Blockchain data query: Percentile values of MON transfers

Soils were created in millennia by the interaction of diverse natural processes. For centuries, however, they have increasingly been shaped by human activities such as land use and material inputs. For most inorganic substances, the starting substrate of soil formation determines the natural (geogenic) basic content of a soil. In addition, there is an anthropogenic component, whereby the ratio of geogenous and anthropogenic proportion varies greatly in element-specific terms. Background values characterize the typical background contents of a substance or group of substances in the soil. In accordance with the procedure of the Federal-State Working Group on Soil Protection (LABO), the 50. percentile (median) and the 90th percentile (median). The percentile is used. The median represents the median background content, which is 90. Percentile is the upper limit of the typical background content. For the purposes of determining background levels, the anthropogenic fraction shall not come from an identifiable individual source or source of pollution. Rather, they must be diffuse, i.e. they must be the result of general large-scale (ubiquitous) distributions of substances over longer periods of time. Specifically polluted soils must therefore be removed from the data before background values are calculated. The substrate is the most important differentiation criterion for natural contents of inorganic substances. The next deeper level of division are horizon groups such as topsoil, subsoil and subsoil. If there is still a sufficient number of cases, the group of topsoil horizons is further subdivided into use classes. The State Office for Geology and Mining Rhineland-Palatinate has been dealing with physical and chemical investigations of soils for many years within the framework of the land survey as well as through cooperation with other state authorities. The information bases could be significantly improved with data collected during the project Bodenbelastungskataster Rheinland-Pfalz. Since this project, comprehensive soil investigations have been successively continued with the Rhineland-Palatinate Soil Status Report. This long-term project of the Ministry of the Environment, Forests and Consumer Protection is carried out on behalf of the State Office for the Environment, Water Management and Trade Inspectorate. In the meantime, about 18 percent of the country's area with at least one investigated site per km2 has been recorded. The data basis for the spatial distribution of the substrate groups comes from the soil specialist information system (FISBO) of the LGB. This system manages data from boch heterogeneous soil mapping (scale 1:25,000 to 1:200,000). Although this leads to recognizably different resolutions and differentiation problems, these data are suitable for nationwide surveys in small-scale applications above the scale of 1:50,000, despite their inhomogeneity. Further information on this topic can be found in the loose-leaf collection background values of the soils of Rhineland-Palatinate.:As a 90.P background value, this is 90. Percentile of a Data Collective. It is the value at which 90% of the cases observed so far have lower levels. The calculation is made after the data group of outliers has been cleaned up. The 90. The percentile often serves as the upper limit of the background range to delineate unusually high levels. The total content is determined from the aqua regia extract (according to DIN ISO 11466 (1997)). The concentration is given in mg/kg. The salary classes take into account, among other things, the pension values of the BBodSchV (1999). These are 15 mg/kg for the soil type sand, 50 mg/kg for clay, silt and heavily silty sand and 70 mg/kg for clay. According to LABO (2003) a sample count of >=20 is required for the calculation of background values. However, the map also shows groups with a sample count >= 10. This information is then only informal and not representative.

The 2000 CDC growth charts are based on national data collected between 1963 and 1994 and include a set of selected percentiles between the 3rd and 97th and LMS parameters that can be used to obtain other percentiles and associated z-scores. Obesity is defined as a sex- and age-specific body mass index (BMI) at or above the 95th percentile. Extrapolating beyond the 97th percentile is not recommended and leads to compressed z-score values. This study attempts to overcome this limitation by constructing a new method for calculating BMI distributions above the 95th percentile using an extended reference population. Data from youth at or above the 95th percentile of BMI-for-age in national surveys between 1963 and 2016 were modelled as half-normal distributions. Scale parameters for these distributions were estimated at each sex-specific 6-month age-interval, from 24 to 239 months, and then smoothed as a function of age using regression procedures. The modelled distributions above the 95th percentile can be used to calculate percentiles and non-compressed z-scores for extreme BMI values among youth. This method can be used, in conjunction with the current CDC BMI-for-age growth charts, to track extreme values of BMI among youth.

