The Global Human Settlement Layer: Population and Built-Up Estimates, and Degree of Urbanization Settlement Model Grid data set provides gridded data on human population (GHS-POP), built-up area (GHS-BUILT), and degree of urbanization (GHS-SMOD) across four time periods: 1975, 1990, 2000, and 2014 (BUILT) or 2015 (POP, SMOD). GHS-BUILT describes the percent built-up area for each 30 arc-second grid cell (approximately 1 km at the equator) based on Landsat imagery from each of the four time periods. GHS-POP consists of census data from the 2010 round of global census from Gridded Population of the World, Version 4, Revision 10 (GPWv4.10) spatially-allocated within census Units based on the percent built-up areas from GHS-BUILT. GHS-SMOD uses GHS-BUILT and GHS-POP in order to develop a standardized classification of degree of urbanization grid. The original data from the Joint Research Centre of the European Commission (JRC-EC) has been combined into a single data package in GeoTIFF format and reprojected from Mollweide Equal Area into WGS84 at 9 arc-second and 30 arc-second horizontal resolutions in order to support integration with a variety of global raster data sets.

Global Human Footprint Index represents the relative human influence in each terrestrial biome expressed as a percentage. The purpose is to provide an updated map of anthropogenic impacts on the environment in geographic projection which can be used in wildlife conservation planning, natural resource management, and research on human-environment interactions. Dataset SummaryThe Global Human Footprint Index Dataset of the Last of the Wild Project, Version 2, 2005 (LWP-2) is the Human Influence Index (HII) normalized by biome and realm. The HII is a global dataset of 1-kilometer grid cells, created from nine global data layers of human population pressure (population density), human land use and infrastructure (built-up areas, nighttime lights, land use/land cover), and human access (coastlines, roads, railroads, navigable rivers). A value of zero represents the least influenced–the “most wild” part of the biome with value of 100 representing the most influenced (least wild) part of the biome. The dataset is produced by the Wildlife Conservation Society (WCS) and the Columbia University Center for International Earth Science Information Network (CIESIN).Recommended CitationWildlife Conservation Society - WCS, and Center for International Earth Science Information Network - CIESIN - Columbia University. 2005. Last of the Wild Project, Version 2, 2005 (LWP-2): Global Human Footprint Dataset (Geographic). Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://dx.doi.org/10.7927/H4M61H5F. Accessed DAY MONTH YEAR.

The Global Human Modification of Terrestrial Systems data set provides a cumulative measure of the human modification of terrestrial lands across the globe at a 1-km resolution. It is a continuous 0-1 metric that reflects the proportion of a landscape modified, based on modeling the physical extents of 13 anthropogenic stressors and their estimated impacts using spatially-explicit global data sets with a median year of 2016.

The Global Human Influence Index Dataset of the Last of the Wild Project, Version 2, 2005 (LWP-2) is a global dataset of 1-kilometer grid cells, created from nine global data layers covering human population pressure (population density), human land use and infrastructure (built-up areas, nighttime lights, land use/land cover), and human access (coastlines, roads, railroads, navigable rivers). The dataset in Clarke 1866 Geographic Coordinate System is produced by the Wildlife Conservation Society (WCS) and the Columbia University Center for International Earth Science Information Network (CIESIN).

“The Global Human Settlement Layer Urban Centres Database (GHS-UCDB) is the most complete database on cities to date, publicly released as an open and free dataset. The database represents the global status on Urban Centres in 2015 by offering cities location, their extent (surface, shape), and describing each city with a set of geographical, socio-economic and environmental attributes, many of them going back 25 or even 40 years in time.”Zusätzliche Informationen The Urban Centres are defined by specific cut-off values on resdient population and built-up surfac share in a 1x1km uniform global grid.See ghs_stat_ucdb2015mt_globe_r2019a_v1_0_web_1.pdf for more information.Views of this layer are used in web maps for the ArcGIS Living Atlas of the World.QuelleGlobal Human Settlement - Urban Centre database R2019A - European Commission | Zuletzt Aufgerufen am 25.04.2025Datenbestand2019

Estimates suggest that by 2023, the number of voice assistants in existence will be roughly equal to the global population, reaching around eight billion. As of 2019, this number stands at around 2.45 billion, implying that the voice assistant industry is set for continued, rapid growth over the coming years.

