This dataset, titled "Health and Lifestyle Survey Data," contains a collection of personal health records and demographic information from a survey of individuals. The data is structured with 24 distinct columns, providing insights into various aspects of the respondents' lives, including their personal habits, health conditions, and preferences.

The dataset includes the following key categories of information:

Demographics: Includes basic information such as Name, Gender, Age Group, Profession, and Region/Locality.

Lifestyle and Habits: Features data on Smoking Habit, Alcohol Consumption, Physical Activity Level, Fast Food Consumption Frequency, Sleep Duration, Sleep Issues, Diet Type, and Water Intake per Day.

Health and Wellness: Details Current Health Conditions, Common Regional Health Concerns, Access to Clean Water & Sanitation, Pollution Exposure Area, Mental Health Frequency, Healthcare Access Method, and Symptoms.

Other Information: Includes Preferred Language for Chatbot and Preferred Platform, which can be used to understand communication preferences. The Blood group and Particular disease fields provide specific medical information.

This dataset is suitable for academic research in public health, sociology, and data analysis. It can be used to study the correlations between lifestyle choices and health outcomes, identify prevalent health issues in specific regions, and analyze healthcare access patterns. The data is presented in a clear, organized format, making it easy for researchers to perform various statistical analyses and generate new insights into community health trends.

This statistic illustrates the results of a survey on individuals following a healthy lifestyle in Sweden in 2018, by habit. According to data provided by Ipsos, a majority of roughly ** percent stated to be in a good health. The most popular healthy habits among Swedes were about sleep quality and nutrition, as ** percent of respondents declared to eat a healthy diet and ** percent of them got enough sleep. On the other hand, they were not as scrupulous about their training conditions as ** percent of interviewees stated to get enough exercise.

Welcome to the 'Healthy Lifestyle' dataset! This collection offers a comprehensive look into factors influencing individual health and well-being. Dive into a rich array of features including specific ailments, food preferences, age, BMI, smoking habits, living environment, hereditary conditions, and adherence to diets. Explore correlations and predictive insights to unlock the secrets of healthy living. Whether you're a data enthusiast, researcher, or health enthusiast, this dataset provides valuable insights for understanding and promoting wellness.

📘 Overview

The Diabetes Health Indicators Dataset provides a rich and realistic representation of patient health data designed for diabetes risk prediction, healthcare analytics, and machine learning experimentation. It is fully preprocessed, consistent, and aligned with medically validated patterns, ensuring reliability for both research and applied modeling.

This dataset integrates multiple health dimensions—demographic, lifestyle, and clinical—to enable robust data-driven insights into diabetes progression and prevention.

🧬 Dataset Description

Each record in this dataset reflects an individual’s health profile, combining demographic attributes, lifestyle behaviors, family medical background, and physiological measurements. The variables simulate realistic medical distributions derived from public health research, maintaining privacy while preserving analytical validity.

The data is suitable for use in:

Predictive modeling (classification or regression)

Exploratory data analysis (EDA)

Hypothesis testing

Health trend visualization

📊 Feature Categories 👨‍👩‍👧 Demographics

Includes age, gender, ethnicity, education level, income category, and employment type — essential for understanding population health disparities.

💪 Lifestyle Indicators

Captures habits such as smoking, alcohol consumption, diet quality, sleep patterns, and physical activity — crucial for preventive health modeling.

🧠 Medical History

Accounts for genetic predisposition and prior conditions such as hypertension or cardiovascular disease, enhancing model interpretability.

❤️ Clinical Measurements

Covers vital and biochemical markers, including body mass index (BMI), blood pressure, cholesterol levels, triglycerides, fasting/post-meal glucose, insulin, and HbA1c metrics.

🎯 Target Variables

Provides both binary and multiclass targets for predicting diabetes diagnosis and stage, supporting diverse modeling approaches.

✅ Data Quality Assurance

Complete & Clean: No missing or duplicate entries.

Medically Realistic: Values fall within validated clinical ranges.

Balanced Distribution: Reflects realistic yet model-friendly patterns.

ML Ready: Ideal for direct integration into predictive workflows.

💡 Potential Use Cases

🩹 Binary Classification: Predict whether a patient has diabetes.

⚕️ Multiclass Prediction: Determine diabetes stage (e.g., Pre-Diabetes, Type 1, Type 2).

