BackgroundFor countries to contribute to Sustainable Development Goal 3.1 of reducing the global maternal mortality ratio (MMR) to less than 70 per 100,000 live births by 2030, identifying the drivers of maternal mortality is critically important. The ability of countries to identify the key drivers is however hampered by the lack of data sources with sufficient observations of maternal death to allow a rigorous analysis of its determinants. This paper overcomes this problem by utilising census data. In the context of Indonesia, we merge individual-level data on pregnancy-related deaths and households’ socio-economic status from the 2010 Indonesian population census with detailed data on the availability and quality of local health services from the Village Census. We use these data to test the hypothesis that health service access and quality are important determinants of maternal death and explain the differences between high maternal mortality and low maternal mortality provinces.MethodsThe 2010 Indonesian Population Census identifies 8075 pregnancy-related deaths and 5,866,791 live births. Multilevel logistic regression is used to analyse the impacts of demographic characteristics and the existence of, distance to and quality of health services on the likelihood of maternal death. Decomposition analysis quantifies the extent to which the difference in maternal mortality ratios between high and low performing provinces can be explained by demographic and health service characteristics.FindingsHealth service access and characteristics account for 23% (CI: 17.2% to 28.5%) of the difference in maternal mortality ratios between high and low-performing provinces. The most important contributors are the number of doctors working at the community health centre (8.6%), the number of doctors in the village (6.9%) and distance to the nearest hospital (5.9%). Distance to health clinics and the number of midwives at community health centres and village health posts are not significant contributors, nor is socio-economic status. If the same level of access to doctors and hospitals in lower maternal mortality Java-Bali was provided to the higher maternal mortality Outer Islands of Indonesia, our model predicts 44 deaths would be averted per 100,000 pregnancies.ConclusionIndonesia has employed a strategy over the past several decades of increasing the supply of midwives as a way of decreasing maternal mortality. While there is evidence of reductions in maternal mortality continuing to accrue from the provision of midwife services at village health posts, our findings suggest that further reductions in maternal mortality in Indonesia may require a change of focus to increasing the supply of doctors and access to hospitals. If data on maternal death is collected in a subsequent census, future research using two waves of census data would prove a useful validation of the results found here. Similar research using census data from other countries is also likely to be fruitful.

Abstract

The Sahel Women Empowerment and Demographic Dividend (P150080) project in Burkina Faso focuses on advancing women's empowerment to spur demographic transition and mitigate gender disparities. This project seeks to empower young women by promoting entrepreneurship through business skills training and grants, and by enhancing access to reproductive health information and contraception, thereby aiming to lower fertility rates.

The World Bank Africa Gender Innovation Lab, along with its partners, is conducting detailed impact evaluations of the SWEDD program’s key initiatives to gauge their effects on child marriage, fertility, and the empowerment of adolescent girls and young women.

This data represents the first round of data collection (baseline) for the impact evaluation and include a household and community level surveys. The household level sample comprises 9857 households, 70,169 individuals and 9382 adolescent girls and young wives aged 24 living in the Boucle du Mouhoun and the East regions of Burkina Faso. The community level sample includes 175 villages.

The insights derived from this survey could help policymakers develop strategies to: - Reduce fertility and child marriage by enhancing access to contraceptives and broadening reproductive health education. - Promote women’s empowerment by increasing their participation in economic activities

This data is valuable for planners who focus on improving living standards, particularly for women. The Ministry of Women, National Solidarity, Family, and Humanitarian Action of Burkina Faso, along with District Authorities, Research Institutions, NGOs, and the general public, stand to benefit from this survey data.

Geographic coverage

Burkina Faso, Regions of Boucle du Mouhoun and East

Analysis unit

The unit of analysis is adolescent girls for the adolescent survey and households for the household survey.

Kind of data

Sample survey data [ssd]

Sampling procedure

We randomly selected 200 villages from the 11 provinces in the two regions of the Boucle du Mouhoun and the East. The 200 villages were selected proportionally, based on the formula (Np/N)*200, where Np represents the number of eligible villages in the province and N the total number of eligible villages. 25 villages were later dropped because of lack of safety.

A census was first administered in each village to identify eligible girls and young wives, as well as households with these eligible individuals. All households with at least one eligible person then constituted the universe from which the survey sample was drawn. In total 9857 households and 9382 girls and young wives were sampled. A village-level questionnaire was also administered.

The objective of the baseline survey was to build a comprehensive dataset, which would serve as a reference point for the entire sample, before treatment and control assignment and program implementation.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The data consists of responses from households to questions pertaining to: 1. List of household members 2. Education of household members 3. Occupations of household members 4. Characteristics of housing and durable goods 5. Food security 6. Household head's aspirations, as well as those of a boy aged 12 to 24 7. Opinions on women's empowerment and gender equality

The questionnaire administrated to girls contains the following sections: 1. Education 2. Marriage and children 3. Aspirations 4. Health and family planning 5. Knowledge of HIV/AIDS 6. Women's empowerment 7. Gender-based violence 8. Income-generating activities 9. Savings and credit 10. Personal relationships and social networks 11. Committee members and community participation

The questionnaire administered at the village-level contains the following sections: 1. Social norms (marriage norms) 2. Ethnic and religious compositions 3. Economic infrastructures (markets and roads) 4. Social services a. Health b. Education

The household questionnaire was administered to the head of the household or to an authorized person capable of answering questions about all individuals in the household. The adolescent questionnaire was administered to each eligible pre-selected individual within the household. Considering the modules of the adolescent questionnaire, it was only administered by female enumerators. The village-level questionnaire was administered to a group of three to five village leaders with enough knowledge of the village. The enumerators were instructed to include women in this group whenever possible. The questionnaires were written in French, translated into the local languages, and programmed on tablets in French using the CAPI program.

Cleaning operations

Data was anonymized through decoding and local suppression.

Demographic parameters are key to understanding population dynamics. Here, we analyse the survival and reproduction of the German wolf population in the 20 years following recolonization. Specifically, we analysed the effects of environmental, ecological, and individual characteristics on i) the survival probability of the population; ii) annual survival rates of age classes; iii) reproduction probability; and iv) reproductive output, measured as the number of detected pups/juveniles. Using the Cox proportional hazards model, we estimated a median survival time of circa 3 years for wolves. Annual survival probabilities were found to be 0.75 for juveniles, 0.75 for subadults, and 0.88 for adults. Survival was lower for juveniles in winter and for subadult males in summer, probably associated with dispersal events. Low habitat suitability was clearly associated with lower survival in juveniles and subadults, but not in adults. Local territory density was related to increased survival. Reproduction probability within a territory was 0.89, but explanatory variables had no effect. Reproductive output was four pups/juveniles on average, positively related to habitat suitability and female experience, but negatively related to territory density. Survival values were very high for the species when compared to other regions. We hypothesize that carrying capacity has not been reached in the study area, thus the survival may decrease in the future if the landscape becomes saturated. Furthermore, our results highlight a spatial pattern in survival and reproduction, with areas of better habitat suitability favouring faster population growth. Thus, targeting conservation measures to low habitat suitability areas will have a strong population effect in the short term by boosting the survival and reproduction of the individuals, while long-term viability should be carefully planned with high suitability areas in mind, as those contain the territories with higher survival and reproduction potential. Methods Wolf individual and territory data for survival and reproduction analyses were provided by the Federal Documentation and Consultation Centre on Wolves (DBBW, www.dbb-wolf.de) and by the Senckenberg Centre for Wildlife Genetics. Information about individuals and territories was grouped into monitoring years (from the 1st of May to the 30th of April next year), starting in 2000 until 2020 (April 2021). Individuals were identified genetically and for the survival analysis, the original dataset was filtered to retain only reliable information on the lifespan of the animals. Thus, individuals with NA ("not available") in the variables 'sex' or 'date of birth' as well as individuals born or died outside the German border were removed, as the environmental data included in the demographic analyses were only available for Germany. Consequently, the status of the individuals (dead, alive) was assessed until April 2021. The age classes were defined as juveniles including pups (0-12 months), subadults (13-24 months), and adults (> 24 months) (Mech and Boitani, 2003). The final dataset contained a total of 1054 individuals. Reproduction data was analysed at the territory level. The number of juvenile counts might be less than the actual number of pups born, thus we defined this variable as ‘minimum reproductive output’. Territories with more than 10 observed pups/ juveniles were removed from analyses to account for the fact that such a high number of pups might stem from a double reproduction and thus belong to one or more females (n = 4). In addition, territories from the first year of pair formation were removed (n = 227), because pairs typically form shortly before or during the breeding season (in autumn or winter) and therefore, there is no opportunity for reproduction in the months prior to the pair formation, which would correspond to the reproduction in the first year in the dataset. The final dataset consisted of 723 entries comprising 205 different territories with data from 1-16 years per territory. Explanatory variables We analysed the survival and reproduction of wolves in relation to environmental and ecological conditions and individual characteristics. For the survival analysis, we used as environmental variables the wolf habitat suitability (Planillo et al., 2024) in an 8 km radius of the territory centroid, wolf local territory density for each year in a 50 km radius, season defined as summer (May-Oct) or winter (Nov-Apr), individual sex and age, with the latter being classified as age classes: juveniles < 12 months, subadults 12 to 24 months, and adults > 24 months old. For the reproduction analysis, environmental and ecological conditions were described by habitat suitability values and local territory density around each breeding territory. As individual characteristics, we included the experience of the reproductive female in the models, measured sequentially as the number of years the same breeding female had reproduced, i.e., the first year that the female reproduced was considered year 1, the second year 2, and so forth. Data Analysis

