Socio-demographic trends between 1996 and 2011.

This dataset captures 15 years(2010-2024) of data on violence against women in Bangladesh, detailing incident counts, death rates, victim age groups, and literacy levels. It provides valuable insights into how Sociology and demographic factors influence the nature and severity of violence over time.

Research Area:

• Trend analysis and visualization

• Sociological and policy research

• Predictive modeling on gender based violence

• Correlation between literacy, age, and violence intensity

This dataset provides an in-depth look at global demographic trends spanning a century, offering detailed insights into population growth, age-gender structure, and dependency changes over time. It is designed to support a wide range of analytical applications, from academic research in demographics to policy-making and socio-economic planning.

The Stata data file "CAP_Demographics_Jumla_Kavre_recoded.dta” and equivalent excel file of the same name comprises data collected by adolescent secondary school students during a "Citizen Science" project in the district of Kavre in the central hills of Nepal during April 2022 and in the district of Jumla in the remote mountains of West Nepal during June 2022. The project was part of a CIFF-funded Children in All Policies 2030 (CAP2030) project.

The data were generated by the students using a mobile device data collection form developed using "Open Data Kit (ODK) Collect" electronic data collection platform by Kathmandu Living Labs (KLL) and University College London (UCL) for the purposes of this study. Researchers from KLL and UCL trained the adolescents to record basic socio-demographic information about themselves and their households including caste/ethnicity, religion, education, water sources, assets, household characteristics, and income sources. The form also asked about their access to mobile phones or other devices and internet and their concerns with respect to climate change. The resulting data describe the participants in the citizen science project, but their names and addresses have been removed. The app and the process of gathering the data are described in a paper entitled "Citizen science for climate change resilience: engaging adolescents to study climate hazards, biodiversity and nutrition in rural Nepal" submitted to Wellcome Open Research in Feb 2023. The data contributed to Tables 2 and 3 of this paper.

A national analysis has been carried out as a follow-up to the exploratory study ‘Major changes in European public opinion with regard to the EU’, which showed how public opinion had changed in the 28 Member States since 1973.

The new national analysis is made up of three Powerpoint presentations that show how public opinion in each of the Member States has changed since 2007.

1. The first presentation, ‘national public opinion trends’, analyses how the answers to key Eurobarometer questions changed in each Member State between 2007 and 2015, in particular: The image of the EP, the role of the EP and the membership of the EU.

2. The second presentation, which also focuses on individual Member States, is devoted to socio demographic trends. It shows the main differences between the EU average and the national results for the key questions referred to above and for others. It breaks trends down by gender, age and socio-professional category.

3. The third presentation deals more specifically with topics relating to ‘identity and EU citizenship’. The changes in public opinion between 2007 and 2015 are dealt with on a national basis and compared with the European average. On a socio-demographic level, a specific analysis was made of the differences between age groups.

North Carolina Population and Housing Statistics

Demographic and Housing Trends in North Carolina

By Matthew Schnars [source]

About this dataset

This comprehensive dataset provides a well-detailed and robust statistical representation of various characteristics related to the population and housing conditions of North Carolina. The dataset originates from NC LINC (Log Into North Carolina), a critical data allocation platform that focuses on sharing information regarding diverse aspects of the state’s overall demographics, socio-economic conditions, education, and employment background.

The dataset highlights a variety of facets such as population estimates by age group, race or ethnic group encompassing multiple demographic groups across different geographic areas within the state including counties and municipalities. Utilizing this expansive set of data could prove instrumental for researchers looking into demographic trends, market estimation studies or any other analysis requiring population certifications.

Revolving around Housing Statistics in North Carolina, this dataset also gives a complete perspective about various ypes of residences available throughout the region. Availability types include both renter-occupied housing units along with owned homes, providing an encapsulating vision into the home ownership versus rental situation in North Carolina. In conjunction with providing insight into occupancy details for vacant homes.

An intriguing section included within these datasets is congregated ethnicity-based data spread across numerous age-groups which can assist research based out on diverse cultures dwelling within this area.

Overall, this dataset constitutes an essential resource for stakeholders interested in understanding demographic transformations over time or gaining insights into housing availability situations across different localities in North Carolina State to inform urban planning strategies and policies beneficially impacting residents’ lives directly

How to use the dataset

This dataset offers a broad range of demographic and housing data for North Carolina, making it an ideal resource for those interested in demographic trends, urban planning, social science research, real estate and economic studies. Here's how to get the most out of it:

• Interpretation: Determine what each column represents in terms of demographic and housing attributes. Familiarize yourself with the unique characteristics that each column represents such as population size, race categories, gender distributions etc.

• Comparison Studies: Analyze different locations within North Carolina by comparing figures across rows (geographic units). This can provide insight on socio-economic disparities or geographical preferences among residents.

