The survey dataset for identifying Shiraz old silo’s new use which includes four components: 1. The survey instrument used to collect the data “SurveyInstrument_table.pdf”. The survey instrument contains 18 main closed-ended questions in a table format. Two of these, concern information on Silo’s decision-makers and proposed new use followed up after a short introduction of the questionnaire, and others 16 (each can identify 3 variables) are related to the level of appropriate opinions for ideal intervention in Façade, Openings, Materials and Floor heights of the building in four values: Feasibility, Reversibility, Compatibility and Social Benefits. 2. The raw survey data “SurveyData.rar”. This file contains an Excel.xlsx and a SPSS.sav file. The survey data file contains 50 variables (12 for each of the four values separated by colour) and data from each of the 632 respondents. Answering each question in the survey was mandatory, therefor there are no blanks or non-responses in the dataset. In the .sav file, all variables were assigned with numeric type and nominal measurement level. More details about each variable can be found in the Variable View tab of this file. Additional variables were created by grouping or consolidating categories within each survey question for simpler analysis. These variables are listed in the last columns of the .xlsx file. 3. The analysed survey data “AnalysedData.rar”. This file contains 6 “SPSS Statistics Output Documents” which demonstrate statistical tests and analysis such as mean, correlation, automatic linear regression, reliability, frequencies, and descriptives. 4. The codebook “Codebook.rar”. The detailed SPSS “Codebook.pdf” alongside the simplified codebook as “VariableInformation_table.pdf” provides a comprehensive guide to all 50 variables in the survey data, including numerical codes for survey questions and response options. They serve as valuable resources for understanding the dataset, presenting dictionary information, and providing descriptive statistics, such as counts and percentages for categorical variables.

We used individual-level death data to estimate county-level life expectancy at 25 (e25) for Whites, Black, AIAN and Asian in the contiguous US for 2000-2005. Race-sex-stratified models were used to examine the associations among e25, rurality and specific race proportion, adjusted for socioeconomic variables. Individual death data from the National Center for Health Statistics were aggregated as death counts into five-year age groups by county and race-sex groups for the contiguous US for years 2000-2005 (National Center for Health Statistics 2000-2005). We used bridged-race population estimates to calculate five-year mortality rates. The bridged population data mapped 31 race categories, as specified in the 1997 Office of Management and Budget standards for the collection of data on race and ethnicity, to the four race categories specified under the 1977 standards (the same as race categories in mortality registration) (Ingram et al. 2003). The urban-rural gradient was represented by the 2003 Rural Urban Continuum Codes (RUCC), which distinguished metropolitan counties by population size, and nonmetropolitan counties by degree of urbanization and adjacency to a metro area (United States Department of Agriculture 2016). We obtained county-level sociodemographic data for 2000-2005 from the US Census Bureau. These included median household income, percent of population attaining greater than high school education (high school%), and percent of county occupied rental units (rent%). We obtained county violent crime from Uniform Crime Reports and used it to calculate mean number of violent crimes per capita (Federal Bureau of Investigation 2010). This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Request to author. Format: Data are stored as csv files. This dataset is associated with the following publication: Jian, Y., L. Neas, L. Messer, C. Gray, J. Jagai, K. Rappazzo, and D. Lobdell. Divergent trends in life expectancy across the rural-urban gradient among races in the contiguous United States. International Journal of Public Health. Springer Basel AG, Basel, SWITZERLAND, 64(9): 1367-1374, (2019).

