the Google Earth engine platform. Vegetation index (NDVI) is an important index for monitoring ground vegetation. The 6th edition data of Terra moderate resolution imaging spectrometer (MODIS) vegetation index level 3 product (mod13q1) are generated every 16 days with a spatial resolution of 250 meters. The annual average NDVI index calculated based on GEE platform can reflect the long-term change trend of vegetation coverage from 2000 to 2018. Meanwhile, the multi-year average NDVI index from 2000 to 2018 reflects the spatial distribution of the Qinghai Tibet Plateau. The spatial-temporal change monitoring of vegetation index (NDVI) is an indispensable basic information and key parameter for environmental change research and sustainable development planning, which is helpful to understand the changes and impacts of some ecological factors (temperature, precipitation) under the background of climate change.

Digital dataset of Land Cover in 2000 of Central Karakoram National Park(CKNP) area, Pakistan. This dataset is extracted from LandSat satellite imagery of 2000 with spatial resolution of 30 m and spectral resolution of 6 bands. land cover classes are classified using Nearest Neighbor method.

DATASET: Alpha version 2010, 2012, 2015, 2020, 2025, 2030, and 2035 estimates of numbers of pregnancies per grid square, with national totals adjusted to match national estimates on numbers of pregnancies made by the Guttmacher Institute (http://www.guttmacher.org/). REGION: Asia SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated pregnancies per grid square MAPPING APPROACH: Tatem AJ, Campbell J, Guerra-Arias M, de Bernis L, Moran A, Matthews Z, 2014, Mapping for maternal and newborn health: the distributions of women of childbearing age, pregnancies and births, International Journal of Health Geographics, 13:2 FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AFG2010pregnancies.tif = Afghanistan (AFG) pregnancies count map for 2010 adjusted to match UN national estimates on numbers of pregnancies. DATE OF PRODUCTION: May 2014