About this dataset

Context

The dataset tabulates the Major County population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Major County. The dataset can be utilized to understand the population distribution of Major County by age. For example, using this dataset, we can identify the largest age group in Major County.

Key observations

The largest age group in Major County, OK was for the group of age 5 to 9 years years with a population of 609 (7.95%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Major County, OK was the 85 years and over years with a population of 212 (2.77%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Major County is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Major County total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Major County Population by Age. You can refer the same here

LivWell is a global longitudinal database which provides a range of key indicators related to women’s socioeconomic status, health and well-being, access to basic services, and demographic outcomes. Data are available at the sub-national level for 52 countries and 447 regions. A total of 134 indicators are based on 199 Demographic and Health Surveys for the period 1990-2019, supplemented by extensive information on socioeconomic and climatic conditions in the respective regions for a total of 190 indicators. The resulting data offer various opportunities for policy-relevant research on gender inequality, inclusive development, and demographic trends at the sub-national level.

For a full description, please refer to the article describing the database here: (link to come)

The companion repository livwelldata allows to easily use the database in R. The R package can be downloaded following the instructions on the following git repository: https://gitlab.pik-potsdam.de/belmin/livwelldata. The version of the database in the package is the same as in this repository.

This dataset contains information about the demographics of all US cities and census-designated places with a population greater or equal to 65,000. This data comes from the US Census Bureau's 2015 American Community Survey. This product uses the Census Bureau Data API but is not endorsed or certified by the Census Bureau.

About this dataset

Context

The dataset tabulates the data for the Major County, OK population pyramid, which represents the Major County population distribution across age and gender, using estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. It lists the male and female population for each age group, along with the total population for those age groups. Higher numbers at the bottom of the table suggest population growth, whereas higher numbers at the top indicate declining birth rates. Furthermore, the dataset can be utilized to understand the youth dependency ratio, old-age dependency ratio, total dependency ratio, and potential support ratio.

Key observations

• Youth dependency ratio, which is the number of children aged 0-14 per 100 persons aged 15-64, for Major County, OK, is 35.7.
• Old-age dependency ratio, which is the number of persons aged 65 or over per 100 persons aged 15-64, for Major County, OK, is 36.2.
• Total dependency ratio for Major County, OK is 71.9.
• Potential support ratio, which is the number of youth (working age population) per elderly, for Major County, OK is 2.8.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group for the Major County population analysis. Total expected values are 18 and are define above in the age groups section.
• Population (Male): The male population in the Major County for the selected age group is shown in the following column.
• Population (Female): The female population in the Major County for the selected age group is shown in the following column.
• Total Population: The total population of the Major County for the selected age group is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Major County Population by Age. You can refer the same here

blockgroupdemographics A selection of variables from the US Census Bureau's American Community Survey 5YR and TIGER/Line publications. Overview The U.S. Census Bureau published it's American Community Survey 5 Year with more than 37,000 variables. Most ACS advanced users will have their personal list of favorites, but this conventional wisdom is not available to occasional analysts. This publication re-shares 174 select demographic data from the U.S. Census Bureau to provide an supplement to Open Environments Block Group publications. These results do not reflect any proprietary or predictive model. Rather, they extract from Census Bureau results. For additional support or more detail, please see the Census Bureau citations below. The first 170 demographic variables are taken from popular variables in the American Community Survey (ACS) including age, race, income, education and family structure. A full list of ACS variable names and definitions can be found in the ACS 'Table Shells' here https://www.census.gov/programs-surveys/acs/technical-documentation/table-shells.html. The dataset includes 4 additional columns from the Census' TIGER/Line publication. See Open Environment's 2023blockgroupcartographics publication for the shapes of each block group. For each block group, the dataset includes land area (ALAND), water area (AWATER), interpolated latitude (INTPTLAT) and longitude (INTPTLON). These are valuable for calculating population density variables which combine ACS populations and TIGER land area. Files The resulting dataset is available with other block group based datasets on Harvard's Dataverse https://dataverse.harvard.edu/ in Open Environment's Block Group Dataverse https://dataverse.harvard.edu/dataverse/blockgroupdatasets/. This data simply requires csv reader software or pythons pandas package. Supporting the data file, is acsvars.csv, a list of the Census variable names and their corresponding description. Citations “American Community Survey 5-Year Data (2019-2023).” Census.gov, US Census Bureau, https://www.census.gov/data/developers/data-sets/acs-5year.html. 2023 "American Community Survey, Table Shells and Table List” Census.gov, US Census Bureau, https://www.census.gov/programs-surveys/acs/technical-documentation/table-shells.html Python Package Index - PyPI. Python Software Foundation. "A simple wrapper for the United States Census Bureau’s API.". Retrieved from https://pypi.org/project/census/