Survival analysis: Survival analysis was calculated for the whole population and for each of the age classes using Cox Proportional Hazards Regression (Therneau and Grambsch 2000).

Reproduction analysis: We analyzed reproduction patterns for i) the probability of reproduction in a territory and ii) the total number of juveniles per reproductive event. In both cases, data were analysed using generalised linear mixed-effects models (GLMMs) with binomial error distribution and logit link for reproduction probability and Poisson error distribution and log link for the reproductive output. Territory identity was included as a random effect. The total number of years that a territory was monitored in the dataset was included as a weighting variable to avoid an inflated effect of the territories observed only for one year. As explanatory variables, we included the mean habitat suitability of the territory, local territory density in a buffer of 50 km, and the quadratic effect of the experience of the female as fixed effects.

Population growth: We used the values of survival and reproduction to estimate population growth (λ) and contrast it with the observed data. We developed a population matrix model using three age classes, based on obtained values of reproduction and annual survival for the age classes, and used the eigenvalue of the matrix as our λ. We explored the observed population growth values with respect to the effects of the minimum and maximum values of habitat suitability. To compute the lambda for the latter cases, we predicted the survival of juveniles and subadults and the number of pups per reproduction in areas with the lowest and highest observed values of habitat suitability.

BackgroundIndividual based models have become a valuable tool for modeling the spatiotemporal dynamics of epidemics, e.g. influenza pandemic, and for evaluating the effectiveness of intervention strategies. While specific contacts among individuals into diverse environments (family, school/workplace) can be modeled in a standard way by employing available socio-demographic data, all the other (unstructured) contacts can be dealt with by adopting very different approaches. This can be achieved for instance by employing distance-based models or by choosing unstructured contacts in the local communities or by employing commuting data.Methods/ResultsHere we show how diverse choices can lead to different model outputs and thus to a different evaluation of the effectiveness of the containment/mitigation strategies. Sensitivity analysis has been conducted for different values of the first generation index G0 , which is the average number of secondary infections generated by the first infectious individual in a completely susceptible population and by varying the seeding municipality. Among the different considered models, attack rate ranges from 19.1% to 25.7% for G0 = 1.1, from 47.8% to 50.7% for G0 = 1.4 and from 62.4% to 67.8% for G0 = 1.7. Differences of about 15 to 20 days in the peak day have been observed. As regards spatial diffusion, a difference of about 100 days to cover 200 km for different values of G0 has been observed.ConclusionTo reduce uncertainty in the models it is thus important to employ data, which start being available, on contacts on neglected but important activities (leisure time, sport mall, restaurants, etc.) and time-use data for improving the characterization of the unstructured contacts. Moreover, all the possible effects of different assumptions should be considered for taking public health decisions: not only sensitivity analysis to various model parameters should be performed, but intervention options should be based on the analysis and comparison of different modeling choices.

This data collection contains de-identified clinical health service utilisation data from Bendigo Health and the General Practitioners Practices associated with the Loddon Mallee Murray Medicare Local. The collection also includes associated population health data from the ABS, AIHW and the Municipal Health Plans. Health researchers have a major interest in how clinical data can be used to monitor population health and health care in rural and regional Australia through analysing a broad range of factors shown to impact the health of different populations. The Population Health data collection provides students, managers, clinicians and researchers the opportunity to use clinical data in the study of population health, including the analysis of health risk factors, disease trends and health care utilisation and outcomes.Temporal range (data time period):2004 to 2014Spatial coverage:Bendigo Latitude -36.758711200000010000, Bendigo Longitude 144.283745899999990000

San Francisco Flood Health Vulnerability

Socioeconomic, Demographic, Health, and Housing Indicators

By City of San Francisco [source]

About this dataset

This dataset provides a comprehensive composite index that captures the relative vulnerability of San Francisco communities to the health impacts of flooding and extreme storms. Predominantly sourced from local governmental health, housing, and public data sources, this index is constructed from an array of socio-economic factors, exposure indices,Health indicators and housing attributes. Used as a valuable planning tool for both health and climate adaptation initiatives throughout San Francisco, this dataset helps to identify vulnerable populations within the city such as areas with high concentrations of children or elderly individuals. Data points included in this index include: census blockgroup numbers; the percentage of population under 18 years old; percentage of population above 65; percentage non-white; poverty levels; education level; yearly precipitation estimates; diabetes prevalence rate; mental health issues reported in the area; asthma cases by geographic location;; disability rates within each block group measure as well as housing quality metrics. All these components provide a broader understanding on how best to tackle issues faced within SF arising from any form of climate change related weather event such as floods or extreme storms

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How to use the dataset

This dataset can be used to analyze the vulnerability of the population in San Francisco to the health impacts of floods and storms. This dataset includes a number of important indicators such as poverty, education, demographic, exposure and health-related information. These indicators can be useful for developing effective strategies for health and climate adaptation in an urban area.

To get started with this dataset: First, review the data dictionary provided in the attachments section of this metadata to understand each variable that you plan on using in your analysis. Second, see if there are any null or missing values in your columns by checking out ‘Null Value’ column provided in this metadata sheet and look at how they will affect your analysis - use appropriate methods to handle those values based on your goals and objectives. Thirdly begin exploring relationships between different variables using visualizations like pandas scatter_matrix() & pandas .corr() . These tools can help you identify potential strong correlations between certain variables that you may have not seen otherwise through simple inspection of the data.

