LANDSCAN GLOBAL 2005

Contact: Human Geography, Geospatial Science and Human Security Division, Oak Ridge National Laboratory

Address: landscan@ornl.gov

Online Resource: https://landscan.ornl.gov

Standard Name: ISO 19139 Geographic Information - Metadata - Implementation Specification

Standard Version: 2007

Title: LandScan Global 2005

Publication Date: 2006-07-01

Creation Date: Human Geography, Geospatial Science and Human Security Division, Oak Ridge National Laboratory

Other Citation Details: https://doi.org/10.48690/1524201

Abstract: Using an innovative approach that combines Geographic Information Science, remote sensing technology, and machine learning algorithms, ORNL’s LandScan is the community standard for global population distribution. At 30 arc-second (approximately 1 km) resolution, LandScan is the finest resolution global population distribution data available representing an “ambient population” (average over 24 hours). The LandScan algorithm, an R&D 100 Award Winner, uses spatial data, high-resolution imagery exploitation, and a multi-variable dasymetric modeling approach to disaggregate census counts within an administrative boundary. LandScan population data are spatially explicit - unlike tabular Census data. Since no single population distribution model can account for the differences in spatial data availability, quality, scale, and accuracy as well as the differences in cultural settlement practices, LandScan population distribution models are tailored to match the data conditions and geographical nature of each individual country and region. By modeling an ambient population, LandScan Global captures the full potential activity space of people throughout the course of the day and night rather than just a residential location.

Purpose: LandScan Global was developed on behalf of the U.S. federal government and is used for rapid consequence and risk assessment as well as emergency planning and management.

Credit: Human Geography, Geospatial Science and Human Security Division, Oak Ridge National Laboratory; US DOD

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Creative Commons Attribution 4.0 International License

https://landscan.ornl.gov/licensing

Oak Ridge National Laboratory

Using an innovative approach with Geographic Information Systems and Remote Sensing, ORNL’s LandScan is the community standard for global population distribution. At 30 arc-second (approximately 1 km) resolution, LandScan is the finest resolution global population distribution data available and represents an “ambient population” (average over 24 hours). The LandScan algorithm, an R&D 100 Award Winner, uses spatial data and imagery analysis technologies and a multi-variable dasymetric modeling approach to disaggregate census counts within an administrative boundary. LandScan population data are spatially explicit - unlike tabular Census data. Since no single population distribution model can account for the differences in spatial data availability, quality, scale, and accuracy as well as the differences in cultural settlement practices, LandScan population distribution models are tailored to match the data conditions and geographical nature of each individual country and region.

The LandScan data set is a worldwide population database compiled on a 30" X 30" latitude/longitude grid. Census counts (at sub-national level) were apportioned to each grid cell based on likelihood coefficients, which are based on proximity to roads, slope, land cover, nighttime lights, and other data sets. LandScan has been developed as part of the Oak Ridge National Laboratory (ORNL) Global Population Project for estimating ambient populations at risk. The LandScan files are available via the internet in ESRI grid format by continent and for the world. You can access the data files after user registration through the data links. For an overview of the methods used to develop LandScan, please read the documentation and FAQs.

[Summary provided by Oak Ridge National Laboratory]

Description to be added

LANDSCAN GLOBAL 2000

Contact: Human Geography, Geospatial Science and Human Security Division, Oak Ridge National Laboratory

Address: landscan@ornl.gov

Online Resource: https://landscan.ornl.gov

Standard Name: ISO 19139 Geographic Information - Metadata - Implementation Specification

Standard Version: 2007

Title: LandScan Global 2000

Publication Date: 2001-07-01

Creation Date: Human Geography, Geospatial Science and Human Security Division, Oak Ridge National Laboratory

Other Citation Details: https://doi.org/10.48690/1524196

Abstract: Using an innovative approach that combines Geographic Information Science, remote sensing technology, and machine learning algorithms, ORNL’s LandScan is the community standard for global population distribution. At 30 arc-second (approximately 1 km) resolution, LandScan is the finest resolution global population distribution data available representing an “ambient population” (average over 24 hours). The LandScan algorithm, an R&D 100 Award Winner, uses spatial data, high-resolution imagery exploitation, and a multi-variable dasymetric modeling approach to disaggregate census counts within an administrative boundary. LandScan population data are spatially explicit - unlike tabular Census data. Since no single population distribution model can account for the differences in spatial data availability, quality, scale, and accuracy as well as the differences in cultural settlement practices, LandScan population distribution models are tailored to match the data conditions and geographical nature of each individual country and region. By modeling an ambient population, LandScan Global captures the full potential activity space of people throughout the course of the day and night rather than just a residential location.

Purpose: LandScan Global was developed on behalf of the U.S. federal government and is used for rapid consequence and risk assessment as well as emergency planning and management.