This update to the Human Footprint (HFP) provides a measure of the direct and indirect human pressures on the environment globally in years 2000, 2005, 2010, and 2013. Per the orinal Human Footprint, this dataset is derived from remotely-sensed and bottom-up survey information compiled on eight measured variables. This represents not only the most current information of its type, but also the first temporally-consistent set of Human Footprint maps. Data on human pressures were acquired or developed for: 1) built environments, 2) population density, 3) electric infrastructure, 4) crop lands, 5) pasture lands, 6) roads, 7) railways, and 8) navigable waterways. This update incorporates updated and higher resolution population, nightlights, pasture, road, and railway input datasets. The Human Footprint maps find a range of uses as proxies for human disturbance of natural systems and can provide an increased understanding of the human pressures that drive macro-ecological patterns, as well as for tracking environmental change and informing conservation science and application. HFP values range from 0 (no human impact) to 50 (heavily human impacted).

See: Venter, O. et al., 2016. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nature Communications, 7, pp.1–11.

This dataset can be downloaded uniquly from UN Biodiversity Lab.
Updated data is made available only to FIP pilot countires at present - rasters are clipped to other FIP data extents.

The global human identification demand is anticipated to accelerate at a CAGR of 9.6%. The market revenue is likely to be valued at US$ 1.2 billion in 2023 and exhibit a revenue growth of US$ 3 billion by 2033.

Attributes	Details
Human Identification Market HCAGR (2017 to 2022)	12.5%
Human Identification Market CAGR (2023 to 2033)	9.6%
Market Size for Human Identification (2023)	US$ 1.2 billion
Market Size for Human Identification (2033)	US$ 3 billion

Region-Wise Overview

Country	United States of America
CAGR (2023 to 2033)	9.4%
HCAGR (2017 to 2022)	12.1%

Country	United Kingdom
CAGR (2023 to 2033)	8.7%
HCAGR (2017 to 2022)	11.1%

Country	China
CAGR (2023 to 2033)	9%
HCAGR (2017 to 2022)	11.7%

Country	Japan
CAGR (2023 to 2033)	8.1%
HCAGR (2017 to 2022)	10.6%

Country	South Korea
CAGR (2023 to 2033)	7.3%
HCAGR (2017 to 2022)	9.2%

Attributes	Details
United States Market Size (2033)	US$ 1.1 billion
United States Market Absolute Dollar Growth (US$ million/billion)	US$ 629.3 million

Attributes	Details
UK Market Size (2033)	US$ 126.3 million
UK Market Absolute Dollar Growth (US$ million/billion)	US$ 71.2 million

Attributes	Details
South Korea Market Size (2033)	US$ 103.5 million
South Korea Market Absolute Dollar Growth (US$ million/billion)	US$ 52.4 million

Attributes	Details
Japan Market Size (2033)	US$ 176 million
Japan Market Absolute Dollar Growth (US$ million/billion)	US$ 95.1 million

First stage action plan...........................

The Global Human Modification of Terrestrial Systems data set provides a cumulative measure of the human modification of terrestrial lands across the globe at a 1-km resolution. It is a continuous 0-1 metric that reflects the proportion of a landscape modified, based on modeling the physical extents of 13 anthropogenic stressors and their estimated impacts using spatially-explicit global data sets with a median year of 2016.

Countries from Natural Earth 50M scale data with a Human Development Index attribute, repeated for each of the following years: 1980, 1985, 1990, 1995, 2000, 2005, 2010, & 2013, to enable time-series display using the YEAR attribute. The Human Development Index measures achievement in 3 areas of human development: long life, good education and income. Specifically, the index is computed using life expectancy at birth, Mean years of schooling, expected years of schooling, and gross national income (GNI) per capita (PPP $). The United Nations categorizes the HDI values into 4 groups. In 2013 these groups were defined by the following HDI values: Very High: 0.736 and higher High: 0.615 to 0.735 Medium: 0.494 to 0.614 Low: 0.493 and lower

Human Development Index attributes are from The World Bank: HDRO calculations based on data from UNDESA (2013a), Barro and Lee (2013), UNESCO Institute for Statistics (2013), UN Statistics Division (2014), World Bank (2014) and IMF (2014).