• Temporal Analysis: Although the dataset doesn't contain specific dates or timeframes directly related to these statistics, you can cross-reference with external datasets from different years to conduct temporal analysis procedures such as observing the growth rates in population or changes in housing statistics.

• Joining Data: Combine this dataset with other relevant datasets like education levels or crime rates which may not be available here but could add multidimensional value when conducting thorough analyses.

• Correlation Studies: Perform correlation studies between different columns e.g., is there a strong correlation between population density and number of occupied houses? Such insights may be valuable for multiple sectors including real estate investment or policy-making purposes.

• Map Visualization: Use geographic tools to map data based on counties/townships providing visual perspectives over raw number comparisons which could potentially lead to more nuanced interpretations of demographic distributions across North Carolina

• Predictive Modelling/Forecasting: Based on historic figures available through this database develop models which predict future trends within demographics & housing sector

8: Presentation/Communication Tool: Whether you're delivering a presentation about social class disparities in NC regions or just curious about where populations are densest versus where there are more mobile homes vs homes owned freely -hamarize and display data in an easy-to-understand format.

Before diving deep, always remember to clean the dataset by eliminating duplicates, filling NA values aptly, and verifying the authenticity of the data. Furthermore, always respect privacy & comply with data regulation policies while handling demographic databases

Research Ideas

• Urban Planning: This dataset can be a val...

This data contains socio-demographic, income, and expenditure information of Iranian elderly collected in 2011 and 2015.

Summary statistics of temporal trend analysis (coefficient and R square) for socio- demographic and ecological variables, (p<0.05).

The analysis of the world's population is a complex and multifaceted endeavor, encompassing a wide range of demographic, economic, social, and environmental factors. Understanding these trends and dynamics is crucial for policymakers, researchers, and organizations to make informed decisions and plan for the future. This article delves into a comprehensive analysis of the world's population, examining its growth patterns, demographic shifts, challenges, and opportunities.

Population Growth. The world's population has experienced remarkable growth over the past century. In 1927, the global population reached its first billion, and since then, it has surged exponentially. As of the latest available data in 2021, the world's population stands at approximately 7.8 billion. Projections indicate that this figure will continue to rise, with estimates suggesting a population of over 9 billion by 2050.

Factors Driving Population Growth. 1. Fertility Rates: High birth rates, particularly in developing countries, have been a significant driver of population growth. Access to healthcare, education, and family planning services plays a crucial role in reducing fertility rates. 2. Increased Life Expectancy: Improvements in healthcare, nutrition, and sanitation have led to longer life expectancy worldwide. This has contributed to population growth, as people are living longer and healthier lives. 3. Demographic Shifts: Demographic shifts are shaping our world in significant ways. In developed countries, an aging population with a higher median age is reshaping healthcare systems, retirement policies, and workforce dynamics. Simultaneously, urbanization is accelerating, with over half of the global population now living in cities, presenting challenges and opportunities for infrastructure, resource management, and social development.

Challenges. 1. Overpopulation: Rapid population growth in certain regions can strain resources, leading to issues such as food scarcity, water shortages, and overcrowding. 2. Aging Workforce: As the global population ages, there may be a shortage of skilled workers, affecting economic productivity and social support systems. 3. Environmental Impact: Population growth is closely linked to increased resource consumption and environmental degradation. Sustainable development and conservation efforts are essential to mitigate these effects.

Opportunities. 1. Demographic Dividend: Countries with youthful populations can benefit from a demographic dividend, where a large working-age population can drive economic growth and innovation. 2. Cultural Diversity: A diverse global population can lead to cultural exchange, creativity, and a richer societal tapestry. 3. Innovation and Technology: Addressing the challenges posed by population growth can drive innovation in areas such as healthcare, agriculture, and energy production.

Analysing the world's population is a complex task that involves understanding its growth patterns, demographic shifts, challenges, and opportunities. As the global population continues to rise, it is essential to address the associated challenges while harnessing the potential benefits of a diverse and dynamic world population. Policymakers, researchers, and organizations must work collaboratively to create sustainable solutions that ensure a prosperous future for all.

Contact restrictions and distancing measures are among the most effective non-pharmaceutical measures to stop the spread of the SARS-CoV2 virus. Yet, research has only begun to understand the wider social consequences of these interventions. This study investigates how individuals' social networks have changed since the outbreak of the pandemic and how this is related to individuals' socio-economic positions and their socio-demographic characteristics. Based on a large quota sample of the German adult population, we investigate the loss and gain of strong and weak social ties during the pandemic. While about one third of respondents reported losing of contact with acquaintances, every fourth person has lost contact to a friend. Forming new social ties occurs less frequently. Only 10–15% report having made new acquaintances (15%) or friends (10%) during the pandemic. Overall, more than half of our respondents did not report any change, however. Changes in social networks are linked to both socio-demographic and socio-economic characteristics, such as age, gender, education, and migration background, providing key insights into a yet underexplored dimension of pandemic-related social inequality.

