Use this application to view the pattern of concentrations of people by race and Hispanic or Latino ethnicity. Data are provided at the U.S. Census block group level, one of the smallest Census geographies, to provide a detailed picture of these patterns. The data is sourced from the U.S Census Bureau, 2020 Census Redistricting Data (Public Law 94-171) Summary File. Definitions: Definitions of the Census Bureau’s categories are provided below. This interactive map shows patterns for all categories except American Indian or Alaska Native and Native Hawaiian or Other Pacific Islander. The total population countywide for these two categories is small (1,582 and 263 respectively). The Census Bureau uses the following race categories:Population by RaceWhite – A person having origins in any of the original peoples of Europe, the Middle East, or North Africa.Black or African American – A person having origins in any of the Black racial groups of Africa.American Indian or Alaska Native – A person having origins in any of the original peoples of North and South America (including Central America) and who maintains tribal affiliation or community attachment.Asian – A person having origins in any of the original peoples of the Far East, Southeast Asia, or the Indian subcontinent including, for example, Cambodia, China, India, Japan, Korea, Malaysia, Pakistan, the Philippine Islands, Thailand, and Vietnam.Native Hawaiian or Other Pacific Islander – A person having origins in any of the original peoples of Hawaii, Guam, Samoa, or other Pacific Islands.Some Other Race - this category is chosen by people who do not identify with any of the categories listed above. People can identify with more than one race. These people are included in the Two or More Races Hispanic or Latino PopulationThe Hispanic/Latino population is an ethnic group. Hispanic/Latino people may be of any race.Other layers provided in this tool included the Loudoun County Census block groups, towns and Dulles airport, and the Loudoun County 2021 aerial imagery.

The number of Reddit users in Africa was forecast to continuously increase between 2024 and 2028 by in total 4.7 million users (+66.67 percent). After the eighth consecutive increasing year, the Reddit user base is estimated to reach 11.78 million users and therefore a new peak in 2028. Notably, the number of Reddit users of was continuously increasing over the past years.User figures, shown here with regards to the platform reddit, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once. Reddit users encompass both users that are logged in and those that are not.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Reddit users in countries like North America and Asia.

Abstract

The South African Population Research Infrastructure Network (SAPRIN) is a national research infrastructure funded through the Department of Science and Technology and hosted by the South African Medical Research Council. One of SAPRIN’s initial goals has been to harmonise the legacy longitudinal data from the three current Health and Demographic Surveillance System (HDSS) Nodes. These long-standing nodes are the MRC/Wits University Agincourt HDSS in Bushbuckridge District, Mpumalanga, established in 1993, with a population of 116 000 people; the University of Limpopo DIMAMO HDSS in the Capricorn District of Limpopo, established in 1996, with a current population of 100 000; and the Africa Health Research Institute (AHRI) HDSS in uMkhanyakude District, KwaZulu-Natal, established in 2000, with a current population of 125 000.

SAPRIN data are processed for longitudinal analysis by organising the demographic data into residence episodes at a geographical location, and membership episodes within a household. Start events include enumeration, birth, in-migration and relocating into a household from within the study population; exit events include death (by cause), out-migration, and relocating to another location in the study population. Variables routinely updated at individual level include health care utilisation, marital status, labour status, education status, as well as recording household asset status. Anticipated outcomes of SAPRIN include: (i) regular releases of up-to-date, longitudinal data, representative of South Africa’s fast-changing poorer communities for research, interpretation and calibration of national datasets; (ii) national statistics triangulation, whereby longitudinal SAPRIN data are triangulated with National Census data for calibration of national statistics and studying the mechanisms driving the national statistics; (iii) An interdisciplinary research platform for conducting observational and interventional research at population level; (iv) policy engagement to provide evidence to underpin policy-making for cost evaluation and targeting intervention programmes, thereby improving the accuracy and efficiency of pro-poor, health and wellbeing interventions; (v) scientific education through training at related universities; and (vi) community engagement, whereby coordinated engagement with communities will enable two-way learning between researchers and community members, and enabling research site communities and service providers to have access to and make effective use of research results.

Geographic coverage

The Agincourt HDSS covers an area of approximately 420km2 and is located in Bushbuckridge District, Mpumalanga in the rural north-east of South Africa close to the Mozambique border. DIMAMO is located in the Capricorn district, Limpopo Province approximately 40 km from Polokwane, the capital city of Limpopo Province and 15-50 km from the University of Limpopo (Turfloop Campus). The site covers an area of approximately 200 km2. AHRI is situated in the south-east portion of the Umkhanyakude district of KwaZulu-Natal province near the town of Mtubatuba. It is bounded on the west by the Umfolozi-Hluhluwe nature reserve, on the south by the Umfolozi river, on the east by the N2 highway (except form portions where the KwaMsane township strandles the highway) and in the north by the Inyalazi river for portions of the boundary. The area is 438km2.

