This dataset contains a small sample of demographic information including names, ages, and cities. It is designed as a demonstration dataset for educational purposes, showcasing basic demographic data structure with three individuals from different major US cities. The dataset includes:

• Name: Individual names (John, Jane, Bob)
• Age: Ages ranging from 20-30 years
• City: Major US cities (New York, Los Angeles, Chicago)

This synthetic dataset can be used for learning basic data analysis techniques, practicing data visualization, or as a starting point for demographic analysis tutorials.

"""

County-Level Demographic: Population, Race, Gender

Overview

This dataset provides a detailed breakdown of demographic information for counties across the United States, derived from the U.S. Census Bureau's 2023 American Community Survey (ACS). The data includes population counts by gender, race, and ethnicity, alongside unique identifiers for each county using State and County FIPS codes.

Dataset Features

The dataset includes the following columns: - County: Name of the county. - State: Name of the state the county belongs to. - State FIPS Code: Federal Information Processing Standard (FIPS) code for the state. - County FIPS Code: FIPS code for the county. - FIPS: Combined State and County FIPS codes, a unique identifier for each county. - Total Population: Total population in the county. - Male Population: Number of males in the county. - Female Population: Number of females in the county. - Total Race Responses: Total race-related responses recorded in the survey. - White Alone: Number of individuals identifying as White alone. - Black or African American Alone: Number of individuals identifying as Black or African American alone. - Hispanic or Latino: Number of individuals identifying as Hispanic or Latino.

Processing Methodology

1. Source:
   • Data was retrieved using the U.S. Census Bureau ACS API.
2. County-Level Aggregation:
   • Each county is uniquely identified using State FIPS Code and County FIPS Code.
   • These codes were concatenated to form the unified FIPS column.
3. Data Cleaning:
   • All numeric columns were converted to appropriate data types.
   • County and state names were extracted from the raw NAME field for clarity.

Why Use This Dataset?

This dataset is highly versatile and suitable for: - Demographic Analysis: - Analyze population distribution by gender, race, and ethnicity. - Geographic Studies: - Use FIPS codes to map counties geographically. - Data Visualizations: - Create visual insights into demographic trends across counties.

File Format

• The dataset is available as a CSV file with 3,000+ rows (one for each county).

Licensing

• Source: Data is sourced from the U.S. Census Bureau's 2023 American Community Survey (ACS).
• License: This dataset is in the public domain and provided under the U.S. Census Bureau’s terms of use. Attribution to the Census Bureau is appreciated.

Acknowledgments

Special thanks to the U.S. Census Bureau for making this data publicly available and to the Kaggle community for fostering a collaborative space for data analysis and exploration. """

The demographic data displayed in this theme of Florida’s Roadmap to Living Healthy are quantitative measures that exhibit the socioeconomic state of Florida’s communities. The data sets comprising this themed map include topics such as population, race, income level, age, education, housing, and lifestyle data for all of Florida’s 67 counties, and other basic demographic characteristics. The Florida Department of Agriculture and Consumer Services has utilized the most current demographic statistical data from trusted sources such as the U.S. Census Bureau, U.S. Department of Housing and Urban Development, U.S. Department of Labor Bureau of Labor Statistics, Florida Department of Children and Families, and Esri to craft this custom visualization. Demographics provide profound perspective to your data analytics and will help you recognize the distinctive characteristics of a population based on its location. This demographic-themed mapping tool will simplify your ability to identify the specific socioeconomic needs of every community in Florida.

This site was retired on February 22, 2022 and replaced with https://opdatahub.dc.gov. The DC Office of Planning (OP) State Data Center Data Visualization Portal is an online, interactive information service that provides people with reliable, up-to-date, data on the demographic trends of District of Columbia.

This is an application based on open data and transparency of information for the public. The user-friendly Data Visualization Portal makes popular demographic charts and data much more accessible for residents, researchers, and other stakeholders.

The data provided by the dashboards on the portal cover a variety of city-wide and ward level indicators ranging from population size to poverty rates, and can be broken down by year, age, race or gender. This data will help citizens, government agencies, and community leaders get the analysis they need to support strategic planning, policy-making, and business development across the District.