BMI- Body mass index, M- Male, F- Female

Context

This dataset provides comprehensive global demographic and socioeconomic indicators for each country, compiled for the year 2024. It includes data on population sizes, growth rates, fertility rates, migration, urbanization, and other critical factors that influence global social and economic trends.

Key Features:

Country Name: The name of each country or region included in the dataset.

Population (2024): Estimated total population of each country for the year 2024, measured in millions or billions.

Population Growth Rate: The annual percentage change in population from one year to the next. It highlights whether the population is growing or declining.

Urbanization Percentage: The proportion of the population living in urban areas, indicating trends in urban migration and the shift from rural to urban living.

Fertility Rate: The average number of children born per woman of childbearing age, a key indicator of population reproduction levels.

Median Age: The median age of the population, reflecting the age distribution and helping to assess population aging or youthfulness.

Life Expectancy at Birth: The average number of years a newborn is expected to live, assuming current mortality rates persist.

Infant Mortality Rate: The number of deaths of infants under one year of age per 1,000 live births, a key indicator of healthcare quality and access.

GDP (Gross Domestic Product): The total monetary or market value of all the goods and services produced within a country’s borders in a given time period (usually measured annually in USD).

GDP per Capita: GDP divided by the total population, reflecting the average economic output per person and serving as a measure of the average income or economic standard of living.

Human Development Index (HDI): A composite index that considers life expectancy, education, and income per capita to provide an overall measure of human development.

Applications of the Dataset: Policy and Development Analysis: Governments, international organizations, and think tanks can use this data to craft development policies, allocate resources, and address issues such as urbanization, aging populations, and fertility rates.

Economic Forecasting and Analysis: Economists and financial institutions can leverage this data for macroeconomic analysis, forecasting, and investment decisions, especially using indicators like GDP, GDP per capita, and HDI.

Social and Health Research: Public health organizations can track health indicators like life expectancy, infant mortality rates, and fertility rates to guide public health interventions and strategies.

Education and Demography: Educators and researchers in the fields of demography, sociology, and global studies can use this dataset to analyze population trends, migration patterns, and social changes across the globe.

Sources of Data:

The data is sourced from reputable international organizations including the United Nations, the World Bank, the World Health Organization (WHO), the International Monetary Fund (IMF), and other national statistical agencies.

Data Use & Limitations:

Use: This dataset is intended for general research, educational, and analytical purposes. It provides a snapshot of global demographic trends and socioeconomic conditions as of 2024. Limitations: While the data is collected from reliable sources, estimates for certain countries may vary slightly due to differing methods of data collection or reporting across regions. Additionally, as some countries may not have updated data for 2024, projections or estimates have been used where necessary.

The Census@Leicester datasets include socio-demographic data from the 2001, 2011, and 2021 Leicester censuses to enable the exploration of recent historical trends. It also includes data from the 2021 census for both Nottingham and Coventry to enable comparisons with other cities.

This online resource that can be used for teaching and research purposes by staff and students and to create a legacy for the Census@Leicester Project.

This dataset includes synthetically produced data from 10 different cities (Istanbul, Nablus, Chattogram, Cox’s Bazaar, Nairobi, Nakuru, Quito, Kokhana, Rapti and Darussalam) for a future urban context. The data includes physical elements in a city such as buildings, roads, and power networks, as well as social elements such as households and individuals. The dataset contains a maximum of 9 different data types, described below. For some cities power and road network data were not considered due to context specific priorities. landuse: The land use plan data depicting how the land will be zoned and used in the next fifty years within the area or interest. The attributes include the land use type, areal coverage in hectares, maximum population density and existing population. building: Data representing the building footprints that will emerge as a result of the future exposure generation procedure. It includes the attributes of the building such as its identifier number, construction type, number of floors, footprint area, occupation type and construction code level. road nodes: Data representing the points where road segments (edges) are connected to each other, including the identifier number for each node. road edges: Data representing the road segments, including the ID numbers of the starting and ending point (node). power nodes: Data representing the points where power lines (edges) are connected to each other, including the identifier number for each node. power edges: Data representing the power segments, including the including the ID numbers of the starting and ending point (node). household: Data that contains social attributes of a household living in a building. The attributes include number of individuals, income level and commonly used facility ID (such as hospital). individual: Data that contains the attributes of the individuals that are a part of a household. The attributes are age, gender, school ID (if relevant), workplace ID (if relevant) and last attained education level. Distribution table: The future projections for each city that identifies the socio-demographic changes and expected physical development in the next 50 years. The data can be used in geospatial platforms. The nomenclature for the data is as follows: “CitynameFutureExposureDataset/Cityname_CommunityCode_DataType”. This dataset was created as case studies for the Tomorrows Cities: Tomorrowville virtual testbed. It is supported by NERC as part of the GCRF Urban Disaster Risk Hub (NE/S009000/1).

