The purpose of this dataset is to provide village-level wealth estimates for places where up-to-date information about geographic wealth distribution is needed. This dataset contains information on buildings, roads, points of interest (POIs), night-time luminosity, population density, and estimated wealth index for 1-mi² inhabited places identified by the underlying datasets. The wealth level is an estimated value of the International Wealth Index which is a comparable asset based wealth index covering the complete developing world.

In 2025, nearly 11.7 percent of the world population in extreme poverty, with the poverty threshold at 2.15 U.S. dollars a day, lived in Nigeria. Moreover, the Democratic Republic of the Congo accounted for around 11.7 percent of the global population in extreme poverty. Other African nations with a large poor population were Tanzania, Mozambique, and Madagascar. Poverty levels remain high despite the forecast decline Poverty is a widespread issue across Africa. Around 429 million people on the continent were living below the extreme poverty line of 2.15 U.S. dollars a day in 2024. Since the continent had approximately 1.4 billion inhabitants, roughly a third of Africa’s population was in extreme poverty that year. Mozambique, Malawi, Central African Republic, and Niger had Africa’s highest extreme poverty rates based on the 2.15 U.S. dollars per day extreme poverty indicator (updated from 1.90 U.S. dollars in September 2022). Although the levels of poverty on the continent are forecast to decrease in the coming years, Africa will remain the poorest region compared to the rest of the world. Prevalence of poverty and malnutrition across Africa Multiple factors are linked to increased poverty. Regions with critical situations of employment, education, health, nutrition, war, and conflict usually have larger poor populations. Consequently, poverty tends to be more prevalent in least-developed and developing countries worldwide. For similar reasons, rural households also face higher poverty levels. In 2024, the extreme poverty rate in Africa stood at around 45 percent among the rural population, compared to seven percent in urban areas. Together with poverty, malnutrition is also widespread in Africa. Limited access to food leads to low health conditions, increasing the poverty risk. At the same time, poverty can determine inadequate nutrition. Almost 38.3 percent of the global undernourished population lived in Africa in 2022.

This dataset is provided by the World Bank Group.

Data have been aggregated to district level.

Overlaying the number of marginality dimensions with percentage of people living below 1.25$/day. This map is included in a global study on mapping marginality focusing on Sub-Saharan Africa and South Asia. The Dimensions of Marginality are based on different data sources representing different spheres of life. The poverty dataset used in this study is based on calculations by Harvest Choice. The underlying Marginality map is based on the approach on Marginality Mapping (http://www.zef.de/fileadmin/webfiles/downloads/zef_wp/wp88.pdf). The respective map can be found here: https://daten.zef.de/#/metadata/ae4ae68c-cea3-44e7-8199-1c2ae04abb88 Quality/Lineage: Poverty Data was provided and generated by Harvest Choice GIS lab. Marginality hotspots are based on the approach by Graw, V. using five dimensions of marginality. In ArcGIS thresholds were defined based on percentages and overlapping dimensions. Using raster data this data was reclassified and overlayed to build a new classification with regard to the here presented purpose. This approach is similar to the overlap over marginality and poverty mass except this map shows percentage of poverty instead of number of poor people. Purpose: This map was created in the MARGIP project to identify the marginalized and poor by highlighting those areas where the "spheres of life" have a low performance. Those areas where multiple "low performance indicators" did overlap got the highest attention for further research.

This dataset is created based on a Nigeria's 1km Poverty map provided by worldpop.org. Data have been aggregated to local government areas level.

Time Series of Satellite Imagery Improve Deep Learning Estimates of Neighborhood-Level Poverty in Africa These maps contain estimates of material wealth at the neighborhood-level across Africa. These estimates were made using a novel deep-learning model for predicting material wealth based on satellite images from Landsat, DMSP and VIIRS. The model was trained on DHS survey data as described in the corresponding paper (Pettersson et al. 2023). The maps cover all populated areas according to the Global Human Settlement Layer. The spatial resolution of the maps is 6.72 x 6.72 km and the unit of measurement is the International Wealth Index (IWI), scaled from 0 to 1. Each map represents a three-year time span between 1990 to 2019. In the tif file these maps are stored as bands in the image, resulting in the following configuration: Band 1: IWI estimates for 1990-1992 Band 2: IWI estimates for 1993-1995 Band 3: IWI estimates for 1996-1998 Band 4: IWI estimates for 1999-2001 Band 5: IWI estimates for 2002-2004 Band 6: IWI estimates for 2005-2007 Band 7: IWI estimates for 2008-2010 Band 8: IWI estimates for 2011-2013 Band 9: IWI estimates for 2014-2016 Band 10: IWI estimates for 2017-2019 For a full explanation of the map-generating process and estimates, see the corresponding paper.