The National Population Database (NPD) is a point-based Geographical Information System (GIS) dataset that combines locational information from providers like the Ordnance Survey with population information about those locations, mainly sourced from Government statistics. The points (and sometimes polygons) represent individual buildings, so the NPD allows detailed local analysis for anywhere in Great Britain.

The Health & Safety Laboratory (HSL) working with Staffordshire University originally created the NPD in 2004 to help its parent organisation, the Health and Safety Executive (HSE), assess the risks to society of major hazard sites e.g. oil refineries, chemical works and gas holders. Of particular interest to HSE were 'sensitive' populations e.g. schools and hospitals where the people at those locations may be more vulnerable to harm and potentially harder to evacuate in an emergency. The data is split into 5 themes: residential, sensitive populations, transport, workplaces and leisure.

More information about the NPD can be found here:

https://www.hsl.gov.uk/what-we-do/better-decisions/geoanalytics/national-population-database

The NPD was created using various datasets available within Government as part of the Public Sector Mapping Agreement (PSMA) and contains other intellectual property so is only available under license and for a fee. Please contact the HSL GIS Team if you would like to discuss gaining access to the sample or full dataset.

INSPIRE dataset for Population Distribution – Demography theme represents the main demographic characteristics for the Lithuanian population and socio-demographic variables grouped by the relevant territorial statistical in Lithuania. Also, certain variables were calculated for different gender and/or age groups, certain economic demographic variables – for different economic activities (by NACE classification). Layers of the theme are shown at a scale of 1: 1 500 000, except for PD.StatisticalDistribution.GRID layer, which is shown at a scale of 1: 25 000.

Hong Kong Social Contact Dynamics

Understanding Age, Gender and Network Dynamics

By [source]

About this dataset

This dataset provides an in-depth look at the dynamics of social interaction, particularly in Hong Kong. It contains comprehensive information regarding individuals, households and interactions between individuals such as their ages, frequency and duration of contact, and genders. This data can be utilized to evaluate various social and economic trends, behaviors, as well as dynamics observed at different levels. For example, this data set is an ideal tool to recognize population-level trends such as age and gender diversification of contacts or investigate the structure of social networks in addition to the implications of contact patterns on health and economic outcomes. Additionally, it offers valuable insights into dissimilar groups of people including their permanent residence activities related to work or leisure by enabling one to understand their interactions along with contact dynamics within their respective populations. Ultimately this dataset is key for attaining a comprehensive understanding of social contact dynamics which are fundamental for grasping why these interactions are crucial in Hong Kong's society today

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How to use the dataset

This dataset provides detailed information about the social contact dynamics in Hong Kong. With this dataset, it is possible to gain a comprehensive understanding of the patterns of various forms of social contact - from permanent residence and work contacts to leisure contacts. This guide will provide an overview and guidelines on how to use this dataset for analysis.

Exploring Trends and Dynamics:

To begin exploring the trends and dynamics of social contact in Hong Kong, start by looking at demographic factors such as age, gender, ethnicity, and educational attainment associated with different types of contacts (permanent residence/work/leisure). Consider the frequency and duration of contacts within these segments to identify any potential differences between them. Additionally, look at how these factors interact with each other – observe which segments have higher levels of interaction with each other or if there are any differences between different population groups based on their demographic characteristics. This can be done through visualizations such as line graphs or bar charts which can illustrate trends across timeframes or population demographics more clearly than raw numbers would alone.

