Dataset

This dataset was created by Muhammad Saeed Akhtar

Contents

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.

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.

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

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 dataset includes: (1) the dataset of rockfalls’ points distributions, surfaces (boundaries) and related attributes in CPEC; (2) the dataset of landslides’ points distributions, surfaces (boundaries) and related attributes in CPEC.

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

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.

Probable informal settlements over Karachi (Pakistan) contains spatial explicit information about position of slums as identified in 2005 and 2010 from ancillary data sources and in 2017 by interpretation of VHR satellite imagery. The level of detail for the classification scheme mainly relies on the input data sources.

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.

Land cover data of Hindu Kush Himalayan region of Pakistan for 2010. This dataset is created using the LandSat 30 meter spatial resolution satellite image of 2010.

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.

Comprehensive assessment of soil erosion dynamics in Pakistan for 2005 and 2015 at 1 km² spatial resolution using the Revised Universal Soil Loss Equation (RUSLE) model and six influencing factors. Soil erosion maps are categorized into four classes: low, medium, high, and very high, revealing an increase from 1.79 to 2.47 ton ha⁻¹ yr⁻¹ on the national level.

The national-scale soil erosion dataset for Pakistan (2005 and 2015) at 1km spatial resolution data is available here:

Citation:

Use of these data require citation of this dataset:

Gilani, Hammad, Ahmad, Adeel, Younes, Isma, & Abbas, Sawaid. (2021). National-scale soil erosion dataset for Pakistan (2005 and 2015) at 1km spatial resolution (1.0) [Dataset]. Zenodo. https://doi.org/10.5281/zenodo.10694225

Original research article:

Gilani, H., Ahmad, A., Younes, I., & Abbas, S. (2021). Impact assessment of land cover and land use changes on soil erosion changes (2005–2015) in Pakistan. Land Degradation & Development, 33(1):204–217. doi.org/10.1002/ldr.4138

OpenStreetMap (OSM) is a collaborative project to create a free editable map of the world. Created in 2004, it was inspired by the success of Wikipedia and more than two million registered users who can add data by manual survey, GPS devices, aerial photography, and other free sources.

OSM is produced as a public good by volunteers, and there are no guarantees about data quality. OpenStreetMap® is open data, licensed under the Open Data Commons Open Database License (ODbL) by the OpenStreetMap Foundation (OSMF).

OSM represents physical features on the ground (e.g. roads or buildings) using tabs attached to its basic data structure (its nodes, ways, and relations). Each tag describes a geographic attribute of the feature being shown by the specific node, way or relation.

Nodes are one of the core elements in the OSM data model. It consists of a single point in space defined by its latitude, longitude and node id. Nodes can be used to define standalone point features.

Includes complete landcover data for Nepal and Bhutan, with partial data for Afghanistan, Pakistan, India, China and Myanmar.
Can be found at the following link: http://www.fao.org/geonetwork/srv/en/main.home?uuid=46d3c2ef-72c3-4f96-8...

The purpose of the land cover database is to provide essential basic information natural resource assessment and management, environmental modelling, and decision-making and policy formulation for a sustainable land management. The strategy adopted combines the need to develop new tools and methodologies for land cover mapping with the necessity to strengthen the capacity of developing countries to produce, manage and distribute spatial information on environmental resources.
The land cover was interpreted from LANDSAT imagery acquired mainly in the period 2000-2005. Current validation was made from ancillary data and free high resolution historical imagery. The field work verification it is planned shortly. Countries involved in the mapping are going to control the classes created/interpretation performed and update the database with new classes according to a more detailed interpretation scale (1:100 000).

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).

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.

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.

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