West Virginia, Mississippi, and Arkansas are the U.S. states with the highest percentage of their population who are obese. The states with the lowest percentage of their population who are obese include Colorado, Hawaii, and Massachusetts. Obesity in the United States Obesity is a growing problem in many countries around the world, but the United States has the highest rate of obesity among all OECD countries. The prevalence of obesity in the United States has risen steadily over the previous two decades, with no signs of declining. Obesity in the U.S. is more common among women than men, and overweight and obesity rates are higher among African Americans than any other race or ethnicity. Causes and health impacts Obesity is most commonly the result of a combination of poor diet, overeating, physical inactivity, and a genetic susceptibility. Obesity is associated with various negative health impacts, including an increased risk of cardiovascular diseases, certain types of cancer, and diabetes type 2. As of 2022, around 8.4 percent of the U.S. population had been diagnosed with diabetes. Diabetes is currently the eighth leading cause of death in the United States.

National Obesity Percentages by State. Explanation of Field Attributes:Obesity - The percent of the state population that is considered obese from the 2015 CDC BRFSS Survey.

Obesity percentages for Lake County, Illinois. Explanation of field attributes:

Pct_Obese – The percent of people in the zip code who are considered obese, defined as having a BMI greater than or equal to 30.

ObsOrOvrwt –The percent of people in the zip code who are considered overweight (defined as having a BMI greater than or equal to 25 but less than 30) or obese (defined as having a BMI greater than or equal to 30).

These data are from the 2013 California Dietary Practices Surveys (CDPS), 2012 California Teen Eating, Exercise and Nutrition Survey (CalTEENS), and 2013 California Children’s Healthy Eating and Exercise Practices Surveys (CalCHEEPS). These surveys have been discontinued. Adults, adolescents, and children (with parental assistance) were asked for their current height and weight, from which, body mass index (BMI) was calculated. For adults, a BMI of 30.0 and above is considered obese. For adolescents and children, obesity is defined as having a BMI at or above the 95th percentile, according to CDC growth charts.

The California Dietary Practices Surveys (CDPS), the California Teen Eating, Exercise and Nutrition Survey (CalTEENS), and the California Children’s Healthy Eating and Exercise Practices Surveys (CalCHEEPS) (now discontinued) were the most extensive dietary and physical activity assessments of adults 18 years and older, adolescents 12 to 17, and children 6 to 11, respectively, in the state of California. CDPS and CalCHEEPS were administered biennially in odd years up through 2013 and CalTEENS was administered biennially in even years through 2014. The surveys were designed to monitor dietary trends, especially fruit and vegetable consumption, among Californias for evaluating their progress toward meeting the Dietary Guidelines for Americans and the Healthy People 2020 Objectives. All three surveys were conducted via telephone. Adult and adolescent data were collected using a list of participating CalFresh households and random digit dial, and child data were collected using only the list of CalFresh households. Older children (9-11) were the primary respondents with some parental assistance. For younger children (6-8), the primary respondent was parents. Data were oversampled for low-income and African American to provide greater sensitivity for analyzing trends among the target population. Wording of the question used for these analyses varied by survey (age group). The questions were worded are as follows: Adult:1) How tall are you without shoes?2) How much do you weigh?Adolescent:1) About how much do you weigh without shoes?2) About how tall are you without shoes? Child:1) How tall is [child's name] now without shoes on?2) How much does [child's name] weigh now without shoes on?

This data represents the age-adjusted prevalence of high total cholesterol, hypertension, and obesity among US adults aged 20 and over between 1999-2000 to 2017-2018. Notes: All estimates are age adjusted by the direct method to the U.S. Census 2000 population using age groups 20–39, 40–59, and 60 and over. Definitions Hypertension: Systolic blood pressure greater than or equal to 130 mmHg or diastolic blood pressure greater than or equal to 80 mmHg, or currently taking medication to lower high blood pressure High total cholesterol: Serum total cholesterol greater than or equal to 240 mg/dL. Obesity: Body mass index (BMI, weight in kilograms divided by height in meters squared) greater than or equal to 30. Data Source and Methods Data from the National Health and Nutrition Examination Surveys (NHANES) for the years 1999–2000, 2001–2002, 2003–2004, 2005–2006, 2007–2008, 2009–2010, 2011–2012, 2013–2014, 2015–2016, and 2017–2018 were used for these analyses. NHANES is a cross-sectional survey designed to monitor the health and nutritional status of the civilian noninstitutionalized U.S. population. The survey consists of interviews conducted in participants’ homes and standardized physical examinations, including a blood draw, conducted in mobile examination centers.