This processed data represents the estimated percentile level of noise energy from transportation. The data is from the U.S. Department of Transportation, Bureau of Transportation Statistics, National Transportation Noise Map, 2018. The census block data was converted into census tract data by the mean of the census blocks within a tract comprising the data associated with each tract. From there the percentile and the rank were calculated. A percentile is a score indicating the value below which a given percentage of observations in a group of observations fall. It indicates the relative position of a particular value within a dataset. For example, the 20th percentile is the value below which 20% of the observations may be found. The rank refers to a process of arranging percentiles in descending order, starting from the highest percentile and ending with the lowest percentile. Once the percentiles are ranked, a normalization step is performed to rescale the rank values between 0 and 10. A rank value of 10 represents the highest percentile, while a rank value of 0 corresponds to the lowest percentile in the dataset. The normalized rank provides a relative assessment of the position of each percentile within the distribution, making it simpler to understand the relative magnitude of differences between percentiles. Normalization between 0 and 10 ensures that the rank values are standardized and uniformly distributed within the specified range. This normalization allows for easier interpretation and comparison of the rank values, as they are now on a consistent scale. For detailed methods, go to connecticut-environmental-justice.circa.uconn.edu.

The Maximum Considered Earthquake Geometric Mean (MCEG) peak ground acceleration (PGA) values of the 2020 NEHRP Recommended Seismic Provisions and 2022 ASCE/SEI 7 Standard are derived from the downloadable data files. For each site class, the MCEG peak ground acceleration (PGA_M) is calculated via the following equation: PGA_M = min[ PGA_MUH, max( PGA_M84th , PGA_MDLL ) ] where PGA_MUH = uniform-hazard peak ground acceleration PGA_M84th = 84th-percentile peak ground acceleration PGA_MDLL = deterministic lower limit spectral acceleration

This indicator represents the tracts ranked by their percentile level of median household incomes per census tract, per capita income. The data source is 2017-2021 American Community Survey, 5-year estimates. The percentile and the rank were calculated. A percentile is a score indicating the value below which a given percentage of observations in a group of observations fall. It indicates the relative position of a particular value within a dataset. For example, the 20th percentile is the value below which 20% of the observations may be found. The rank refers to a process of arranging percentiles in descending order, starting from the highest percentile and ending with the lowest percentile. Once the percentiles are ranked, a normalization step is performed to rescale the rank values between 0 and 10. A rank value of 10 represents the highest percentile, while a rank value of 0 corresponds to the lowest percentile in the dataset. The normalized rank provides a relative assessment of the position of each percentile within the distribution, making it simpler to understand the relative magnitude of differences between percentiles. Normalization between 0 and 10 ensures that the rank values are standardized and uniformly distributed within the specified range. This normalization allows for easier interpretation and comparison of the rank values, as they are now on a consistent scale. For detailed methods, go to connecticut-environmental-justice.circa.uconn.edu.

Blockchain data query: Percentile values of gas fees

This indicator represents the tracts ranked by their percentile level of daily 8-hour annual average surface-level PM2.5 concentrations modeled over by 1km x 1km plots in 2019. The data is from Atmospheric Composition Analysis, Washington University in St. Louis. The census block data was converted into census tract data by the mean of the census blocks within a tract comprising the data associated with each tract. From there the percentile and the rank were calculated. A percentile is a score indicating the value below which a given percentage of observations in a group of observations fall. It indicates the relative position of a particular value within a dataset. For example, the 20th percentile is the value below which 20% of the observations may be found. The rank refers to a process of arranging percentiles in descending order, starting from the highest percentile and ending with the lowest percentile. Once the percentiles are ranked, a normalization step is performed to rescale the rank values between 0 and 10. A rank value of 10 represents the highest percentile, while a rank value of 0 corresponds to the lowest percentile in the dataset. The normalized rank provides a relative assessment of the position of each percentile within the distribution, making it simpler to understand the relative magnitude of differences between percentiles. Normalization between 0 and 10 ensures that the rank values are standardized and uniformly distributed within the specified range. This normalization allows for easier interpretation and comparison of the rank values, as they are now on a consistent scale.For detailed methods, go to connecticut-environmental-justice.circa.uconn.edu.

