Explore the spatial patterns of the Human Development Index (HDI) to identify regional pat- terns and causal factors in the data. The GeoInquiry activity is available here.Educational standards addressed:APHG: VI:B2 Analyze spatial patterns of social and economic development – GNI per capita. APHG: VI:B1 Explain social and economic measures of development – HDI, Gender Inequali- ty Index (GII), Total Fertility Rate (TRF).APHG: VI:B6 Social and economic measures of development — Changes in fertilityand mortalityThis map is part of a Human Geography GeoInquiry activity. Learn more about GeoInquiries.

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).

Switzerland had the highest level of the Human Development Index (HDI) worldwide in 2022 with a value of 0.967. With a score of 0.966, Norway followed closely behind Switzerland and had the second highest level of human development in that year. The rise of the Asian tigers In the decades after the Cold War, the four so-called Asian tigers, South Korea, Singapore, Taiwan, and Hong Kong (now a Special Administrative Region of China) experienced rapid economic growth and increasing human development. At number four and number nine of the HDI, respectively, Hong Kong and Singapore are the only Asian locations within the top 10 highest HDI scores. Both locations have experienced tremendous economic growth since the 1980’s and 1990’s. In 1980, the per capita GDP of Hong Kong was 5,703 U.S. dollars, increasing throughout the decades until reaching 50,029 in 2023, which is expected to continue to increase in the future. Meanwhile, in 1989, Singapore had a GDP of nearly 31 billion U.S. dollars, which has risen to nearly 501 billion U.S. dollars today and is also expected to keep increasing. Growth of the UAE The United Arab Emirates (UAE) is the only Middle Eastern country besides Israel within the highest ranking HDI scores globally. Within the Middle East and North Africa (MENA) region, the UAE has the third largest GDP behind Saudi Arabia and Israel, reaching nearly 507 billion U.S. dollars by 2022. Per capita, the UAE GDP was around 21,142 U.S. dollars in 1989, and has nearly doubled to 43,438 U.S. dollars by 2021. Moreover, this is expected to reach over 67,538 U.S. dollars by 2029. On top of being a major oil producer, the UAE has become a hub for finance and business and attracts millions of tourists annually.

This layer is a part of Esri GeoInquiries at http://www.esri.com/geoinquiries The HDI was created to emphasize that people and their capabilities should be the ultimate criteria for assessing the development of a country, not economic growth alone. The HDI can also be used to question national policy choices, asking how two countries with the same level of GNI per capita can end up with different human development outcomes. These contrasts can stimulate debate about government policy priorities. The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions. The health dimension is assessed by life expectancy at birth, the education dimension is measured by mean of years of schooling for adults aged 25 years and more and expected years of schooling for children of school entering age. The standard of living dimension is measured by gross national income per capita. The HDI uses the logarithm of income, to reflect the diminishing importance of income with increasing GNI. The scores for the three HDI dimension indices are then aggregated into a composite index using geometric mean. Refer to Technical notes for more details. [source, 2020]This dataset includes the fields:HDI_Rank_2019HDI_2019Life_expectancy_at_birth_inYearExpected_years_of_schoolingMean_years_of_schooling_2019GNI_per_capita_2019Data sources:UN Development Programhttp://hdr.undp.org/en/content/2019-human-development-index-rankingHistoric HDI data source:http://hdr.undp.org/en/data#

Compared to other African countries, Seychelles scored the highest in the Human Development Index (HDI) in 2022. The country also ranked 67th globally, as one of the countries with a very high human development. This was followed by Mauritius, Libya, Egypt, and Tunisia, with scores ranging from 0.80 to 0.73 points. On the other hand, Central African Republic, South Sudan, and Somalia were among the countries in the region with the lowest index scores, indicating a low level of human development.

Students will explore the spatial patterns of the Human Development Index (HDI) to identifyregional patterns and causal factors in the data. The activity uses a web-based map and is tied to the AP Human Geography benchmarks. Learning outcomes: Students will be able to analyze development statistics and see how development correlates with other APHG topics (for example, fertility and mortality).Find more advanced human geography geoinquiries and explore all geoinquiries at http://www.esri.com/geoinquiries

Human development index of China increased by 0.77% from 0.78 score in 2019 to 0.78 score in 2020. Since the 1.60% rise in 2010, human development index surged by 11.89% in 2020. A composite index measuring average achievement in three basic dimensions of human development—a long and healthy life, knowledge and a decent standard of living. 1=the most developed.

From 1990 to 2022, the Human development index (HDI) of Spain has shown an upward trend. In 1990, the country had a HDI score of 0.755. By 2022, the score had increased to 0.911, indicating that Spain has reached very high levels of human development.

The HDI itself is a statistic that combines life-expectancy, education levels and GDP per capita. Countries with scores over 0.800 are considered to have very high levels of development, compared with countries that score lower.