This submission includes publicly available data extracted in its original form. Please reference the Related Publication listed here for source and citation information: https://catalog.data.gov/dataset/smart-location-database7 If you have questions about the underlying data stored here, please contact Thomas John (thomas.john@epa.gov). If you have questions or recommendations related to this metadata entry and extracted data, please contact the CAFE Data Management team at: climatecafe@bu.edu. "The Smart Location Database is a nationwide geographic data resource for measuring location efficiency. It includes more than 90 attributes summarizing characteristics, such as housing density, diversity of land use, neighborhood design, destination accessibility, transit service, employment and demographics. Most attributes are available for every census block group in the United States. A large body of research has demonstrated that land use and urban form can have a significant effect on transportation outcomes. People who live and/or work in compact neighborhoods with a walkable street grid and easy access to public transit, jobs, stores, and services are more likely to have several transportation options to meet their everyday needs. As a result, they can choose to drive less, which reduces their emissions of greenhouse gases and other pollutants compared to people who live and work in places that are not location efficient. Walking, biking, and taking public transit can also save people money and improve their health by encouraging physical activity. The Smart Location Database summarizes several demographic, employment, and built environment variables for every census block group (CBG) in the United States. The database includes indicators of the commonly cited “D” variables shown in the transportation research literature to be related to travel behavior. The Ds include residential and employment density, land use diversity, design of the built environment, access to destinations, and distance to transit. SLD variables can be used as inputs to travel demand models, baseline data for scenario planning studies, and combined into composite indicators characterizing the relative location efficiency of CBG within U.S. metropolitan regions. EPA first released a beta version of the Smart Location Database in 2011. The initial full version was released in 2013, and the database was updated to its current version in 2021." Quote from https://www.epa.gov/smartgrowth/smart-location-mapping and https://catalog.data.gov/dataset/smart-location-database7

The purpose of this dataset is to enable the replication of the research results presented in the article: Michał Lasota, Aleksandra Zabielska, Marianna Jacyna, Piotr Gołębiowski, Renata Żochowska, Mariusz Wasiak. Method for Delivery Planning in Urban Areas with Environmental Aspects. Sustainability 2024, 16(4), 1571. https://doi.org/10.3390/su16041571 - published online: 2024-02-13, which a method of large-criteria decision-making support was developed in the field of urban supply planning, taking into account the minimization of harmful compound emissions.

Dataset contains:

• Readme.txt: description of the dataset
• InputData.xlsx: Contains the input data in the model. The data is presented in three tables.
• OutputOptimization.xlsx: Contains the output optimization data. The data is prsented in four tables.
• OutputSummary.xlsx: Contains contains the final results of the aggregated variable.

The dataset was created as part of the E-Laas project (Energy optimal urban logistics As A Service).
Project implemented as part of the call ERA-NET Cofund Urban Accessibility and Connectivity (ENUAC China Call) organized by JPI Urban Europe and the National Natural Science Foundation of China (NSFC). This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 875022.
E-Laas project is carried out in an international consortium. Project coordinator in Europe: Chalmers University of Technology (Sweden), project coordinator in China: Shanghai University (China), consortium members: Tsinghua University (China), Warsaw University of Technology (Poland), cooperation partners: Stockholms stad, Trafikkontoret (Sweden), ParkUnload (Spain), Metropolis GZM (Poland), Shanghai Urban-Rural Construction and Transportation Department (China), Volvo Group Trucks Technology and Operations (Sweden).
- The Chinese part of the project is funded by National Natural Science Foundation of China.
- The Swedish part of the project is funded by Swedish Energy Agency.
- The Polish part of the project is funded by the National Science Centre, Poland (project no. 2022/04/Y/ST8/00134). The value of the co-financing is PLN 878,107.00. Project duration 27/04/2023 - 26/04/2026 (36 months).

The database "Census tracts variables data" contains fifteen variables associated with urban inequality. The database "Data_PSL_S-III" contains thirty four variables associated with urban inequality. The data are from the city of Maringá, Paraná, Brazil. The primary source is the Brazilian Institute of Geography and Statistics (IBGE).

