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.

This activity uses interactive web maps to visualise and explore the human development index (HDI), crime rates, fertility rates, gender inequality, and economic indicators. The patterns that you see in these web maps will help to shape your understanding of global development patterns and the impact they have on people.Read through the material and answer the questions in yellow.

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).

Human Development Index by country for 2013. This is a filtered layer based on the "Human Development Index by country, 1980-2010 time-series" layer.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 Human Development: 0.736 and higher High Human Development: 0.615 to 0.735 Medium Human Development: 0.494 to 0.614 Low Human Development: 0.493 and lower

Country shapes from Natural Earth 50M scale data. 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).

Countries ranked by child-based capability index.

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.

The purpose of LADOT's data dashboards is to measure performance of meaningful indicators related to the department's values and goals.

BackgroundEducation and health are both constituents of human capital that enable people to earn higher wages and enhance people’s capabilities. Human capabilities may lead to fulfilling lives by enabling people to achieve a valuable combination of human functionings—i.e., what people are able to do or be as a result of their capabilities. A better understanding of how these different human capabilities are produced together could point to opportunities to help jointly reduce the wide disparities in health and education across populations.Methods and findingsWe use nationally and regionally representative individual-level data from Demographic and Health Surveys (DHS) for 55 low- and middle-income countries (LMICs) to examine patterns in human capabilities at the national and regional levels, between 2000 and 2017 (N = 1,657,194 children under age 5). We graphically analyze human capabilities, separately for each country, and propose a novel child-based Human Development Index (HDI) based on under-five survival, maternal educational attainment, and measures of a child’s household wealth. We normalize the range of each component using data on the minimum and maximum values across countries (for national comparisons) or first-level administrative units within countries (for subnational comparisons). The scores that can be generated by the child-based HDI range from 0 to 1.We find considerable heterogeneity in child health across countries as well as within countries. At the national level, the child-based HDI ranged from 0.140 in Niger (with mean across first-level administrative units = 0.277 and standard deviation [SD] 0.114) to 0.755 in Albania (with mean across first-level administrative units = 0.603 and SD 0.089). There are improvements over time overall between the 2000s and 2010s, although this is not the case for all countries included in our study. In Cambodia, Malawi, and Nigeria, for instance, under-five survival improved over time at most levels of maternal education and wealth. In contrast, in the Philippines, we found relatively few changes in under-five survival across the development spectrum and over time. In these countries, the persistent location of geographical areas of poor child health across both the development spectrum and time may indicate within-country poverty traps.Limitations of our study include its descriptive nature, lack of information beyond first- and second-level administrative units, and limited generalizability beyond the countries analyzed.ConclusionsThis study maps patterns and trends in human capabilities and is among the first, to our knowledge, to introduce a child-based HDI at the national and subnational level. Areas of chronic deprivation may indicate within-country poverty traps and require alternative policy approaches to improving child health in low-resource settings.

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.

Selected characteristics of study countries.

India's performance on UNDP's Human Development Index (HDI) - score, rank, and comparison with global peers.

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ම ය ම නව ස වර ධන දර ශකය අන ව රටවල ල ය ස ත වක World map of countries by Human Development Index categories in increments

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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#

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.

Data publication: 2014-06-01

Supplemental Information:

ClimAfrica was an international project funded by European Commission under the 7th Framework Programme (FP7) for the period 2010-2014. The ClimAfrica consortium was formed by 18 institutions, 9 from Europe, 8 from Africa, and the Food and Agriculture Organization of United Nations (FAO).

ClimAfrica was conceived to respond to the urgent international need for the most appropriate and up-to-date tools and methodologies to better understand and predict climate change, assess its impact on African ecosystems and population, and develop the correct adaptation strategies. Africa is probably the most vulnerable continent to climate change and climate variability and shows diverse range of agro-ecological and geographical features. Thus the impacts of climate change can be very high and can greatly differ across the continent, and even within countries.

The project focused on the following specific objectives:

1. Develop improved climate predictions on seasonal to decadal climatic scales, especially relevant to SSA;

2. Assess climate impacts in key sectors of SSA livelihood and economy, especially water resources and agriculture;

3. Evaluate the vulnerability of ecosystems and civil population to inter-annual variations and longer trends (10 years) in climate;

4. Suggest and analyse new suited adaptation strategies, focused on local needs;

5. Develop a new concept of 10 years monitoring and forecasting warning system, useful for food security, risk management and civil protection in SSA;

6. Analyse the economic impacts of climate change on agriculture and water resources in SSA and the cost-effectiveness of potential adaptation measures.

The work of ClimAfrica project was broken down into the following work packages (WPs) closely connected. All the activities described in WP1, WP2, WP3, WP4, WP5 consider the domain of the entire South Sahara Africa region. Only WP6 has a country specific (watershed) spatial scale where models validation and detailed processes analysis are carried out.