Lastly if needed use modelling techniques like regression analysis or other quantitative methods like ANOVA’s etc., for further elaboration on understanding relationships between different parameters involved as per need basis

Research Ideas

• Developing targeted public health interventions focused on high-risk areas/populations as identified in the vulnerability index.
• Establishing criteria for insurance premiums and policies within high-risk areas/populations to incentivize adaption to climate change.
• Visual mapping of individual indicators in order to identify trends and correlations between flood risk and socioeconomic indicators, resource availability, and/or healthcare provision levels at a granular level

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: san-francisco-flood-health-vulnerability-1.csv | Column name | Description | |:---------------------------|:----------------------------------------------------------------------------------------| | Census Blockgroup | Unique numerical identifier for each block in the city. (Integer) | | Children | Percentage of population under 18 years of age. (Float) | | Children_wNULLvalues | Percentage of population under 18 years of age with null values. (Float) | | Elderly | Percentage of population over 65 years of age. (Float) | | Elderly_wNULLvalues | Percentage of population over 65 years of age with null values. (Float) | | NonWhite | Percentage of non-white population. (Float) ...

Description

This comprehensive dataset provides a wealth of information about all countries worldwide, covering a wide range of indicators and attributes. It encompasses demographic statistics, economic indicators, environmental factors, healthcare metrics, education statistics, and much more. With every country represented, this dataset offers a complete global perspective on various aspects of nations, enabling in-depth analyses and cross-country comparisons.

Key Features

• Country: Name of the country.
• Density (P/Km2): Population density measured in persons per square kilometer.
• Abbreviation: Abbreviation or code representing the country.
• Agricultural Land (%): Percentage of land area used for agricultural purposes.
• Land Area (Km2): Total land area of the country in square kilometers.
• Armed Forces Size: Size of the armed forces in the country.
• Birth Rate: Number of births per 1,000 population per year.
• Calling Code: International calling code for the country.
• Capital/Major City: Name of the capital or major city.
• CO2 Emissions: Carbon dioxide emissions in tons.
• CPI: Consumer Price Index, a measure of inflation and purchasing power.
• CPI Change (%): Percentage change in the Consumer Price Index compared to the previous year.
• Currency_Code: Currency code used in the country.
• Fertility Rate: Average number of children born to a woman during her lifetime.
• Forested Area (%): Percentage of land area covered by forests.
• Gasoline_Price: Price of gasoline per liter in local currency.
• GDP: Gross Domestic Product, the total value of goods and services produced in the country.
• Gross Primary Education Enrollment (%): Gross enrollment ratio for primary education.
• Gross Tertiary Education Enrollment (%): Gross enrollment ratio for tertiary education.
• Infant Mortality: Number of deaths per 1,000 live births before reaching one year of age.
• Largest City: Name of the country's largest city.
• Life Expectancy: Average number of years a newborn is expected to live.
• Maternal Mortality Ratio: Number of maternal deaths per 100,000 live births.
• Minimum Wage: Minimum wage level in local currency.
• Official Language: Official language(s) spoken in the country.
• Out of Pocket Health Expenditure (%): Percentage of total health expenditure paid out-of-pocket by individuals.
• Physicians per Thousand: Number of physicians per thousand people.
• Population: Total population of the country.
• Population: Labor Force Participation (%): Percentage of the population that is part of the labor force.
• Tax Revenue (%): Tax revenue as a percentage of GDP.
• Total Tax Rate: Overall tax burden as a percentage of commercial profits.
• Unemployment Rate: Percentage of the labor force that is unemployed.
• Urban Population: Percentage of the population living in urban areas.
• Latitude: Latitude coordinate of the country's location.
• Longitude: Longitude coordinate of the country's location.

Potential Use Cases

• Analyze population density and land area to study spatial distribution patterns.
• Investigate the relationship between agricultural land and food security.
• Examine carbon dioxide emissions and their impact on climate change.
• Explore correlations between economic indicators such as GDP and various socio-economic factors.
• Investigate educational enrollment rates and their implications for human capital development.
• Analyze healthcare metrics such as infant mortality and life expectancy to assess overall well-being.
• Study labor market dynamics through indicators such as labor force participation and unemployment rates.
• Investigate the role of taxation and its impact on economic development.
• Explore urbanization trends and their social and environmental consequences.

Data Source: This dataset was compiled from multiple data sources

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Techsalerator’s Location Sentiment Data for Ukraine

Techsalerator’s Location Sentiment Data for Ukraine provides a detailed collection of insights crucial for businesses, researchers, and policymakers. This dataset offers in-depth information about regional sentiment, social dynamics, and environmental factors across Ukraine’s diverse areas.

For access to the full dataset, contact us at info@techsalerator.com or visit Techsalerator Contact Us.

Techsalerator’s Location Sentiment Data for Ukraine

Techsalerator’s Location Sentiment Data for Ukraine delivers a comprehensive analysis of regional sentiment and environmental factors, essential for businesses, government agencies, and developers working in Ukraine. This data supports AI research, social studies, marketing, and urban planning.

Top 5 Key Data Fields

• Location of Sentiment Analysis – Identifies the specific geographic regions within Ukraine where sentiment data is captured, helping researchers assess regional variations.
• Sentiment Score – Measures the positive, negative, or neutral sentiment of a given area based on social media, public opinion, and regional discussions.
• Demographic Insights – Provides detailed demographic data associated with sentiment, including age, gender, and socio-economic factors.
• Time of Sentiment Analysis – Records the date and time of sentiment capture, allowing for temporal trend analysis.
• Sentiment Influencers – Identifies key events, news, or social movements that have influenced public sentiment in specific regions.

Top 5 Sentiment Trends in Ukraine

• Political Sentiment – Political discussions and national events greatly impact sentiment, especially in the context of ongoing geopolitical issues and elections.
• Economic Sentiment – Economic factors, such as inflation, unemployment rates, and GDP performance, contribute significantly to shifts in public sentiment.
• Social Issues – Public sentiment related to social movements, such as those for gender equality and anti-corruption, is a growing trend across urban centers in Ukraine.
• Conflict and Crisis Impact – Regions affected by the ongoing conflict show distinct changes in sentiment, with many areas experiencing heightened stress and resilience.
• Cultural Shifts – The evolving cultural identity in response to regional influences and international relations impacts sentiment, especially in cities like Kyiv and Lviv.

Top 5 Applications of Location Sentiment Data in Ukraine

• Urban Planning and Development – Sentiment data assists urban planners in understanding local public opinion on infrastructure projects and development initiatives.
• Political Campaigns – Candidates and political parties can leverage sentiment data to tailor their campaigns to the specific needs and concerns of different regions.
• Social Research – Sentiment analysis helps sociologists and anthropologists understand shifts in public opinion, cultural trends, and societal changes.
• Marketing and Advertising – Brands and marketers can use location-based sentiment data to create personalized campaigns that resonate with local audiences.
• Crisis Management – Governments and NGOs can use sentiment data to monitor public reaction to crises and adjust their response strategies accordingly.

Accessing Techsalerator’s Location Sentiment Data

To obtain Techsalerator’s Location Sentiment Data for Ukraine, contact info@techsalerator.com with your specific requirements. Techsalerator offers customized datasets based on desired fields, with delivery available within 24 hours. Ongoing access options can also be discussed.

Included Data Fields

• Location of Sentiment Analysis
• Sentiment Score
• Demographic Insights
• Time of Sentiment Analysis
• Sentiment Influencers
• Political Sentiment
• Economic Sentiment
• Social Issue Sentiment
• Crisis Impact
• Cultural Sentiment Trends
• Contact Information

For valuable insights into regional sentiment trends across Ukraine, Techsalerator’s dataset is an indispensable tool for businesses, researchers, and decision-makers.