Credit: Human Geography, Geospatial Science and Human Security Division, Oak Ridge National Laboratory; US DOD

​

Creative Commons Attribution 4.0 International License

https://landscan.ornl.gov/licensing

Oak Ridge National Laboratory

Developed for the U. S. Department of Defense. Allows for quick and easy assessment, estimation, and visualization of populations-at-risk.

Homeland Infrastructure Foundation-Level Data (HIFLD) geospatial data sets containing information on LandScan USA 2020.

The LandScan 2000 global population data set is a worldwide population database compiled on a 30" X 30" latitude/longitude grid. Census counts (at sub-national level) were apportioned to each grid cell based on likelihood coefficients, which are based on proximity to roads, slope, land cover, nighttime lights, and other data sets. LandScan has been developed as part of the Oak Ridge National Laboratory (ORNL) Global Population Project for estimating ambient populations at risk. The LandScan files are available via the internet in ESRI grid format by continent and for the world. At approximately 1 km resolution (30" X 30"), There is also a "Layer" file (lspop2000.lyr) for ArcGIS. LandScan is the finest resolution global population distribution data available and represents an ambient population (average over 24 hours). This dataset is part of the LandScan 2000 Global Population Database (2000-2010).

Homeland Infrastructure Foundation-Level Data (HIFLD) geospatial data sets containing information on Landscan Symbology.

Web Map Service that supports the IRENA Global Atlas for Renewable EnergyThe LandScan 2018 Global Population Database was developed by Oak Ridge National Laboratory (ORNL) for the United States Department of Defense (DoD).ORNL’s LandScan™ is a community standard for global population distribution data. At approximately 1 km (30″ X 30″) spatial resolution, it represents an ambient population (average over 24 hours) distribution. The database is refreshed annually and released to the broader user community around October. LandScan™ is now available at no cost to the educational community. The latest LandScan™ dataset available is LandScan Global 2018. Older LandScan Global data sets (LandScan 1998, 2000-2017) are available through site. These data set can be licensed for commercial and other applications through multiple third-party vendors. LandScan is developed using best available demographic (Census) and geographic data, remote sensing imagery analysis techniques within a multivariate dasymetric modeling framework to disaggregate census counts within an administrative boundary. Since no single population distribution model can account for the differences in spatial data availability, quality, scale, and accuracy as well as the differences in cultural settlement practices, LandScan population distribution is essentially a combination of locally adoptive models that are tailored to match the data conditions and geographical nature of each individual country and region.

LandScan Global Population Database 2011. Population counts at 30 arc second resolution.

Detailed information are to be found in cover_letter_ls11.pdf.

RDW_HIFLD/LandScan_Hawaii_Night

ชุดข้อมูล LandScan ซึ่งจัดทำโดยห้องทดลองแห่งชาติ Oak Ridge (ORNL) มีชุดข้อมูลการกระจายประชากรทั่วโลกที่ครอบคลุมและมีความละเอียดสูง ซึ่งเป็นแหล่งข้อมูลที่มีค่าสำหรับแอปพลิเคชันต่างๆ มากมาย LandScan ใช้ประโยชน์จากเทคนิคการประมาณพื้นที่ที่ทันสมัยที่สุดและแหล่งข้อมูลเชิงพื้นที่ขั้นสูง เพื่อแสดงข้อมูลโดยละเอียดเกี่ยวกับจํานวนประชากรและ…

These data were produced by the WorldPop Research Group at the University of Southampton. These data include gridded estimates of population at approximately 100m and 1km resolution for 2020, along with estimates of the number of people belonging to individual age-sex groups. These results were produced using Subnational Population Statistics 2020 for Ukraine provided in the Common Operational Dataset on Population Statistics (COD-PS) and ORNL LandScan HD for Ukraine 2022 settlement layer.

The datasets are produced using the "top-down" method, with both the unconstrained and constrained top-down disaggregation methods used to produce two different datasets. The differences between constrained and un-constrained methods are described here .

Main data sources

• Subnational Population Statistics for Ukraine provided by Common Operational Dataset on Population Statistics (COD-PS). The subnational population statistics were estimated using baseline information from the 2001 Population Census of Ukraine and annual birth and death registration data since the last census.
• Settlement layer ORNL LandScan HD for Ukraine.
• Subnational Administrative Boundaries for Ukraine provided by OCHA.
• Geospatial covariate layers available at WorldPop.

For further details, please, read the Release Statement.

