This dataset contains information on antibody testing for COVID-19: the number of people who received a test, the number of people with positive results, the percentage of people tested who tested positive, and the rate of testing per 100,000 people, stratified by ZIP Code Tabulation Area (ZCTA) neighborhood poverty group. These data can also be accessed here: https://github.com/nychealth/coronavirus-data/blob/master/totals/antibody-by-poverty.csv Exposure to COVID-19 can be detected by measuring antibodies to the disease in a person’s blood, which can indicate that a person may have had an immune response to the virus. Antibodies are proteins produced by the body’s immune system that can be found in the blood. People can test positive for antibodies after they have been exposed, sometimes when they no longer test positive for the virus itself. It is important to note that the science around COVID-19 antibody tests is evolving rapidly and there is still much uncertainty about what individual antibody test results mean for a single person and what population-level antibody test results mean for understanding the epidemiology of COVID-19 at a population level. These data only provide information on people tested. People receiving an antibody test do not reflect all people in New York City; therefore, these data may not reflect antibody prevalence among all New Yorkers. Increasing instances of screening programs further impact the generalizability of these data, as screening programs influence who and how many people are tested over time. Examples of screening programs in NYC include: employers screening their workers (e.g., hospitals), and long-term care facilities screening their residents. In addition, there may be potential biases toward people receiving an antibody test who have a positive result because people who were previously ill are preferentially seeking testing, in addition to the testing of persons with higher exposure (e.g., health care workers, first responders.) Neighborhood-level poverty groups were classified in a manner consistent with Health Department practices to describe and monitor disparities in health in NYC. Neighborhood poverty measures are defined as the percentage of people earning below the Federal Poverty Threshold (FPT) within a ZCTA. The standard cut-points for defining categories of neighborhood-level poverty in NYC are: • Low: <10% of residents in ZCTA living below the FPT • Medium: 10% to <20% • High: 20% to <30% • Very high: ≥30% residents living below the FPT The ZCTAs used for classification reflect the first non-missing address within NYC for each person reported with an antibody test result. Rates were calculated using interpolated intercensal population estimates updated in 2019. These rates differ from previously reported rates based on the 2000 Census or previous versions of population estimates. The Health Department produced these population estimates based on estimates from the U.S. Census Bureau and NYC Department of City Planning. Rates for poverty were calculated using direct standardization for age at diagnosis and weighting by the US 2000 standard population. Antibody tests are categorized based on the date of specimen collection and are aggregated by full weeks starting each Sunday and ending on Saturday. For example, a person whose blood was collected for antibody testing on Wednesday, May 6 would be categorized as tested during the week ending May 9. A person tested twice in one week would only be counted once in that week. This dataset includes testing data beginning April 5, 2020. Data are updated daily, and the dataset preserves historical records and source data changes, so each extract date reflects the current copy of the data as of that date. For example, an extract date of 11/04/2020 and extract date of 11/03/2020 will both contain all records as they were as of that extract date. Without filtering or grouping by extract date, an analysis will almost certain

Global Covid-19 Data

Global Covid-19 data on cases, deaths, vaccinations, and more

By Valtteri Kurkela [source]

About this dataset

The dataset is constantly updated and synced hourly to ensure up-to-date information. With over several columns available for analysis and exploration purposes, users can extract valuable insights from this extensive dataset.

Some of the key metrics covered in the dataset include:

1. Vaccinations: The dataset covers total vaccinations administered worldwide as well as breakdowns of people vaccinated per hundred people and fully vaccinated individuals per hundred people.

2. Testing & Positivity: Information on total tests conducted along with new tests conducted per thousand people is provided. Additionally, details on positive rate (percentage of positive Covid-19 tests out of all conducted) are included.

3. Hospital & ICU: Data on ICU patients and hospital patients are available along with corresponding figures normalized per million people. Weekly admissions to intensive care units and hospitals are also provided.

4. Confirmed Cases: The number of confirmed Covid-19 cases globally is captured in both absolute numbers as well as normalized values representing cases per million people.

5.Confirmed Deaths: Total confirmed deaths due to Covid-19 worldwide are provided with figures adjusted for population size (total deaths per million).

6.Reproduction Rate: The estimated reproduction rate (R) indicates the contagiousness of the virus within a particular country or region.

7.Policy Responses: Besides healthcare-related metrics, this comprehensive dataset includes policy responses implemented by countries or regions such as lockdown measures or travel restrictions.

8.Other Variables of InterestThe data encompasses various socioeconomic factors that may influence Covid-19 outcomes including population density,membership in a continent,gross domestic product(GDP)per capita;

For demographic factors: -Age Structure : percentage populations aged 65 and older,aged (70)older,median age -Gender-specific factors: Percentage of female smokers -Lifestyle-related factors: Diabetes prevalence rate and extreme poverty rate

1. Excess Mortality: The dataset further provides insights into excess mortality rates, indicating the percentage increase in deaths above the expected number based on historical data.

The dataset consists of numerous columns providing specific information for analysis, such as ISO code for countries/regions, location names,and units of measurement for different parameters.

Overall,this dataset serves as a valuable resource for researchers, analysts, and policymakers seeking to explore various aspects related to Covid-19

How to use the dataset

Introduction:

• Understanding the Basic Structure:

  • The dataset consists of various columns containing different data related to vaccinations, testing, hospitalization, cases, deaths, policy responses, and other key variables.
  • Each row represents data for a specific country or region at a certain point in time.