Disturbance is a fundamental ecological process and driver of population dynamics. Ecologists seek to understand the effects of disturbance on ecological systems and to use disturbance to modify habitats degraded by anthropogenic change. Demographic responses by plants to disturbance are often well described, but demographic responses by animals are less understood. This limits development of applied strategies that leverage disturbance to augment animal populations. We estimated demographic and behavioural responses of an endangered butterfly, Fender's blue, Plebejus icarioides fenderi, to experimental burning in Oregon, USA. We monitored butterfly vital rates for four years post-fire. Prescribed fire killed Fender's blue larvae. However, fecundity was higher relative to reference/unburned areas for two years after the burn and overwinter larval survivorship was higher for a year after the burn. Fire treatments did not influence adult movement behaviour. We used matrix models to project butterfly population dynamics in fire-driven successional landscapes. We compared optimal burn strategies given targeted burns, such as prescribed fire, to undirected burns, such as wildfires. Disturbance enhances population growth rate under both strategies, and the optimal proportion of landscape burned is similar in both cases. However, targeted burning leads to substantially higher population growth rates. Synthesis and applications. Demographic models allow planning of long-term and large-scale disturbance by balancing initial costs of disturbance with subsequent benefits. We use matrix models to project population growth in fire-driven successional landscapes and contrast prescribed burns with undirected burns (wildfires). We also use these models to evaluate the influence of local vs. non-local dispersal. Because random (non-local) dispersal allows individuals to disperse into areas that were just burned, non-local dispersal always increases population growth rates in this system. This contrasts with source-sink dynamics in stationary environments, in which local dispersal leads to higher population growth rates. Our matrix modelling approach has broad application to other disturbance-dependent taxa surviving in anthropogenically modified landscapes, and could be more widely applied to animal populations.

Life history traits and environmental conditions influence reproductive success in animals, and consequences of these can influence subsequent survival and recruitment into breeding populations. Understanding influences on demographic rates is required to determine the causes of decline. Migratory species experience spatially and temporally variable conditions across their annual cycle, making identifying where the factors influencing demographic rates operate challenging. Here, we use the Whinchat Saxicola rubetra as a model declining long-distance migrant bird. We analyse 10 years of data from 247 nesting attempts and 2519 post-fledging observations of 1193 uniquely marked nestlings to examine the influence of life history traits, habitat characteristics and weather on survival of young from the nestling stage to local recruitment into the natal population. We detected potential silver spoon effects where conditions during the breeding stage influence subsequent apparent local recruitment rates, with higher recruitment for fledglings from larger broods, and recruitment rate negatively related to rainfall that chicks experienced in-nest. Additionally, extreme temperatures experienced pre- and post-fledging increased fledging success and recruitment rate. However, we could not determine whether this was driven by temperature influencing mortality during the post-fledging period or later in the annual cycle. Brood size declined with hatching date. In-nest survival increased with brood size and was highest at local temperature extremes. Furthermore, nest survival was highest at nests surrounded with 40–60% vegetation cover of Bracken Pteridium aquilinum within 50m of the nest. Our results show that breeding phenology and environmental factors may influence fledging success and recruitment in songbird populations, with conditions experienced during the nestling stage influencing local recruitment rates in Whinchats (i.e. silver spoon effect). Recruitment rates are key drivers of songbird population dynamics. Our results help identify some of the likely breeding season mechanisms that could be important population drivers.

Methods Study area Our study was conducted between 2013–2022 at RSPB Geltsdale nature reserve in the North Pennines in Cumbria, UK (54.9°N −2.6°S), which is jointly owned by the Royal Society for the Protection of Birds and the Weir Trust. The survey area is an ~11km2 sub-section of the reserve comprising blanket bog, heathland and acid grassland, with an altitude of 220–440m. Study species and field methods Whinchats are short-lived (<8 years) Afro-Palearctic migrants, breeding in grassland habitats throughout Europe and Western Asia and migrating annually to sub-Saharan Africa for the northern winter. Whinchats are ground nesting and usually lay a single clutch of 4–7 eggs. The incubation period is 12–14 days with young provisioned by both parents for ~13 days before fledging. Young are capable of flight 3–5 days after fledging, with a further 9–15 days spent close to their natal nest while they are still dependent on their parents for food (Collar 2005, Tome & Denac 2012). Post-independence, fledglings typically remain in their natal area for 1–2 months, when they undergo a partial moult prior to southerly migration (Collar 2005). We began searching for nests when Whinchats arrived at Geltsdale in May, with searches performed almost daily until nesting had ceased in July. Nests were located by observing adult behaviour (male singing, nest building, guarding, and incubating). Males are typically more conspicuous than females during the breeding season, so the male of a pair was usually identified first, but once a nest was located females were also identified. We visited each nest every 3–7 days, recording clutch size, brood size and to confirm number fledged (see Table 1 for definitions). We estimated first egg laying date through back-calculations from either observation of incomplete clutches assuming one egg is laid daily, or for nests found post-hatching, by back-calculating based on chick development stage assuming 14 days incubation and a clutch size equal to brood size plus the number of unhatched eggs. All chicks were ringed with a unique combination of three colour rings and a numbered metal ring 6–8 days post-hatching. Fledging success was usually determined from resighting of fledglings. Fledging date was estimated from chick development stage observed from nest visits. For our analyses, we used the nest visit data to determine how many chicks successfully hatched and whether a nest successfully fledged at least one chick. Nest visits and bird handling were undertaken by field workers with ringing permits granted by the British Trust for Ornithology, and to minimise nest disturbance, no active nest was intrusively monitored on more than four occasions. Colour-marked Whinchat fledglings were monitored in the year of fledging until autumn departure and were then searched for in the adult breeding population in subsequent years. Searches were made almost daily from late April until early September in all years 2013–2022. Typically, multiple observers independently surveyed the whole study area almost daily from May – July of each year. Late in the season fledglings and adults congregated to moult in certain areas, and these hotspots were surveyed more frequently in August and September. Despite rigorous and frequent searches of the field site, some nesting attempts would inevitably have been missed. Because failed nests are active for a shorter period than successful nests, they are more likely to be missed, so a direct estimate of nest success rates from failed vs fledged nests may overestimate fledging success. To account for this, we performed a nest survival analysis (Mayfield 1975, Dinsmore et al. 2002), which estimated the probability of nest survival from the total number of days that each nest survived (i.e. exposure days). Using this approach, we estimated a nest survival rate of 73.3%, which is slightly lower than the direct estimate from our data (80.6%). However, this is unlikely to affect our assessment of the systematic factors influencing nest survival unless these factors likewise influenced the likelihood of observers finding a nest. Given that nests were usually found by locating calling adults we find it unlikely that any of the key factors we investigate (e.g. vegetation, weather) should affect the likelihood that a nest failed prior to being located. For further details on survival analysis see supplementary material. Vegetation and habitat sampling The vegetation substrate on which a nest was built and the vegetation within 5m, 50m and 200m radii of each nest were recorded between May – July of each year 2013–2014 and 2017–2019 after the nest had concluded. Radii boundaries were first marked, then observers measured the total area occupied by each individual vegetation type within this area, as follows: Bracken (Pteridium sp.), Tree scrub (e.g. Crataegus sp.), Tufted hair grass (Deschampsia caespitosa), Purple moor grass (Molinia caerulea), Rush (Juncus sp.), Bilberry etc. (Vaccinium myrtillus), Heather (Calluna vulgaris) and other grass (e.g. Holcus lanatus). These measures were then used to calculate the relative percentage cover by each vegetation type within each area. The number of trees and presence or absence of key features (e.g. fence post, wall) was also recorded within each radius; for the 5m radius the number of trees was counted exactly, whereas for 50m and 200m radii, the number of trees was estimated and binned. For further details on habitat sampling, see Table 1 and Table S1 (supplementary material). Additionally, the elevation at which each nest was located was recorded. Weather data To determine the effect of environmental conditions pre- and post-fledging we estimated relevant time windows for in-nest and post-fledging periods for each nest. The in-nest period, when chicks are flightless and dependent on parents for food, was defined as the period between the day of hatching and day of fledging. To account for variation of in-nest stage duration such as from the shorter periods of failed nests, this period was standardised as the median 13-days post-hatching in all cases (Fig. S1, supplementary material). The post-fledging period covers from when initially flightless chicks leave the nest until migratory dispersal. Upon fledging, Whinchat remain within 5 – 10m of their natal nest for 3 – 5 days when mortality risk is most acute (Tome & Denac 2012), then typically spend a further 10–12 days within 50–75m of their nest before increasing their range to >200m for the remainder of the post-fledging period (Tome & Denac 2012). To estimate dispersal date, an analysis of the final observation dates of fledglings at Geltsdale in the year of fledging indicated large drop-offs at 40- and 50-days post-fledging, with few observed after 50 days, suggesting most have either dispersed or died 50 days after fledging (Fig. S2). To minimise estimation error, we used two different period lengths, 40 days and 50 days after fledging; however, due to high correlation between these data, no final model included both post-fledging period lengths. Whilst our analysis cannot distinguish post-fledging mortality from mortality during subsequent stages of the annual cycle, we aimed to investigate links between post-fledging conditions and survival to recruitment as these conditions may impact recruitment directly via mortality during the post-fledging period, and indirectly by influencing subsequent survival in later stages of the annual cycle. We downloaded data on daily interpolated maximum and minimum temperature and total precipitation for the 5x5 km square encompassing Geltsdale (Eastings: 360000–365000; Northings: 555000–560000) for June-September each year from CEDA (Hollis et al. 2018). We then averaged the daily