Release content

1. ukr_pop_2020_100m_unconstrained_v1_0.zip
2. ukr_pop_2020_100m_constrained_v1_0.zip
3. ukr_pop_2020_1km_unconstrained_v1_0.zip
4. ukr_pop_2020_1km_constrained_v1_0.zip
5. ukr_agesex_2020_100m_unconstrained_v1_0.zip
6. ukr_agesex_2020_100m_constrained_v1_0.zip
7. ukr_agesex_2020_1km_unconstrained_v1_0.zip
8. ukr_agesex_2020_1km_constrained_v1_0.zip
9. ukr_agesex_0_18_2020_1km_unconstrained_v1_0.zip
10. ukr_agesex_0_18_2020_1km_constrained_v1_0.zip

Recommended citations

Bondarenko M., Sorichetta A., Leasure DR. and Tatem AJ. 2022 Gridded population estimates for Ukraine using UN COD-PS estimates 2020, version 1.0. WorldPop, University of Southampton. doi:10.5258/SOTON/WP00734

License

These data may be distributed using a Creative Commons Attribution 4.0 International (CC BY 4.0) License, specified in legal code. Contact release[at]worldpop.org for more information.

The authors followed rigorous procedures designed to ensure that the used data, the applied method and thus the results are appropriate and of reasonable quality. If users encounter apparent errors or misstatements, they should contact WorldPop at release[at]worldpop.org.

WorldPop, University of Southampton, and their sponsors offer these data on a "where is, as is" basis; do not offer an express or implied warranty of any kind; do not guarantee the quality, applicability, accuracy, reliability or completeness of any data provided; and shall not be liable for incidental, consequential, or special damages arising out of the use of any data that they offer.

This raster dataset is a grid of world countries. These are the standard country boundaries. Also included is a DBF (countries.dbf) giving the country name for each country "number" in the grid and has demographic factors similar to the Admin1 table. This dataset is part of the LandScan global 2013.

This data set provides the first global inventory of the spatial distribution and density of constructed impervious surface area (ISA) based on the brightness of satellite observed and calibrated nighttime lights [U.S. Air Force Defense Meteorological Satellite Program (DMSP) Operational Linescan System (OLS)] and population count from ORNL LandScan 2004 [which includes input from three satellite data sources: NASA MODIS land cover, the topographic data from the Shuttle Radar Topography Mission (SRTM), and the high resolution Controlled Image Base (CIB) from the U.S. National Geospatial Intelligence Agency (NGA)]. Examples of ISA include roads, parking lots, buildings, driveways, sidewalks, and other manmade surfaces. While high spatial resolution is required to observe these features, this product was made at one km2 resolution. The reference data used in the calibration were derived from 30-meter resolution ISA estimates of the USA from the U.S. Geological Survey. Nominally the product is for the years 2000-2001 since both the nighttime lights and reference data are from those two years. Investigators used the product to estimate the world’s total ISA, to rank the leading countries in total ISA and to calculate the quantity of ISA per person for individual countries. In addition, they aggregated the ISA density for the major watershed units of the world to identify those watersheds impacted by the proliferation of ISA. Investigators found that 1.05% of the United States land area is impervious surface (83,337 km2) and 0.43% of the world's land surface (579,703 km2) is constructed impervious surface. China has more ISA than any other country (87,182 km2), but has only 67 m2 of ISA per person, compared to 297 m2 per person in the USA. Hydrologic and environmental impacts of ISA begin to be exhibited when the density of ISA reaches 10% of the land surface. An examination of the areas with 10% or more ISA in watersheds finds that with the exception of Europe, the majority of watershed areas have less than 0.4% of their area at or above the 10% ISA threshold. The investigators believe the next step for improving the product is to include reference ISA data from many more areas around the world. For additional information, see Elvidge, C.D., B.T. Tuttle, P.C. Sutton, K.E. Baugh, A.T. Howard, C. Milesi, B.L. Bhaduri, and R. Nemani. 2007. Global Distribution and Density of Constructed Impervious Surfaces. Sensors 7: 1962-1979.

The PAR differences are reported here as proportions of the total national population of the corresponding countries as estimated by the United Nations Population Division (UNPD) for 2008. The top 10 countries with the highest PAR disparity are listed, alongside the top 10 by PAR disparity for countries with populations over one million. A detailed list of all countries has been provided in Table S1.

LandScan HD proporciona estimaciones de población en cuadrículas con una resolución de 3 segundos de arco (~100 m). Los valores de cada celda de LandScan HD representan una estimación del recuento de población ambiental (es decir, el promedio de 24 horas). De esta manera, los datos capturan todo el espacio de actividad potencial de las personas a lo largo del día y la noche.

This raster dataset is a grid of world countries. These are the standard country boundaries. Also included is a DBF (countries.dbf) giving the country name for each country "number" in the grid and has demographic factors similar to the Admin1 table. This dataset is part of the LandScan 2012 Global Population Database.

Data from the Oak Ridge National Laboratory, LandScan Global Population 1998 Database. Estimates for rural population were obtained by excluding the Urban Population Areas. This was achieved by removing settled and partly settled grid cells from the Landcover Dataset and removing(limiting) propulation density figures to produce a net result which approximates the known rural population. Data-set has been exported as Binary format.