Analysis unit

Exposure episodes

Universe

Households resident in dwellings within the study area will be eligible for inclusion in the household component of SAPRIN. All individuals identified by the household proxy informant as a member of the household will be enumerated. A resident household member is an individual that intends to sleep the majority of time at the dwelling occupied by the household over a four-month period. Households will include resident and non-resident members. An individual is a non-resident member if they have close ties to the household, but do not physically reside with the household most of the time. They can also be called temporary migrants and they are enumerated within the household list. Because household membership is not tied to physical residency, an individual may be a member of more than one household.

Kind of data

Event/transaction data

Sampling procedure

This dataset is not based on a sample but contains information from the complete demographic surveillance areas.

🇬🇧 United Kingdom English This dataset shows different breakdowns of London's resident population by their country of birth. Data used comes from ONS' Annual Population Survey (APS). The APS has a sample of around 320,000 people in the UK (around 28,000 in London). As such all figures must be treated with some caution. 95% confidence interval levels are provided. Numbers have been rounded to the nearest thousand and figures for smaller populations have been suppressed. Four files are available for download: Country of Birth - Borough: Shows country of birth estimates in their broad groups such as European Union, South East Asia, North Africa, etc. broken down to borough level. Detailed Country of Birth - London: Shows country of birth estimates for specific countries such as France, Bangladesh, Nigeria, etc. available for London as a whole Demography Update 09-2015: A GLA Demography report that uses APS data to analyse the trends in London for the period 2004 to 2014. A supporting data file is also provided. Country of Birth Borough 2004-2016 Analysis Tool: A tool produced by GLA Demography that allows users to explore different breakdowns of country of birth data. An accompanying Tableau visualisation tool has also been produced which maps data from 2004 to 2015. Nationality data can be found here: https://data.london.gov.uk/dataset/nationality Nationality refers to that stated by the respondent during the interview. Country of birth is the country in which they were born. It is possible that an individual’s nationality may change, but the respondent’s country of birth cannot change. This means that country of birth gives a more robust estimate of change over time.

Survey of Young People in Egypt (SYPE) is a nationally representative sample of young people in the Middle East and North Africa. The 2009 survey included 15,000 young people between the ages of 10 and 29 from 11,000 households. The 2014 survey follows more than 10,000 original respondents. Gender-disaggregated information on health, schooling, employment, and civic engagement is available.

🇬🇧 United Kingdom English This dataset shows different breakdowns of London's resident population by their nationality. Data used comes from ONS' Annual Population Survey (APS). The APS has a sample of around 320,000 people in the UK (around 28,000 in London). As such all figures must be treated with some caution. 95% confidence interval levels are provided. Numbers have been rounded to the nearest thousand and figures for smaller populations have been suppressed. Two files are available to download: Nationality - Borough: Shows nationality estimates in their broad groups such as European Union, South East Asia, North Africa, etc. broken down to borough level. Detailed Nationality - London: Shows nationality estimates for specific countries such as France, Bangladesh, Nigeria, etc. available for London as a whole. A Tableau visualisation tool is also available. Country of Birth data can be found here: https://data.london.gov.uk/dataset/country-of-birth Nationality refers to that stated by the respondent during the interview. Country of birth is the country in which they were born. It is possible that an individual’s nationality may change, but the respondent’s country of birth cannot change. This means that country of birth gives a more robust estimate of change over time.

This dataset shows different breakdowns of London's resident population by their country of birth. Data used comes from ONS' Annual Population Survey (APS). The APS has a sample of around 320,000 people in the UK (around 28,000 in London). As such all figures must be treated with some caution. 95% confidence interval levels are provided. Numbers have been rounded to the nearest thousand and figures for smaller populations have been suppressed. Four files are available for download: Country of Birth - Borough: Shows country of birth estimates in their broad groups such as European Union, South East Asia, North Africa, etc. broken down to borough level. Detailed Country of Birth - London: Shows country of birth estimates for specific countries such as France, Bangladesh, Nigeria, etc. available for London as a whole Demography Update 09-2015: A GLA Demography report that uses APS data to analyse the trends in London for the period 2004 to 2014. A supporting data file is also provided. Country of Birth Borough 2004-2016 Analysis Tool: A tool produced by GLA Demography that allows users to explore different breakdowns of country of birth data. An accompanying Tableau visualisation tool has also been produced which maps data from 2004 to 2015. 2011 Census Country of Birth data can be found here: https://data.london.gov.uk/census/themes/diversity/ Nationality data can be found here: https://data.london.gov.uk/dataset/nationality Nationality refers to that stated by the respondent during the interview. Country of birth is the country in which they were born. It is possible that an individual’s nationality may change, but the respondent’s country of birth cannot change. This means that country of birth gives a more robust estimate of change over time.