BackgroundBetter medication adherence among people with diabetes mellitus was found to be associated with improved glycaemic control. However, medication non-adherence is a significant concern in older people with uncontrolled type 2 diabetes mellitus.PurposeTo explore the perspectives of older people with uncontrolled type 2 diabetes mellitus towards medication adherence.DesignA qualitative descriptive exploratory study.MethodologyA purposive sample of older people with uncontrolled type 2 diabetes mellitus living in the community was recruited. Snowball sampling was applied in community recruitment. In‐depth telephone interviews were conducted using a semi‐structured interview guide. Interviews were transcribed verbatim. Thematic analysis was used in data analysis. The consolidated criteria for reporting qualitative research (COREQ) guidelines were followed.ResultsThe emerged six themes were: (a) impact of knowledge, attitudes and practices on medication adherence, (b) treatment-related barriers to medication adherence, (c) impact of age-related changes on medication adherence, (d) person-related barriers to medication adherence, (e) impact of COVID-19 on medication adherence and, (f) role of support systems in medication adherence. Knowledge of the disease process and medications, attitudes towards medication adherence, the practice of different treatment approaches, self-medication and dosing, negative experiences related to medications, polypharmacy, changes in lifestyle and roles, the influence of work-life, motivation, negligence, family support, support received from health workers, facilities available and financial capability are the main factors influence medication adherence. Age-related memory impairment, visual disturbances and physical weaknesses affect medication adherence in older people. Additionally, COVID-19-related guidelines imposed by the government and healthcare system-related issues during the COVID-19 pandemic also affected medication adherence.ConclusionAdherence to medications among older people is hampered by a variety of factors, including their knowledge, attitudes and practices, person and treatment-related factors and age-related changes. The COVID-19 pandemic has brought additional challenges. Individualised patient care for older people with uncontrolled type 2 diabetes mellitus to improve medication adherence is timely. Strengthening support mechanisms for the above population is essential.

Changes in demographics will fundamentally shift the types of consumers that insurers need to target, as well as the types of products they need to provide. An aging population will put increased strain on state pensions and social services like public healthcare. A declining middle class due to median incomes not increasing as fast as other core goods and services means young people are buying a house, getting married, and starting families at later points in life. And a larger proportion of the population living in urban areas leads to increased health risk due to pollution, poor hygiene, and other urban lifestyle factors. These three factors will help shape the insurance industry going forward. Read More

Taabo HDSS aims to monitor health trends and demographic changes over time. The initiative is part of a larger network of health and demographic surveillance systems that aim to provide comprehensive insights into population health, mortality, and morbidity patterns. It includes continuously updated socio-demographic information for more than 40,000 participants who live in Taabo, a primarily rural area of south-central Côte d’Ivoire. Baseline data collection took place in 2009 and participants were initially followed up every three months. As of 2011, participants have been followed up every four months.

This dataset provides historical data on the global population from 1950 to 2023, which can be used to analyze global demographic trends over this significant period of time. The data is presented in an easy-to-understand format, allowing researchers, social scientists, and policy practitioners to explore and better understand global population dynamics.

The DG for Development launched this survey in order to address the issue of public opinion in the NMS12, to provide incentives for national and European debates on development aid issues, and to provide a starting point for shaping EC communication on the topic. The survey was carried out in the 12 new Member States, between 25 May and 30 June 2007. This report consists of two main parts: i) Perceptions of EU development aid and ii) Information concerning EU development aid It presents the main findings for the NMS12 as a whole as well as a country-by-country analysis. The results are also analysed in socio-demographic terms. i) Perceptions of EU development aid and ii) Information concerning EU development aid It presents the main findings for the NMS12 as a whole as well as a country-by-country analysis. The results are also analysed in socio-demographic terms.

The results by volumes are distributed as follows:

• Volume A: Countries
• Volume AA: Groups of countries
• Volume A' (AP): Trends
• Volume AA' (AAP): Trends of groups of countries
• Volume B: EU/socio-demographics
• Volume B' (BP) : Trends of EU/ socio-demographics
• Volume C: Country/socio-demographics ---- Researchers may also contact GESIS - Leibniz Institute for the Social Sciences: https://www.gesis.org/eurobarometer

Sensitivity Analysis. (XLS)