Investigating Social Networks:

The data collected through this dataset also allows for investigation into social networks – understanding who connects with who in both real-life interactions as well as through digital channels (if applicable). Focus on analyzing individual or family networks rather than larger groups in order to get a clearer picture without having too much complexity added into the analysis time. Analyze commonalities among individuals within a network even after controlling for certain factors that could affect interaction such as age or gender – utilize clustering techniques for this step if appropriate– then focus on comparing networks between individuals/families overall using graph theory methods such as length distributions (the average number of relationships one has) , degrees (the number of links connected from one individual or family unit), centrality measures(identifying individuals who serve an important role bridging two different parts fo he network) etc., These methods will help provide insights into varying structures between large groups rather than focusing only on small-scale personal connections among friends / colleagues / relatives which may not always offer accurate portrayals due to their naturally limited scope

Modeling Health Implications:

Finally, consider modeling health implications stemming from these observed patterns– particularly implications that may not be captured by simpler measures like count per contact hour (which does not differentiate based on intensity). Take into account aspects like viral transmission risk by analyzing secondary effects generated from contact events captured in the data – things like physical proximity when multiple people meet up together over multiple days

Research Ideas

• Analyzing the age, gender and contact dynamics of different areas within Hong Kong to understand the local population trends and behavior.
• Investigating the structure of social networks to study how patterns of contact vary among socio economic backgro...

This database contains cross-sectional data for all municipalities in the Netherlands in two separate periods 1877-1879 and 1908-1910. Per municipality a wide variety of data is given for the two short periods. It contains demographic data: population size, infant mortality, stillbirths, births, male births, illegitimate births, and migration figures. Religious data: absolute numbers of adherents per religion. Medical data: numbers per medical personnel category and various vaccination figures. Furthermore it has participation data: unemployment rate, percentage employed in agriculture, labor participation of women, and the number of eligible voters in municipal elections. Finally it contains data on the water pipe supply, economic region, and the soil type of the municipality. It is the basis for the historical-demographic research article "Regional differentials in infant mortality of the Netherlands in the late 19th and early 20th century: Evaluating the importance of demographic, sociocultural, environmental, and medical factors". It contains the original Excel Database (with all relevant data), a STATA do-file (which 'cleans' the data), spatial datasets, and associated spatial weight files.

It also includes data derived from 'Mourits, Rick J; Boonstra, Onno; Knippenberg, Hans; Hofstee, Evert W; Zijdeman, Richard L, 2016, "Historische Database Nederlandse Gemeenten", https://hdl.handle.net/10622/RPBVK4, IISH Data Collection, V5' and the (adjusted) shapefiles are from 'Boonstra, O.W.A. (2007). NLGis shapefiles. DANS. https://doi.org/10.17026/dans-xb9-t677'. Make sure to cite both when using this database!

Knowing who your consumers are is essential for businesses, marketers, and researchers. This detailed demographic file offers an in-depth look at American consumers, packed with insights about personal details, household information, financial status, and lifestyle choices. Let's take a closer look at the data:

Personal Identifiers and Basic Demographics At the heart of this dataset are the key details that make up a consumer profile:

Unique IDs (PID, HHID) for individuals and households Full names (First, Middle, Last) and suffixes Gender and age Date of birth Complete location details (address, city, state, ZIP) These identifiers are critical for accurate marketing and form the base for deeper analysis.

Geospatial Intelligence This file goes beyond just listing addresses by including rich geospatial data like:

Latitude and longitude Census tract and block details Codes for Metropolitan Statistical Areas (MSA) and Core-Based Statistical Areas (CBSA) County size codes Geocoding accuracy This allows for precise geographic segmentation and localized marketing.

Housing and Property Data The dataset covers a lot of ground when it comes to housing, providing valuable insights for real estate professionals, lenders, and home service providers:

Homeownership status Dwelling type (single-family, multi-family, etc.) Property values (market, assessed, and appraised) Year built and square footage Room count, amenities like fireplaces or pools, and building quality This data is crucial for targeting homeowners with products and services like refinancing or home improvement offers.