Part of the Integrated Vulnerability Assessment in the Arab Region, this 1km pixel resolution raster dataset provides a representation of Vulnerability index, in the Middle East and North Africa Region, 1986-2005 reference period. Pixel values are classified according to level of vulnerability, from low 1 to high 10. Vulnerability is a concept used to express the complex interaction of climate change effects and the susceptibility of a system to its impacts. The integrated vulnerability assessment methodology is based on an understanding of vulnerability as a function of a system’s climate change exposure, sensitivity and adaptive capacity to cope with climate change effects, consistent with the approach put forward by the Intergovernmental Panel on Climate Change (IPCC) in its Fourth Assessment Report (AR4). The integrated vulnerability assessment combines a series of single vulnerability assessments for several water-related climate change impacts on different sectors. It adopts the time periods generally used by the IPCC and other regional climate modelling experiments, and runs climate simulations based on future time periods that are compared with a historical reference period. Sectors and sub-sectors (indicators): Agriculture Agriculture - Water Available for Crops Agriculture - Water Available for Livestock Biodiversity and Ecosystems Biodiversity and Ecosystems - Area Covered by Forests Biodiversity and Ecosystems - Area Covered by Wetlands Infrastructure and Human Settlements Infrastructure and Human Settlements - Inland Flooding Area People People - Employment Rate for the Agricultural Sector People - Human Health Conditions due to Heat Stress People - Water Available for Drinking Water Water - Water Availability

Abstract

The Human Sciences Research Council (HSRC) carried out the Migration and Remittances Survey in South Africa for the World Bank in collaboration with the African Development Bank. The primary mandate of the HSRC in this project was to come up with a migration database that includes both immigrants and emigrants. The specific activities included: · A household survey with a view of producing a detailed demographic/economic database of immigrants, emigrants and non migrants · The collation and preparation of a data set based on the survey · The production of basic primary statistics for the analysis of migration and remittance behaviour in South Africa.

Like many other African countries, South Africa lacks reliable census or other data on migrants (immigrants and emigrants), and on flows of resources that accompanies movement of people. This is so because a large proportion of African immigrants are in the country undocumented. A special effort was therefore made to design a household survey that would cover sufficient numbers and proportions of immigrants, and still conform to the principles of probability sampling. The approach that was followed gives a representative picture of migration in 2 provinces, Limpopo and Gauteng, which should be reflective of migration behaviour and its impacts in South Africa.

Geographic coverage

Two provinces: Gauteng and Limpopo

Limpopo is the main corridor for migration from African countries to the north of South Africa while Gauteng is the main port of entry as it has the largest airport in Africa. Gauteng is a destination for internal and international migrants because it has three large metropolitan cities with a great economic potential and reputation for offering employment, accommodations and access to many different opportunities within a distance of 56 km. These two provinces therefore were expected to accommodate most African migrants in South Africa, co-existing with a large host population.

Analysis unit

• Household
• Individual

Universe

The target group consists of households in all communities. The survey will be conducted among metro and non-metro households. Non-metro households include those in: - small towns, - secondary cities, - peri-urban settlements and - deep rural areas. From each selected household, one adult respondent will be selected to participate in the study.

Kind of data

Sample survey data [ssd]

Sampling procedure

Migration data for South Africa are available for 2007 only at the level of local governments or municipalities from the 2007 Census; for smaller areas called "sub places" (SPs) only as recently as the 2001 census, and for the desired EAs only back so far as the Census of 1996. In sum, there was no single source that provided recent data on the five types of migrants of principal interest at the level of the Enumeration Area, which was the area for which data were needed to draw the sample since it was going to be necessary to identify migrant and non-migrant households in the sample areas in order to oversample those with migrants for interview.

In an attempt to overcome the data limitations referred to above, it was necessary to adopt a novel approach to the design of the sample for the World Bank's household migration survey in South Africa, to identify EAs with a high probability of finding immigrants and those with a low probability. This required the combined use of the three sources of data described above. The starting point was the CS 2007 survey, which provided data on migration at a local government level, classifying each local government cluster in terms of migration level, taking into account the types of migrants identified. The researchers then spatially zoomed in from these clusters to the so-called sub-places (SPs) from the 2001 Census to classifying SP clusters by migration level. Finally, the 1996 Census data were used to zoom in even further down to the EA level, using the 1996 census data on migration levels of various typed, to identify the final level of clusters for the survey, namely the spatially small EAs (each typically containing about 200 households, and hence amenable to the listing operation in the field).