SEMCOG"s Demographic Emphasis Areas tool is great for dynamically visualizing demographic indicators in Southeast Michigan. The map allows the user to look at combinations of indicators and visualize the data on the fly. Use this dataset to extend the capabilities of the online map.This tool has over 20 indicators across 2 geography types (Community and Census Tracts).IndicatorsMinorityNon-Hispanic BlackHispanicNon-Hispanic AsianOther Non-White Non-Hispanic RacesYouthPopulation Age 5 to 17Disengaged YouthOlder AdultsDisabilityPersons in PovertyForeign BornHouseholds in PovertyLimited English ProficiencyTransit Dependent HouseholdsNo Car HouseholdsFemale Headed HouseholdsHousing Cost BurdenMedian Household IncomeUnemployment RateMedian Income

Demographic statistics broken down by zip code

Demographic Trends and Health Outcomes in the U.S

Inequalities,Risk Factors and Access to Care

By Data Society [source]

About this dataset

This dataset contains key demographic, health status indicators and leading cause of death data to help us understand the current trends and health outcomes in communities across the United States. By looking at this data, it can be seen how different states, counties and populations have changed over time. With this data we can analyze levels of national health services use such as vaccination rates or mammography rates; review leading causes of death to create public policy initiatives; as well as identify risk factors for specific conditions that may be associated with certain populations or regions. The information from these files includes State FIPS Code, County FIPS Code, CHSI County Name, CHSI State Name, CHSI State Abbreviation, Influenza B (FluB) report count & expected cases rate per 100K population , Hepatitis A (HepA) Report Count & expected cases rate per 100K population , Hepatitis B (HepB) Report Count & expected cases rate per 100K population , Measles (Meas) Report Count & expected cases rate per 100K population , Pertussis(Pert) Report Count & expected case rate per 100K population , CRS report count & expected case rate per 100K population , Syphilis report count and expected case rate per 100k popuation. We also look at measures related to preventive care services such as Pap smear screen among women aged 18-64 years old check lower/upper confidence intervals seperately ; Mammogram checks among women aged 40-64 years old specified lower/upper conifence intervals separetly ; Colonosopy/ Proctoscpushy among men aged 50+ measured in lower/upper limits ; Pneumonia Vaccination amongst 65+ with loewr/upper confidence level detail Additionally we have some interesting trend indicating variables like measures of birth adn death which includes general fertility ratye ; Teen Birth Rate by Mother's age group etc Summary Measures covers mortality trend following life expectancy by sex&age categories Vressionable populations access info gives us insight into disablilty ratio + access to envtiromental issues due to poor quality housing facilities Finally Risk Factors cover speicfic hoslitic condtiions suchs asthma diagnosis prevelance cancer diabetes alcholic abuse smoking trends All these information give a good understanding on Healthy People 2020 target setings demograpihcally speaking hence will aid is generating more evience backed policies

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

How to use the dataset

What the Dataset Contains

This dataset contains valuable information about public health relevant to each county in the United States, broken down into 9 indicator domains: Demographics, Leading Causes of Death, Summary Measures of Health, Measures of Birth and Death Rates, Relative Health Importance, Vulnerable Populations and Environmental Health Conditions, Preventive Services Use Data from BRFSS Survey System Data , Risk Factors and Access to Care/Health Insurance Coverage & State Developed Types of Measurements such as CRS with Multiple Categories Identified for Each Type . The data includes indicators such as percentages or rates for influenza (FLU), hepatitis (HepA/B), measles(MEAS) pertussis(PERT), syphilis(Syphilis) , cervical cancer (CI_Min_Pap_Smear - CI_Max\Pap \Smear), breast cancer (CI\Min Mammogram - CI \Max \Mammogram ) proctoscopy (CI Min Proctoscopy - CI Max Proctoscopy ), pneumococcal vaccinations (Ci min Pneumo Vax - Ci max Pneumo Vax )and flu vaccinations (Ci min Flu Vac - Ci Max Flu Vac). Additionally , it provides information on leading causes of death at both county levels & national level including age-adjusted mortality rates due to suicide among teens aged between 15-19 yrs per 100000 population etc.. Furthermore , summary measures such as age adjusted percentage who consider their physical health fair or poor are provided; vulnerable populations related indicators like relative importance score for disabled adults ; preventive service use related ones ranging from self reported vaccination coverage among men40-64 yrs old against hepatitis B virus etc...

Getting Started With The Dataset

To get started with exploring this dataset first your need to understand what each column in the table represents: State FIPS Code identifies a unique identifier used by various US government agencies which denote states . County FIPS code denotes counties wi...