• Selecting Desired Columns:

  • Identify the specific columns that are relevant to your analysis or research needs.
  • Some important columns include population, total cases, total deaths, new cases per million people, and vaccination-related metrics.

• Filtering Data:

  • Use filters based on specific conditions such as date ranges or continents to focus on relevant subsets of data.
  • This can help you analyze trends over time or compare data between different regions.

• Analyzing Vaccination Metrics:

  • Explore variables like total_vaccinations, people_vaccinated, and people_fully_vaccinated to assess vaccination coverage in different countries.
  • Calculate metrics such as people_vaccinated_per_hundred or total_boosters_per_hundred for standardized comparisons across populations.

• Investigating Testing Information:

  • Examine columns such as total_tests, new_tests, and tests_per_case to understand testing efforts in various countries.
  • Calculate rates like tests_per_case to assess testing efficiency or identify changes in testing strategies over time.

• Exploring Hospitalization and ICU Data:

  • Analyze variables like hosp_patients, icu_patients, and hospital_beds_per_thousand to understand healthcare systems' strain.
  • Calculate rates like icu_patients_per_million or hosp_patients_per_million for cross-country comparisons.

• Assessing Covid-19 Cases and Deaths:

  • Analyze variables like total_cases, new_ca...

IT: Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 15.300 % in 2021. This records a decrease from the previous number of 15.600 % for 2020. IT: Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 14.050 % from Dec 1977 (Median) to 2021, with 36 observations. The data reached an all-time high of 16.200 % in 1993 and a record low of 9.700 % in 1982. IT: Proportion of People Living Below 50 Percent Of Median Income: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Italy – Table IT.World Bank.WDI: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

This dataset measures food availability and access for 76 low- and middle-income countries. The dataset includes annual country-level data on area, yield, production, nonfood use, trade, and consumption for grains and root and tuber crops (combined as R&T in the documentation tables), food aid, total value of imports and exports, gross domestic product, and population compiled from a variety of sources. This dataset is the basis for the International Food Security Assessment 2015-2025 released in June 2015. This annual ERS report projects food availability and access for 76 low- and middle-income countries over a 10-year period. Countries (Spatial Description, continued): Democratic Republic of the Congo, Ecuador, Egypt, El Salvador, Eritrea, Ethiopia, Gambia, Georgia, Ghana, Guatemala, Guinea, Guinea-Bissau, Haiti, Honduras, India, Indonesia, Jamaica, Kenya, Kyrgyzstan, Laos, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Moldova, Mongolia, Morocco, Mozambique, Namibia, Nepal, Nicaragua, Niger, Nigeria, North Korea, Pakistan, Peru, Philippines, Rwanda, Senegal, Sierra Leone, Somalia, Sri Lanka, Sudan, Swaziland, Tajikistan, Tanzania, Togo, Tunisia, Turkmenistan, Uganda, Uzbekistan, Vietnam, Yemen, Zambia, and Zimbabwe. Resources in this dataset:Resource Title: CSV File for all years and all countries. File Name: gfa25.csvResource Title: International Food Security country data. File Name: GrainDemandProduction.xlsxResource Description: Excel files of individual country data. Please note that these files provide the data in a different layout from the CSV file. This version of the data files was updated 9-2-2021

More up-to-date files may be found at: https://www.ers.usda.gov/data-products/international-food-security.aspx

India IN: Poverty Headcount Ratio at $3.65 a Day: 2017 PPP: % of Population data was reported at 44.000 % in 2021. This records a decrease from the previous number of 48.200 % for 2020. India IN: Poverty Headcount Ratio at $3.65 a Day: 2017 PPP: % of Population data is updated yearly, averaging 62.000 % from Dec 1977 (Median) to 2021, with 14 observations. The data reached an all-time high of 89.100 % in 1977 and a record low of 44.000 % in 2021. India IN: Poverty Headcount Ratio at $3.65 a Day: 2017 PPP: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s India – Table IN.World Bank.WDI: Social: Poverty and Inequality. Poverty headcount ratio at $3.65 a day is the percentage of the population living on less than $3.65 a day at 2017 international prices.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

This dataset contains data included in the San Diego County Regional Equity Indicators Report led by the Office of Equity and Racial Justice (OERJ). The full report can be found here: https://data.sandiegocounty.gov/stories/s/7its-kgpt.

Geographic data used to create maps in the report can be found here: https://data.sandiegocounty.gov/dataset/Equity-Report-Data-Geography/p6uw-qxpv

Filter by the Indicator column to select data for a particular indicator.

User notes: 10/9/25 - for the report year 2025, data for the following indicators were uploaded with changes relative to report year 2023: Crime Rate: As of January 1, 2021, the FBI replaced the Summary Reporting System (SRS) with the National Incident Based Reporting System (NIBRS), which expands how crimes were recorded and classified. This report uses California’s version of NIBRS, the California Incident Based Reporting System (CIBRS), obtained from the SANDAG Open Data Portal. Crime rates are not disaggregated by jurisdiction, as in the previous Equity Indicator Report. Internet access: The age group variable was incorporated to account for notable disparities in internet access by age. Police Stops and Searches: refined methods. Agency data was aggregated to San Diego County because data was available for all agencies; previously data was available for three agencies. Analysis of RIPA data was updated to exclude stops where the stop was made in response to a call for service, combine transgender women and transgender men into a transgender category, and limit to contraband found during search. Used term “discovery rate” instead of “hit rate.” Removed comparison to traffic collision data and instead compared to population estimates from the American Community Survey. Jail Incarceration: new data sources. The numerator data for the average daily population data in jail was obtained from the San Diego County Sheriff's Office. Population data to calculate the rates was obtained from the San Diego Association of Governments (SANDAG). The terms for conviction status were corrected to "locally sentenced" and "unsentenced" for sentencing status. For jail population data, East African was reclassified as Black and Middle Eastern as White to allow for calculation of rates using SANDAG population estimates.