A higher score or weight was attached to the 2007 Community Survey municipality-level (MN) data than to the Census 2001 sub-place (SP) data, which in turn was given a greater weight than the 1996 enumerator area (EA) data. The latter was derived exclusively from the Census 1996 EA data, but has then been reallocated to the 2001 EAs proportional to geographical size. Although these weights are purely arbitrary since it was composed from different sources, they give an indication of the relevant importance attached to the different migrant categories. These weighted migrant proportions (secondary strata), therefore constituted the second level of clusters for sampling purposes.

In addition, a system of weighting or scoring the different persons by migrant type was applied to ensure that the likelihood of finding migrants would be optimised. As part of this procedure, recent migrants (who had migrated in the preceding five years) received a higher score than lifetime migrants (who had not migrated during the preceding five years). Similarly, a higher score was attached to international immigrants (both recent and lifetime, who had come to SA from abroad) than to internal migrants (who had only moved within SA's borders). A greater weight also applied to inter-provincial (internal) than to intra-provincial migrants (who only moved within the same South African province).

How the three data sources were combined to provide overall scores for EA can be briefly described. First, in each of the two provinces, all local government units were given migration scores according to the numbers or relative proportions of the population classified in the various categories of migrants (with non-migrants given a score of 1.0. Migrants were assigned higher scores according to their priority, with international migrants given higher scores than internal migrants and recent migrants higher scores than lifetime migrants. Then within the local governments, sub-places were assigned scores assigned on the basis of inter vs. intra-provincial migrants using the 2001 census data. Each SP area in a local government was thus assigned a value which was the product of its local government score (the same for all SPs in the local government) and its own SP score. The third and final stage was to develop relative migration scores for all the EAs from the 1996 census by similarly weighting the proportions of migrants (and non-migrants, assigned always 1.0) of each type. The the final migration score for an EA is the product of its own EA score from 1996, the SP score of which it is a part (assigned to all the EAs within the SP), and the local government score from the 2007 survey.

Based on all the above principles the set of weights or scores was developed.

In sum, we multiplied the proportion of populations of each migrant type, or their incidence, by the appropriate final corresponding EA scores for persons of each type in the EA (based on multiplying the three weights together), to obtain the overall score for each EA. This takes into account the distribution of persons in the EA according to migration status in 1996, the SP score of the EA in 2001, and the local government score (in which the EA is located) from 2007. Finally, all EAs in each province were then classified into quartiles, prior to sampling from the quartiles.

From the EAs so classified, the sampling took the form of selecting EAs, i.e., primary sampling units (PSUs, which in this case are also Ultimate Sampling Units, since this is a single stage sample), according to their classification into quartiles. The proportions selected from each quartile are based on the range of EA-level scores which are assumed to reflect weighted probabilities of finding desired migrants in each EA. To enhance the likelihood of finding migrants, much higher proportions of EAs were selected into the sample from the quartiles with the higher scores compared to the lower scores (disproportionate sampling). The decision on the most appropriate categorisations was informed by the observed migration levels in the two provinces of the study area during 2007, 2001 and 1996, analysed at the lowest spatial level for which migration data was available in each case.

Because of the differences in their characteristics it was decided that the provinces of Gauteng and Limpopo should each be regarded as an explicit stratum for sampling purposes. These two provinces therefore represented the primary explicit strata. It was decided to select an equal number of EAs from these two primary strata.

The migration-level categories referred to above were treated as secondary explicit strata to ensure optimal coverage of each in the sample. The distribution of migration levels was then used to draw EAs in such a way that greater preference could be given to areas with higher proportions of migrants in general, but especially immigrants (note the relative scores assigned to each type of person above). The proportion of EAs selected into the sample from the quartiles draws upon the relative mean weighted migrant scores (referred to as proportions) found below the table, but this is a coincidence and not necessary, as any disproportionate sampling of EAs from the quartiles could be done, since it would be rectified in the weighting at the end for the analysis.