Author: Joseph Kerski, post_secondary_educator, Esri and University of DenverGrade/Audience: high school, ap human geography, post secondary, professional developmentResource type: lessonSubject topic(s): population, maps, citiesRegion: africa, asia, australia oceania, europe, north america, south america, united states, worldStandards: All APHG population tenets. Geography for Life cultural and population geography standards. Objectives: 1. Understand how population change and demographic characteristics are evident at a variety of scales in a variety of places around the world. 2. Understand the whys of where through analysis of change over space and time. 3. Develop skills using spatial data and interactive maps. 4. Understand how population data is communicated using 2D and 3D maps, visualizations, and symbology. Summary: Teaching and learning about demographics and population change in an effective, engaging manner is enriched and enlivened through the use of web mapping tools and spatial data. These tools, enabled by the advent of cloud-based geographic information systems (GIS) technology, bring problem solving, critical thinking, and spatial analysis to every classroom instructor and student (Kerski 2003; Jo, Hong, and Verma 2016).

The North Carolina State Demographer data platform houses the latest data produced by the Office of the State Demographer. The platform allows users to create visualizations, download full (or partial) datasets, and create maps. Registered users can save their visualizations and be notified of dataset updates. This new platform is a subdomain of OSBM’s Log In to North Carolina (LINC) – a service containing over 900 data items including items pertaining to population, labor force, education, transportation, etc. LINC includes topline statistics from the State Demographer’s population estimates and projections while the North Carolina State Demographer data platform includes more detailed datasets for users requiring more detailed demographic information.

Demographic data from Chapter 4 "Measuring audio visualization performance" from the PhD thesis "Semantic Audio Tools for Radio Production" by Chris Baume.

Sourcing accurate and up-to-date GIS data across Asia and MENA has historically been difficult for retail brands looking to expand their store networks in these regions. Either the data does not exist or it isn't readily accessible or updated regularly.

GapMaps uses known population data combined with billions of mobile device location points to provide highly accurate and globally consistent GIS data across Asia and MENA at 150m x 150m grid levels in major cities and 1km grids outside of major cities.

With this information, brands can get a detailed understanding of who lives in a catchment, where they work and their spending potential which allows you to:

• Better understand your customers
• Identify optimal locations to expand your retail footprint
• Define sales territories for franchisees
• Run targeted marketing campaigns.

GapMaps GIS data for Asia and MENA can be utilized in any GIS platform and includes the latest Demographic estimates (updated annually) including:

1. Population (how many people live in your local catchment)
2. Census Demographics (who lives within your local catchment)
3. Worker population (how many people work within your local catchment)
4. Consuming Class and Premium Consuming Class (who can can afford to buy goods & services beyond their basic needs and /or shop at premium retailers)
5. Retail Spending (Food & Beverage, Grocery, Apparel, Other). How much are consumers spending on retail goods and services by category.

GapMaps GIS Data also includes Point-Of-Interest (POI) Data updated monthly across a range of categories including Fast Food, Cafe, Health & Fitness and Supermarket/ Grocery

Primary Use Cases for GapMaps GIS Data:

1. Retail Site Selection - identify optimal locations for future expansion and benchmark performance across existing locations.
2. Customer Profiling: get a detailed understanding of the demographic profile of your customers, where they work and their spending potential
3. Analyse your trade areas at a granular 150m x 150m grid levels using all the key metrics
4. Target Marketing: Develop effective marketing strategies to acquire more customers.
5. Integrate GapMaps GIS data with your existing GIS or BI platform to generate powerful visualizations.

TrashVisualization.RR code that merges and analyzes all of the data. SizesOfObjects:Table of sizes of objects we compare in the VR. WPP2017_POP_F01_1_TOT:United Nations, Department of Economic and Social Affairs, Population Division (2017). World Population Prospects: The 2017 Revision, DVD Edition.Population:Cleaned population data from UN data set above taking only 2015.1260352_SupportingFile:Jambeck JR, Geyer R, Wilcox C, Siegler TR, Perryman M, Andrady A, et al. Marine pollution. Plastic waste inputs from land into the ocean. Science. 2015 Feb 13;347(6223):768–71.DetailedSummary-Earth (+1-2):Coastal Cleanup Day Data from 2016-2018 https://www.coastalcleanupdata.org/WCD:World Cleanup Day Data for 2018https://www.letsdoitworld.org/wp-content/uploads/2019/01/WCD_2018_Waste_Report_FINAL_26.01.2019.pdfAnything with the word "Key":A key used for merging country names between data sets.