This dataset is created based on a Tanzania's 1km Poverty map provided by worldpop.org. Data have been aggregated to district level.

Poverty density at $1.25/day and $2.00/day for select countries in Africa south of the Sahara (SSA). Map published in Atlas of African Agriculture Research & Development (Sebastian, ed. 2014). p.76-77 Poverty Original content from Carlo Azzarri (HarvestChoice/IFPRI).

For more information: http://agatlas.org/contents/poverty/

Poverty prevalence (percent) and density (number per square km) at $1.25/day and $2.00/day for select countries in Africa south of the Sahara (SSA). The value $1.25/day represents extreme poverty and $2.00/day represents moderate poverty but when mapped also includes the extremely poor. All $ values are expressed in terms of average 2005 purchasing power parity rates.

Kenya’s population has nearly tripled in the last 35 years, from 16.3 million in 1980 to about 47 million today yet majority of the population are below the poverty line. poverty in Kenya is a widespread problem concentrated in the rural areas. This data set shows poverty rates within the Kenyan counties.

Poverty density at $1.25/day and $2.00/day for select countries in Africa south of the Sahara (SSA). Map published in Atlas of African Agriculture Research & Development (Sebastian, ed. 2014). p.76-77 Poverty Original content from Carlo Azzarri (HarvestChoice/IFPRI).

For more information: http://agatlas.org/contents/poverty/

For the past several censuses, the Census Bureau has invited people to self-respond before following up in-person using census takers. The 2010 Census invited people to self-respond predominately by returning paper questionnaires in the mail. The 2020 Census allows people to self-respond in three ways: online, by phone, or by mail. The 2020 Census self-response rates are self-response rates for current census geographies. These rates are the daily and cumulative self-response rates for all housing units that received invitations to self-respond to the 2020 Census. The 2020 Census self-response rates are available for states, counties, census tracts, congressional districts, towns and townships, consolidated cities, incorporated places, tribal areas, and tribal census tracts. The Self-Response Rate of Los Angeles County is 65.1% for 2020 Census, which is slightly lower than 69.6% of California State rate. More information about these data are available in the Self-Response Rates Map Data and Technical Documentation document associated with the 2020 Self-Response Rates Map or review our FAQs. Animated Self-Response Rate 2010 vs 2020 is available at ESRI site SRR Animated Maps and can explore Census 2020 SRR data at ESRI Demographic site Census 2020 SSR Data. Following Demographic Characteristics are included in this data and web maps to visualize their relationships with Census Self-Response Rate (SRR)..1. Population Density2. Poverty Rate3. Median Household income4. Education Attainment5. English Speaking Ability6. Household without Internet Access7. Non-Hispanic White Population8. Non-Hispanic African-American Population9. Non-Hispanic Asian Population10. Hispanic Population

BackgroundOf the total 56 million deaths worldwide during 2012, 38 million (68%) were due to noncommunicable diseases (NCDs), particularly cardiovascular diseases (17.5 million deaths) cancers (8.2 million) which represents46.2% and 21.7% of NCD deaths, respectively). Nearly 80 percent of the global CVD deaths occur in low- and middle-income countries. Some of the major CVDs such as ischemic heart disease (IHD) and stroke and CVD risk conditions, namely, hypertension and dyslipidaemia share common modifiable risk factors including smoking, unhealthy diets, harmful use of alcohol and physical inactivity. The CVDs are now putting a heavy strain of the health systems at both national and local levels, which have previously largely focused on infectious diseases and appalling maternal and child health. We set out to estimate district-level co-occurrence of two cardiovascular diseases (CVDs), namely, ischemic heart disease (IHD) and stroke; and two major risk conditions for CVD, namely, hypertension and dyslipidaemia in South Africa.MethodThe analyses were based on adults health collected as part of the 2012 South African National Health and Nutrition Examination Survey (SANHANES). We used joint disease mapping models to estimate and map the spatial distributions of risks of hypertension, self-report of ischaemic heart disease (IHD), stroke and dyslipidaemia at the district level in South Africa. The analyses were adjusted for known individual social demographic and lifestyle factors, household and district level poverty measurements using binary spatial models.ResultsThe estimated prevalence of IHD, stroke, hypertension and dyslipidaemia revealed high inequality at the district level (median value (range): 5.4 (0–17.8%); 1.7 (0–18.2%); 32.0 (12.5–48.2%) and 52.2 (0–71.7%), respectively). The adjusted risks of stroke, hypertension and IHD were mostly high in districts in the South-Eastern parts of the country, while that of dyslipidaemia, was high in Central and top North-Eastern corridor of the country.ConclusionsThe study has confirmed common modifiable risk factors of two cardiovascular diseases (CVDs), namely, ischemic heart disease (IHD) and stroke; and two major risk conditions for CVD, namely, hypertension and dyslipidaemia. Accordingly, an integrated intervention approach addressing cardiovascular diseases and associated risk factors and conditions would be more cost effective and provide stronger impacts than individual tailored interventions only. Findings of excess district-level variations in the CVDs and their risk factor profiles might be useful for developing effective public health policies and interventions aimed at reducing behavioural risk factors including harmful use of alcohol, physical inactivity and high salt intake.