This dataset includes data on adult's diet, physical activity, and weight status from Behavioral Risk Factor Surveillance System. This data is used for DNPAO's Data, Trends, and Maps database, which provides national and state specific data on obesity, nutrition, physical activity, and breastfeeding.

Obesity rates for each Census Tract in Allegheny County were produced for the study “Developing small-area predictions for smoking and obesity prevalence in the United States." The data is not explicitly based on population surveys or data collection conducted in Allegheny County, but rather estimated using statistical modeling techniques. In this technique, researchers applied the obesity rate of a demographically similar census tract to one in Allegheny County to compute an obesity rate.

BackgroundUnderstanding the social environmental around obesity has been limited by available data. One promising approach used to bridge similar gaps elsewhere is to use passively generated digital data.PurposeThis article explores the relationship between online social environment via web-based social networks and population obesity prevalence.MethodsWe performed a cross-sectional study using linear regression and cross validation to measure the relationship and predictive performance of user interests on the online social network Facebook to obesity prevalence in metros across the United States of America (USA) and neighborhoods within New York City (NYC). The outcomes, proportion of obese and/or overweight population in USA metros and NYC neighborhoods, were obtained via the Centers for Disease Control and Prevention Behavioral Risk Factor Surveillance and NYC EpiQuery systems. Predictors were geographically specific proportion of users with activity-related and sedentary-related interests on Facebook.ResultsHigher proportion of the population with activity-related interests on Facebook was associated with a significant 12.0% (95% Confidence Interval (CI) 11.9 to 12.1) lower predicted prevalence of obese and/or overweight people across USA metros and 7.2% (95% CI: 6.8 to 7.7) across NYC neighborhoods. Conversely, greater proportion of the population with interest in television was associated with higher prevalence of obese and/or overweight people of 3.9% (95% CI: 3.7 to 4.0) (USA) and 27.5% (95% CI: 27.1 to 27.9, significant) (NYC). For activity-interests and national obesity outcomes, the average root mean square prediction error from 10-fold cross validation was comparable to the average root mean square error of a model developed using the entire data set.ConclusionsActivity-related interests across the USA and sedentary-related interests across NYC were significantly associated with obesity prevalence. Further research is needed to understand how the online social environment relates to health outcomes and how it can be used to identify or target interventions.

About this dataset

Context

This list ranks the 50 states in the United States by White population, as estimated by the United States Census Bureau. It also highlights population changes in each states over the past five years.

Content

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

• 2019-2023 American Community Survey 5-Year Estimates
• 2018-2022 American Community Survey 5-Year Estimates
• 2017-2021 American Community Survey 5-Year Estimates
• 2016-2020 American Community Survey 5-Year Estimates
• 2015-2019 American Community Survey 5-Year Estimates

Variables / Data Columns

• Rank by White Population: This column displays the rank of states in the United States by their White population, using the most recent ACS data available.
• states: The states for which the rank is shown in the previous column.
• White Population: The White population of the states is shown in this column.
• % of Total states Population: This shows what percentage of the total states population identifies as White. Please note that the sum of all percentages may not equal one due to rounding of values.
• % of Total U.S. White Population: This tells us how much of the entire United States White population lives in that states. Please note that the sum of all percentages may not equal one due to rounding of values.
• 5 Year Rank Trend: TThis column displays the rank trend across the last 5 years.

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/.

State of Illinois Obesity Percentages by County. Explanation of field attributes: Obesity - The percent of each Illinois county’s population that is considered obese from the 2015 CDC BRFSS Survey.

The share of the population with overweight in Canada was forecast to continuously increase between 2024 and 2029 by in total 1.6 percentage points. After the fifteenth consecutive increasing year, the overweight population share is estimated to reach 74.45 percent and therefore a new peak in 2029. Notably, the share of the population with overweight of was continuously increasing over the past years.Overweight is defined as a body mass index (BMI) of more than 25.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the share of the population with overweight in countries like Mexico and United States.