Context:

Digital marketing platforms today rely heavily on user profiling to decide which advertisements should be displayed to which audience. Social networks collect demographic information such as age, gender, and income to understand user behavior and improve ad targeting. This dataset captures how different user demographics respond to online advertisements, making it valuable for studying customer behavior, marketing strategies, and purchase prediction.

The dataset is widely used in machine learning education and projects because it is simple, clean, and ideal for building classification models. It helps beginners and professionals understand how demographic features influence a user’s decision to purchase a product after viewing an ad.

Content :

This dataset contains user demographic information and their response to an advertisement. Each row represents one individual from a social media platform, including:

Age : The age of the user

Estimated Salary : Approximate annual salary of the user

Purchased : Target variable indicating whether the user bought the advertised product

0 = No purchase

1 = Purchase

The dataset can be used for:

• Predicting purchase behavior using machine learning models

• Understanding how age and income affect ad response

• Performing exploratory data analysis (EDA)

• Demonstrating classification algorithms such as Logistic Regression, KNN, SVM, Trees, etc.

• Practicing feature scaling, model training, evaluation, and visualization

Sources: U.S. Census Bureau, Census 2020; generated by CCRPC staff; using 2020 Census Demographic Data Map Viewer; https://www.census.gov/library/visualizations/2021/geo/demographicmapviewer.html; (18 August 2021); U.S. Census Bureau; Census 2000, Summary File 1, Table DP-1; generated by CCRPC staff; using American FactFinder; http://factfinder2.census.gov; (30 December 2015). U.S. Census Bureau; Census 2010, Summary File 1, Table P1; generated by CCRPC staff; using American FactFinder; http://factfinder2.census.gov; (30 December 2015). U.S. Census Bureau; 1980 Census of Population, Volume 1: Characteristics of the Population, Chapter A: Number of Inhabitants, Part 15: Illinois, PC80-1-A15, Table 2, Land Area and Population: 1930-1980. U.S. Census Bureau; Fourteenth Census of the United States; State Compendium Illinois, Table 1. - Area and Population of Counties: 1850 to 1920; https://www.census.gov/library/publications/1924/dec/state-compendium.html; (23 August 2018).

Geolocet offers a rich repository of European demographic data, providing you with a robust foundation for data-driven decisions. Our datasets encompass a diverse range of attributes, but it's important to note that the attributes available may vary significantly from country to country. This variation reflects the unique demographic reporting standards and data availability in each region.

Attributes include essential demographic factors such as Age Bands, Gender, and Marital Status, as a minimum. In some countries, we provide cross-referenced attributes, such as Marital Status per Age Band, Marital Status per Gender, or even intricate combinations like Marital Status per Gender and Age. Additionally, for select countries, we offer insights into income, employment status, household composition, housing status, and many more.

🌐 Trusted Source Data

Our demographic data is derived exclusively from official census sources, ensuring the highest level of accuracy and reliability. We take pride in using data that is available under open licenses for commercial use. However, it's important to note that our data is not a direct representation of the original census data. Instead, we use this source data to create comprehensive demographic models that are tailored to your needs.

🔄 Annual Data Updates

To keep your insights fresh and accurate, our data is updated once per year. We offer annual subscriptions, allowing you to access the latest demographic information and maintain the relevance of your analyses.

🌍 Geographic Coverage

While our demographic data spans across the majority of European countries and their administrative divisions' boundaries, it's important to inquire about specific attributes and coverage for each region of interest. We understand that your data needs may vary depending on your target regions, and our team is here to assist you in selecting the most relevant datasets for your objectives.

Contact us to explore our offerings and learn how our data can elevate your decision-making processes.

🌐 Enhanced with Spatial Insights: Administrative Boundaries Spatial Data

Geolocet's demographic data isn't limited to numbers; it's brought to life through seamless integration with our Administrative Boundaries Spatial Data. This integration offers precise boundary mapping, allowing you to visualize demographic distributions, patterns, and densities on a map. This spatial perspective unlocks geo patterns and insights, aiding in strategic decision-making. Whether you're planning localized marketing strategies, optimizing resource allocation, or selecting ideal expansion sites, the geographic context adds depth to your data-driven strategies. Contact us today to explore how this spatial synergy can enhance your decision-making.