The repercussions of climate change in Africa profound. The effects are recorded notably on agricultural and water systems across the continent. This data brief presents historical data and the first of its kind maps of readiness of Africa to climate change adaptation. The data are technically validated through a newly developed readiness Index (ClimAdaptCap Index). The Data and maps show that readiness for climate change adaptation is driven by the intensity of climate forcing and adaptive capacity. The climate data includes precipitation and temperature for the period 1991-2016. The latter were culled from the World Bank Climate Portal. The adaptive capacity data included proxies such as poverty and literacy rates for the same historical period. These were collected from the World Bank and Macrotrends. These climate data were normalized using the normalization function to enhance interpretation, comparison/fusion into the Index. Missing poverty and literacy rate data were estimated by linear interpolation of the poverty and literacy rate data. The readiness maps and data show that North and Southern Africa are the readiest for climate change while West Africa is the least ready and Middle and East Africa are in the middle. The data shows further that, readiness has a positive correlation with literacy rates and an inverse correlation with poverty rates. The data, maps and index can be adapted in different contexts principally around Africa.

Goal 1: End poverty in all its forms everywhereGlobally, the number of people living in extreme poverty has declined by more than half from 1.9 billion in 1990. However, 836 million people still live in extreme poverty. About one in five persons in developing regions lives on less than $1.25 per day.Southern Asia and sub-Saharan Africa are home to the overwhelming majority of people living in extreme poverty.High poverty rates are often found in small, fragile and conflict-affected countries.One in four children under age five in the world has inadequate height for his or her age.The all India Poverty Head Count Ratio (PHCR) has been brought down from 47% in 1990 to 21% in 2011-2012, nearly halved.This map layer is offered by Esri India, for ArcGIS Online subscribers. If you have any questions or comments, please let us know via content@esri.in.

IntroductionYoung people with disabilities face major barriers in accessing sexual reproductive health (SRH) services in resource-poor settings, including Sub Saharan Africa (SSA). Although, there is increasing recognition of their unique SRH needs, the availability, acceptability, and uptake of SRH service delivery interventions for this population group remain understudied. Young people with disabilities encounter barriers to accessing SRH services due to stigma, poverty, lack of information and physical barriers. We aim to map existing literature on SRH service delivery interventions targeting young people with disabilities in SSA through a scoping review.Methods and analysisThe scoping review will be guided by the Arksey and O’Malley methodological framework. Articles will be searched in PubMed, African Index Medicus, Google Scholar, African Journals Online, Web of Science and Embase electronic databases as well as grey literature database, Open Grey. We will also do a citation search of references of eligible papers for literature that may have been overlooked in other searches. A two-step process will be used to screen retrieved articles i) title and abstract screening ii) full text screening. Results of the scoping review will be reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA-P) extension for scoping reviews.DiscussionThere is a paucity of knowledge on availability, acceptability and uptake of SRH service delivery interventions for young people with disabilities in SSA. This scoping review is poised to fill the gap by demonstrating the breadth of literature on availability, acceptability and uptake of SRH service delivery interventions for young people with disabilities The scoping review aims to map the availability of SRH service delivery interventions for young people with disabilities. This mapping of evidence has the potential to identify whether there is a need for SRH service delivery interventions for young people with disabilities. Our scoping review will map which SRH service delivery interventions work or do not work, as well as gaps in SRH service delivery interventions for young people with disabilities. This information is useful for policy making and for designing effective SRH service delivery interventions for young people with disabilities.