📈 Regression Modeling: Estimate glucose, HbA1c, or overall risk scores.

🧩 Exploratory Analysis: Discover relationships between lifestyle and clinical indicators.

🤖 Machine Learning Research: Develop, benchmark, and validate healthcare prediction models.

📉 Statistical Testing: Analyze the significance of lifestyle or demographic risk factors.

📂 File Information

Format: CSV (comma-separated)

Structure: One record per patient

Content: Demographic, lifestyle, medical, and target variables

🔍 Attribution

This dataset was generated using statistically inspired methods based on clinical and public health literature. All entries are synthetic, ensuring privacy protection while maintaining realistic distributions suitable for healthcare AI applications.

For more information see here

In 2023, more than half the population of over 85 years of age in spain led a sedentary lifestyle. Over 56 percent of men and over 73 percent of women in this age group didn't practice physical activity in their free time. Accross all age groups, men led a generally less sedentary lifestyle.

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Dataset Overview: The Sleep Health and Lifestyle Dataset comprises 400 rows and 13 columns, covering a wide range of variables related to sleep and daily habits. It includes details such as gender, age, occupation, sleep duration, quality of sleep, physical activity level, stress levels, BMI category, blood pressure, heart rate, daily steps, and the presence or absence of sleep disorders.

Key Features of the Dataset: Comprehensive Sleep Metrics: Explore sleep duration, quality, and factors influencing sleep patterns. Lifestyle Factors: Analyze physical activity levels, stress levels, and BMI categories. Cardiovascular Health: Examine blood pressure and heart rate measurements. Sleep Disorder Analysis: Identify the occurrence of sleep disorders such as Insomnia and Sleep Apnea.

Dataset Columns: Person ID: An identifier for each individual. Gender: The gender of the person (Male/Female). Age: The age of the person in years. Occupation: The occupation or profession of the person. Sleep Duration (hours): The number of hours the person sleeps per day. Quality of Sleep (scale: 1-10): A subjective rating of the quality of sleep, ranging from 1 to 10. Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily. Stress Level (scale: 1-10): A subjective rating of the stress level experienced by the person, ranging from 1 to 10. BMI Category: The BMI category of the person (e.g., Underweight, Normal, Overweight). Blood Pressure (systolic/diastolic): The blood pressure measurement of the person, indicated as systolic pressure over diastolic pressure. Heart Rate (bpm): The resting heart rate of the person in beats per minute. Daily Steps: The number of steps the person takes per day. Sleep Disorder: The presence or absence of a sleep disorder in the person (None, Insomnia, Sleep Apnea).

Details about Sleep Disorder Column:

None: The individual does not exhibit any specific sleep disorder. Insomnia: The individual experiences difficulty falling asleep or staying asleep, leading to inadequate or poor-quality sleep. Sleep Apnea: The individual suffers from pauses in breathing during sleep, resulting in disrupted sleep patterns and potential health risks.

Around **** million people in Germany were especially interested in healthy eating and a healthy lifestyle as of 2024, in a population of roughly ** million. Figures for this category did not change noticeably during the timeline shown on the graph. In fact, the distribution between the different answers to the corresponding survey remained similar. The Allensbach Market and Advertising Media Analysis (Allensbacher Markt- und Werbeträgeranalyse or AWA in German) determines attitudes, consumer habits and media usage of the population in Germany on a broad statistical basis. Food, drink and cigarettes Despite the availability of constantly expanding information on what consumers can do to stay healthy, everyone’s understanding of this still differs. Personal preferences and circumstances play a role in decision-making and motivation. While most people may indeed want to eat healthy and lead the accompanying lifestyle, they cannot always afford to, literally. Rising food prices in recent years have put a strain on households, with product categories across the board recording significant increases. 2024 saw saw relief, however. To make an example of foods typically associated with healthy eating, vegetable prices decreased by around *** percent, while fruit prices grew by roughly *** percent. All the same, a recent survey on health-conscious behavior revealed encouraging results. Around ** percent of respondents stated they did not smoke, and ** percent did not drink excessively. Making a move With movement and exercise being vital parts of leading a healthy lifestyle, it is interesting to see which types of sports Germans prefer. Based on a survey published in 2024, fitness studios had around **** million members and were in the lead. Other leading types of physical activity pursued included soccer, gymnastics and tennis. These are all activities that require additional spending, as they usually include going to a particular venue and using specific equipment, as well as working with a trainer. There are, of course, “free” types of exercise that contribute positively to leading a healthy life, such as walking and cycling. Both can be a regular part of daily routines and commutes, without extra planning. Especially when it comes to shorter distances, cycling to work, school or university is a popular alternative to using the car or public transport for many Germans.