The resultant proportions of migrants then led to the following proportional allocation of sampled EAs (Quartile 1: 5 per cent (instead of 25% as in an equal distribution), Quartile 2: 15 per cent (instead

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

Many species of migratory birds have been declining on the Palearctic-African flyways in recent decades due to human population pressure and land-use intensification. Models predict that the declining trends of migratory birds will continue into the foreseeable future across much of Africa, likely exacerbated by climate change. While sub-Saharan Africa is viewed as less important for these migrants than the Sahel, the region still receives many migrant species. We use the citizen science Southern African Bird Atlas Project data sets (SABAP1 and SABAP2) to determine relative change between atlas periods (1987–1991; 2007–2021). Firstly, we validate our metrics of population change on a dataset of 581 species that occur frequently in South Africa, Lesotho and Eswatini by examining change in relation to migratory status (Palearctic, Intra-Africa or Resident) and other species’ traits. We found greatest declines in migrants but with magnitudes not as great as expected, with largest relative decreases for Palearctic migrants, and little difference between Intra-Africa migrants and residents. Declines were best described by size independent of migratory status, even when controlling for phylogeny. For the set of Palearctic migrants, we then examine if change is related to Northern Hemisphere distribution. We found greater decreases for birds with breeding grounds in southern Asia (India and south-eastern Asia) relative to Europe. These results are useful for conservation agencies wishing to extend ties to relevant researchers and conservationists in these regions, and highlights potential challenge areas for this set of birds on their breeding grounds.

The idea was to find out insights from the terrorism dataset. The entire project was done in Tableau. Below are the worksheet in png format with the insights drawn.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2Fcf18424620e5a2e1ebcd388febaf5b5d%2FTerror%20Attacks%20per%20country.png?generation=1687427293213688&alt=media" alt="">

Highest number of terror attacks have taken place in Iraq(13617), followed by Pakistan (8965), Afghanistan (6489) and India (5805).

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2Fa57ab3db7c096ccc635dd047053da15a%2FKilled%20per%20Country.png?generation=1687427452648782&alt=media" alt="">

Highest number of people have been killed in Iraq(43576), followed by Afghanistan (19950), Pakistan (13560), Nigeria (13258) & India (9103).

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2Fcdd2ef0c624fc41cb11cf8de6c8891a0%2FWounded%20per%20country.png?generation=1687427625201371&alt=media" alt="">

Highest number of people have been wounded in Iraq(78285), followed by Pakistan (23518), Afghanistan (21282), United States of America (19089) & India (14434).

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2F818c9b407d5e5ba181e218acd373c2bc%2FRegion%20Wise%20Attacks.png?generation=1687427784192435&alt=media" alt="">

Highest number of terror attacks region wise have been in Middle East & North Africa (25794), followed by South Asia (24269), Sub Saharan Africa (8343), South East Asia (6224) & South America (5967).

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2Ff1cbd7494c9bbe66fa5347891e11c8a8%2FRegion%20Wise%20Killed.png?generation=1687427932150641&alt=media" alt="">

Highest number of people killed region wise have been in Middle East & North Africa (71594), followed by South Asia (50330), Sub Saharan Africa (40919), South America (9905) & South East Asia (7126) .

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2Fed0d59bb75953dbf279fb18df15acfde%2FNationality%20Targeted.png?generation=1687428321642976&alt=media" alt="">

Nationalities that have been targeted include Iraqis, followed by Pakistan , India & then Afghanistan.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2F5f64288cb9d56d36f0769d78beb9fcc9%2FTop%20Attackers.png?generation=1687428457783642&alt=media" alt="">

Attackers who have committed the most terror attacks include Taliban (26.93%), followed by ISIL (17.82%), Al-Shabaab (13.68%), FARC (8.96%), Boko Haram (8.7%), New People's Army (8.36%), Shining Path (8.31%) & CPI Maoist India (7.23%).

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2F6cfb68a782708e98ddabead7d8e24d5a%2FTarget%20Type.png?generation=1687428638719468&alt=media" alt="">

Highest number of terror attacks have been targeted at Private Citizens & Property (32.78%), Military (20.36%) & Police (18.49%).

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2F24d0d90ac90a73710ac48113c5a6a62d%2FAttack%20Type.png?generation=1687428760434222&alt=media" alt="">

Highest number of terror attacks have been conducted via Bombing/Explosions (55.29%), Armed Assault (27.17%), Assassination (10.46%) & Kidnappings (7.09%).

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2Fdae0541f9dc62d22c963d95a73184fe7%2FAttacks%20year%20wise.png?generation=1687428871842867&alt=media" alt="">

2014 was the deadliest year with 16902 terror attacks followed by 2013 (12036) and 2016 (5082)

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2F77553ea453cc8d1bd35a4fdbd1cc3c5c%2FSuicide%20Attack%20year%20wise.png?generation=1687428987867030&alt=media" alt="">

2014 was the deadliest year with 744 suicide attacks followed by 2013 (621) and 2016 (319).