This data release contains the input-data files and R scripts associated with the analysis presented in [citation of manuscript]. The spatial extent of the data is the contiguous U.S. The input-data files include one comma separated value (csv) file of county-level data, and one csv file of city-level data. The county-level csv (“county_data.csv”) contains data for 3,109 counties. This data includes two measures of water use, descriptive information about each county, three grouping variables (climate region, urban class, and economic dependency), and contains 18 explanatory variables: proportion of population growth from 2000-2010, fraction of withdrawals from surface water, average daily water yield, mean annual maximum temperature from 1970-2010, 2005-2010 maximum temperature departure from the 40-year maximum, mean annual precipitation from 1970-2010, 2005-2010 mean precipitation departure from the 40-year mean, Gini income disparity index, percent of county population with at least some college education, Cook Partisan Voting Index, housing density, median household income, average number of people per household, median age of structures, percent of renters, percent of single family homes, percent apartments, and a numeric version of urban class. The city-level csv (city_data.csv) contains data for 83 cities. This data includes descriptive information for each city, water-use measures, one grouping variable (climate region), and 6 explanatory variables: type of water bill (increasing block rate, decreasing block rate, or uniform), average price of water bill, number of requirement-oriented water conservation policies, number of rebate-oriented water conservation policies, aridity index, and regional price parity. The R scripts construct fixed-effects and Bayesian Hierarchical regression models. The primary difference between these models relates to how they handle possible clustering in the observations that define unique water-use settings. Fixed-effects models address possible clustering in one of two ways. In a "fully pooled" fixed-effects model, any clustering by group is ignored, and a single, fixed estimate of the coefficient for each covariate is developed using all of the observations. Conversely, in an unpooled fixed-effects model, separate coefficient estimates are developed only using the observations in each group. A hierarchical model provides a compromise between these two extremes. Hierarchical models extend single-level regression to data with a nested structure, whereby the model parameters vary at different levels in the model, including a lower level that describes the actual data and an upper level that influences the values taken by parameters in the lower level. The county-level models were compared using the Watanabe-Akaike information criterion (WAIC) which is derived from the log pointwise predictive density of the models and can be shown to approximate out-of-sample predictive performance. All script files are intended to be used with R statistical software (R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org) and Stan probabilistic modeling software (Stan Development Team. 2017. RStan: the R interface to Stan. R package version 2.16.2. http://mc-stan.org).

25%

The High-resolution Urban Meteorology for Impacts Dataset, HUMID, will be useful for studies examining spatial variability of near surface meteorology and the impacts of urban heat islands across many disciplines including epidemiology, ecology, and climatology. We have explicitly included representation of spatial meteorological variability over urban areas in the contiguous United States (CONUS) as compared to other observation-only gridded meteorology products by employing the High-Resolution Land Data Assimilation System (HRLDAS), which accounts for the fine-scale impacts of spatiotemporally varying land surfaces on weather. Further, we include in situ meteorological observations such as local mesonets to bias correct the HRLDAS output, creating a model-observation fusion product. The data spans 1 January 1981 to 31 December 2018, covering all of CONUS at 1 km grid spacing. The dataset includes daily maximum, minimum, and mean values for a variety of temperature estimates such as 2 m temperature, skin temperature, urban temperatures, as well as specific humidity and surface energy budget terms. The full variable list with corresponding file and variable metadata is in this file [https://rda.ucar.edu/OS/web/datasets/d314008/docs/humid_dataset_readme.pdf].

Using the variable data calculated and processed by the SolVES model, the influence of the natural environmental variables of Mianyang People’s Park and the social value index of ecosystem services is discussed.

Important note: If you get a message that .zip archive is corrupt, please try updating WinRAR or right-click the folder and select Extract All on Windows or use unzip command on Linux terminal. If the issue persists, email: kamath.harsh@utexas.edu