📘 Description:

This synthetic health dataset simulates real-world lifestyle and wellness data for individuals. It is designed to help data scientists, machine learning engineers, and students build and test health risk prediction models safely — without using sensitive medical data.

The dataset includes features such as age, weight, height, exercise habits, sleep hours, sugar intake, smoking, alcohol consumption, marital status, and profession, along with a synthetic health_risk label generated using a heuristic rule-based algorithm that mimics realistic risk behavior patterns.

🧾 Columns Description:

🧩 Use Cases:

1. Health Risk Prediction: Train classification models (Logistic Regression, RandomForest, XGBoost, CatBoost) to predict health risk (low / high).

2. Feature Importance Analysis: Identify which lifestyle factors most influence health risk.

3. Data Preprocessing & EDA Practice: Use this dataset for data cleaning, encoding, and visualization practice.

4. Model Explainability Projects: Use SHAP or LIME to explain how different lifestyle habits affect predictions.

5. Streamlit or Flask Web App Development: Build a real-time web app that predicts health risk from user input.

💡 Case Study Example:

Imagine you are a data scientist building a Health Risk Prediction App for a wellness startup. You want to analyze how exercise, sleep, and sugar intake affect overall health risk. This dataset helps you simulate those relationships without handling sensitive medical data.

You could:

• Perform EDA to find correlations between age, BMI, and health risk.
• Train a model using Random Forest to predict health_risk.
• Deploy a Streamlit app where users can input their lifestyle information and get a risk score instantly.

⚙️ Technical Information:

• Rows: 5,000 (adjustable, you can create more)
• Columns: 12
• Target variable: health_risk
• Data type: Mixed (Numeric + Categorical)
• Source: Fully synthetic, generated using Python (NumPy, Faker)

📈 License:

CC0: Public Domain You are free to use this dataset for research, learning, or commercial projects.

🌍 Author:

Created by Arif Miah Machine Learning Engineer | Kaggle Expert | Data Scientist 📧 arifmiahcse@gmail.com

This table provides an overview of the key figures on health and care available on StatLine. All figures are taken from other tables on StatLine, either directly or through a simple conversion. In the original tables, breakdowns by characteristics of individuals or other variables are possible. The period after the year of review before data become available differs between the data series. The number of exam passes/graduates in year t is the number of persons who obtained a diploma in school/study year starting in t-1 and ending in t.

Data available from: 2001

Status of the figures:

2024: Most available figures are definite. Figures are provisional for: - causes of death; - youth care; - persons employed in health and welfare; - persons employed in healthcare; - Mbo health care graduates; - Hbo nursing graduates / medicine graduates (university).

2023: Most available figures are definite. Figures are provisional for: - perinatal mortality at pregnancy duration at least 24 weeks; - diagnoses known to the general practitioner; - hospital admissions by some diagnoses; - average period of hospitalisation; - supplied drugs; - AWBZ/Wlz-funded long term care; - physicians and nurses employed in care; - persons employed in health and welfare; - average distance to facilities; - profitability and operating results at institutions. Figures are revised provisional for: - expenditures on health and welfare.

2022: Most available figures are definite. Figures are revised provisional for: - expenditures on health and welfare.

2021: Most available figures are definite, Figures are revised provisional for: - expenditures on health and welfare.f

2020 and earlier: All available figures are definite.

Changes as of 4 July 2025: More recent figures have been added for: - causes of death; - life expectancy; - life expectancy in perceived good health; - self-perceived health; - hospital admissions by some diagnoses; - sickness absence; - average period of hospitalisation; - contacts with health professionals; - youth care; - smoking, heavy drinkers, physical activity; - overweight; - high blood pressure; - physicians and nurses employed in care; - persons employed in health and welfare; - persons employed in healthcare; - Mbo health care graduates; - Hbo nursing graduates / medicine graduates (university); - expenditures on health and welfare; - profitability and operating results at institutions.