8/1/25 - for the report year 2025, the following change were made: Business Ownership: the minority and nonminority labels were switched for the population estimates and some of the race/ethnicity data for nonemployer businesses were corrected. Homelessness: added asterisks to category name for unincorporated regions to allow for a footnote in the figure in the story page.

7/11/25 - for the report year 2025, the following changes were made: Beach Water Quality: the number of days with advisories was corrected for Imperial Beach municipal beach, San Diego Bay, and Ocean Beach.

5/22/25 - for the report year 2023, the following changes were made: Youth poverty/Poverty: IPUMS identified an error in the POVERTY variable for multi-year ACS samples. In July 2024, they released a revised version of all multi-year ACS samples to IPUMS USA, which included corrected POVERTY values. The corrected POVERTY values were downloaded, and the analysis was rerun for this indicator using the 2021 ACS 5-year Estimates. Youth Poverty: data source label corrected to be 2021 for all years. Employment, Homeownership, and Cost-Burdened Households - Notes were made consistent for rows where category = Race/Ethnicity.

5/9/25 - Excluding data for the crime section indicators, data were appended on May 9, 2025 and the report will be updated to reflect the new data in August 2025. The following changes in methods were made: For indicators based on American Community Survey (ACS) data, the foreign-born category name was changed to Nativity Status. Internet access: Group quarters is a category included in the survey sample, but it is not part of the universe for the analysis. For the 2025 Equity Report year, respondents in group quarters were excluded from the analysis, whereas for the 2023 Equity Report year, these respondents were included. Adverse childhood experiences - new data source.

Prepared by: Office of Evaluation, Performance, and Analytics and the Office of Equity and Racial Justice, County of San Diego, in collaboration with the San Diego Regional Policy & Innovation Center (https://www.sdrpic.org).

Timor-Leste TL: Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 5.400 % in 2014. This records an increase from the previous number of 4.900 % for 2007. Timor-Leste TL: Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 5.400 % from Dec 2001 (Median) to 2014, with 3 observations. The data reached an all-time high of 11.900 % in 2001 and a record low of 4.900 % in 2007. Timor-Leste TL: Proportion of People Living Below 50 Percent Of Median Income: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Timor-Leste – Table TL.World Bank.WDI: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

Serbia RS: Poverty Headcount Ratio at $2.15 a Day: 2017 PPP: % of Population data was reported at 1.200 % in 2021. This records a decrease from the previous number of 1.600 % for 2020. Serbia RS: Poverty Headcount Ratio at $2.15 a Day: 2017 PPP: % of Population data is updated yearly, averaging 5.350 % from Dec 2012 (Median) to 2021, with 10 observations. The data reached an all-time high of 6.900 % in 2014 and a record low of 1.200 % in 2021. Serbia RS: Poverty Headcount Ratio at $2.15 a Day: 2017 PPP: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Serbia – Table RS.World Bank.WDI: Social: Poverty and Inequality. Poverty headcount ratio at $2.15 a day is the percentage of the population living on less than $2.15 a day at 2017 purchasing power adjusted prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