Abstract

We introduce GLObal Building heights for Urban Studies (UT-GLOBUS), a dataset providing building heights and urban canopy parameters (UCPs) for major cities worldwide. UT-GLOBUS combines open-source spaceborne altimetry (ICESat-2 and GEDI) and coarse resolution urban canopy elevation data with a random forest model to estimate building-level information. Validation using LiDAR data from six U.S. cities showed UT-GLOBUS-derived building heights had an RMSE of 9.1 meters, and mean building height within 1-km² grid cells had an RMSE of 7.8 meters. Testing the UCPs in the urban Weather Research and Forecasting (WRF-Urban) model resulted in a significant improvement (~55% in RMSE) in intra-urban air temperature representation compared to the existing table-based local climate zone approach in Houston, TX. Additionally, we demonstrated the dataset's utility for simulating heat mitigation strategies and building energy consumption using WRF-Urban, with test cases in Chicago, IL, and Austin, TX. Street-scale mean radiant temperature simulations using the SOlar and LongWave Environmental Irradiance Geometry (SOLWEIG) model, incorporating UT-GLOBUS and LiDAR-derived building heights, confirmed the dataset’s effectiveness in modeling human thermal comfort at Baltimore, MD (daytime RMSE = 2.85°C). Thus, UT-GLOBUS can be used for modeling urban hazards with significant socioeconomic and ecological risks, enabling finer scale urban climate simulations and overcoming previous limitations due to the lack of building information.

Data

We are also supplying a vector file to represent the data coverage, and this file will receive updates as data for new city is added. Building-level data is accessible in vector file format (GeoPackage: .gpkg), which can be converted into raster file format (geoTIFF). These formats are compatible with the SUEWS and SOLWEIG models for the simulation of urban energy balance and thermal comfort. The vector files employ the Universal Transverse Mercator (UTM) projection. Both the vector and raster files are compatible with GIS platforms like QGIS and ArcGIS and can be imported for analysis using programming languages such as Python. We are also providing UCPs required by the BEP-BEM urban model in the urban WRF system in binary file format. Additionally, we provide the urban fractions calculated using ESA world cover dataset (https://esa-worldcover.org/en) for WRF model in binary file format. These files can be directly incorporated into the WRF pre-processing system (WPS). The UT-GLOBUS UCPs are determined using a moving kernel with a size of 1 km2 and spacing of 300 meters in both the X and Y directions

Data coverage

The 'Coverage_xxxx.gpkg' files provide that geographical extents of cities that are included in our dataset.

How to find your city in the UT-GLOBUS dataset

Open the 'coverage' geopackage (.gpkg) files in QGIS or ArcGIS. Click on the city polygons and get the 'Label'/City name. Find a folder with the same 'Label'/City name. All the data for the periticular city will be in the folder.

How to run BEP-BEM model in WRF using UT-GLOBUS urban canopy parameters

Unstandardized values.

Abstract

The dataset is a relational dataset of 8,000 households households, representing a sample of the population of an imaginary middle-income country. The dataset contains two data files: one with variables at the household level, the other one with variables at the individual level. It includes variables that are typically collected in population censuses (demography, education, occupation, dwelling characteristics, fertility, mortality, and migration) and in household surveys (household expenditure, anthropometric data for children, assets ownership). The data only includes ordinary households (no community households). The dataset was created using REaLTabFormer, a model that leverages deep learning methods. The dataset was created for the purpose of training and simulation and is not intended to be representative of any specific country.

The full-population dataset (with about 10 million individuals) is also distributed as open data.

Geographic coverage

The dataset is a synthetic dataset for an imaginary country. It was created to represent the population of this country by province (equivalent to admin1) and by urban/rural areas of residence.

Analysis unit

Household, Individual

Universe

The dataset is a fully-synthetic dataset representative of the resident population of ordinary households for an imaginary middle-income country.

Kind of data

ssd

Sampling procedure

The sample size was set to 8,000 households. The fixed number of households to be selected from each enumeration area was set to 25. In a first stage, the number of enumeration areas to be selected in each stratum was calculated, proportional to the size of each stratum (stratification by geo_1 and urban/rural). Then 25 households were randomly selected within each enumeration area. The R script used to draw the sample is provided as an external resource.

Mode of data collection

other

Research instrument

The dataset is a synthetic dataset. Although the variables it contains are variables typically collected from sample surveys or population censuses, no questionnaire is available for this dataset. A "fake" questionnaire was however created for the sample dataset extracted from this dataset, to be used as training material.