The HDI was created to emphasize that people and their capabilities should be the ultimate criteria for assessing the development of a country, not economic growth alone. The HDI can also be used to question national policy choices, asking how two countries with the same level of GNI per capita can end up with different human development outcomes. These contrasts can stimulate debate about government policy priorities. The Human Development Index (HDI) is a summary measure of average achievement in key dimensions of human development: a long and healthy life, being knowledgeable and have a decent standard of living. The HDI is the geometric mean of normalized indices for each of the three dimensions. The health dimension is assessed by life expectancy at birth, the education dimension is measured by mean of years of schooling for adults aged 25 years and more and expected years of schooling for children of school entering age. The standard of living dimension is measured by gross national income per capita. The HDI uses the logarithm of income, to reflect the diminishing importance of income with increasing GNI. The scores for the three HDI dimension indices are then aggregated into a composite index using geometric mean. Refer to Technical notes for more details. The HDI simplifies and captures only part of what human development entails. It does not reflect on inequalities, poverty, human security, empowerment, etc. The HDRO offers the other composite indices as broader proxy on some of the key issues of human development, inequality, gender disparity and poverty. A fuller picture of a country's level of human development requires analysis of other indicators and information presented in the statistical annex of the report.

The average for 2022 based on 27 countries was 0.903 points. The highest value was in Denmark: 0.952 points and the lowest value was in Bulgaria: 0.799 points. The indicator is available from 1980 to 2022. Below is a chart for all countries where data are available.

The Human Development Index (HDI) of the United Kingdom has increased from 0.804 in 1990 to 0.940 by 2022, indicating that the UK has reached very high levels of human development. HDI is a statistic that combines life-expectancy, education levels and GDP per capita. Countries with scores over 0.800 are considered to have very high levels of development, compared with countries that score lower.

Countries ranked by child-based capability index.

School life expectancy, primary to tertiary, gender parity index (GPI) in Russia was reported at 1.027 GPI in 2018, according to the World Bank collection of development indicators, compiled from officially recognized sources. Russia - School life expectancy, primary to tertiary, gender parity index - actual values, historical data, forecasts and projections were sourced from the World Bank on March of 2025.

Students will explore the spatial patterns of the Human Development Index (HDI) to identify regional patterns and causal factors in the data. The activity uses a web-based map. Learning outcomes: Students will be able to analyse development statistics and see how development correlates with other topics such as fertility and morality.Other New Zealand GeoInquiry instructional material freely available at https://arcg.is/1GPDXe

Explore factors that define levels of development. The GeoInquiry activity is available here.Educational standards addressed:APHG: VI.B1. Analyze spatial variation in the Human Development Index. APHG: VI.B1. Explain social and economic measures of development.This map is part of a Human Geography GeoInquiry activity. Learn more about GeoInquiries.

There is a considerable gap linking human dimensions and marine ecosystem services with Sustainable Development Goals, and one of these issues relate to differing perspectives and ideas around concepts of human development. There is also a lack of contemporary evaluations of coastal communities from developing nations under the lens of wellbeing and social vulnerability indexes. This study contributes to that discussion by presenting an analysis of Brazilian coastal municipalities, based on two indexes: The Social Vulnerability Index (SVI) and the Municipal Human Development Index (MHDI). These indicators intend to map some aspects of social well-being and development in the Brazilian territory under different perspectives. MHDI illustrates the average population conditions in a certain territory for humans to thrive, while the SVI points more specifically to the lack of assets necessary for wellbeing in a territory. The main aims are to map inequalities between coastal municipalities based on these two indexes and to provide a critical view reinforcing the importance of also considering natural capital as a key issue for wellbeing. Both indexes were developed with data from the Brazilian Institute of Geography and Statistics Census of 2010, the most recent one available for municipalities. Overall, 65.9 and 78% of a total of 387 Brazilian coastal municipalities assessed were ranked below SVI and MHDI country average values, respectively. Both indexes indicated higher human development conditions in Southern municipalities than in Northern ones, especially for income and education conditions, also showing large heterogeneity of discrepancies among and within regions. The importance of combined approaches for local socioeconomic wellbeing improvements, as measured by the MHDI and the SVI, and natural capital optimization seems essential for improvements in coastal communities’ quality-of-life conditions.

Countries from Natural Earth 50M scale data with a Human Development Index attribute for each of the following years: 1980, 1985, 1990, 1995, 2000, 2005, 2010, 2013, 2015, & 2017. 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

In 2015 & 2017 these groups were defined by the following HDI values: Very High: 0.800 and higher High: 0.700 to 0.799 Medium: 0.550 to 0.699 Low: 0.549 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). 2015 & 2017 values source: HDRO calculations based on data from UNDESA (2017a), UNESCO Institute for Statistics (2018), United Nations Statistics Division (2018b), World Bank (2018b), Barro and Lee (2016) and IMF (2018).