RTB Maps is a cloud-based electronic Atlas. We used ArGIS 10 for Desktop with Spatial Analysis Extension, ArcGIS 10 for Server on-premise, ArcGIS API for Javascript, IIS web services based on .NET, and ArcGIS Online combining data on the cloud with data and applications on our local server to develop an Atlas that brings together many of the map themes related to development of roots, tubers and banana crops. The Atlas is structured to allow our participating scientists to understand the distribution of the crops and observe the spatial distribution of many of the obstacles to production of these crops. The Atlas also includes an application to allow our partners to evaluate the importance of different factors when setting priorities for research and development. The application uses weighted overlay analysis within a multi-criteria decision analysis framework to rate the importance of factors when establishing geographic priorities for research and development.Datasets of crop distribution maps, agroecology maps, biotic and abiotic constraints to crop production, poverty maps and other demographic indicators are used as a key inputs to multi-objective criteria analysis.Further metadata/references can be found here: http://gisweb.ciat.cgiar.org/RTBmaps/DataAvailability_RTBMaps.htmlDISCLAIMER, ACKNOWLEDGMENTS AND PERMISSIONS:This service is provided by Roots, Tubers and Bananas CGIAR Research Program as a public service. Use of this service to retrieve information constitutes your awareness and agreement to the following conditions of use.This online resource displays GIS data and query tools subject to continuous updates and adjustments. The GIS data has been taken from various, mostly public, sources and is supplied in good faith.RTBMaps GIS Data Disclaimer• The data used to show the Base Maps is supplied by ESRI.• The data used to show the photos over the map is supplied by Flickr.• The data used to show the videos over the map is supplied by Youtube.• The population map is supplied to us by CIESIN, Columbia University and CIAT.• The Accessibility map is provided by Global Environment Monitoring Unit - Joint Research Centre of the European Commission. Accessibility maps are made for a specific purpose and they cannot be used as a generic dataset to represent "the accessibility" for a given study area.• Harvested area and yield for banana, cassava, potato, sweet potato and yam for the year 200, is provided by EarthSat (University of Minnesota’s Institute on the Environment-Global Landscapes initiative and McGill University’s Land Use and the Global Environment lab). Dataset from Monfreda C., Ramankutty N., and Foley J.A. 2008.• Agroecology dataset: global edapho-climatic zones for cassava based on mean growing season, temperature, number of dry season months, daily temperature range and seasonality. Dataset from CIAT (Carter et al. 1992)• Demography indicators: Total and Rural Population from Center for International Earth Science Information Network (CIESIN) and CIAT 2004.• The FGGD prevalence of stunting map is a global raster datalayer with a resolution of 5 arc-minutes. The percentage of stunted children under five years old is reported according to the lowest available sub-national administrative units: all pixels within the unit boundaries will have the same value. Data have been compiled by FAO from different sources: Demographic and Health Surveys (DHS), UNICEF MICS, WHO Global Database on Child Growth and Malnutrition, and national surveys. Data provided by FAO – GIS Unit 2007.• Poverty dataset: Global poverty headcount and absolute number of poor. Number of people living on less than $1.25 or $2.00 per day. Dataset from IFPRI and CIATTHE RTBMAPS GROUP MAKES NO WARRANTIES OR GUARANTEES, EITHER EXPRESSED OR IMPLIED AS TO THE COMPLETENESS, ACCURACY, OR CORRECTNESS OF THE DATA PORTRAYED IN THIS PRODUCT NOR ACCEPTS ANY LIABILITY, ARISING FROM ANY INCORRECT, INCOMPLETE OR MISLEADING INFORMATION CONTAINED THEREIN. ALL INFORMATION, DATA AND DATABASES ARE PROVIDED "AS IS" WITH NO WARRANTY, EXPRESSED OR IMPLIED, INCLUDING BUT NOT LIMITED TO, FITNESS FOR A PARTICULAR PURPOSE. By accessing this website and/or data contained within the databases, you hereby release the RTB group and CGCenters, its employees, agents, contractors, sponsors and suppliers from any and all responsibility and liability associated with its use. In no event shall the RTB Group or its officers or employees be liable for any damages arising in any way out of the use of the website, or use of the information contained in the databases herein including, but not limited to the RTBMaps online Atlas product.APPLICATION DEVELOPMENT:• Desktop and web development - Ernesto Giron E. (GeoSpatial Consultant) e.giron.e@gmail.com• GIS Analyst - Elizabeth Barona. (Independent Consultant) barona.elizabeth@gmail.comCollaborators:Glenn Hyman, Bernardo Creamer, Jesus David Hoyos, Diana Carolina Giraldo Soroush Parsa, Jagath Shanthalal, Herlin Rodolfo Espinosa, Carlos Navarro, Jorge Cardona and Beatriz Vanessa Herrera at CIAT, Tunrayo Alabi and Joseph Rusike from IITA, Guy Hareau, Reinhard Simon, Henry Juarez, Ulrich Kleinwechter, Greg Forbes, Adam Sparks from CIP, and David Brown and Charles Staver from Bioversity International.Please note these services may be unavailable at times due to maintenance work.Please feel free to contact us with any questions or problems you may be having with RTBMaps.

This shows fuel poor households as a proportion of all households in the geographical area (modelled) using the Low Income Low Energy Efficiency (LILEE) measure. Since 2021 (2019 data) the LILEE indicator considers a household to be fuel poor if: it is living in a property with an energy efficiency rating of band D, E, F or G as determined by the most up-to-date Fuel Poverty Energy Efficiency Rating (FPEER) methodologyits disposable income (income after housing costs (AHC) and energy needs) would be below the poverty line. The Government is interested in the amount of energy people need to consume to have a warm, well-lit home, with hot water for everyday use, and the running of appliances. Therefore, fuel poverty is measured based on required energy bills rather than actual spending. This ensures that those households who have low energy bills simply because they actively limit their use of energy at home, Fuel poverty statistics are based on data from the English Housing Survey (EHS). Estimates of fuel poverty at the regional level are taken from the main fuel poverty statistics. Estimates at the sub-regional level should only be used to look at general trends and identify areas of particularly high or low fuel poverty. They should not be used to identify trends over time.Data is Powered by LG Inform Plus and automatically checked for new data on the 3rd of each month.

This shows fuel poor households as a proportion of all households in the geographical area (modelled) using the Low Income Low Energy Efficiency (LILEE) measure. Since 2021 (2019 data) the LILEE indicator considers a household to be fuel poor if: it is living in a property with an energy efficiency rating of band D, E, F or G as determined by the most up-to-date Fuel Poverty Energy Efficiency Rating (FPEER) methodologyits disposable income (income after housing costs (AHC) and energy needs) would be below the poverty line. The Government is interested in the amount of energy people need to consume to have a warm, well-lit home, with hot water for everyday use, and the running of appliances. Therefore, fuel poverty is measured based on required energy bills rather than actual spending. This ensures that those households who have low energy bills simply because they actively limit their use of energy at home, Fuel poverty statistics are based on data from the English Housing Survey (EHS). Estimates of fuel poverty at the regional level are taken from the main fuel poverty statistics. Estimates at the sub-regional level should only be used to look at general trends and identify areas of particularly high or low fuel poverty. They should not be used to identify trends over time.Data is Powered by LG Inform Plus and automatically checked for new data on the 4th of each month.