Cleaning operations

The synthetic data generation process included a set of "validators" (consistency checks, based on which synthetic observation were assessed and rejected/replaced when needed). Also, some post-processing was applied to the data to result in the distributed data files.

Response rate

This is a synthetic dataset; the "response rate" is 100%.

The purpose of this project was to conduct an evaluation of the impact on crime of the closing, renovation, and subsequent reopening of selected public housing developments under the United States Department of Housing and Urban Development's (HUD) Housing Opportunities for People Everywhere (HOPE VI) initiative. The study examined crime displacement and potential diffusion of benefits in and around five public housing developments that, since 2000, had been redeveloped using funds from HUD's HOPE VI initiative and other sources. In Milwaukee, Wisconsin, three sites were selected for inclusion in the study. However, due to substantial overlap between the various target sites and displacement zones, the research team ultimately decided to aggregate the three sites into a single target area. A comparison area was then chosen based on recommendations from the Housing Authority of the City of Milwaukee (HACM). In Washington, DC, two HOPE VI sites were selected for inclusion in the study. Based on recommendations from the District of Columbia Housing Authority (DCHA), the research team selected a comparison site for each of the two target areas. Displacement areas were then drawn as concentric rings ("buffers") around the target areas in both Milwaukee, Wisconsin and Washington, DC. Address-level incident data were collected for the city of Milwaukee from the Milwaukee Police Department for the period January 2002 through February 2010. Incident data included all "Group A" offenses as classified under National Incident Based Reporting System (NIBRS). The research team classified the offenses into personal and property offenses. The offenses were aggregated into monthly counts, yielding 98 months of data (Part 1: Milwaukee, Wisconsin Data). Address-level data were also collected for Washington, DC from the Metropolitan Police Department for the time period January 2000 through September 2009. Incident data included all Part I offenses as classified under the Uniform Crime Report (UCR) system. The data were classified by researchers into personal and property offenses and aggregated by month, yielding 117 months of data (Part 2: Washington, DC Data). Part 1 contains 15 variables, while Part 2 contains a total of 27 variables. Both datasets include variables on the number of personal offenses reported per month, the number of property offenses reported per month, and the total number of incidents reported per month for each target site, buffer zone area (1000 feet or 2000 feet), and comparison site. Month and year indicators are also included in each dataset.

City-level dataset. List of variables considered in the current study for city-level analyses, together with their acronyms, the levels attainable by each variable, and data source.

This dataset contains counts for urban accessibility geography by region for selected variables from the 2018, 2013, and 2006 censuses. Estimated resident populations for 1996–2020 are also included.

Urban accessibility measures the degree of urban influence New Zealand’s urban areas have on surrounding rural areas. It classifies the geographic accessibility of rural statistical area 1s (SA1s) and small urban areas according to their proximity, or degree of remoteness, to larger urban areas. To find out more about the urban accessibility classification see Urban accessibility – methodology and classification.

The urban accessibility categories are:

· major urban area – 100,000 or more residents

· large urban area – 30,000–99,999 residents

· medium urban area – 10,000–29,999 residents

· high urban accessibility – small urban areas (1,000–9,999 residents) and rural SA1s within 0 to 15 minutes from major urban areas

· medium urban accessibility – small urban areas and rural SA1s within: 15 to 25 minutes from major urban areas, 0 to 25 minutes from large urban areas, 0 to 15 minutes from medium urban areas

· low urban accessibility – small urban areas and rural SA2s within: 25 to 60 minutes from major or large urban areas, 15 to 60 minutes from medium urban areas

· remote – small urban areas and rural SA1s within 60 to 120 minutes from major, large, or medium urban areas

· very remote – small urban areas and rural SA1s more than 120 minutes from major, large, or medium urban areas

· water areas – inland water, inlet, oceanic.

The dataset uses geographic boundaries (SA1, urban area, regional council) as at 1 January 2018. For explanation of geographies see Statistical standard for geographic areas 2018.