1. Density regulation of the population growth rate occurs through negative feedbacks on underlying vital rates, in response to increasing population size. Here, we examine in a capital breeder how vital rates of different life history stages, their elasticities, and population growth rates are affected by changes in population size. 2. We developed an integrated population model for a local population of Svalbard barnacle geese, Branta leucopsis, using counts, reproductive data and individual-based mark-recapture data (1990-2017) to model age class-specific survival, reproduction and number of individuals. Based on these estimates, we quantified the changes in demographic structure and the effect of population size on age class-specific vital rates and elasticities, as well as the population growth rate. 3. Local density regulation at the breeding grounds acted to reduce population growth through negative effects on reproduction; however, population size could not explain substantial variation in survival rates, although there was some support for density-dependent first year survival. 4. With the use of prospective perturbation analysis of the density-dependent projection matrix, we show that the elasticities to different vital rates changed as population size increased. As population size approached carrying capacity, the influence of reproductive rates and early life-survival on the population growth rate were reduced, whereas the influence of adult survival increased. A retrospective perturbation analysis revealed that density dependence resulted in a positive contribution of reproductive rates, and a negative contribution of the numbers of individuals in the adult age class, to the realised population growth rate. 5. The patterns of density dependence in this population of barnacle geese were different from those recorded in income breeding birds, where density regulation mainly occurs through an effect on early life survival. This indicates that the population dynamics of capital breeders, such as the barnacle goose, are likely to be more reproduction-driven than is the case for income breeders.

BackgroundEffective integration of home visit interventions focused on early childhood development into existing service platforms is important for expanding access in low- and middle-income countries (LMICs). We designed and evaluated a home visit intervention integrated into community health worker (CHW) operations in South Africa.Methods and findingsWe conducted a cluster-randomized controlled trial in Limpopo Province, South Africa. CHWs operating in ward-based outreach teams (WBOTs; clusters) and caregiver–child dyads they served were randomized to the intervention or control group. Group assignment was masked from all data collectors. Dyads were eligible if they resided within a participating CHW catchment area, the caregiver was at least 18 years old, and the child was born after December 15, 2017. Intervention CHWs were trained on a job aid that included content on child health, nutrition, developmental milestones, and encouragement to engage in developmentally appropriate play-based activities, for use during regular monthly home visits with caregivers of children under 2 years of age. Control CHWs provided the local standard of care. Household surveys were administered to the full study sample at baseline and endline. Data were collected on household demographics and assets; caregiver engagement; and child diet, anthropometry, and development scores. In a subsample of children, electroencephalography (EEG) and eye-tracking measures of neural function were assessed at a lab concurrent with endline and at 2 interim time points. Primary outcomes were as follows: height-for-age z-scores (HAZs) and stunting; child development scores measured using the Malawi Developmental Assessment Tool (MDAT); EEG absolute gamma and total power; relative EEG gamma power; and saccadic reaction time (SRT)—an eye-tracking measure of visual processing speed. In the main analysis, unadjusted and adjusted impacts were estimated using intention-to-treat analysis. Adjusted models included a set of demographic covariates measured at baseline. On September 1, 2017, we randomly assigned 51 clusters to intervention (26 clusters, 607 caregiver–child dyads) or control (25 clusters, 488 caregiver–child dyads). At endline (last assessment June 11, 2021), 432 dyads (71%) in 26 clusters remained in the intervention group, and 332 dyads (68%) in 25 clusters remained in the control group. In total, 316 dyads attended the first lab visit, 316 dyads the second lab visit, and 284 dyads the third lab visit. In adjusted models, the intervention had no significant impact on HAZ (adjusted mean difference (aMD) 0.11 [95% confidence interval (CI): −0.07, 0.30]; p = 0.220) or stunting (adjusted odds ratio (aOR) 0.63 [0.32, 1.25]; p = 0.184), nor did the intervention significantly impact gross motor skills (aMD 0.04 [−0.15, 0.24]; p = 0.656), fine motor skills (aMD −0.04 [−0.19, 0.11]; p = 0.610), language skills (aMD −0.02 [−0.18, 0.14]; p = 0.820), or social–emotional skills (aMD −0.02 [−0.20, 0.16]; p = 0.816). In the lab subsample, the intervention had a significant impact on SRT (aMD −7.13 [−12.69, −1.58]; p = 0.012), absolute EEG gamma power (aMD −0.14 [−0.24, −0.04]; p = 0.005), and total EEG power (aMD −0.15 [−0.23, −0.08]; p < 0.001), and no significant impact on relative gamma power (aMD 0.02 [−0.78, 0.83]; p = 0.959). While the effect on SRT was observed at the first 2 lab visits, it was no longer present at the third visit, which coincided with the overall endline assessment. At the end of the first year of the intervention period, 43% of CHWs adhered to monthly home visits. Due to the COVID-19 pandemic, we were not able to assess outcomes until 1 year after the end of the intervention period.ConclusionsWhile the home visit intervention did not significantly impact linear growth or skills, we found significant improvement in SRT. This study contributes to a growing literature documenting the positive effects of home visit interventions on child development in LMICs. This study also demonstrates the feasibility of collecting markers of neural function like EEG power and SRT in low-resource settings.Trial registrationPACTR 201710002683810; https://pactr.samrc.ac.za/TrialDisplay.aspx?TrialID=2683; South African Clinical Trials Registry, SANCTR 4407