Population data are from (1) United Nations Population Division. World Population Prospects, (2) United Nations Statistical Division. Population and Vital Statistics Report (various years), (3) Census reports and other statistical publications from national statistical offices, (4) Eurostat: Demographic Statistics, (5) Secretariat of the Pacific Community: Statistics and Demography Programme, and (6) U.S. Census Bureau: International Database.

It is widely accepted that the main driver of the observed decline in biological diversity is increasing human pressure on Earth's ecosystems. However, the spatial patterns of change in human pressure and their relation to conservation efforts are less well known. We developed a spatially and temporally explicit map of global change in human pressure over two decades between 1990 and 2010 at a resolution of 10 km². We evaluated 22 spatial data sets representing different components of human pressure and used them to compile a Temporal Human Pressure Index (THPI) based on 3 data sets: human population density, land transformation, and electrical power infrastructure. We investigated how the THPI within protected areas correlate to International Union for Conservation of Nature (IUCN) management categories and the Human Development Index (HDI), as well as how the THPI was correlated to accumulative pressure using the original Human footprint. Since the early 90's, human pressure increased 64% in terrestrial areas; the largest increases were in Southeast Asia. Protected areas also exhibited overall increases in human pressure, the degree of which varied with location and IUCN management category. Only wilderness areas and natural monuments (management categories Ib and III) exhibited decreases in pressure. Protected areas not assigned any category exhibited the greatest increases. High HDI values and greater mean elevation correlated with greater reductions in pressure across protected areas, while increasing age of the protected area correlated with increases in pressure. Our analysis is an initial step toward mapping changes in human pressure on the natural world over time. That only 3 data sets could be included in our spatio-temporal global pressure map, highlights the challenge to measuring pressure changes over time.

The Gross Domestic Product per capita (gross domestic product divided by mid-year population converted to international dollars, using purchasing power parity rates) has been identified as an important determinant of susceptibility and vulnerability by different authors and used in the Disaster Risk Index 2004 (Peduzzi et al. 2009, Schneiderbauer 2007, UNDP 2004) and is commonly used as an indicator for a country’s economic development (e.g. Human Development Index). Despite some criticisms (Brooks et al. 2005) it is still considered useful to estimate a population’s susceptibility to harm, as limited monetary resources are seen as an important factor of vulnerability. However, collection of data on economic variables, especially sub-national income levels, is problematic, due to various shortcomings in the data collection process. Additionally, the informal economy is often excluded from official statistics. Night time lights satellite imagery of NOAA grid provides an alternative means for measuring economic activity. NOAA scientists developed a model for creating a world map of estimated total (formal plus informal) economic activity. Regression models were developed to calibrate the sum of lights to official measures of economic activity at the sub-national level for some target Country and at the national level for other countries of the world, and subsequently regression coefficients were derived. Multiplying the regression coefficients with the sum of lights provided estimates of total economic activity, which were spatially distributed to generate a 30 arc-second map of total economic activity (see Ghosh, T., Powell, R., Elvidge, C. D., Baugh, K. E., Sutton, P. C., & Anderson, S. (2010).Shedding light on the global distribution of economic activity. The Open Geography Journal (3), 148-161). We adjusted the GDP to the total national GDPppp amount as recorded by IMF (International Monetary Fund) for 2010 and we divided it by the population layer from Worldpop Project. Further, we ran a focal statistics analysis to determine mean values within 10 cell (5 arc-minute, about 10 Km) of each grid cell. This had a smoothing effect and represents some of the extended influence of intense economic activity for local people. Finally we apply a mask to remove the area with population below 1 people per square Km. This dataset has been produced in the framework of the "Climate change predictions in Sub-Saharan Africa: impacts and adaptations (ClimAfrica)" project, Work Package 4 (WP4). More information on ClimAfrica project is provided in the Supplemental Information section of this metadata.

This web map is part of SDGs Today. Please see sdgstoday.orgSDG 11 aims to “make cities and human settlements inclusive, safe, resilient and sustainable.” Prevalence of and access to public space is critical for achieving SDG 11. Aside from environmental benefits, public space can also help improve public health, bolster community, and encourage economic exchange. As one measure of progress, the UN has established SDG Indicator 11.7.1: “Average share of the built-up area of cities that is open space for public use for all, by sex, age and persons with disabilities.” Using open data, CIESIN computed SDG Indicator 11.7.1 results for 8,873 urban centers across 180 countries. Urban center extents and identifying names from the European Commission's Joint Research Center’s Urban Center Database (UCDB) were used for the production of this data set. This work was completed in support of the Group on Earth Observations (GEO) Human Planet Initiative (HPI). CIESIN plans to update the SDG Indicator 11.7.1: Urban Public Space - Availability and Access data set annually to help countries track their progress towards SDG 11 and to facilitate international comparisons.