Please note this dataset supersedes previous versions on the Climate Data Portal. It has been uploaded following an update to the dataset in March 2023. This means sea level rise is approximately 1cm higher (larger) compared to the original data release (i.e. the previous version available on this portal) for all UKCP18 site-specific sea level projections at all timescales. For more details please refer to the technical note.What does the data show?The exploratory extended time-mean sea-level projections to 2300 show the amount of sea-level change (in cm) for each coastal location (grid-box) around the British Isles for several emission scenarios. Sea-level rise is the primary mechanism by which we expect coastal flood risk to change in the UK in the future. The amount of sea-level rise depends on the location around the British Isles and increases with higher emission scenarios. Here, we provide the relative time-mean sea-level projections to 2300, i.e. the local sea-level change experienced at a particular location compared to the 1981-2000 average, produced as part of UKCP18.For each grid box the time-mean sea-level change projections are provided for the end of each decade (e.g. 2010, 2020, 2030 etc) for three emission scenarios known as Representative Concentration Pathways (RCP) and for three percentiles.The emission scenarios are:RCP2.6RCP4.5RCP8.5The percentiles are:5th percentile50th percentile95th percentileImportant limitations of the dataWe cannot rule out substantial additional sea-level rise associated with ice sheet instability processes that are not represented in the UKCP18 projections, as discussed in the recent IPCC Sixth Assessment Report (AR6). These exploratory projections show sea levels continue to increase beyond 2100 even with large reductions in greenhouse gas emissions. It should be noted that these projections have a greater degree of uncertainty than the 21st Century Projections and should therefore be treated as illustrative of the potential future changes. They are designed to be used alongside the 21st Century projections for those interested in exploring post-2100 changes.What are the naming conventions and how do I explore the data?The data is supplied so that each row corresponds to the combination of a RCP emissions scenario and percentile value e.g. 'RCP45_50' is the RCP4.5 scenario and the 50th percentile. This can be viewed and filtered by the field 'RCP and Percentile'. The columns (fields) correspond to the end of each decade and the fields are named by sea level anomaly at year x, e.g. '2050 seaLevelAnom' is the sea level anomaly at 2050 compared to the 1981-2000 average.Please note that the styling and filtering options are independent of each other and the attribute you wish to style the data by can be set differently to the one you filter by. Please ensure that you have selected the RCP/percentile and decade you want to both filter and style the data by. Select the cell you are interested in to view all values.To understand how to explore the data please refer to the New Users ESRI Storymap.What are the emission scenarios?The 21st Century time-mean sea level projections were produced using some of the future emission scenarios used in the IPCC Fifth Assessment Report (AR5). These are RCP2.6, RCP4.5 and RCP8.5, which are based on the concentration of greenhouse gases and aerosols in the atmosphere. RCP2.6 is an aggressive mitigation pathway, where greenhouse gas emissions are strongly reduced. RCP4.5 is an intermediate ‘stabilisation’ pathway, where greenhouse gas emissions are reduced by varying levels. RCP8.5 is a high emission pathway, where greenhouse gas emissions continue to grow unmitigated. Further information is available in the Understanding Climate Data ESRI Storymap and the RCP Guidance on the UKCP18 website.What are the percentiles?The UKCP18 sea-level projections are based on a large Monte Carlo simulation that represents 450,000 possible outcomes in terms of global mean sea-level change. The Monte Carlo simulation is designed to sample the uncertainties across the different components of sea-level rise, and the amount of warming we see for a given emissions scenario across CMIP5 climate models. The percentiles are used to characterise the uncertainty in the Monte Carlo projections based on the statistical distribution of the 450,000 individual simulation members. For example, the 50th percentile represents the central estimate (median) amongst the model projections. Whilst the 95th percentile value means 95% of the model distribution is below that value and similarly the 5th percentile value means 5% of the model distribution is below that value. The range between the 5th to 95th percentiles represent the projection range amongst models and corresponds to the IPCC AR5 “likely range”. It should be noted that, there may be a greater than 10% chance that the real-world sea level rise lies outside this range.Data sourceThis data is an extract of a larger dataset (every year and more percentiles) which is available on CEDA at https://catalogue.ceda.ac.uk/uuid/a077f4058cda4cd4b37ccfbdf1a6bd29Data has been extracted from the v20221219 version (downloaded 17/04/2023) of three files:seaLevelAnom_marine-sim_rcp26_ann_2007-2300.ncseaLevelAnom_marine-sim_rcp45_ann_2007-2300.ncseaLevelAnom_marine-sim_rcp85_ann_2007-2300.ncUseful links to find out moreFor a comprehensive description of the underpinning science, evaluation and results see the UKCP18 Marine Projections Report (Palmer et al, 2018).For a discussion on ice sheet instability processes in the latest IPCC assessment report, see Fox-Kemper et al (2021). Technical note for the update to the underpinning data: https://www.metoffice.gov.uk/binaries/content/assets/metofficegovuk/pdf/research/ukcp/ukcp_tech_note_sea_level_mar23.pdf.Further information in the Met Office Climate Data Portal Understanding Climate Data ESRI Storymap.