Included in this dataset:

· estimated resident population at 30 June 1996-2020

· 2006, 2013, and 2018 Census usually resident population and sex

· 2018 Census usually resident: age (10-year groups), median age, ethnic group, birthplace, work and labour force status, status in employment, occupation, industry, highest qualification, sources of personal income, total personal income (grouped), median income, individual home ownership, languages spoken, religious affiliation, main means of travel to work by usual residence address, main means of travel to education by usual residence address, New Zealand Index of deprivation

· 2018 Census dwellings: dwelling type, main types of heating used, dwelling dampness, dwelling mould

· 2018 Census households: tenure of household, access to telecommunication systems; number of motor vehicles.

The data uses fixed random rounding to protect confidentiality. Some counts of less than 6 are suppressed according to 2018 confidentiality rules. Values of ‘-999’ indicate suppressed data.

Medians are calculated from unrounded counts, with input noise added to or subtracted from each contributing value during measures calculation. Medians based on less than six individuals are suppressed.

For further information on this dataset please refer to the 2018 Census urban accessibility dataset** **on the 2018 Census webpage - Excel workbook (including data quality ratings and footnotes).

Dataset Description

This dataset contains a simulated collection of 1,00000 patient records designed to explore hypertension management in resource-constrained settings. It provides comprehensive data for analyzing blood pressure control rates, associated risk factors, and complications. The dataset is ideal for predictive modelling, risk analysis, and treatment optimization, offering insights into demographic, clinical, and treatment-related variables.

Dataset Structure

1. Dataset Volume

   • Size: 10,000 records. • Features: 19 variables, categorized into Sociodemographic, Clinical, Complications, and Treatment/Control groups.

2. Variables and Categories

A. Sociodemographic Variables

1. Age:
•  Continuous variable in years.
•  Range: 18–80 years.
•  Mean ± SD: 49.37 ± 12.81.
2. Sex:
•  Categorical variable.
•  Values: Male, Female.
3. Education:
•  Categorical variable.
•  Values: No Education, Primary, Secondary, Higher Secondary, Graduate, Post-Graduate, Madrasa.
4. Occupation:
•  Categorical variable.
•  Values: Service, Business, Agriculture, Retired, Unemployed, Housewife.
5. Monthly Income:
•  Categorical variable in Bangladeshi Taka.
•  Values: <5000, 5001–10000, 10001–15000, >15000.
6. Residence:
•  Categorical variable.
•  Values: Urban, Sub-urban, Rural.

B. Clinical Variables

7. Systolic BP:
•  Continuous variable in mmHg.
•  Range: 100–200 mmHg.
•  Mean ± SD: 140 ± 15 mmHg.
8. Diastolic BP:
•  Continuous variable in mmHg.
•  Range: 60–120 mmHg.
•  Mean ± SD: 90 ± 10 mmHg.
9. Elevated Creatinine:
•  Binary variable (\geq 1.4 \, \text{mg/dL}).
•  Values: Yes, No.
10. Diabetes Mellitus:
•  Binary variable.
•  Values: Yes, No.
11. Family History of CVD:
•  Binary variable.
•  Values: Yes, No.
12. Elevated Cholesterol:
•  Binary variable (\geq 200 \, \text{mg/dL}).
•  Values: Yes, No.
13. Smoking:
•  Binary variable.
•  Values: Yes, No.

C. Complications

14. LVH (Left Ventricular Hypertrophy):
•  Binary variable (ECG diagnosis).
•  Values: Yes, No.
15. IHD (Ischemic Heart Disease):
•  Binary variable.
•  Values: Yes, No.
16. CVD (Cerebrovascular Disease):
•  Binary variable.
•  Values: Yes, No.
17. Retinopathy:
•  Binary variable.
•  Values: Yes, No.