In 2008 the National Center for Health Statistics released a dual energy x-ray absorptiometry (DXA) whole body dataset from the NHANES population-based sample acquired with modern fan beam scanners in 15 counties across the United States from 1999 through 2004. The NHANES dataset was partitioned by gender and ethnicity and DXA whole body measures of %fat, fat mass/height2, lean mass/height2, appendicular lean mass/height2, %fat trunk/%fat legs ratio, trunk/limb fat mass ratio of fat, bone mineral content (BMC) and bone mineral density (BMD) were analyzed to provide reference values for subjects 8 to 85 years old. DXA reference values for adults were normalized to age; reference values for children included total and sub-total whole body results and were normalized to age, height, or lean mass. We developed an obesity classification scheme by using estabbody mass index (BMI) classification thresholds and prevalences in young adults to generate matching classification thresholds for Fat Mass Index (FMI; fat mass/height2). These reference values should be helpful in the evaluation of a variety of adult and childhood abnormalities involving fat, lean, and bone, for establishing entry criteria into clinical trials, and for other medical, research, and epidemiological uses.

Some say climate change is the biggest threat of our age while others say it’s a myth based on dodgy science. We are turning some of the data over to you so you can form your own view.

Even more than with other data sets that Kaggle has featured, there’s a huge amount of data cleaning and preparation that goes into putting together a long-time study of climate trends. Early data was collected by technicians using mercury thermometers, where any variation in the visit time impacted measurements. In the 1940s, the construction of airports caused many weather stations to be moved. In the 1980s, there was a move to electronic thermometers that are said to have a cooling bias.

Given this complexity, there are a range of organizations that collate climate trends data. The three most cited land and ocean temperature data sets are NOAA’s MLOST, NASA’s GISTEMP and the UK’s HadCrut.

We have repackaged the data from a newer compilation put together by the Berkeley Earth, which is affiliated with Lawrence Berkeley National Laboratory. The Berkeley Earth Surface Temperature Study combines 1.6 billion temperature reports from 16 pre-existing archives. It is nicely packaged and allows for slicing into interesting subsets (for example by country). They publish the source data and the code for the transformations they applied. They also use methods that allow weather observations from shorter time series to be included, meaning fewer observations need to be thrown away.

In this dataset, we have include several files:

Global Land and Ocean-and-Land Temperatures (GlobalTemperatures.csv):

• Date: starts in 1750 for average land temperature and 1850 for max and min land temperatures and global ocean and land temperatures
• LandAverageTemperature: global average land temperature in celsius
• LandAverageTemperatureUncertainty: the 95% confidence interval around the average
• LandMaxTemperature: global average maximum land temperature in celsius
• LandMaxTemperatureUncertainty: the 95% confidence interval around the maximum land temperature
• LandMinTemperature: global average minimum land temperature in celsius
• LandMinTemperatureUncertainty: the 95% confidence interval around the minimum land temperature
• LandAndOceanAverageTemperature: global average land and ocean temperature in celsius
• LandAndOceanAverageTemperatureUncertainty: the 95% confidence interval around the global average land and ocean temperature

Other files include:

• Global Average Land Temperature by Country (GlobalLandTemperaturesByCountry.csv)
• Global Average Land Temperature by State (GlobalLandTemperaturesByState.csv)
• Global Land Temperatures By Major City (GlobalLandTemperaturesByMajorCity.csv)
• Global Land Temperatures By City (GlobalLandTemperaturesByCity.csv)

The raw data comes from the Berkeley Earth data page.

In 2023, Washington, D.C. had the highest population density in the United States, with 11,130.69 people per square mile. As a whole, there were about 94.83 residents per square mile in the U.S., and Alaska was the state with the lowest population density, with 1.29 residents per square mile. The problem of population density Simply put, population density is the population of a country divided by the area of the country. While this can be an interesting measure of how many people live in a country and how large the country is, it does not account for the degree of urbanization, or the share of people who live in urban centers. For example, Russia is the largest country in the world and has a comparatively low population, so its population density is very low. However, much of the country is uninhabited, so cities in Russia are much more densely populated than the rest of the country. Urbanization in the United States While the United States is not very densely populated compared to other countries, its population density has increased significantly over the past few decades. The degree of urbanization has also increased, and well over half of the population lives in urban centers.