Cities are sometimes characterized as homogenous with species assemblages composed of abundant, generalist species having similar ecological functions. Under this assumption, rare species, or species observed infrequently, would have especially high conservation value in cities for their potential to increase functional diversity. Management to increase the number of rare species in cities could be an important conservation strategy in a rapidly urbanizing world. However, most studies of species rarity define rarity in relatively pristine environments where human management and disturbance is minimized. We know little about what species are rare, how many species are rare, and what management practices promote rare species in urban environments. Here, we identified which plants and species of birds and bees that control pests and pollinate crops are rare in urban gardens and assessed how social, biophysical factors, and cross-taxonomic comparisons influence rare species richness. We found overwhelming numbers of rare species, with over 50% of plant cultivars observed classified as rare. Our results highlight the importance of women, older individuals, and gardeners who live closer to garden sites in increasing the number of rare plants within urban areas. Fewer rare plants were found in older gardens and gardens with more bare soil. There were more rare bird species in larger gardens and more rare bee species where canopy cover was higher. We also found that in some cases, rarity begets rarity, with positive correlations found between the number of rare plants and bee species and between bee and bird species. Overall, our results suggest that urban gardens include a high number of species existing at low frequency and that social and biophysical factors promoting rare, planned biodiversity can cascade down to promote rare, associated biodiversity. Methods Study Region We worked in 18 urban community gardens in three counties (Santa Clara, Santa Cruz, and Monterey) in the central coast region of California, USA. The gardens differ in local habitat (structural and compositional diversity of both crop and non-crop species) and landscape context (amount of natural, agricultural, and urban land cover in the surrounding area). All gardens have been cultivated for five to 47 years and range from 444 to 15,525 m2 in size. All of the gardens use organic management practices and prohibit the use of chemical pesticides and insecticides. Gardens were chosen because they represent sites across a gradient of urban, natural, and agricultural landscapes and were separated from each other by >2 km, the farthest distance between gardens was 90 km and the closest was 2 km (Cohen et al., 2020; Egerer et al., 2017; Philpott and Bichier, 2017). Gardener demographic data indicates that gardeners are diverse in their make-up, covering a range of family sizes, education, salary, and food insecurity levels (Egerer et al., 2017; Philpott et al., 2020). Data Collection We provide the following framework (Fig. 1) to help visualize the specific set of questions posed in this study and the data and analyses used to address them. First, we ask which gardener characteristics (Q1), and which local and landscape garden features affect the number of rare plant cultivars (Q2a) and rare bird and bee species (Q2b) in urban community gardens. We include cultivars as distinct types per (Reiss and Drinkwater 2018). Subsequently, we ask if there is an association between the number of rare plant cultivars and the number of rare bird and bee species (Q3), and if the number of rare bird and bee species are also related to one another (Q4).
The data analyzed for this research was collected in two summer field seasons (2015, 2017), from May to September, which is the peak urban garden growing season for the region. Gardener characteristics data (defined below) and gardener self-reported plant data were collected in summer 2017 to address Q1 (Fig. 1). Direct sampling of biodiversity (plants, bees, birds) and garden characteristics was done in summer 2015 to address Q2-4 (Fig. 1). Though structural equation modeling (SEMs) was considered, there is no direct way to compare data from 2017 and 2015 because of the methodological differences outlined below. Thus, separate statistical analyses are conducted for 2017 and 2015 data. We can test the relationship between gardener characteristics and number of rare plant cultivars because gardeners reported what plants they grew in our surveys. We cannot directly test how gardener characteristics influenced the number of rare bird and bee species because gardeners were not asked about these species. Instead, we infer effects of gardener characteristics on bees and birds indirectly via the overall research framework in Figure 1. We explain the specific methods for each type of data collection and the analysis below. Gardener characteristics data We surveyed gardeners from 18 urban community gardens during the 2017 summer field season. Survey questionnaires collected information on gardener demographic information as well as gardening experience and use data (Table 1). Specifically, we surveyed 185 gardeners in total, or six to 14 gardeners per garden (9.5-65% of the gardener population in a site). We only included surveys in our analysis if plant information on the survey was completed (n=162). We administered surveys in English (n=123), Spanish (n=38), and Bosnian (n=1) and either read the survey out loud in person (n=138) or via phone (n=1), and either had the gardener fill out the survey themselves (n=21) or had a gardener read the survey to another gardener (n=1). Two of the surveys did not have information on the method of survey administration. We also note that despite best efforts to surveys gardens equally, uneven gardener availability resulted in unequal gardener sampling across the 18 community gardens, requiring us to calculate the number of rare plant cultivars in gardener-reported data (2017) by gardener surveys rather than by garden as was done in direct field-based data (2015) described below. Gardener-reported plant data Gardeners were asked to identify and list the plant species and cultivars that they planted in their plots. We then classified gardener-reported plants into either crop or ornamental species. Crop species included fruits, vegetables, herbs, and other consumable plants. Ornamental species included plants grown for decorative purposes, such as flowers and non-food providing crops. Though we included plant cultivars as distinct types, gardeners varied in the level of cultivar specificity provided, which we acknowledge is a limitation to our study. We looked up scientific names for common names provided and supplemented these results with direct field-based plant data where researchers identified species and cultivars in the field using methods described in detail below. Garden characteristics data Landscape-level garden data For each garden, we measured the surrounding landscape composition within buffers surrounding gardens at the 0.5, 1, and 3 km scale. We used the 2011 National Land Cover Database (NLCD) (Jin et al. 2015) to calculate the percentage of urban NLCD land cover class using ArcGIS (v. 10.1) (ESRI 2011). Urban land cover was calculated by combining developed low, medium, and high intensity developed land. Urban land cover is correlated with many other land use categories (e.g., natural land), thus we chose to focus on only urban land cover in our models because we were most interested in the effects of urbanization on biodiversity; further, urban land cover has been a significant predictor of biodiversity in previous analyses of these gardens (Quistberg et al. 2016, Egerer et al. 2017). Urban cover at the 1 km scale best predicted pooled species rarity across taxa, exhibiting the lowest AIC of all the scale models (Appendix S1: Table S1), thus the 1 km spatial scale was used for all subsequent analyses. Local-level garden data To collect local-scale garden characteristics, we established a 20 x 20 m plot in the center of each garden. In this plot, we measured canopy cover using a spherical densiometer at the center and N, S, E, and W edges of the plot, counted the number and species of trees and shrubs, and counted the number of trees or shrubs in flower within the plot. We determined age and size of each garden by examining historic Google Earth images and noting the first appearance of the gardens, and then we used ground-truthed GPS points taken from each garden to calculate size. For a few of the gardens older than 35 years, we used historical information gained through community resources or discussions with farm management to determine age. We measured ground characteristics using four 1 x 1 m sub-plots within the 20 x 20 m plots. The 1 x 1 m sub-plots were randomly placed anywhere (including pathways) within the 20 x 20 m plots. Within each 1 x 1 m sub-plot, we measured the height of the tallest herbaceous vegetation and estimated ground cover composition (percent bare soil, rocks, leaf litter, grass, mulch). We repeated sampling once per month between May and September 2015 and calculated the mean value for each environmental variable for each garden at each time point. Field-based biodiversity data Field-based plant data We measured plant biodiversity using the same four 1 x 1 m sub-plots within the 20 x 20 m plots. Within each sub-plot, we identified the species and cultivars of all herbaceous plants and measured the percent cover for each species and cultivar. This was measured once per month for five sampling periods, separated by roughly 21 days. As with gardener-reported plant data, researchers classified field-based plant data into either crop or ornamental species and cultivars. Plants that did not fit crop or ornamental categories were designated weeds. Gardeners were not asked to report any weeds, thus not classified in gardener-reported plant

Abstract This research used cross-section data, from 2016, to analyze the effect of public spending distribution and some economic, demographic and political preferences on municipalities’ development from Minas Gerais (i.e.: Índice Mineiro de Responsabilidade Social - IMRS) and their sub-dimensions (i.e.: education, health, social vulnerability, security, sanitation/housing/environment and culture/sport/leisure). Thus, the variables were selected by Extreme Bounds Analysis - EBA and econometric-spatial models were estimated. The results indicate that development is typical from smaller cities, far from the capital (safer), with higher elderly people proportion (less subject to crime), lower fertility (trait of safer and less vulnerable places), good tax autonomy (attribute of less vulnerable areas, with more sanitation and culture/leisure), lower spending on education and public administration and higher on housing. Furthermore, the results minimize the state (i.e.: municipal administration) and the local productive profile relevance on development and reveal negative externalities from the state capital.