Wealth and Financial Data For a deeper dive into consumer wealth, the file includes:

Estimated household income Wealth scores Credit card usage Mortgage info (loan amounts, rates, terms) Home equity estimates and investment property ownership These indicators are invaluable for financial services, luxury brands, and fundraising organizations looking to reach affluent individuals.

Lifestyle and Interests One of the most useful features of the dataset is its extensive lifestyle segmentation:

Hobbies and interests (e.g., gardening, travel, sports) Book preferences, magazine subscriptions Outdoor activities (camping, fishing, hunting) Pet ownership, tech usage, political views, and religious affiliations This data is perfect for crafting personalized marketing campaigns and developing products that align with specific consumer preferences.

Consumer Behavior and Purchase Habits The file also sheds light on how consumers behave and shop:

Online and catalog shopping preferences Gift-giving tendencies, presence of children, vehicle ownership Media consumption (TV, radio, internet) Retailers and e-commerce businesses will find this behavioral data especially useful for tailoring their outreach.

Demographic Clusters and Segmentation Pre-built segments like:

Household, neighborhood, family, and digital clusters Generational and lifestage groups make it easier to quickly target specific demographics, streamlining the process for market analysis and campaign planning.

Ethnicity and Language Preferences In today's multicultural market, knowing your audience's cultural background is key. The file includes:

Ethnicity codes and language preferences Flags for Hispanic/Spanish-speaking households This helps ensure culturally relevant and sensitive communication.

Education and Occupation Data The dataset also tracks education and career info:

Education level and occupation codes Home-based business indicators This data is essential for B2B marketers, recruitment agencies, and education-focused campaigns.

Digital and Social Media Habits With everyone online, digital behavior insights are a must:

Internet, TV, radio, and magazine usage Social media platform engagement (Facebook, Instagram, LinkedIn) Streaming subscriptions (Netflix, Hulu) This data helps marketers, app developers, and social media managers connect with their audience in the digital space.

Political and Charitable Tendencies For political campaigns or non-profits, this dataset offers:

Political affiliations and outlook Charitable donation history Volunteer activities These insights are perfect for cause-related marketing and targeted political outreach.

Neighborhood Characteristics By incorporating census data, the file provides a bigger picture of the consumer's environment:

Population density, racial composition, and age distribution Housing occupancy and ownership rates This offers important context for understanding the demographic landscape.

Predictive Consumer Indexes The dataset includes forward-looking indicators in categories like:

Fashion, automotive, and beauty products Health, home decor, pet products, sports, and travel These predictive insights help businesses anticipate consumer trends and needs.

Contact Information Finally, the file includes ke...

The American alligator (Alligator mississippiensis) is a species of ecological and economic importance in the southeastern United States. Within South Carolina, alligators are subject to private and public harvest programs, as well as nuisance removal. These management activities can have different impacts across alligator size classes that may not be apparent through widely-used monitoring techniques such as nightlight surveys. We synthesized multiple datasets within an integrated population model (IPM) to estimate size class-specific survival and abundance estimates, that would not be estimable through separate, non-integrated modeling frameworks. The IPM framework included a multistate mark-recapture-recovery model that used mark-recapture-recovery data from the Tom Yawkey Wildlife Center and growth transition probabilities that were estimated outside of the IPM framework. The IPM also included a state-space count model, which used nightlight survey counts of alligtaors from two survey routes: 1) Great Pee Dee and Waccamaw Rivers; and 2) South Santee Rivers. The IPM modeling framework also used mean clutch size data from the Tom Yawkey Wildlife Center and public and private harvest data within the state model. Lastly, we evaluated the effects of capture effort on capture probability, as well as the effects of water temperature and relative water level on count detection probability, and provide all covariate datasets. Our IPM framework determined that size class-specific survival rates were relatively high for all non-hatchling size classes, and abundance trends differed between the two nightlight survey sites.