Data on the extent, patterns, and trends of human land use are critically important to support global and national priorities for conservation and sustainable development. To inform these issues, we created a series of detailed global datasets for 1990, 1995, 2000, 2005, 2010, 2015, and 2017 to evaluate temporal changes and spatial patterns of land use modification of terrestrial lands (excluding Antarctica). These data were calculated using the degree of human modification approach that combines the proportion of a pixel of a given stressor (i.e. footprint) times the intensity of that stressor (ranging from 0 to 1.0). Our novel datasets are detailed (0.09 km^2 resolution), temporally consistent (for 1990-2015, every 5 years), comprehensive (11 change stressors, 14 current), robust (using an established framework and incorporating classification errors and parameter uncertainty), and strongly validated. We also provide a dataset that represents ~2017 conditions and has 14 stressors for an even more comprehensive dataset, but the 2017 results should not be used to calculate change with the other datasets (1990-2015). Note that because of repo file size limits, the datasets for the for the HM overall for 1990 and 1995, as well as major stressors for all years, are located this Google Drive.

This version 1.5 provides the following updates:

Datasets are provided for each of the 6 stressor groups: built-up areas (BU), agricultural/timber harvest (AG), extractive energy and mining (EX), human intrusions (HI), natural system modifications (NS), and transportation & infrastructure (TI), available now at 300 m resolution for each of the time steps in the 1990-2015 time series.

It provides the addition datasets for the years 1995 and 2005, calculated using linear interpolation when stressor data do not provide data at the specific year.

The ESA 150 m water-mask dataset (Lamarche et al. 2017) was used to provide better and more consistent alignment of datasets at the ocean-land-inland water interfaces.

The built-up stressor uses an updated version of the Global Human Settlement Layer (v2022A).

Values provided are 32-bit floating point values, with human modification values ranging from 0.0 to 1.0.

For more details on the approach and methods, please see: Theobald, D. M., Kennedy, C., Chen, B., Oakleaf, J., Baruch-Mordo, S., and Kiesecker, J.: Earth transformed: detailed mapping of global human modification from 1990 to 2017, Earth Syst. Sci. Data., https://doi.org/10.5194/essd-2019-252, 2020.

Version 1.5 was completed in collaboration with the Center for Biodiversity and Global Change at Yale University and supported by the E.O. Wilson Biodiversity Foundation.

The deadliest animals in the world based on the number of human deaths per year is not a creature that humans usually find scary, such as a lion or snake. Mosquitos are by far the deadliest creature in the world when it comes to annual human deaths, causing around one million deaths per year, compared to 100,000 deaths from snakes and 250 from lions. Perhaps surpringly, dogs are the third deadliest animal to humans. Dogs are responsible for around 30,000 human deaths per year, with the vast majority of these deaths resulting from rabies that is transmitted from the dog.

Malaria

Mosquitos are the deadliest creature in the world because they transmit a number of deadly diseases, the worst of which is malaria. Malaria is a mosquito-borne disease caused by a parasite that results in fever, chills, headache, vomiting and, if left untreated, death. Malaria disproportionately affects poorer regions of the world such as Africa and South-East Asia. In 2020, there were around 627,000 deaths from malaria worldwide.

Mosquito-borne diseases in the U.S.

The most common mosquito-borne diseases reported in the United States include West Nile virus, malaria, and dengue viruses. Many of these cases, however, are from travelers who contracted the disease in another country - this is especially true for malaria, Zika, and dengue. In 2018, the states of California, New York, and Texas reported the highest number of mosquito-borne disease cases in the United States.

The average for 2024 based on 175 countries was 4.98 index points. The highest value was in Samoa: 10 index points and the lowest value was in Australia: 0.3 index points. The indicator is available from 2007 to 2024. Below is a chart for all countries where data are available.