This table contains data on the modified retail food environment index for California, its regions, counties, cities, towns, and census tracts. An adequate, nutritious diet is a necessity at all stages of life. Pregnant women and their developing babies, children, adolescents, adults, and older adults depend on adequate nutrition for optimum development and maintenance of health and functioning. Nutrition also plays a significant role in causing or preventing a number of illnesses, such as cardiovascular disease, some cancers, obesity, type-2 diabetes, and anemia. Peoples’ food choices and their likelihood of being overweight or obese are also influenced by their food environment: the foods available in their neighborhoods including stores, restaurants, schools, and worksites.

The modified retail food environment index table is part of a series of indicators in the Healthy Communities Data and Indicators Project (HCI) of the Office of Health Equity. The goal of HCI is to enhance public health by providing data, a standardized set of statistical measures, and tools that a broad array of sectors can use for planning healthy communities and evaluating the impact of plans, projects, policy, and environmental changes on community health. The creation of healthy social, economic, and physical environments that promote healthy behaviors and healthy outcomes requires coordination and collaboration across multiple sectors, including transportation, housing, education, agriculture and others. Statistical metrics, or indicators, are needed to help local, regional, and state public health and partner agencies assess community environments and plan for healthy communities that optimize public health. More information on HCI can be found here: https://www.cdph.ca.gov/Programs/OHE/CDPH%20Document%20Library/Accessible%202%20CDPH_Healthy_Community_Indicators1pager5-16-12.pdf

The format of the modified retail food environment table is based on the standardized data format for all HCI indicators. As a result, this data table contains certain variables used in the HCI project (e.g., indicator ID, and indicator definition). Some of these variables may contain the same value for all observations.

Colombia CO: Prevalence of Overweight: Weight for Height: % of Children Under 5 data was reported at 5.700 % in 2016. This records an increase from the previous number of 4.800 % for 2010. Colombia CO: Prevalence of Overweight: Weight for Height: % of Children Under 5 data is updated yearly, averaging 4.850 % from Dec 1986 (Median) to 2016, with 6 observations. The data reached an all-time high of 5.700 % in 2016 and a record low of 4.200 % in 2005. Colombia CO: Prevalence of Overweight: Weight for Height: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Colombia – Table CO.World Bank.WDI: Social: Health Statistics. Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's 2006 Child Growth Standards.;UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.;See SH.STA.OWGH.ME.ZS for aggregation;Estimates of overweight children are from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues.

Content

The "Global Country Rankings Dataset" is a comprehensive collection of metrics and indicators that ranks countries worldwide based on their socioeconomic performance. This datasets are providing valuable insights into the relative standings of nations in terms of key factors such as GDP per capita, economic growth, and various other relevant criteria.

Researchers, analysts, and policymakers can leverage this dataset to gain a deeper understanding of the global economic landscape and track the progress of countries over time. The dataset covers a wide range of metrics, including but not limited to:

Economic growth: the rate of change of real GDP- Country rankings: The average for 2021 based on 184 countries was 5.26 percent.The highest value was in the Maldives: 41.75 percent and the lowest value was in Afghanistan: -20.74 percent. The indicator is available from 1961 to 2021.

GDP per capita, Purchasing Power Parity - Country rankings: The average for 2021 based on 182 countries was 21283.21 U.S. dollars.The highest value was in Luxembourg: 115683.49 U.S. dollars and the lowest value was in Burundi: 705.03 U.S. dollars. The indicator is available from 1990 to 2021.

GDP per capita, current U.S. dollars - Country rankings: The average for 2021 based on 186 countries was 17937.03 U.S. dollars.The highest value was in Monaco: 234315.45 U.S. dollars and the lowest value was in Burundi: 221.48 U.S. dollars. The indicator is available from 1960 to 2021.

GDP per capita, constant 2010 dollars - Country rankings: The average for 2021 based on 184 countries was 15605.8 U.S. dollars.The highest value was in Monaco: 204190.16 U.S. dollars and the lowest value was in Burundi: 261.02 U.S. dollars. The indicator is available from 1960 to 2021.

source: https://www.theglobaleconomy.com/

The dataset includes information on earthquakes that occurred in February of 2023, as well as historical earthquakes that took place in 2020 in Izmir and 2011 in Van. The inclusion of data on earthquakes from different years allows for analysis of trends and patterns in seismic activity in Turkey, and provides valuable context for understanding the intensity and impact of earthquakes that occur in the future.