Contact points:

Metadata Contact: FAO-Data

Resource Contact: Selvaraju Ramasamy

Resource constraints:

copyright

Online resources:

GDP per capita

Project deliverable D4.1 - Scenarios of major production systems in Africa

Climafrica Website - Climate Change Predictions In Sub-Saharan Africa: Impacts And Adaptations

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(1) Human well-being on the Qinghai Tibet Plateau based on the human development index: the human well-being on the Qinghai Tibet Plateau (Qinghai and Xizang provinces) is measured quantitatively using the comprehensive human development index, an objective well-being assessment indicator. Referring to the functional structure framework of human welfare in China in the new era, the functional structure of human groups is divided into basic functions, harmonious functions, development functions and sustainable functions. On the basis of the four functions, functional indicators and specific indicator systems are further designed, that is, health, education, integration of urban and rural areas, living standards and coping with climate change account for 1/5 of the five functional indicators, and the secondary indicators are also set with equal rights. This data can reflect the comprehensive development level of human beings in Qinghai and Xizang to a certain extent, and has certain reference significance for the future development planning of the Qinghai Tibet Plateau. (2) Regional Social Relations Comprehensive Index: Based on data collected from the 2010-2019 China Regional Economic Statistical Yearbook, China Urban Statistical Yearbook, China Civil Affairs Statistical Yearbook, Provincial (Autonomous Region) Statistical Yearbook and Statistical Bulletin, relevant City Statistical Bulletin, etc., a regional social relations evaluation index system was constructed on the basis of regional social relations analysis in provincial-level areas of the Qinghai Tibet Plateau. The weights of various indicators were determined, and the regional social relations comprehensive index of 37 prefecture level cities on the Qinghai Tibet Plateau was calculated. Based on this data, obtain a spatiotemporal distribution map of regional social relations at the prefecture level on the Qinghai Tibet Plateau. (3) Human economic well-being related data: Based on data from the China Statistical Yearbook of six provinces in the Qinghai Tibet Plateau region from 2000 to 2017, and considering the complexity of human well-being, 18 indicators were selected to construct a human economic well-being indicator system suitable for evaluating the Qinghai Tibet Plateau region from four aspects: income and consumption, production materials, living materials, and resource acquisition capacity; Based on data from 17 prefecture level cities in the Qinghai Tibet Plateau region from 2007 to 2018, including the China Urban Statistical Yearbook, provincial (autonomous region) statistical yearbooks and bulletins, and relevant urban statistical bulletins, and considering the actual situation of typical cities in the Qinghai Tibet Plateau region, 22 indicators were selected to construct a human welfare index system from five aspects: income and consumption, means of production, means of livelihood, resource acquisition ability, and physical health. This indicator helps to better understand the actual conditions of basic living conditions such as economy, material resources, and means of production of residents in various regions of the Qinghai Tibet Plateau. (4) Habitat quality of the Qinghai Tibet Plateau: This dataset is based on the InVEST model and uses land use data, road data, and terrain data to calculate the habitat quality of the Qinghai Tibet Plateau from 2000 to 2020. The data span is 20 years, with data provided every 5 years and a resolution of 1000m. Among them, the land use data is sourced from the global 30 meter land cover fine classification product（ http://data.casearth.cn/sdo/list ）. The DEM data is sourced from the National Qinghai Tibet Plateau Science Data Center（ http://data.tpdc.ac.cn ）. The road data is sourced from the OpenStreetMap website（ http://openstreetmap.org/ ）. (5) Educational welfare: Based on the education statistical data of various provinces from 2013 to 2021 released on the official website of the Ministry of Education of the People's Republic of China, the compilation of science and technology statistical data of higher education institutions, the Statistical Yearbook of China's Disability Affairs, the Statistical Yearbook of China's Education Funds, relevant research reports, and other publicly available data, the entropy weight method is selected to objectively determine the weights of each evaluation indicator. The natural breakpoint method is used to grade the various educational welfare evaluation data obtained in 2013 and 2021, and to draw educational welfare evaluation maps and comprehensive educational welfare evaluation maps of various levels and types of schools. This provides a more accurate understanding of the spatiotemporal pattern of various educational welfare and comprehensive educational welfare on the Qinghai Tibet Plateau, and provides scientific basis and decision-making reference for relevant departments. (6) Human welfare in the Dadu River Basin: Based on meteorological data from

يعرض هذا الملف درجة وترتيب دولة قطر في مؤشر التنمية البشرية خلال الفترة 2014–2025. المؤشر هو مقياس مركب يشمل مستوى الصحة، ومستوى التعليم، ومستوى الدخل الإجمالي للفرد. ويقدم المؤشر صورة إجمالية عن مستوى الرفاهية، ويُستخدم لتصنيف الدول حسب مستوى التنمية وقياس أثر السياسات الاقتصادية على نوعية الحياة.

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