Abstract

The data was collected using the High Frequency Survey (HFS), the new regional data collection tool & methodology launched in the Americas. The survey allowed for better reaching populations of interest with new remote modalities (phone interviews and self-administered surveys online) and improved sampling guidance and strategies. It includes a set of standardized regional core questions while allowing for operation-specific customizations. The core questions revolve around populations of interest's demographic profile, difficulties during their journey, specific protection needs, access to documentation & regularization, health access, coverage of basic needs, coping capacity & negative mechanisms used, and well-being & local integration. The data collected has been used by countries in their protection monitoring analysis and vulnerability analysis.

Geographic coverage

National coverage

Analysis unit

Household

Universe

All people of concern.

Kind of data

Sample survey data [ssd]

Sampling procedure

In the absence of a well-developed sampling-frame for forcibly displaced populations in the Americas, the High Frequency Survey employed a multi-frame sampling strategy where respondents entered the sample through one of three channels: (i) those who opt-in to complete an online self-administered version of the questionnaire which was widely circulated through refugee social media; (ii) persons identified through UNHCR and partner databases who were remotely-interviewed by phone; and (iii) random selection from the cases approaching UNHCR for registration or assistance. The total sample size was 129 households. At the time of the survey, the population of concern was estimated at around 11000 individuals.

Mode of data collection

Other [oth]

Research instrument

The questionnaire contained the following sections: journey, family composition, vulnerability, basic Needs, coping capacity, well-being, COVID-19 Impact.

Abstract

The Project for Statistics on Living standards and Development was a countrywide World Bank Living Standards Measurement Survey. It covered approximately 9000 households, drawn from a representative sample of South African households. The fieldwork was undertaken during the nine months leading up to the country's first democratic elections at the end of April 1994. The purpose of the survey was to collect statistical information about the conditions under which South Africans live in order to provide policymakers with the data necessary for planning strategies. This data would aid the implementation of goals such as those outlined in the Government of National Unity's Reconstruction and Development Programme.

Geographic coverage

National

Analysis unit

Households

Universe

All Household members. Individuals in hospitals, old age homes, hotels and hostels of educational institutions were not included in the sample. Migrant labour hostels were included. In addition to those that turned up in the selected ESDs, a sample of three hostels was chosen from a national list provided by the Human Sciences Research Council and within each of these hostels a representative sample was drawn on a similar basis as described above for the households in ESDs.

Kind of data

Sample survey data [ssd]

Sampling procedure

(a) SAMPLING DESIGN

Sample size is 9,000 households. The sample design adopted for the study was a two-stage self-weighting design in which the first stage units were Census Enumerator Subdistricts (ESDs, or their equivalent) and the second stage were households. The advantage of using such a design is that it provides a representative sample that need not be based on accurate census population distribution in the case of South Africa, the sample will automatically include many poor people, without the need to go beyond this and oversample the poor. Proportionate sampling as in such a self-weighting sample design offers the simplest possible data files for further analysis, as weights do not have to be added. However, in the end this advantage could not be retained, and weights had to be added.

(b) SAMPLE FRAME

The sampling frame was drawn up on the basis of small, clearly demarcated area units, each with a population estimate. The nature of the self-weighting procedure adopted ensured that this population estimate was not important for determining the final sample, however. For most of the country, census ESDs were used. Where some ESDs comprised relatively large populations as for instance in some black townships such as Soweto, aerial photographs were used to divide the areas into blocks of approximately equal population size. In other instances, particularly in some of the former homelands, the area units were not ESDs but villages or village groups. In the sample design chosen, the area stage units (generally ESDs) were selected with probability proportional to size, based on the census population. Systematic sampling was used throughout that is, sampling at fixed interval in a list of ESDs, starting at a randomly selected starting point. Given that sampling was self-weighting, the impact of stratification was expected to be modest. The main objective was to ensure that the racial and geographic breakdown approximated the national population distribution. This was done by listing the area stage units (ESDs) by statistical region and then within the statistical region by urban or rural. Within these sub-statistical regions, the ESDs were then listed in order of percentage African. The sampling interval for the selection of the ESDs was obtained by dividing the 1991 census population of 38,120,853 by the 300 clusters to be selected. This yielded 105,800. Starting at a randomly selected point, every 105,800th person down the cluster list was selected. This ensured both geographic and racial diversity (ESDs were ordered by statistical sub-region and proportion of the population African). In three or four instances, the ESD chosen was judged inaccessible and replaced with a similar one. In the second sampling stage the unit of analysis was the household. In each selected ESD a listing or enumeration of households was carried out by means of a field operation. From the households listed in an ESD a sample of households was selected by systematic sampling. Even though the ultimate enumeration unit was the household, in most cases "stands" were used as enumeration units. However, when a stand was chosen as the enumeration unit all households on that stand had to be interviewed.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

All the questionnaires were checked when received. Where information was incomplete or appeared contradictory, the questionnaire was sent back to the relevant survey organization. As soon as the data was available, it was captured using local development platform ADE. This was completed in February 1994. Following this, a series of exploratory programs were written to highlight inconsistencies and outlier. For example, all person level files were linked together to ensure that the same person code reported in different sections of the questionnaire corresponded to the same person. The error reports from these programs were compared to the questionnaires and the necessary alterations made. This was a lengthy process, as several files were checked more than once, and completed at the beginning of August 1994. In some cases, questionnaires would contain missing values, or comments that the respondent did not know, or refused to answer a question.

These responses are coded in the data files with the following values: VALUE MEANING -1 : The data was not available on the questionnaire or form -2 : The field is not applicable -3 : Respondent refused to answer -4 : Respondent did not know answer to question

Data appraisal

The data collected in clusters 217 and 218 should be viewed as highly unreliable and therefore removed from the data set. The data currently available on the web site has been revised to remove the data from these clusters. Researchers who have downloaded the data in the past should revise their data sets. For information on the data in those clusters, contact SALDRU http://www.saldru.uct.ac.za/.

In order to anticipate the impact of local public policies, a synthetic population reflecting the characteristics of the local population provides a valuable test bed. While synthetic population datasets are now available for several countries, there is no open-source synthetic population for Canada. We propose an open-source synthetic population of individuals and households at a fine geographical level for Canada for the years 2021, 2023 and 2030. Based on 2016 census data and population projections, the synthetic individuals have detailed socio-demographic attributes, including age, sex, income, education level, employment status and geographic locations, and are related into households. A comparison of the 2021 synthetic population with 2021 census data over various geographical areas validates the reliability of the synthetic dataset. Users can extract populations from the dataset for specific zones, to explore ‘what if’ scenarios on present and future populations. They can extend the dataset using local survey data to add new characteristics to individuals. Users can also run the code to generate populations for years up to 2042.

To capture the full social and economic benefits of AI, new technologies must be sensitive to the diverse needs of the whole population. This means understanding and reflecting the complexity of individual needs, the variety of perceptions, and the constraints that might guide interaction with AI. This challenge is no more relevant than in building AI systems for older populations, where the role, potential, and outstanding challenges are all highly significant.

The RAIM (Responsible Automation for Inclusive Mobility) project will address how on-demand, electric autonomous vehicles (EAVs) might be integrated within public transport systems in the UK and Canada to meet the complex needs of older populations, resulting in improved social, economic, and health outcomes. The research integrates a multidisciplinary methodology - integrating qualitative perspectives and quantitative data analysis into AI-generated population simulations and supply optimisation. Throughout the project, there is a firm commitment to interdisciplinary interaction and learning, with researchers being drawn from urban geography, ageing population health, transport planning and engineering, and artificial intelligence.