This dataset highlights the important facts we extracted from various Census tables. The data is related to Census Block Groups which are at the core of our Opportunity Project. We have included information about income, spending, education and family/ household composition. The source data is from the American Community Survey (2016 5yr estimates)

For our Big Data Project we spent a lot of time learning about Altmetrics and its data before concluding on a topic to Analyze. We researched about the company, Altmetric itself and looked in depth at the data sets provided to us. We used python tools to open the files and physically read through them to get a comprehensive understanding of what the Altmetrics dataset was. Having no prior knowledge of any type of Big Data or data analysis it took some time to really understand what the data was and how we would be able to analyze it. With all of our team members having a lot of experience programming we decided to use R for data analysis. Coming from a Java, R was a bit of a learning curve due to all of the packages, however we quickly realized these packages were R’s main strength as it made the tools needed to analyze Altmetric data readily and easily available.

          In doing this research our team began to realize the importance of Altmetric. The data

collected was extremely relevant, organized and easy to analyze on both a macro and micro level. This allows us to be able to see where science is going and see what people in general are interested in and which people are interested in specific topics. Prior to learning about Altmetrics are team would have never known data like this was available. The fact that Altmetrics has this information and is allowing people to study these important trends.

          Once we familiarized ourselves with the basics of what we needed to get started, it was

time to choose a topic. Something that originally peaked our interest was the demographics section of the Altmetric dataset. We chose to focus on the locations in the demographics section. For our final question that we came up with, How did trending article types change over time in different countries?. This would determine how significant new research is and contrast the differences between different countries’ preferences towards research papers.

The Turkey Demographic and Health Survey (DHS) 2008 has been conducted by the Haccettepe University Institute of Population Studies in collaboration with the Ministry of health General Directorate of Mother and Child Health and Family Planning and Undersecretary of State Planning Organization. The Turkey Demographic and Health Survey 2008 has been financed the scientific and Technological research Council of Turkey (TUBITAK) under the support program for Research Projects of Public Institutions. The primary objective of the Turkey DHS 2008 is to provide data on fertility, contraceptive methods, maternal and child health. Detailed information on these issues is obtained through questionnaires, filled by face-to face interviews with ever-married women in reproductive ages (15-49). Another important objective of the survey, with aims to contribute to the knowledge on population and health as well, is to maintain the flow of information for the related organizations in Turkey on the Turkish demographic structure and change in the absence of reliable vital registration system and ascertain the continuity of data on demographic and health necessary for sustainable development in the absence of a reliable vital registration system. In terms of survey methodology and content, the Turkey DHS 2008 is comparable with the previous demographic surveys in Turkey (MEASURE DHS+).

A collection of population life tables covering a multitude of countries and many years. Most of the HLD life tables are life tables for national populations, which have been officially published by national statistical offices. Some of the HLD life tables refer to certain regional or ethnic sub-populations within countries. Parts of the HLD life tables are non-official life tables produced by researchers. Life tables describe the extent to which a generation of people (i.e. life table cohort) dies off with age. Life tables are the most ancient and important tool in demography. They are widely used for descriptive and analytical purposes in demography, public health, epidemiology, population geography, biology and many other branches of science. HLD includes the following types of data: * complete life tables in text format; * abridged life tables in text format; * references to statistical publications and other data sources; * scanned copies of the original life tables as they were published. Three scientific institutions are jointly developing the HLD: the Max Planck Institute for Demographic Research (MPIDR) in Rostock, Germany, the Department of Demography at the University of California at Berkeley, USA and the Institut national d''��tudes d��mographiques (INED) in Paris, France. The MPIDR is responsible for maintaining the database.

This project aimed to understand the public acceptability of a Low Emission Zone in the city of Bath, UK (formally known as the 'Clean Air Zone'). The dataset consists of socio-demographic, travel-related, and psychological variables, and a measure of Low Emission Zone acceptability.