D. Treatment and Control

18. Treatment:
•  Categorical variable indicating therapy type.
•  Values: Single Drug, Combination Drugs.
19. Control Status:
•  Binary variable.
•  Values: Controlled, Uncontrolled.

Dataset Applications

1. Predictive Modeling:
•  Develop models to predict blood pressure control status using demographic and clinical data.
2. Risk Analysis:
•  Identify significant factors influencing hypertension control and complications.
3. Severity Scoring:
•  Quantify hypertension severity for patient risk stratification.
4. Complications Prediction:
•  Forecast complications like IHD, LVH, and CVD for early intervention.
5. Treatment Guidance:
•  Analyze therapy efficacy to recommend optimal treatment strategies.

Abstract (en): The purpose of this project was to measure and estimate the distribution of personal income and related economic factors in both rural and urban areas of the People's Republic of China. The principal investigators based their definition of income on cash payments and on a broad range of additional components. Data were collected through a series of questionnaire-based interviews conducted in rural and urban areas at the end of 2002. There are ten separate datasets. The first four datasets were derived from the urban questionnaire. The first contains data about individuals living in urban areas. The second contains data about urban households. The third contains individual-level economic variables copied from the initial urban interview form. The fourth contains household-level economic variables copied from the initial urban interview form. The fifth dataset contains village-level data, which was obtained by interviewing village leaders. The sixth contains data about individuals living in rural areas. The seventh contains data about rural households, as well as most of the data from a social network questionnaire which was presented to rural households. The eighth contains the rest of the data from the social network questionnaire and is specifically about the activities of rural school-age children. The ninth dataset contains data about individuals who have migrated from rural to urban areas, and the tenth dataset contains data about rural-urban migrant households. Dataset 1 contains 151 variables and 20,632 cases (individual urban household members). Dataset 2 contains 88 variables and 6,835 cases (urban households). Dataset 3 contains 44 variables and 27,818 cases, at least 6,835 of which are empty cases used to separate households in the file. The remaining cases from dataset 3 match those in dataset 1. Dataset 4 contains 212 variables and 6,835 cases, which match those in dataset 2. Dataset 5 contains 259 variables and 961 cases (villages). Dataset 6 contains 84 variables and 37,969 cases (individual rural household members). Dataset 7 contains 449 variables and 9,200 cases (rural households). Dataset 8 contains 38 variables and 8,121 cases (individual school-age children). Dataset 9 contains 76 variables and 5,327 cases (individual rural-urban migrant household members). Dataset 10 contains 129 variables and 2,000 cases (rural-urban migrant households). The Chinese Household Income Project collected data in 1988, 1995, 2002, and 2007. ICPSR holds data from the first three collections, and information about these can be found on the series description page. Data collected in 2007 are available through the China Institute for Income Distribution. The purpose of this project was to measure and estimate the distribution of personal income in both rural and urban areas of the People's Republic of China. The study was interview-based. Five main questionnaire forms (Urban, Rural, Rural Migrant, Social Network, and Village) were filled in by interviewers at the various locations, based on questions asked of respondents. Individuals were not all interviewed directly; household members were allowed to answer questions on behalf of other members. In addition, interviewers made some direct observations about the households. Respondents in datasets 1-4 and 6-10 were members and heads of households. In dataset 5, respondents were village representatives: for each village, interviewers asked questions of the party branch secretary, the head of the village committee, or the village accountant. Village authorities were encouraged to use existing statistical data where it was available. All datasets contain a wide range of demographic and economic variables, including income, assets, liabilities, and expenditures. Cases are coded such that individuals can be linked to the information about their households and villages in other datasets. Datasets about individuals (datasets 1, 6, and 9) all include demographic variables such as household composition, gender, age, nationality, marital status, party membership, educational history, and health information. Dataset 1 is about individuals living in urban areas. It contains standard demographic variables as well as economic variables such as medical insurance and expenditures, economically productive social contacts, and employment information including occupation, sector, income, hours, conditions, job history, and training. Dataset 2 is about households in urban areas...