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F10868729%2F9b5249cd73542d7e5300339e720ccdde%2FDashboard%201%20(2).png?generation=1687429041975253&alt=media" alt="">

Final Dashboard

The number of Pinterest users in Africa was forecast to continuously increase between 2024 and 2028 by in total 15 million users (+68.81 percent). After the ninth consecutive increasing year, the Pinterest user base is estimated to reach 36.81 million users and therefore a new peak in 2028. Notably, the number of Pinterest users of was continuously increasing over the past years.User figures, shown here regarding the platform pinterest, have been estimated by taking into account company filings or press material, secondary research, app downloads and traffic data. They refer to the average monthly active users over the period and count multiple accounts by persons only once.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of Pinterest users in countries like Europe and Australia & Oceania.

Part of the Integrated Vulnerability Assessment in the Arab Region, this 1km pixel resolution raster dataset provides a representation of future change in vulnerability for People sector, in the Middle East and North Africa Region. The Raster grid was generated for high representative concentration pathway (RCP8.5) emission scenario developed by the Intergovernmental Panel on Climate Change (IPCC), for time period End-Century (2081-2100). Pixel values are classified according to level of vulnerability, from low 1 to high 10. Vulnerability is a concept used to express the complex interaction of climate change effects and the susceptibility of a system to its impacts. The integrated vulnerability assessment methodology is based on an understanding of vulnerability as a function of a system’s climate change exposure, sensitivity and adaptive capacity to cope with climate change effects, consistent with the approach put forward by the IPCC in its Fourth Assessment Report (AR4). It adopts the time periods generally used by the IPCC and other regional climate modelling experiments, and runs climate simulations based on future time periods that are compared with a historical reference period.

Part of the Integrated Vulnerability Assessment in the Arab Region, this 1km pixel resolution raster dataset provides a representation of future change in vulnerability for People: Employment Rate for the Agricultural Sector sub-sector, in the Middle East and North Africa Region. The Raster grid was generated for high representative concentration pathway (RCP8.5) emission scenario developed by the Intergovernmental Panel on Climate Change (IPCC), for time period End-Century (2081-2100). Pixel values are classified according to level of vulnerability, from low 1 to high 10. Vulnerability is a concept used to express the complex interaction of climate change effects and the susceptibility of a system to its impacts. The integrated vulnerability assessment methodology is based on an understanding of vulnerability as a function of a system’s climate change exposure, sensitivity and adaptive capacity to cope with climate change effects, consistent with the approach put forward by the IPCC in its Fourth Assessment Report (AR4). It adopts the time periods generally used by the IPCC and other regional climate modelling experiments and runs climate simulations based on future time periods that are compared with a historical reference period.

Context

This dataset was collected from data received via this APi.

Content

“[Recovered cases are a] more important metric to track than Confirmed cases.”— Researchers for the University of Virginia’s COVID-19 dashboard

If the number of total cases were accurately known for every country then the number of cases per million people would be a good indicator as to how well various countries are handling the pandemic.

Countries:

Datasets contend data about covid_19 from 232 countries - Afghanistan - Albania - Algeria - Andorra - Angola - Anguilla - Antigua-and-Barbuda - Argentina - Armenia - Aruba - Australia - Austria - Azerbaijan - Bahamas - Bahrain - Bangladesh - Barbados - Belarus - Belgium - Belize - Benin - Bermuda - Bhutan - Bolivia - Bosnia-and-Herzegovina - Botswana - Brazil - British-Virgin-Islands - Brunei - Bulgaria - Burkina-Faso - Burundi - Cabo-Verde - Cambodia - Cameroon - Canada - CAR - Caribbean-Netherlands - Cayman-Islands - Chad - Channel-Islands - Chile - China - Colombia - Comoros - Congo - Cook-Islands - Costa-Rica - Croatia - Cuba - Curaçao - Cyprus - Czechia - Denmark - Diamond-Princess - Diamond-Princess- - Djibouti - Dominica - Dominican-Republic - DRC - Ecuador - Egypt - El-Salvador - Equatorial-Guinea - Eritrea - Estonia - Eswatini - Ethiopia - Faeroe-Islands - Falkland-Islands - Fiji - Finland - France - French-Guiana - French-Polynesia - Gabon - Gambia - Georgia - Germany - Ghana - Gibraltar - Greece - Greenland - Grenada - Guadeloupe - Guam - Guatemala - Guinea - Guinea-Bissau - Guyana - Haiti - Honduras - Hong-Kong - Hungary - Iceland - India - Indonesia - Iran - Iraq - Ireland - Isle-of-Man - Israel - Italy - Ivory-Coast - Jamaica - Japan - Jordan - Kazakhstan - Kenya - Kiribati - Kuwait - Kyrgyzstan - Laos - Latvia - Lebanon - Lesotho - Liberia - Libya - Liechtenstein - Lithuania - Luxembourg - Macao - Madagascar - Malawi - Malaysia - Maldives - Mali - Malta - Marshall-Islands - Martinique - Mauritania - Mauritius - Mayotte - Mexico - Micronesia - Moldova - Monaco - Mongolia - Montenegro - Montserrat - Morocco - Mozambique - MS-Zaandam - MS-Zaandam- - Myanmar - Namibia - Nepal - Netherlands - New-Caledonia - New-Zealand - Nicaragua - Niger - Nigeria - Niue - North-Macedonia - Norway - Oman - Pakistan - Palau - Palestine - Panama - Papua-New-Guinea - Paraguay - Peru - Philippines - Poland - Portugal - Puerto-Rico - Qatar - Réunion - Romania - Russia - Rwanda - S-Korea - Saint-Helena - Saint-Kitts-and-Nevis - Saint-Lucia - Saint-Martin - Saint-Pierre-Miquelon - Samoa - San-Marino - Sao-Tome-and-Principe - Saudi-Arabia - Senegal - Serbia - Seychelles - Sierra-Leone - Singapore - Sint-Maarten - Slovakia - Slovenia - Solomon-Islands - Somalia - South-Africa - South-Sudan - Spain - Sri-Lanka - St-Barth - St-Vincent-Grenadines - Sudan - Suriname - Sweden - Switzerland - Syria - Taiwan - Tajikistan - Tanzania - Thailand - Timor-Leste - Togo - Tonga - Trinidad-and-Tobago - Tunisia - Turkey - Turks-and-Caicos - UAE - Uganda - UK - Ukraine - Uruguay - US-Virgin-Islands - USA - Uzbekistan - Vanuatu - Vatican-City - Venezuela - Vietnam - Wallis-and-Futuna - Western-Sahara - Yemen - Zambia - Zimbabw-

Abstract

From December 2009 to March 2010 the World Bank with the help of Moroccan government conducted a study of the country's young people and their engagement in economic and social activities. Researchers from the World Bank's Sustainable Development Sector of the Middle East and North Africa region utilized a mixed-method approach to study factors that impede the economic and social inclusion of Moroccans aged 15 to 29. The Morocco Household and Youth Survey (MHYS) used two survey instruments to gather quantitative data: Household Questionnaire and Youth Questionnaire.

The study used a nationally representative sample of 2,000 households, in which 1,216 households were located in urban areas and 784 households in the rural areas. The Youth Questionnaire was administered to 2,883 young people between the ages of 15 and 29, representing about 90 percent of the youth in the surveyed households. Information was collected on topics such as economic inclusion, community participation, and use of key public services. The survey was able to examine little studied issues relating to youth such as participation in the labor force, intermediation, career choice, perceived job possibilities, use of time, use of recreational and educational activities targeting young people who have completed formal education.

The focus groups discussions supplemented MHYS.

Geographic coverage

National coverage

Analysis unit

• Households,
• Individuals between the ages of 15 and 29.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample size for the Household Questionnaire was 2,000 households with 1,216 found in urban locations and 784 in rural locations. The 2,000 households were drawn from the 2004 General Census of Population and Housing. For determining the number of households in urban and rural locations, proportionality of the possible locations was used to ensure representativeness. The proportionality was based off the disaggregation of Morocco into primary units in which there are about 600 households. In the end, 125 primary units were randomly selected, with 76 rural primary units and 49 urban primary units. From these 125 primary units, 16 households were randomly selected giving us the total sample size of 2,000 households.

For the Youth Questionnaire, the sample size was 2,883 individuals between the ages of 15 and 29. These 2,883 individuals came from the selected households in the Household Questionnaire. If there was an individual or individuals between the ages of 15 and 29 living at the selected household, the Youth Questionnaire was administrated. More details on sample design are provided in Appendix 2 in "MHYS Basic Information Document".

Mode of data collection

Face-to-face [f2f]

Research instrument

Household Questionnaire covers the following topics: Educational Characteristics, Economic Activities in last 12 months, Secondary Economic Activities in last 12 months, Economic Activities in the last 7 days, Unemployment, Health and Social Security, Housing Characteristics and Durables, Agricultural Assets, Climate Change and Shocks in Agriculture,Incidence of Shocks and Household Responses, Assistance from Social Programs, Migration of Household Members, Migration of non-residents, Migration and Climate Change, Decisions on Consumption in the Household, Expenditures on Frequently Consumed Food Items; Less Frequent Non-Food and Food Expenditures Household Consumption expenditures and food source procurement, Expenditure on less frequent non-food and food products,Infrequent Expenditures, Women in Decision Making

Youth Questionnaire includes the following sections: Employment Preferences, Education, Employment during the last 7 days, First Job, Employment History, Entrepreneurship and Independent Farming, Unemployment, Job Search, Job Services Access, Financial Behavior, Participation of Youth in Educational Institutions and in Youth Centers, Participation of Youth in Family, Access of Youth to Recreation and Social Activities, Satisfaction and Communication, and Time Use.