Explore the patterns of world population in terms of total population, arithmetic density, total fertility rate, natural increase rate, life expectancy, and infant mortality rate. The GeoInquiry activity is available here.Educational standards addressed:APHG: II.A. Analyze the distribution patterns of human populations.APHG: II.B. Understand that populations grow and decline over time and space.This map is part of a Human Geography GeoInquiry activity. Learn more about GeoInquiries.

We developed datasets on the human modification of global terrestrial ecosystems from 1990 to 2020. The methods and data sources associated with these data are fully described in:

Theobald, D.M., Oakleaf, J.R., Moncrieff, G., Voigt, M., Kiesecker, J., and Kennedy, C.M.

For each 5-year step from 1990 to 2020, 9 raster datasets are provided in cloud-optimized GeoTIFF format (300 m resolution, EPSG:4326). The naming convention is as follows: HMv2024080101_

These data are available as Google Earth Engine assets via this script (including 90 m): https://code.earthengine.google.com/1b7b5976fdd6189c6533ca00a46386d1

The Google Earth Engine script to calculate human modification is here: https://code.earthengine.google.com/59c0f7da25579422ce4d459abeae1b7d

The Google Earth Engine script to clip out custom extents and export to GeoTIFF is here: https://code.earthengine.google.com/44c9f092472edb9bac3c45096aa5091d

Please see companion repo here for datasets for 2022: https://zenodo.org/uploads/14502573

The global human identification market size reached USD 2.1 Billion in 2024. Looking forward, IMARC Group expects the market to reach USD 5.3 Billion by 2033, exhibiting a growth rate (CAGR) of 10.17% during 2025-2033. There are various factors that are driving the market, which include rising applications in healthcare and personal genomics, increasing crime rates and terrorism activities, and integration of advanced technologies like fingerprinting and facial recognition.

Report Attribute	Key Statistics
Base Year	2024
Forecast Years	2025-2033
Historical Years	2019-2024
Market Size in 2024	USD 2.1 Billion
Market Forecast in 2033	USD 5.3 Billion
Market Growth Rate 2025-2033	10.17%

IMARC Group provides an analysis of the key trends in each sub-segment of the global human identification market report, along with forecasts at the global, regional and country level from 2025-2033. Our report has categorized the market based on product & service, technology, application and end user.

The Global Human Footprint Dataset of the Last of the Wild Project, Version 1, 2002 (LWP-1) is the Human Influence Index (HII) normalized by biome and realm. The HII is a global dataset of 1-kilometer grid cells, created from nine global data layers covering human population pressure (population density, population settlements), human land use and infrastructure (built up areas, nighttime lights, land use/land cover), and human access (coastlines, roads, railroads, navigable rivers). The dataset in Clarke 1866 Geographic Coordinate System is produced by the Wildlife Conservation Society (WCS) and the Columbia University Center for International Earth Science Information Network (CIESIN).

JO: Human Capital Index (HCI): Male: Lower Bound: Scale 0-1 data was reported at 0.516 NA in 2017. JO: Human Capital Index (HCI): Male: Lower Bound: Scale 0-1 data is updated yearly, averaging 0.516 NA from Dec 2017 (Median) to 2017, with 1 observations. JO: Human Capital Index (HCI): Male: Lower Bound: Scale 0-1 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Jordan – Table JO.World Bank: Human Capital Index. The HCI lower bound reflects uncertainty in the measurement of the components and the overall index. It is obtained by recalculating the HCI using estimates of the lower bounds of each of the components of the HCI. The range between the upper and lower bound is the uncertainty interval. While the uncertainty intervals constructed here do not have a rigorous statistical interpretation, a rule of thumb is that if for two countries they overlap substantially, the differences between their HCI values are not likely to be practically meaningful.; ; World Bank staff calculations based on the methodology described in World Bank (2018). https://openknowledge.worldbank.org/handle/10986/30498; ;

Students will explore the patterns of world population in terms of total population, arithmetic density, total fertility rate, natural increase rate, and infant mortality rate. The activity uses a web-based map and is tied to the AP Human Geography benchmarks. Learning outcomes:Students will be able to identify and explain the spatial patterns and distribution of world population based on total population, density, total fertility rate, natural increase rate, and infant mortality rate.Find more advanced human geography geoinquiries and explore all geoinquiries at http://www.esri.com/geoinquiries