This dataset demonstrates the difference in calculating percentile Intervals as approximation for Highest Density Intervals (HDI) vs. Highest Posterior Density (HPD). This is demonstrated with extended partial liver resection data (ZeLeR-study, ethical vote: 2018-1246-Material). The data includes Computed Tomography (CT) liver volume measurements of patients before (POD 0) and after partial hepatectomy. Liver volume was normalized per patient to the preoperative liver volume. was used to screen the liver regeneration courses. The Fujifilm Synapse3D software was used to calculate volume estimates from CT images. The data is structured in a tabular separated value file of the PEtab format.

As a 90.P background value, that's 90. Percentile of a Data Collective. It is the value at which 90% of the cases observed so far have lower levels. The calculation is made after the data group of outliers has been cleaned up. The 90. The percentile often serves as the upper limit of the background range to delineate unusually high levels. The total content is determined from the aqua regia extract (according to DIN ISO 11466 (1997)). The concentration is given in mg/kg. The salary classes take into account, among other things, the pension values of the BBodSchV (1999). These are 30 mg/kg for sand, 60 mg/kg for clay, silt and very silty sand and 100 mg/kg for clay. According to LABO (2003) a sample count of >=20 is required for the calculation of background values. However, the map also shows groups with a sample count >= 10. This information is then only informal and not representative.