Cleaning operations

The MHYS contains several data files, with each file pertinent to a specific section. For the case in which there are multiple sections per data file, it is because they share similar levels of observations.

The households are identified by the variable "hid" which consists of the region, province, commune, and enumerator area in which the household is located. This allows the household members to remain anonymous yet statistically unique. This is extremely important especially when it comes to merging different datasets.

Merging data sets will depend on which files are being merged. The key to merging the MHYS data files will be to use unique variables.

For the data sets, the "hid" variable will be the unique variable used to perform the merge at household level; "memid" will be the unique variable used to perform the merge at individual level.

The variable "q5" which signifies enumeration area is scrambled to preserve anonymity of sampled households.

The weights are provided in the data file "weights" and can be merged.

Abstract

South Asia Regional Flagship: More and Better Jobs in South Asia

Employment is a major issue throughout the world. To enjoy life, people need productive jobs that remove them from the daily struggle of making ends meet. According to the International Labour Organization (ILO), as many as 30 million people lost their jobs as a result of the 2008 crisis. Youth unemployment is especially high and inequality has increased. As recent events in the Middle East and North Africa demonstrate, joblessness and inequality can trigger political instability and unrest.

When the World Bank South Asia Region decided to initiate a yearly Flagship Report series, it was clear that the very first report needed to concentrate on the important topic of More and Better Jobs in South Asia. Although one of the fastest growing regions, South Asia is still home to the largest number of the world's poor and the pace of creating productive jobs has lagged behind economic growth. Conflict and social and gender issues also increase the challenge of generating more and more productive jobs. Without urgent action, the potential for the demographic dividend from about 150 million entrants to the labor force over the next decade may not be realized.

The Flagship seeks to answer four questions, which could have implications beyond South Asia. • How is South Asia performing in creating more and better jobs? • Where are the better jobs? • What are constraints in supply and demand in moving towards better jobs? • How does conflict affect job creation?

Geographic coverage

National

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Abstract

South Asia Regional Flagship: More and Better Jobs in South Asia

Employment is a major issue throughout the world. To enjoy life, people need productive jobs that remove them from the daily struggle of making ends meet. According to the International Labour Organization (ILO), as many as 30 million people lost their jobs as a result of the 2008 crisis. Youth unemployment is especially high and inequality has increased. As recent events in the Middle East and North Africa demonstrate, joblessness and inequality can trigger political instability and unrest.

When the World Bank South Asia Region decided to initiate a yearly Flagship Report series, it was clear that the very first report needed to concentrate on the important topic of More and Better Jobs in South Asia. Although one of the fastest growing regions, South Asia is still home to the largest number of the world's poor and the pace of creating productive jobs has lagged behind economic growth. Conflict and social and gender issues also increase the challenge of generating more and more productive jobs. Without urgent action, the potential for the demographic dividend from about 150 million entrants to the labor force over the next decade may not be realized.

The Flagship seeks to answer four questions, which could have implications beyond South Asia. • How is South Asia performing in creating more and better jobs? • Where are the better jobs? • What are constraints in supply and demand in moving towards better jobs?

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Abstract

South Asia Regional Flagship: More and Better Jobs in South Asia

Employment is a major issue throughout the world. To enjoy life, people need productive jobs that remove them from the daily struggle of making ends meet. According to the International Labour Organization (ILO), as many as 30 million people lost their jobs as a result of the 2008 crisis. Youth unemployment is especially high and inequality has increased. As recent events in the Middle East and North Africa demonstrate, joblessness and inequality can trigger political instability and unrest.

When the World Bank South Asia Region decided to initiate a yearly Flagship Report series, it was clear that the very first report needed to concentrate on the important topic of More and Better Jobs in South Asia. Although one of the fastest growing regions, South Asia is still home to the largest number of the world's poor and the pace of creating productive jobs has lagged behind economic growth. Conflict and social and gender issues also increase the challenge of generating more and more productive jobs. Without urgent action, the potential for the demographic dividend from about 150 million entrants to the labor force over the next decade may not be realized.

The Flagship seeks to answer four questions, which could have implications beyond South Asia. • How is South Asia performing in creating more and better jobs? • Where are the better jobs? • What are constraints in supply and demand in moving towards better jobs?

Geographic coverage

National

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]