One in four families in Toronto does not have the income necessary to live a healthy life and participate fully in their community. Inequality can be based on geography; some neighbourhoods experience greater poverty than others. People from Aboriginal and racialized communities, newcomers to Canada, people with disabilities, youth and children, lone parents, and others are dealing with the biggest challenges. Many people work more than one job, yet still have low incomes. Having an education is not proving to be a pathway to well-paid jobs for almost 1/4 of graduates. More than 1 million visits to food banks in Toronto means families, especially with children, are unable to put food on the table every day. Housing costs can take more than 70% of household income, yet can be poorly maintained or inadequate for the size of the family. The Phase 1 engagement (November, 2014 to February, 2015) of the Toronto Poverty Reduction Strategy aimed to seek feedback in four key areas: The drivers of poverty in Toronto; A vision for the kind of Toronto we want; What we are doing that helps address poverty and what else could be done; and, An engagement plan for the next phase of the work. The data contained in the Excel file titled Phase 1 Questionnaire Responses, was collected through an online questionnaire. The information recorded in the PDF file, titled Phase 1 Community Conversations was collected by community members using a PDF Facilitation Guide developed by City staff. The information recorded in the PDF file, titled Phase 1 Multisector Dialogue was collected by staff and table facilitators at a full day workshop on November 28, 2014. Phase 2 engagement (February, 2015 to April, 2015) built on community input from Phase 1 and gathered public feedback on key actions for various themes, (Access to Services; Child Care; Employment and Income, Food Access; Housing; and Transportation), and on principles that should guide City decisions. The Phase Two data includes two Excel files: The Days of Dialogue data that was transcribed into Excel from the ten community meetings. The Online Feedback data that was collected through an online feedback form. For more information visit TOProsperity <w:LsdException Locked="false" Priority="48

A broad and generalized selection of 2011-2015 US Census Bureau 2015 5-year American Community Survey poverty data estimates, obtained via Census API and joined to the appropriate geometry (in this case, New Mexico Census tracts). The selection is not comprehensive, but allows a first-level characterization of populations living below the poverty level, as grouped by age, sex, education, workforce status, and nativity. The determination of which estimates to include was based upon level of interest and providing a manageable dataset for users.The U.S. Census Bureau's American Community Survey (ACS) is a nationwide, continuous survey designed to provide communities with reliable and timely demographic, housing, social, and economic data every year. The ACS collects long-form-type information throughout the decade rather than only once every 10 years. The ACS combines population or housing data from multiple years to produce reliable numbers for small counties, neighborhoods, and other local areas. To provide information for communities each year, the ACS provides 1-, 3-, and 5-year estimates. ACS 5-year estimates (multiyear estimates) are “period” estimates that represent data collected over a 60-month period of time (as opposed to “point-in-time” estimates, such as the decennial census, that approximate the characteristics of an area on a specific date). ACS data are released in the year immediately following the year in which they are collected. ACS estimates based on data collected from 2009–2014 should not be called “2009” or “2014” estimates. Multiyear estimates should be labeled to indicate clearly the full period of time. While the ACS contains margin of error (MOE) information, this dataset does not. Those individuals requiring more complete data are directed to download the more detailed datasets from the ACS American FactFinder website. This dataset is organized by Census tract boundaries in New Mexico. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2010 Census Participant Statistical Areas Program. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

Abstract

The Survey on Income and Living Conditions, introduced as part of the European Union harmonisation efforts, aims to produce data on income distribution, relative poverty by income, living conditions and social exclusion comparable with European Union member states. The study which uses a panel survey method is repeated every year and monitors sample of household members for four years. Every year, the study attempts to obtain two datasets: cross-sectional and panel.

The Income and Living Conditions Survey 2009 has been conducted to provide annual and regular cross-sectional data to answer questions such as:

• How equally is the income in the country distributed and how has it changed as compared to the previous years?
• How many poor people are there in the country and how do they distribute across regions? How has this situation changed as compared to the previous years?
• Who is poor? Has there been a change over time?
• How has this gap between the poor and the rich evolved over time?
• What kind of a change or transition occurs in the incomes of individuals and households? How does the direction of this change depends on characteristics and circumstances, does it decline or grow?
• How is the income distributed across sectors, types of income and household characteristics?
• How do people's living conditions change or improve over time?
• The study also aims to provide panel data to calculate indicators such as persistent income poverty and to measure net changes over time.

The cross-sectional database 2009 is documented here.

Geographic coverage

All settlements within the borders of the Republic of Turkey have been included.

Universe

All household members living in households within the borders of the Republic of Turkey. However, the study excludes the population defined as institutional population living in hospices, elderly homes, prisons, military barracks, private hospitals and in childcare centres. Migrant population has also been excluded due to practical challenges.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling method: Stratified, multi-stage, clustered sampling.

Sampling unit: Household.

Sampling framework: Sampling framework has been derived from two sources:

1. For the settlements with municipal status; General Building Census conducted in 2000 by TurkStat and Numbering Study (conducted in 2000) Form Population 1 data have been used.
2. For the settlements without municipal status (Villages); data of General Population Census conducted in 2000 have been used to select the blocks which constituted the sampling unit of the first stage.

Selection of sample households: for the purposes of the study which used a two-staged sampling design; entire Turkey has been divided into blocks which covered 100 households each.