List of Subdatasets: Long-term data: 2000-2021 5th percentile (p05) monthly time-series: 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021 50th percentile (p50) monthly time-series: 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021 95th percentile (p95) monthly time-series: 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021 General Description The monthly aggregated Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) dataset is derived from 250m 8d GLASS V6 FAPAR. The data set is derived from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and LAI data using several other FAPAR products (MODIS Collection 6, GLASS FAPAR V5, and PROBA-V1 FAPAR) to generate a bidirectional long-short-term memory (Bi-LSTM) model to estimate FAPAR. The dataset time spans from March 2000 to December 2021 and provides data that covers the entire globe. The dataset can be used in many applications like land degradation modeling, land productivity mapping, and land potential mapping. The dataset includes: Long-term: Derived from monthly time-series. This dataset provides linear trend model for the p95 variable: (1) slope beta mean (p95.beta_m), p-value for beta (p95.beta_pv), intercept alpha mean (p95.alpha_m), p-value for alpha (p95.alpha_pv), and coefficient of determination R2 (p95.r2_m). Monthly time-series: Monthly aggregation with three standard statistics: (1) 5th percentile (p05), median (p50), and 95th percentile (p95). For each month, we aggregate all composites within that month plus one composite each before and after, ending up with 5 to 6 composites for a single month depending on the number of images within that month. Data Details Time period: March 2000 – December 2021 Type of data: Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) How the data was collected or derived: Derived from 250m 8 d GLASS V6 FAPAR using Python running in a local HPC. The time-series analysis were computed using the Scikit-map Python package. Statistical methods used: for the long-term, Ordinary Least Square (OLS) of p95 monthly variable; for the monthly time-series, percentiles 05, 50, and 95. Limitations or exclusions in the data: The dataset does not include data for Antarctica. Coordinate reference system: EPSG:4326 Bounding box (Xmin, Ymin, Xmax, Ymax): (-180.00000, -62.0008094, 179.9999424, 87.37000) Spatial resolution: 1/480 d.d. = 0.00208333 (250m) Image size: 172,800 x 71,698 File format: Cloud Optimized Geotiff (COG) format. Support If you discover a bug, artifact, or inconsistency, or if you have a question please raise a GitHub issue: https://github.com/Open-Earth-Monitor/Global_FAPAR_250m/issues Reference Hackländer, J., Parente, L., Ho, Y.-F., Hengl, T., Simoes, R., Consoli, D., Şahin, M., Tian, X., Herold, M., Jung, M., Duveiller, G., Weynants, M., Wheeler, I., (2023?) "Land potential assessment and trend-analysis using 2000–2021 FAPAR monthly time-series at 250 m spatial resolution", submitted to PeerJ, preprint available at: https://doi.org/10.21203/rs.3.rs-3415685/v1 Name convention To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are: generic variable name: fapar = Fraction of Absorbed Photosynthetically Active Radiation variable procedure combination: essd.lstm = Earth System Science Data with bidirectional long short-term memory (Bi–LSTM) Position in the probability distribution / variable type: p05/p50/p95 = 5th/50th/95th percentile Spatial support: 250m Depth reference: s = surface Time reference begin time: 20000301 = 2000-03-01 Time reference end time: 20211231 = 2022-12-31 Bounding box: go = global (without Antarctica) EPSG code: epsg.4326 = EPSG:4326 Version code: v20230628 = 2023-06-28 (creation date)

The Maximum Considered Earthquake Geometric Mean (MCEG) peak ground acceleration (PGA) values of the 2020 NEHRP Recommended Seismic Provisions and 2022 ASCE/SEI 7 Standard are derived from the downloadable data files. For each site class, the MCEG peak ground acceleration (PGA_M) is calculated via the following equation: PGA_M = min[ PGA_MUH, max( PGA_M84th , PGA_MDLL ) ] where PGA_MUH = uniform-hazard peak ground acceleration PGA_M84th = 84th-percentile peak ground acceleration PGA_MDLL = deterministic lower limit spectral acceleration