The RAIM project will produce a diverse set of outputs that are intended to promote change and discussion in transport policymaking and planning. As a primary goal, the project will simulate and evaluate the feasibility of an on-demand EAV system for older populations. This requires advances around the understanding and prediction of the complex interaction of physical and cognitive constraints, preferences, locations, lifestyles and mobility needs within older populations, which differs significantly from other portions of society. With these patterns of demand captured and modelled, new methods for meeting this demand through optimisation of on-demand EAVs will be required. The project will adopt a forward-looking, interdisciplinary approach to the application of AI within these research domains, including using Deep Learning to model human behaviour, Deep Reinforcement Learning to optimise the supply of EAVs, and generative modelling to estimate population distributions.

A second component of the research involves exploring the potential adoption of on-demand EAVs for ageing populations within two regions of interest. The two areas of interest - Manitoba, Canada, and the West Midlands, UK - are facing the combined challenge of increasing older populations with service issues and reducing patronage on existing services for older travellers. The RAIM project has established partnerships with key local partners, including local transport authorities - Winnipeg Transit in Canada, and Transport for West Midlands in the UK - in addition to local support groups and industry bodies. These partnerships will provide insights and guidance into the feasibility of new AV-based mobility interventions, and a direct route to influencing future transport policy. As part of this work, the project will propose new approaches for assessing the economic case for transport infrastructure investment, by addressing the wider benefits of improved mobility in older populations.

At the heart of the project is a commitment to enhancing collaboration between academic communities in the UK and Canada. RAIM puts in place opportunities for cross-national learning and collaboration between partner organisations, ensuring that the challenges faced in relation to ageing mobility and AI are shared. RAIM furthermore will support the development of a next generation of researchers, through interdisciplinary mentoring, training, and networking opportunities.

The distribution of spatial genetic variation across a region can shape evolutionary dynamics and impact population persistence. Local population dynamics and among-population dispersal rates are strong drivers of this spatial genetic variation, yet for many species we lack a clear understanding of how these population processes interact in space to shape within-species genetic variation. Here, we used extensive genetic and demographic data from 10 subpopulations of greater sage-grouse to parameterize a simulated approximate Bayesian computation (ABC) model and (i) test for regional differences in population density and dispersal rates for greater sage-grouse subpopulations in Wyoming, and (ii) quantify how these differences impact subpopulation regional influence on genetic variation. We found a close match between observed and simulated data under our parameterized model and strong variation in density and dispersal rates across Wyoming. Sensitivity analyses suggested that changes in dispersal (via landscape resistance) had a greater influence on regional differentiation, whereas changes in density had a greater influence on mean diversity across all subpopulations. Local subpopulations, however, varied in their regional influence on genetic variation. Decreases in the size and dispersal rates of central populations with low overall and net immigration (i.e. population sources) had the greatest negative impact on genetic variation. Overall, our results provide insight into the interactions among demography, dispersal and genetic variation and highlight the potential of ABC to disentangle the complexity of regional population dynamics and project the genetic impact of changing conditions.

This analysis, produced by the Office for National Statistics (ONS), examines how taxes and benefits redistribute income between various groups of households in the United Kingdom. It shows where different types of households and individuals are in the income distribution and looks at the changing levels of income inequality over time. The main sources of data for this study are:

• Family Expenditure Survey (FES) from 1977-2001
• Expenditure and Food Survey (EFS) from 2001-2007
• Living Costs and Food Survey (LCF) from 2008 onwards

Some variables have been created by combining data from the LCF (previously FES or EFS) with control totals from a variety of different government sources, including:

• United Kingdom National Accounts (ONS Blue Book)
• HM Revenue and Customs (HMRC)
• Department for Transport (DfT)
• Department of Health (DH)
• Department for Education and Employment (DfEE)
• Department for Communities and Local Government (DCLG)

The Effects of Taxes and Benefits on Household Income (ETB) has been produced each year since 1961 and is an annual analysis looking at how taxes and benefits affect the income of households in the UK. The estimates in this analysis are based mainly on data derived from the LCF Survey, which replaced the Family Expenditure Survey (FES) from 2001/02, and was known as the EFS until 2008. The LCF is an annual survey of the expenditure and income of private households. People living in hotels, lodging houses, and in institutions such as old people’s homes are excluded. Each person aged 16 and over keeps a full record of payments made during 14 consecutive days and answers questions about hire purchase and other payments; children aged 7 to 15 keep a simplified diary. The respondents also give detailed information, where appropriate, about income (including cash benefits received from the state) and payments of Income Tax. Information on age, occupation, education received, family composition and housing tenure is also obtained. The survey is continuous, interviews being spread evenly over the year to ensure that seasonal effects are covered. The Family Spending publication also includes an outline of the survey design.

The LCF data used in this analysis are grossed so that totals reflect the total population of private households in the UK. The weights are produced in two stages. First the data is weighted to compensate for non-response (sample-based weighting). The non-response weights are then calibrated so that weighted totals match population totals for males and females in different age groups and for different regions and countries (population-based weighting). The results in the analysis are weighted so that statistics represent the total population in private households in the UK based on 2011 Census data. In 2013/14, an additional calibration to Labour Force Survey (LFS) employment totals was also applied.

There are a number of different measures of income used, the most common of which is probably household disposable income. This is the total income households receive from employment (including self-employment), income from private pensions, investments and other sources, plus cash benefits (including the state pension), minus direct taxes (including income tax, NI and council tax). Income is normally analysed at the household level as this provides a better measure of people's economic well-being; while income is usually received by individuals, it is normally shared with other household members (e.g. spouse/partner and children).

In 2018/19 a further adjustment was applied to the data to adjust for the under coverage and under-reporting of income of the richest individuals. This method is often referred to as the 'SPI adjustment' owing to its use of HM Revenue and Customs (HMRC's) Survey of Personal Incomes (SPI). For further details please see the ETB https://www.ons.gov.uk/peoplepopulationandcommunity/personalandhouseholdfinances/incomeandwealth/methodologies/theeffectsoftaxesandbenefitsonukhouseholdincome" style="background-color: rgb(255, 255, 255);">Quality and methodology information webpage and the https://www.ons.gov.uk/peoplepopulationandcommunity/personalandhouseholdfinances/incomeandwealth/articles/theeffectsoftaxesandbenefitsonhouseholdincome/financialyearending2019" style="background-color: rgb(255, 255, 255);">Effects of taxes and benefits on household income technical report.

The Living Costs and Food Survey (LCF) is the source of the microdata on households from 2008-09 onwards. Previously, the Expenditure and Food Survey (EFS) was the data source. Derived variables are created using information from LCF and control totals from a variety of different government sources including the United Kingdom National Accounts (ONS Blue Book), HM Revenue and Customs, Department for Transport, Department of Health, Department for Education and Employment, and Department for Communities and Local Government.

For further information, see the ONS Effects of taxes and benefits on household income webpage.

Variables available in the Secure Access version
The Secure Access version of the ETB datasets include additional variables not included in the standard End User Licence (EUL) versions (available under GN 33299). Extra variables include:

• CASENO (case number): all years
• CESAGE (age of chief economic supporter): 1991-2015
• CESEMPST (economic position of chief economic supporter): 1991-2015
• GGOR (Government Office Region): 2000-2015
• CES (chief economic supporter flag): 2001-2015

Prospective users of a Secure Access version of the ETB will need to fulfil additional requirements, commencing with the completion of an extra application form to demonstrate to the data owners exactly why they need access to the extra, more detailed variables, in order to obtain permission to use that version. Secure Access users must also complete face-to-face training and agree to Secure Access' User Agreement (see 'Access' section below). Therefore, users are encouraged to download and inspect the EUL version of the data prior to ordering the Secure Access version.

The second edition (June 2021) includes data files for 2016/17, 2017/18 and 2018/19. The documentation has been updated accordingly.