• At the first stage, blocks were selected as the first stage sampling unit
• At the second stage, households were selected from among the previously selected blocks as the final sampling unit. Prior to the selection of sample households, addresses at the blocks were updated through an "address screening study"

Sample size: Annual sampling size is 13,414 households in respect of the estimation, objectives and targeted variables of the study and in consideration of the attritions in the sample.

Substitution principle: Substitution has not been used as the sample size had been calculated by taking account of non-response.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

• Household registry form The form filled at the beginning of the survey provides brief information on access to the address of the household, condition of the household and of the survey. Moreover, following the first field application, modalities are identified for filling in the monitoring forms if the households included in the panel survey move home.

• Personal registry form These forms aim to identify basic demographic characteristics of the household members, changes that occur in the status of household membership of the individuals included in the panel survey, reasons for their leaving the household, the date of their departure etc. as well as individuals who join the household.

• Household and personal follow-up form There is need for following up the households which have moved home and the sample individuals who have left the household to join or found another one. Household and personal follow-up forms are used to identify their new addresses and access their contact information.

• Household questionnaire These forms attempt to collect information on the type of the occupied dwelling, status of ownership, information relating to the dwelling (number of rooms, the space actually used, heating system, dwelling facilities, goods owned etc), problems of the dwelling of the neighbourhood, status of indebtedness, rent payments, expenditures for the dwelling, the extent to which households are able to meet their general economic and basic needs and incomes earned at household level.

• Personal questionnaire These forms attempt to collect information on education, health, employment and marital status of the household members aged 15 and over, as well as the dates of employment and incomes earned during the reference year.

The Young Lives survey is an innovative long-term project investigating the changing nature of childhood poverty in four developing countries. The purpose of the project is to improve understanding of the causes and consequences of childhood poverty and examine how policies affect children's well-being, in order to inform the development of future policy and to target child welfare interventions more effectively. The study is being conducted in Ethiopia, India (in Andhra Pradesh), Peru and Vietnam. These countries were selected because they reflect a range of cultural, geographical and social contexts and experience differing issues facing the developing world; high debt burden, emergence from conflict, and vulnerability to environmental conditions such as drought and flood. The Young Lives study aims to track the lives of 12,000 children over a 15-year period, surveyed once every 3-4 years. Round 1 of Young Lives surveyed two groups of children in each country, at 1 year old and 5 years old. Round 2 returned to the same children who were then aged 5 and 12 years old. Round 3 surveyed the same children again at aged 7-8 years and 14-15 years, and Round 4 surveyed them at 12 and 19 years old. Thus the younger children are being tracked from infancy to their mid-teens and the older children through into adulthood, when some will become parents themselves. The survey consists of three main elements: a child questionnaire, a household questionnaire and a community questionnaire. The household data gathered is similar to other cross-sectional datasets (such as the World Bank's Living Standards Measurement Study). It covers a range of topics such as household composition, livelihood and assets, household expenditure, child health and access to basic services, and education. This is supplemented with additional questions that cover caregiver perceptions, attitudes, and aspirations for their child and the family. Young Lives also collects detailed time-use data for all family members, information about the child's weight and height (and that of caregivers), and tests the children for school outcomes (language comprehension and mathematics). An important element of the survey asks the children about their daily activities, their experiences and attitudes to work and school, their likes and dislikes, how they feel they are treated by other people, and their hopes and aspirations for the future. The community questionnaire provides background information about the social, economic and environmental context of each community. It covers topics such as ethnicity, religion, economic activity and employment, infrastructure and services, political representation and community networks, crime and environmental changes. The Young Lives survey is carried out by teams of local researchers, supported by the Principal Investigator and Data Manager in each country. Further information about the survey, including publications, can be downloaded from the Young Lives website. School surveys were introduced into Young Lives in 2010 in order to capture detailed information about children's experiences of schooling, and to improve our understanding of: the relationships between learning outcomes, and children's home backgrounds, gender, work, schools, teachers and class and school peer-groups. school effectiveness, by analysing factors explaining the development of cognitive and non-cognitive skills in school, including value-added analysis of schooling and comparative analysis of school-systems. equity issues (including gender) in relation to learning outcomes and the evolution of inequalities within education The survey allows us to link longitudinal information on household and child characteristics from the household survey with data on the schools attended by the Young Lives children and children's achievements inside and outside the school. It provides policy-relevant information on the relationship between child development (and its determinants) and children's experience of school, including access, quality and progression. This combination of household, child and school-level data over time constitutes the comparative advantage of Young Lives. Findings are all available on our Education theme pages and our publications page. Further information is available from the Young Lives School Survey webpages.

This shows fuel poor households as a proportion of all households in the geographical area (modelled) using the Low Income Low Energy Efficiency (LILEE) measure. Since 2021 (2019 data) the LILEE indicator considers a household to be fuel poor if: it is living in a property with an energy efficiency rating of band D, E, F or G as determined by the most up-to-date Fuel Poverty Energy Efficiency Rating (FPEER) methodologyits disposable income (income after housing costs (AHC) and energy needs) would be below the poverty line. The Government is interested in the amount of energy people need to consume to have a warm, well-lit home, with hot water for everyday use, and the running of appliances. Therefore, fuel poverty is measured based on required energy bills rather than actual spending. This ensures that those households who have low energy bills simply because they actively limit their use of energy at home, Fuel poverty statistics are based on data from the English Housing Survey (EHS). Estimates of fuel poverty at the regional level are taken from the main fuel poverty statistics. Estimates at the sub-regional level should only be used to look at general trends and identify areas of particularly high or low fuel poverty. They should not be used to identify trends over time.