Changes as of 18 december 2024: - Distance to facilities: the figures withdrawn on 5 June have been replaced (unchanged). - Youth care: the previously published final results for 2021 and 2022 have been adjusted due to improvements in the processing. - Due to a revision of the statistics Expenditure on health and welfare 2021, figures for expenditure on health and welfare care have been replaced from 2021 onwards. - Due to the revision of the National Accounts, the figures on persons employed in health and welfare have been replaced for all years. - AWBZ/Wlz-funded long term care: from 2015, the series Wlz residential care including total package at home has been replaced by total Wlz care. This series fits better with the chosen demarcation of indications for Wlz care.

When will new figures be published? New figures will be published in December 2025.

This statistic displays the interest in a healthy lifestyle among younger people in the Netherlands in 2017. When asked to what extent they are conscious about a healthy lifestyle, half of the respondents taking part in this survey stated to be very much concerned about living a healthy life.

This statistical report presents a range of information on obesity, physical activity and diet, drawn together from a variety of sources. The topics covered include: Overweight and obesity prevalence among adults and children Physical activity levels among adults and children Trends in purchases and consumption of food and drink and energy intake Health outcomes of being overweight or obese. This report contains seven chapters which consist of the following: Chapter 1: Introduction; this summarises government policies, targets and outcome indicators in this area, as well as providing sources of further information and links to relevant documents. Chapters 2 to 6 cover obesity, physical activity and diet and provides an overview of the key findings from these sources, whilst maintaining useful links to each section of these reports. Chapter 7: Health Outcomes; presents a range of information about the health outcomes of being obese or overweight which includes information on health risks, hospital admissions and prescription drugs used for treatment of obesity. Figures presented in Chapter 7 have been obtained from a number of sources and presented in a user-friendly format. Some of the data contained in the chapter have been published previously by the NHS Information Centre (NHS IC) or the National Audit Office. Previously unpublished figures on obesity-related Finished Hospital Episodes and Finished Consultant Episodes for 2010/11 are presented using data from the NHS IC's Hospital Episode Statistics as well as data from the Prescribing Unit at the NHS IC on prescription items dispensed for treatment of obesity.

The NHS Information Centre for health and social care commissioned the National Centre for Social Research to produce model-based estimates of healthy lifestyle behaviours, using information from the Health Survey for England (HSfE). These estimates were produced to help meet users' requirements for more up-to-date information at the local area level, and will be published on the Neighbourhood Statistics (NeSS) website which is managed by the Office for National Statistics (ONS) on Wednesday 14 May 2008. Please visit http://www.neighbourhood.statistics.gov.uk to get access to the model based estimates of healthy lifestyle behaviours. The instructions to get to the NeSS Healthy Lifestyle Behaviours: Model Based Estimates, 2003-2005 are as follows: go to the NeSS Home Page - http://www.neighbourhood.statistics.gov.uk click on I want to 'view or download data by topic', click on Health and Care under 'Neighbourhood Statistics' topics. after this select the Healthy Lifestyle Behaviours: Model Based Estimates, 2003-2005 dataset. A model-based approach to producing healthy lifestyle prevalence estimates for each Primary care Organisation (PCO) in England was used because the sample size of national surveys such as the Health Survey for England was too small to provide reliable estimates at a small area level. Model-based estimates and 95% confidence intervals have been produced using 2003-2005 data from the Health Survey for England covering the prevalence of the following healthy lifestyle indicators for adults aged 16 or over: smoking among adults binge drinking for adults obesity among adults consumption of 5 or more portions of fruit and vegetables a day among adults Model-based estimates for children's fruit and vegetable consumption have not been released at PCO level. A relative lack of precision (shown by the wide confidence intervals) indicated a poor fit of the models to the data. The 2003-2005 estimates are the second set of model-based healthy lifestyle prevalence estimates to be published on NeSS. Differences in geographical boundaries, modeling methodologies and data sources, however, mean that they are not comparable to the preceding estimates for 2000-2002. Minority ethnic group (MEG) direct estimates and 95% confidence intervals have also been produced at sub national level in England using the 2004 HSE (including the ethnic boost sample) and cover smoking, binge drinking, obesity and fruit and vegetable consumption amongst adults. These estimates have not involved any statistical modelling, hence are different from model based estimates.