With the recent additions of gender identity and gender expression to the Canadian Human Rights Act and the Criminal Code as well as some sources of administrative data changing from sex to gender, it is necessary to distinguish the concepts of sex and gender within the National Statistical System. In response, Statistics Canada has released a revised variable, 'sex of person', a new variable, 'gender of person' and their related classifications.

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GapMinder collects data from a handful of sources, including the Institute for Health Metrics and Evaluation, the US Census Bureau’s International Database, the United Nations Statistics Division, and the World Bank.

Variable Name & Description of Indicator:

• country: Unique Identifier
• incomeperperson: Gross Domestic Product per capita in constant 2000 US$. The inflation but not the differences in the cost of living between countries has been taken into account.
• Internetuserate: Internet users (per 100 people) Internet users are people with access to the worldwide network.
• urbanrate: Urban population (% of total) Urban population refers to people living in urban areas as defined by national statistical offices (calculated using World Bank population estimates and urban ratios from the United Nations World Urbanization Prospects)

More information is available at www.gapminder.org

Cardiovascular Disease Prevalence in Travis County (2014-2018)

Assessing Risk Factors in an Urban Community

By City of Austin [source]

About this dataset

This dataset provides invaluable insight into the prevalence of cardiovascular disease in Travis County, Texas between 2014 and 2018. By utilizing data from the Behavioral Risk Factor Surveillance System (BRFSS), this dataset offers a comprehensive look at the health of the adult population in Travis County. Are your heart health concerns growing or declining? This dataset has the answer. Through its detailed analysis, you can quickly identify any changes in cardiovascular disease over time as well as understand how disability and other factors such as age may be connected to heart-related diagnosis rates. Investigate how diabetes, lifestyle habits and other factors are affecting residents of Travis County with this insightful strategic measure!

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How to use the dataset

This dataset provides valuable insight into the prevalence of cardiovascular disease among adults in Travis County from 2014 to 2018. The data includes a Date_Time variable, which is the date and time of the survey, as well as a Year variable and Percent variable detailing prevalence within that year. This data can be used for further research into cardiovascular health outcomes in Travis County over time.

The first step in using this dataset is understanding its contents. This data contains information on each year’s percent of residents with cardiovascular disease and was collected during annual surveys by Behavioral Risk Factor Surveillance System (BRFSS). With this information, users can compare yearly changes in cardiovascular health across different cohorts. They can also use it to identify particular areas with higher or lower prevalence of cardiovascular disease throughout Travis County.

Now that you understand what’s included and what it describes, you can start exploring deeper insights within your analysis. Try examining demographic factors such as age group or sex to uncover potential trends underlying the increase or decrease in overall percentage over time . Additionally, look for other data sources relevant to your research topic and explore how prevalence differs across different factors within Travis County like specific counties or cities within it or types of geographies like rural versus urban settings . By overlaying additional datasets such as these , you will learn more about any correlations between them and this BRFSS-surveyed measure overtime .

Finally remember that any findings related to this dataset should always be interpreted carefully given their scale relative to our broader population . Yet by digging deep into the changes taking place , we are able to answer important questions about howCV risk factors might vary from county-to-county across Texas while also providing insight on where public health funding should be directed towards next !

Research Ideas

• Evaluating the correlation between cardiovascular disease prevalence and socio-economic factors such as income, education, and occupation in Travis County over time.
• Building an interactive data visualization tool to help healthcare practitioners easily understand the current trends in cardiovascular disease prevalence for adults in Travis County.
• Developing a predictive model to forecast the future prevalence of cardiovascular disease for adults in Travis County over time given relevant socio-economic factors

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: strategic-measure-percentage-of-residents-with-cardiovascular-disease-1.csv | Column name | Description | |:--------------|:---------------------------------------------------------------------------| | Date_Time | Date and time of the survey. (DateTime) | | Year | Year of the survey. (Integer) | | Percent | Percentage of adults in Travis County with cardiovascular disease. (Float) |

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit City of Austin.