Data is Powered by LG Inform Plus and automatically checked for new data on the 4th of each month and shows MSOAs (Middle Layer Super Output Areas) at the 2021 Census Geography.

Context

Dataset aims to further a county by county analysis of potential risk factors that could heighten Covid 19 transmission rates or deaths. The data has now been split between general population and over 60 estimates and converted to counts for ease of use.

Content

It includes a subset of county by county ACS estimates of:

The data includes information on:

1) County level indicators for over 60 populations including population density, race, poverty level, housing size, sources of income, employment status, whether living alone, language barriers, immigration status, and disability status.

2) County level indicators for the general population including race, poverty level, housing size, sources of income, employment status, whether living alone, language barriers, immigration status, and disability status, modes of transportation stats, and industry stats.

Acknowledgements

For traceability and recreation purposes, I published a kernel with the R code outlining the process used to produce the data set. https://www.kaggle.com/jtourkis/kernel3f7cd0a961

The information comes from 2018 5 Year estimates from the American Community Survey (ACS).

ACSST5Y2018.S0102 ACSST5Y2018.S0804 ACSST5Y2018.S2403

Note: ACS five year estimates are selections limited to counties with populations over 20,000. https://www.census.gov/programs-surveys/acs/guidance/estimates.html

Link to ACS/Census Tables:

https://data.census.gov/cedsci/table?q=United%20States&tid=ACSDP1Y2018.DP05&hidePreview=true&vintage=2018&layer=VT_2018_040_00_PY_D1&cid=S0103_C01_001E

It also includes 4/16 and 4/22 Daily Spread Estimates from John Hopkins and Population Density from the CDC Social Vulnerability Index.

Centers for Disease Control and Prevention/ Agency for Toxic Substances and Disease Registry/ Geospatial Research, Analysis, and Services Program. Social Vulnerability Index 2018 Database US. data-and-tools-download.html. Accessed on 4/16.

Inspiration

I hope this data will help bring people closer to understanding what economic factors correlate to or influence disease spread.

Abstract

The Ouagadougou Health and Demographic Surveillance System (Ouagadougou HDSS), located in five neighborhoods at the northern periphery of the capital of Burkina Faso, was established in 2008. Data on vital events (births, deaths, unions, migration events) are collected during household visits that have taken place every 10 months.

The areas were selected to contrast informal neighborhoods (40,000 residents) with formal areas (40,000 residents), with the aims of understanding the problems of the urban poor, and testing innovative programs that promote the well-being of this population. People living in informal areas tend to be marginalized in several ways: they are younger, poorer, less educated, farther from public services and more often migrants. Half of the residents live in the Sanitary District of Kossodo and the other half in the District of Sig-Nonghin.

The Ouaga HDSS has been used to study health inequalities, conduct a surveillance of typhoid fever, measure water quality in informal areas, study the link between fertility and school investments, test a non-governmental organization (NGO)-led program of poverty alleviation and test a community-led targeting of the poor eligible for benefits in the urban context. Key informants help maintain a good rapport with the community.

The areas researchers follow consist of 55 census tracks divided into 494 blocks. Researchers mapped all the census tracks and blocks using fieldworkers with handheld global positioning system (GPS) receivers and ArcGIS. During a first census (October 2008 to March 2009), the demographic surveillance system was explained to every head of household and a consent form was signed; during subsequent censuses, new households were enrolled in the same way.

Geographic coverage

Ouagadougou is the capital city of Burkina Faso and lies at the centre of this country, located in the middle of West Africa (128 North of the Equator and 18 West of the Prime Meridian).

Analysis unit

Individual

Universe

Resident household members of households resident within the demographic surveillance area. Inmigrants (visitors) are defined by intention to become resident, but actual residence episodes of less than six months (180 days) are censored. Outmigrants are defined by intention to become resident elsewhere, but actual periods of non-residence less than six months (180 days) are censored. Children born to resident women are considered resident by default, irrespective of actual place of birth. The dataset contains the events of all individuals ever residents during the study period (03 Oct. 2009 to 31 Dec. 2014).

Kind of data

Event history data

Frequency of data collection

This dataset contains rounds 0 to 7 of demographic surveillance data covering the period from 07 Oct. 2008 to 31 December 2014.

Sampling procedure

This dataset is not based on a sample, it contains information from the complete demographic surveillance area of Ouagadougou in Burkina Faso.

Reponse units (households) by Round: Round Households
2008 4941
2009 19159 2010 21168
2011 12548 2012 24174 2013 22326

Sampling deviation

None

Mode of data collection

Proxy Respondent [proxy]

Research instrument

List of questionnaires:

Collective Housing Unit (UCH) Survey Form - Used to register characteristics of the house - Use to register Sanitation installations - All registered house as at previous round are uploaded behind the PDA or tablet.

Household registration (HHR) or update (HHU) Form - Used to register characteristics of the HH - Used to update information about the composition of the household - All registered households as at previous rounds are uploaded behind the PDA or tablet.

Household Membership Registration (HMR) or update (HMU) - Used to link individuals to households. - Used to update information about the household memberships and member status observations - All member status observations as at previous rounds are uploaded behind the PDA or tablet.

Presences registration form (PDR) - Used to uniquely identify the presence of each individual in the household and to identify the new individual in the household - Mainly to ensure members with multiple household memberships are appropriately captured - All presences observations as at previous rounds are uploaded behind the PDA or tablet.