🌍 Enhanced with Robust Aggregated POI Data

Geolocet doesn't stop at demographics; we enhance your analysis by offering Geolocet's POI Aggregated Data. This data source provides a comprehensive understanding of local areas, enabling you to craft detailed local area profiles. It's not just about numbers; it's about uncovering the essence of each locality.

🔍 Crafting Local Area Profiles

When you combine our POI Aggregated Data with our Demographics Data, you have the tools to craft insightful local area profiles. Dive into the specific data points for various sectors, such as the number of hospitals, schools, hotels, restaurants, pubs, casinos, groceries, clothing stores, gas stations, and more within designated areas. This level of granularity allows you to paint a vivid picture of each locality, understanding its unique characteristics and offerings.

Contact us today to explore how this synergy can elevate your strategic decision-making and enrich your insights into local communities.

🔍 Customized Data Solutions with DaaS

Geolocet's Data as a Service (DaaS) offers flexibility tailored to your needs. Our transparent pricing model ensures cost-efficiency, allowing you to pay only for the data you require.

SEMCOG's Community Explorer tool is great for dynamically visualizing demographic and economic data in Southeast Michigan. Use this dataset to extend Community Explorer and make your own visualization.This tool has over 40 indicators across 4 geography types (County, Community, School Districts, Census Tracts). Not only are the data columns available, but we also include the Margin of Error (MOE) to better understand the reliability of each column.IndicatorsTotal PopulationPopulation Density (Persons/Acre)Median AgePercent Age 65+Percent Age 65+ Living AlonePercent Ages 5 to 17Ratio Youth to SeniorsPercent Bachelor's Degree or HigherPercent People in PovertyPercent AsianPercent BlackPercent HispanicPercent WhiteTotal HouseholdsAverage Household SizePercent Households with SeniorsPercent Households with ChildrenPercent Households with No CarPercent Households with Internet AccessTotal Households without Internet AccessPercent Households with Broadband Internet AccessTotal Households without Broadband Internet AccessPercentage Households with Computing DevicesTotal Households without a Desktop or LaptopPercent Seniors with Broadband Internet AccessPercent Children without Broadband Internet AccessPercent Children without Computing DevicesTotal Housing UnitsPercent VacantPercent Owner OccupiedPercent Renter OccupiedPercent Single FamilyPercent Multi-FamilyTotal JobsJob Density (Jobs/Acre)Unemployment RateLabor Force Participation RateMedian Household IncomePer Capita IncomeMedian Housing ValueAverage Commute Time (Minutes)Percent Drive Alone to WorkPercent Commute by Transit

Population Numbers By New York City Neighborhood Tabulation Areas

The data was collected from Census Bureaus' Decennial data dissemination (SF1).
Neighborhood Tabulation Areas (NTAs), are aggregations of census tracts that are subsets of New York City's 55 Public Use Microdata Areas (PUMAs). Primarily due to these constraints, NTA boundaries and their associated names may not definitively represent neighborhoods.
This report shows change in population from 2000 to 2010 for each NTA.
Compiled by the Population Division – New York City Department of City Planning.

County Buddy is a dataset detailing the presence, count, and institutions of special populations (incarcerated individuals, college students, military personnel, and Native Americans) at the U.S. county and census tract levels. It offers geographic and demographic context to help explain variation in socio-economic indicators like life expectancy, income, and education.

Administrative Boundaries of Indian Villages with demographic data like Population, Sex, Household, etc

State Boundary: 28 Union Territory: 8 District Boundary: 735

Attributes: State Boundaries, Population, Gender, Sex, and household

State Boundary: 28 Union Territory: 8 District Boundary: 735 Attributes: State Boundaries, Population, Gender, Sex, and household

These files are supplementary information to illustrate the metadata reports and default visualizations provided as end-user products for WorldPop Random Forest-based population mapping. This collection contains those reports outlining all ancillary covariates and model fitting, as well as KML for each case-stady country outlined in Stevens, et al. (2015) which describes the methods to produce these data in detail.

Stevens FR, Gaughan AE, Linard C, Tatem AJ (2015) Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data. PLoS ONE 10(2): e0107042. doi:10.1371/journal.pone.0107042