The historical earthquakes can be used as a benchmark for comparing the magnitude and impact of the more recent earthquakes in the dataset. Researchers can use this information to gain insights into how seismic activity has changed over time, and to identify areas that are particularly prone to earthquakes.

In addition to information on the earthquakes themselves, the dataset also includes demographic data on the populations affected by the 6 February 2023 earthquakes. The number of schools, teachers, and students in the affected areas can help identify populations that are particularly vulnerable to the effects of earthquakes, and can inform disaster preparedness and response efforts.

Overall, the dataset provides a valuable resource for researchers, policymakers, and disaster response teams who are interested in understanding the impact of earthquakes on communities in Turkey.

In this dataset: .csv that starts with h means High Schools. .csv that starts with e means Elementary schoolSchools. .csv that starts with m means Middle Schools. .csv that starts with p means Primary Schools.

The education-related columns in the dataset are labelled in Turkish. Here are the English translations and explanations for these column names:

Şehir: City Resmi: State School Özel: Private School Toplam: Summation R Erkek: State School and Male R Kadın: State School and Female R Toplam: State School Summation Ö Erkek: Private School and Male Ö Kadın: Private School and Female Ö Toplam: Private School and Summation Okul Türü: School Types Din Öğretimi: Faith-Based Schools Mesleki ve Teknik Ortaöğretim: Vocational and Technical Education Schools Genel Ortaöğretim: General Schools

There is a dataset called "ilcelervekoordinatlar" which contains Turkish words. Here are the English translations and meanings of these words: İlçeler: Districts Kayıtlı Nüfus: Population

The data has been scraped from open sources and is freely available to use. You can access the data and related links through the sources provided.

For earthquakes: https://deprem.afad.gov.tr/event-catalog For any data about population and economic numbers: https://www.tuik.gov.tr/ For datas about education: https://istatistik.meb.gov.tr/

We lost more than 50,000 people in what has been the biggest disaster for Turkey since its establishment. If you would like to donate to support the affected communities, please find below a list of websites where you can make a donation:

https://ahbap.org/disasters-turkey https://en.afad.gov.tr/earthquake-humanitarian-aid-campaign

Note:I will be updating this dataset with satellite images, tweets, and other relevant information regarding the disaster.

Gut microbiota has been shown to have an important influence on host health. The microbial composition of the human gut microbiota is modulated by diet and other lifestyle habits and it has been reported that microbial diversity is altered in obese people. Obesity is a worldwide health problem that negatively impacts the quality of life. Currently, the widespread treatment for obesity is bariatric surgery. Interestingly, gut microbiota has been shown to be a relevant factor in effective weight loss after bariatric surgery. Since that the human gut microbiota of normal subjects differs between geographic regions, it is possible that rearrangements of the gut microbiota in dysbiosis context are also region-specific. To better understand how gut microbiota contribute to obesity, this study compared the composition of the human gut microbiota of obese and lean people from six different regions and showed that the microbiota compositions in the context of obesity were specific to each studied geographic location. Furthermore, we analyzed the functional patterns using shotgun DNA metagenomic sequencing and compared the results with other obesity-related metagenomic studies, we observed that microbial contribution to functional pathways were country-specific. Nevertheless, our study showed that although microbial composition of obese patients was country-specific, the overall metabolic functions appeared to be the same between countries, indicating that different microbiota components contribute to similar metabolic outcomes to yield functional redundancy. Furthermore, we studied the microbiota functional changes of obese patients after bariatric surgery, by shotgun metagenomics sequencing and observed that changes in functional pathways were specific to the type of obesity treatment. In all, our study provides new insights into the differences and similarities of obese gut microbiota in relation to geographic location and obesity treatments.

About this dataset

Context

The dataset tabulates the East Rochester population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of East Rochester across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of East Rochester was 6,158, a 0.76% decrease year-by-year from 2022. Previously, in 2022, East Rochester population was 6,205, a decline of 1.04% compared to a population of 6,270 in 2021. Over the last 20 plus years, between 2000 and 2023, population of East Rochester decreased by 500. In this period, the peak population was 6,711 in the year 2012. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the East Rochester is shown in this column.
• Year on Year Change: This column displays the change in East Rochester population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. 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 East Rochester Population by Year. You can refer the same here