This dataset provides 'synthetic estimates' and confidence intervals for a range of healthy lifestyle variables. The variables are: 1) Current smoking among adults (aged 16 or over). 2) Binge drinking for adults (aged 16 or over). 3) Obesity among adults (aged 16 or over). 4) Consumption of 5 or more portions of fruit and vegetables a day among adults (aged 16 or over). Source: The Information Centre for health and social care (IC) Publisher: Neighbourhood Statistics Geographies: Middle Layer Super Output Area (MSOA), Local Authority District (LAD), Primary Care Trust (PCT), Strategic Health Authority (SHA) Geographic coverage: England Time coverage: 2003-2005 Type of data: Modelled data Guidelines: For reference purposes each MSOA/LA is compared to the direct (i.e. non model-based) estimate for England by examining whether its 95% confidence interval overlaps with the confidence interval for the Health Survey for England national estimate (NE). A score of 1 indicates that the MSOA/LA estimate is significantly lower than the national estimate; 2 implies no significant difference and 3 indicates an estimate that is significantly higher.

Note 09/05/2013 A presentation error has been identified in the data in tables 7.1 and 7.2 originally included in this publication. The tables have been republished with corrected figures. The accompanying errata note provides more detail. The Health and Social Care Information Centre apologise for any inconvenience this may have caused. Summary: This statistical report presents a range of information on obesity, physical activity and diet, drawn together from a variety of sources. The topics covered include: Overweight and obesity prevalence among adults and children Physical activity levels among adults and children Trends in purchases and consumption of food and drink and energy intake Health outcomes of being overweight or obese. This report contains seven chapters which consist of the following: Chapter 1: Introduction; this summarises government policies, targets and outcome indicators in this area, as well as providing sources of further information and links to relevant documents. Chapters 2 to 6 cover obesity, physical activity and diet and provides an overview of the key findings from these sources, whilst maintaining useful links to each section of these reports. Chapter 7: Health Outcomes; presents a range of information about the health outcomes of being obese or overweight which includes information on health risks, hospital admissions and prescription drugs used for treatment of obesity. Figures presented in Chapter 7 have been obtained from a number of sources and presented in a user-friendly format. Some of the data contained in the chapter have been published previously by the Health and Social Care Information Centre (HSCIC) or the National Audit Office. Previously unpublished figures on obesity-related Finished Hospital Episodes and Finished Consultant Episodes for 2011/12 are presented using data from the HSCIC's Hospital Episode Statistics as well as data from the Prescribing Unit at the HSCIC on prescription items dispensed for treatment of obesity.

ABSTRACT Introduction Outdoor sports can help people develop good living habits and improve people’s physical fitness. For this reason, it is very important to cultivate sports hobbies and analyze the factors of healthy sports. Objective To understand the factors that affect the healthy sports behavior of college students, we provide a reference for the relevant departments of the school and physical education teachers. Methods The thesis uses literature data method, questionnaire survey method and mathematical statistics method to analyze sports influencing factors with college students as the research object. Results The physical education method and the completeness of the facilities will affect the students’ interest in sports. Students from different family backgrounds have very different preferences for healthy sports. Conclusions The school environment and sports atmosphere are the main factors that constitute the school sports environment. College students’ cognition and understanding of healthy sports will affect their own sports situation. Level of evidence II; Therapeutic studies - investigation of treatment results.

This report presents information on obesity, physical activity and diet drawn together from a variety of sources for England. More information can be found in the source publications which contain a wider range of data and analysis. Each section provides an overview of key findings, as well as providing links to relevant documents and sources. Some of the data have been published previously by NHS Digital. A data visualisation tool (link provided within the key facts) allows users to select obesity related hospital admissions data for any Local Authority (as contained in the data tables), along with time series data from 2013/14. Regional and national comparisons are also provided. The report includes information on: Obesity related hospital admissions, including obesity related bariatric surgery. Obesity prevalence. Physical activity levels. Walking and cycling rates. Prescriptions items for the treatment of obesity. Perception of weight and weight management. Food and drink purchases and expenditure. Fruit and vegetable consumption. Key facts cover the latest year of data available: Hospital admissions: 2018/19 Adult obesity: 2018 Childhood obesity: 2018/19 Adult physical activity: 12 months to November 2019 Children and young people's physical activity: 2018/19 academic year

Between 2022 and 2023, around **** percent of adults in Italy were found to live a sedentary lifestyle. A sedentary lifestyle is here defined as not doing any physical activity in leisure time and not having a job, or having a sedentary job, or a job that even if it requires physical effort is not continuous over time. This statistic highlights regional differences in this figure. According to the data, this kind of lifestyle was more common among people living in Southern regions than among individuals from the rest of Italy. The autonomous province of Bolzano stood out among other regions, with only *** percent of its residents declaring to be sedentary. This statistic shows the share of individuals living a sedentary lifestyle in Italy between 2022 and 2023, by region.