Visitor registration form (VDR) - Used register the characteristics of the new individual in the household - Used to capt the internal migration - Use matching form to facilitate pairing migration

Out Migration notification form (MGN) - Used to record change in the status of residency of individuals or households - Migrants are tracked and updated in the database

Pregnancy history form (PGH) & pregnancy outcome notification form (PON) - Records details of pregnancies and their outcomes - Only if woman is a new member - Only if woman has never completed WHL or WGH - All member pregnancy without pregnancy outcome as at previous rounds are uploaded behind the PDA or tablet.

Death notification form (DTN) - Records all deaths that have recently occurred - Includes information about time, place, circumstances and possible cause of death

Updated Basic information Form (UBIF) - Use to change the individual basic information

Health questionnaire (adults, women, child, elder) - Family planning - Chronic illnesses - Violence and accident - Mental health - Nutrition, alcohol, tobacco - Access to health services - Anthropometric measures - Physical limitations - Self-rated health - Food security

Variability of climate and water accessibility - accessibility to water - child health outcomes - gender outcomes - data on rainfall, temperatures, water quality

Cleaning operations

The data collection system is composed by two databases: - A temporary database, which contains data collected and transferred each day during the round. - A reference database, which contains all data of Ouagadougou Health and Demographic Surveillance System, in which is transferred the data of the temporary database to the end of each round. The temporary database is emptied at the end of the round for a new round.

The data processing takes place in two ways:

1) When collecting data with PDAs or tablets and theirs transfers by Wi-Fi, data consistency and plausibility are controlled by verification rules in the mobile application and in the database. In addition to these verifications, the data from the temporary database undergo validation. This validation is performed each week and produces a validation report for the data collection team. After the validation, if the error is due to an error in the data collection, the field worker equipped with his PDA or tablet go back to the field to revisit and correct this error. At the end of this correction, the field worker makes again the transfer of data through the wireless access points on the server. If the error is due to data inconsistencies that might not be directly related to an error in data collection, the case is remanded to the scientific team of the main database that could resolve the inconsistency directly in the database or could with supervisors perform a thorough investigation in order to correct the error.

2) At the end of the round, the data from the temporary database are automatically transferred into the reference database by a transfer program. After the success of this transfer, further validation is performed on the data in the database to ensure data consistency and plausibility. This still produces a validation report for the data collection team. And the same process of error correction is taken.

Response rate

Household response rates are as follows (assuming that if a household has not responded for 2 years following the last recorded visit to that household, that the household is lost to follow-up and no longer part of the response rate denominator):

Year Response Rate
2008 100%
2009 100%
2010 100%
2011 98% 2012 100% 2013 95%

Sampling error estimates

Not applicable

Data appraisal

CentreId MetricTable QMetric Illegal Legal Total Metric RunDate BF041 MicroDataCleaned Starts 151624 2017-05-16 13:36
BF041 MicroDataCleaned Transitions 0 314778 314778 0 2017-05-16 13:36
BF041 MicroDataCleaned Ends 151624 2017-05-16 13:36
BF041 MicroDataCleaned SexValues 314778 2017-05-16 13:36
BF041 MicroDataCleaned DoBValues 314778 2017-05-16 13:36

Poverty continues to decline in Lao People’s Democratic Republic (PDR). Recent estimates from the Laos Expenditure and Consumption Survey (LECS 5) show that the pro portion of poor people-those whose consumption is less than the nationalpoverty line, declined by 4.3 percentage points from 27.56 percent in 2007/8 to 23.24 percent in 2012/13. As Figure 1 shows, the same trend is observed when you consider the proportion of people living on less than 1.25 PPP dollars a day.

IntroductionPeople experiencing homelessness (PEH) are disproportionally affected by the COVID-19 pandemic. The realities of their daily lives have been given little consideration in the pandemic response. They are not represented in existing health information campaigns, and many are structurally excluded from digital information. The project aimed to develop inclusive COVID-19-information material to strengthen infection prevention and control of PEH.Material and methodsIn a participatory process, PEH were involved in the planning, production, and evaluation of poster and video information material on COVID-19. Various stakeholders were consulted for external supervision. Service providers all over Germany were informed about the material that could be ordered free of charge. For the evaluation, semi-structured interviews with homeless service providers and PEH were conducted, and the online views of the videos were measured.ResultsSixteen PEH participated actively in the project. Two COVID-19-information videos were launched in 5 languages in February 2021. Posters promoting vaccination against COVID-19 were produced in 9 languages. As of May 2022, the videos have been viewed more than 2,000 times. A total of 163 service providers for PEH and public institutions received the posters, thereof 72 upon request. Twelve service providers and 8 PEH participated in the evaluation. They pointed out the lack of targeted information material for PEH. The consideration of the concerns and the diverse representation of PEH was perceived as particularly important. Most of the service providers were unable to show the videos due to technical and spatial limitations. Digital challenges for PEH, like the lack of and maintenance of a smart phone, became apparent.ConclusionThe cooperation of research, practice and the community were key factors for the realization of this project. Strong links to the community and the involvement of relevant stakeholders are indispensable when working with PEH. Exclusion from digital information is an increasingly important component of the structural marginalization of PEH. Digital inclusion for PEH and service providers can help to counteract social and health inequalities. The lessons learned through this project can contribute to strengthen participation of PEH and to consider their perspectives in future health communication strategies.