BackgroundOne of the greatest obstacles facing efforts to address quality of care in low and middle income countries is the absence of relevant and reliable data. This article proposes a methodology for creating a single “Quality Index” (QI) representing quality of maternal and neonatal health care based upon data collected as part of the Demographic and Health Survey (DHS) program.MethodsUsing the 2012 Indonesian Demographic and Health Survey dataset, indicators of quality of care were identified based on the recommended guidelines outlined in the WHO Integrated Management of Pregnancy and Childbirth. Two sets of indicators were created; one set only including indicators available in the standard DHS questionnaire and the other including all indicators identified in the Indonesian dataset. For each indicator set composite indices were created using Principal Components Analysis and a modified form of Equal Weighting. These indices were tested for internal coherence and robustness, as well as their comparability with each other. Finally a single QI was chosen to explore the variation in index scores across a number of known equity markers in Indonesia including wealth, urban rural status and geographical region.ResultsThe process of creating quality indexes from standard DHS data was proven to be feasible, and initial results from Indonesia indicate particular disparities in the quality of care received by the poor as well as those living in outlying regions.ConclusionsThe QI represents an important step forward in efforts to understand, measure and improve quality of MNCH care in developing countries.

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) ...

About this dataset

Context

The dataset tabulates the Major County population by gender and age. The dataset can be utilized to understand the gender distribution and demographics of Major County.

Content

The dataset constitues the following two datasets across these two themes

• Major County, OK Population Breakdown by Gender
• Major County, OK Population Breakdown by Gender and Age

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Introducing a data set that specifically compares females and males can be done in various ways, depending on the purpose and context of the data set. Here's a general introduction that you can use as a starting point:

Title: Female vs Male Data Set: A Comparative Analysis

Introduction:

The "Female vs Male Data Set" is a comprehensive collection of information that aims to provide insights into the similarities and differences between females and males across various domains. This data set has been curated to facilitate analysis and exploration of characteristics, traits, preferences, and other factors that may vary between the two genders.

Dataset Description:

The Female vs Male Data Set comprises a wide range of data points sourced from diverse fields, including demographics, biology, psychology, sociology, economics, education, and more. It encompasses both quantitative and qualitative data, allowing for statistical analysis as well as qualitative interpretations.

The data set covers a multitude of aspects, such as:

Demographic Information: Age, ethnicity, geographical distribution, and other relevant demographic factors that distinguish females and males.

Physiological and Biological Factors: Biological traits, genetic variations, hormonal differences, and anatomical characteristics that are unique or more prevalent in one gender compared to the other.

Social and Cultural Factors: Gender roles, societal expectations, cultural norms, and their impacts on behavior, relationships, and social dynamics between females and males.

Psychological and Personality Traits: Differences or similarities in personality traits, cognitive abilities, emotional patterns, and psychological attributes between females and males.

Educational and Professional Data: Educational attainment, career choices, employment statistics, wage disparities, and other factors related to education and professional domains.

Health and Wellness: Variances in health outcomes, disease prevalence, risk factors, and responses to treatment between females and males.

Usage and Applications:

The Female vs Male Data Set can be utilized for a wide range of research, analysis, and decision-making purposes. Some potential applications include:

Gender Studies: Conducting in-depth studies on gender differences and gender-related topics. Social Sciences: Exploring the societal impacts of gender and investigating gender inequalities. Marketing and Consumer Behavior: Understanding gender-based preferences and consumption patterns. Health and Medicine: Investigating gender-specific health concerns and developing targeted interventions. Education: Analyzing gender gaps and formulating strategies for educational equality. Policy-making: Informing evidence-based policies and initiatives aimed at gender equity. It's important to note that this data set should be used responsibly and with an understanding that gender is a complex and multifaceted concept. Care should be taken to avoid generalizations and to respect individual variations within each gender.

Disclaimer: The data set does not endorse or perpetuate stereotypes or biases, but rather aims to provide a foundation for further exploration and understanding of gender-related aspects.

By utilizing the Female vs Male Data Set, researchers, analysts, and policymakers can gain valuable insights into the similarities and differences between females and males, leading to a more informed and nuanced understanding of gender dynamics in various fields.