Health Poverty Index - Intervening Factors: Lifestyle Source: Department of Health (DoH) Publisher: Health Poverty Index Geographies: Local Authority District (LAD), National Geographic coverage: England Time coverage: 2001 Type of data: Modelled data Notes: Information supplied by Health Survey for England 2001, General Household Survey 2000-2001, Omnibus Survey 2001, Joint Survey Unit of the National Centre for Social Research and the Department of Epidemiology and Public Health, Hospital Episode Statistics (HES) 1998/99-2001/02

Dataset Overview:The Sleep Health and Lifestyle Dataset comprises 400 rows and 13 columns, covering a wide range of variables related to sleep and daily habits. It includes details such as gender, age, occupation, sleep duration, quality of sleep, physical activity level, stress levels, BMI category, blood pressure, heart rate, daily steps, and the presence or absence of sleep disorders.Key Features of the Dataset:Comprehensive Sleep Metrics: Explore sleep duration, quality, and factors influencing sleep patterns.Lifestyle Factors: Analyze physical activity levels, stress levels, and BMI categories.Cardiovascular Health: Examine blood pressure and heart rate measurements.Sleep Disorder Analysis: Identify the occurrence of sleep disorders such as Insomnia and Sleep Apnea.Dataset Columns:Person ID: An identifier for each individual.Gender: The gender of the person (Male/Female).Age: The age of the person in years.Occupation: The occupation or profession of the person.Sleep Duration (hours): The number of hours the person sleeps per day.Quality of Sleep (scale: 1-10): A subjective rating of the quality of sleep, ranging from 1 to 10.Physical Activity Level (minutes/day): The number of minutes the person engages in physical activity daily.Stress Level (scale: 1-10): A subjective rating of the stress level experienced by the person, ranging from 1 to 10.BMI Category: The BMI category of the person (e.g., Underweight, Normal, Overweight).Blood Pressure (systolic/diastolic): The blood pressure measurement of the person, indicated as systolic pressure over diastolic pressure.Heart Rate (bpm): The resting heart rate of the person in beats per minute.Daily Steps: The number of steps the person takes per day.Sleep Disorder: The presence or absence of a sleep disorder in the person (None, Insomnia, Sleep Apnea).Details about Sleep Disorder Column:None: The individual does not exhibit any specific sleep disorder.Insomnia: The individual experiences difficulty falling asleep or staying asleep, leading to inadequate or poor-quality sleep.Sleep Apnea: The individual suffers from pauses in breathing during sleep, resulting in disrupted sleep patterns and potential health risks.

Range of modelled indicators of health lifestyle choices. These estimates must be used with caution. They will almost certainly not mirror precisely any available measures from local studies or surveys (although research by NatCen and others have shown that they tend to be related). 1) Current smoking among adults (aged 16 or over). Current smokers were defined in the HSfE if the respondent reported that they were a 'current cigarette smoker'. 2) Binge drinking for adults (aged 16 or over). Adult respondents to the HSfE were defined to be binge drinkers if they reported that in the last week they had drunk 8 or more units of alcohol if they were a man, or 6 or more units of alcohol if they were a woman, on any one day or more. 3) Obesity among adults (aged 16 or over). Adult respondents to the HSfE were defined to be obese if they were recorded as having a body mass index (BMI) of 30 or above. 4) Consumption of 5 or more portions of fruit and vegetables a day among adults (aged 16 or over). They had reported that they had consumed 5 or more portions of fruit and vegetables on the previous day Modelled data for Obesity, Binge drinking, Smoking and Fruit and Vegetable consumption is available for MSOAs from the HNA website. Modelled sports participation data for LSOAs is also available from the same site. Neighbourhood Statistics are also available from the Office for National Statistics here.