IntroductionYoung people with disabilities face major barriers in accessing sexual reproductive health (SRH) services in resource-poor settings, including Sub Saharan Africa (SSA). Although, there is increasing recognition of their unique SRH needs, the availability, acceptability, and uptake of SRH service delivery interventions for this population group remain understudied. Young people with disabilities encounter barriers to accessing SRH services due to stigma, poverty, lack of information and physical barriers. We aim to map existing literature on SRH service delivery interventions targeting young people with disabilities in SSA through a scoping review.Methods and analysisThe scoping review will be guided by the Arksey and O’Malley methodological framework. Articles will be searched in PubMed, African Index Medicus, Google Scholar, African Journals Online, Web of Science and Embase electronic databases as well as grey literature database, Open Grey. We will also do a citation search of references of eligible papers for literature that may have been overlooked in other searches. A two-step process will be used to screen retrieved articles i) title and abstract screening ii) full text screening. Results of the scoping review will be reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA-P) extension for scoping reviews.DiscussionThere is a paucity of knowledge on availability, acceptability and uptake of SRH service delivery interventions for young people with disabilities in SSA. This scoping review is poised to fill the gap by demonstrating the breadth of literature on availability, acceptability and uptake of SRH service delivery interventions for young people with disabilities The scoping review aims to map the availability of SRH service delivery interventions for young people with disabilities. This mapping of evidence has the potential to identify whether there is a need for SRH service delivery interventions for young people with disabilities. Our scoping review will map which SRH service delivery interventions work or do not work, as well as gaps in SRH service delivery interventions for young people with disabilities. This information is useful for policy making and for designing effective SRH service delivery interventions for young people with disabilities.

IntroductionYoung people with disabilities face major barriers in accessing sexual reproductive health (SRH) services in resource-poor settings, including Sub Saharan Africa (SSA). Although, there is increasing recognition of their unique SRH needs, the availability, acceptability, and uptake of SRH service delivery interventions for this population group remain understudied. Young people with disabilities encounter barriers to accessing SRH services due to stigma, poverty, lack of information and physical barriers. We aim to map existing literature on SRH service delivery interventions targeting young people with disabilities in SSA through a scoping review.Methods and analysisThe scoping review will be guided by the Arksey and O’Malley methodological framework. Articles will be searched in PubMed, African Index Medicus, Google Scholar, African Journals Online, Web of Science and Embase electronic databases as well as grey literature database, Open Grey. We will also do a citation search of references of eligible papers for literature that may have been overlooked in other searches. A two-step process will be used to screen retrieved articles i) title and abstract screening ii) full text screening. Results of the scoping review will be reported following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA-P) extension for scoping reviews.DiscussionThere is a paucity of knowledge on availability, acceptability and uptake of SRH service delivery interventions for young people with disabilities in SSA. This scoping review is poised to fill the gap by demonstrating the breadth of literature on availability, acceptability and uptake of SRH service delivery interventions for young people with disabilities The scoping review aims to map the availability of SRH service delivery interventions for young people with disabilities. This mapping of evidence has the potential to identify whether there is a need for SRH service delivery interventions for young people with disabilities. Our scoping review will map which SRH service delivery interventions work or do not work, as well as gaps in SRH service delivery interventions for young people with disabilities. This information is useful for policy making and for designing effective SRH service delivery interventions for young people with disabilities.

Lake County, Illinois Demographic Data. Explanation of field attributes: Total Population – The entire population of Lake County. White – Individuals who are of Caucasian race. This is a percent.African American – Individuals who are of African American race. This is a percent.Asian – Individuals who are of Asian race. This is a percent. Hispanic – Individuals who are of Hispanic ethnicity. This is a percent. Does not Speak English- Individuals who speak a language other than English in their household. This is a percent. Under 5 years of age – Individuals who are under 5 years of age. This is a percent. Under 18 years of age – Individuals who are under 18 years of age. This is a percent. 18-64 years of age – Individuals who are between 18 and 64 years of age. This is a percent. 65 years of age and older – Individuals who are 65 years old or older. This is a percent. Male – Individuals who are male in gender. This is a percent. Female – Individuals who are female in gender. This is a percent. High School Degree – Individuals who have obtained a high school degree. This is a percent. Associate Degree – Individuals who have obtained an associate degree. This is a percent. Bachelor’s Degree or Higher – Individuals who have obtained a bachelor’s degree or higher. This is a percent. Utilizes Food Stamps – Households receiving food stamps/ part of SNAP (Supplemental Nutrition Assistance Program). This is a percent. Median Household Income - A median household income refers to the income level earned by a given household where half of the homes in the area earn more and half earn less. This is a dollar amount. No High School – Individuals who have not obtained a high school degree. This is a percent. Poverty – Poverty refers to families and people whose income in the past 12 months is below the poverty level. This is a percent.

Increasing poor people's access to financial services can help them weather personal financial crises and increase their chances of climbing out of poverty. The FSP interactive map tool plots financial service locations throughout Africa. This tool can be used to identify gaps in access to financial services, and to design policy and inform decision-making.