Abstract Background Expressing the risk of atherosclerotic cardiovascular disease (ASCVD) as percentiles of the distribution according to sex and age may provide a better perception of the risk. Objectives To determine percentiles of the 10-year ASCVD risk distribution according to sex and age in a sample of the Brazilian population; to characterize individuals at low 10-year risk but high risk percentile. Methods We analyzed individuals aged 40 to 75 years who underwent routine health evaluations from 2010 to 2020. Persons with known clinical ASCVD, diabetes mellitus, chronic kidney disease, or LDL-cholesterol ≥ 190 mg/dL were excluded. The 10-year ASCVD risk was calculated by the ACC/AHA pooled cohort equations. Local polynomial regression was used to determine risk percentiles. Two-sided p-values < 0.050 were considered statistically significant. Results Our sample comprised 54,145 visits (72% male, median age [interquartile range] 48 [43, 53] years). We constructed sex-specific graphs plotting age against ASCVD risk corresponding to the 10th, 25th, 50th, 75th, and 90th percentiles. Most males up to 47 years and females up to 59 years above the 75th percentile had a 10-year risk < 5%. Individuals at low 10-year risk and risk percentile ≥ 75th had a high prevalence of excess weight and median (interquartile range) LDL-cholesterol levels 136 (109, 158) mg/dL (males) and 126 (105, 147) mg/dL (females). Conclusions We established ASCVD risk percentiles according to sex and age in a large sample of the Brazilian population. This approach may increase risk awareness and help identify younger persons at low 10-year risk who may benefit from more aggressive risk factor control.

General Description The monthly aggregated Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) dataset is derived from 250m 8d GLASS V6 FAPAR. The data set is derived from Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance and LAI data using several other FAPAR products (MODIS Collection 6, GLASS FAPAR V5, and PROBA-V1 FAPAR) to generate a bidirectional long-short-term memory (Bi-LSTM) model to estimate FAPAR. The dataset time spans from March 2000 to December 2021 and provides data that covers the entire globe. The dataset can be used in many applications like land degradation modeling, land productivity mapping, and land potential mapping. The dataset includes: Long-term: Derived from monthly time-series. This dataset provides linear trend model for the p95 variable: (1) slope beta mean (p95.beta_m), p-value for beta (p95.beta_pv), intercept alpha mean (p95.alpha_m), p-value for alpha (p95.alpha_pv), and coefficient of determination R2 (p95.r2_m). Monthly time-series: Monthly aggregation with three standard statistics: (1) 5th percentile (p05), median (p50), and 95th percentile (p95). For each month, we aggregate images inside the months and one image before and after, about 5 to 6 images for a single month depending on the number of images inside the month. Data Details Time period: March 2000 – December 2021 Type of data: Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) How the data was collected or derived: Derived from 250m 8 d GLASS V6 FAPAR using Python running in a local HPC. Cloudy pixels were removed and only positive values of water vapor were considered to compute the statistics. The time-series gap-filling and time-series analysis were computed using the Scikit-map Python package. Statistical methods used: for the long-term, trend analysis of p95 monthly variable; for the monthly time-series, percentiles 05, 50, and 95. Limitations or exclusions in the data: The dataset does not include data for Antarctica. Coordinate reference system: EPSG:4326 Bounding box (Xmin, Ymin, Xmax, Ymax): (-180.00000, -62.0008094, 179.9999424, 87.37000) Spatial resolution: 1/480 d.d. = 0.00208333 (250m) Image size: 172,800 x 71,698 File format: Cloud Optimized Geotiff (COG) format. Support If you discover a bug, artifact, or inconsistency, or if you have a question please use some of the following channels: Technical issues and questions about the code: GitLab Issues General questions and comments: LandGIS Forum Name convention To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are: generic variable name: fapar = Fraction of Absorbed Photosynthetically Active Radiation variable procedure combination: essd.lstm = Earth System Science Data with bidirectional long short-term memory (Bi–LSTM) Position in the probability distribution / variable type: p05/p50/p95 = 5th/50th/95th percentile Spatial support: 250m Depth reference: s = surface Time reference begin time: 20000301 = 2000-03-01 Time reference end time: 20211231 = 2022-12-31 Bounding box: go = global (without Antarctica) EPSG code: epsg.4326 = EPSG:4326 Version code: v20230628 = 2023-06-28 (creation date)

This feature layer visualizes the 2018 overall SVI for U.S. counties and tractsSocial Vulnerability Index (SVI) indicates the relative vulnerability of every U.S. county and tract15 social factors grouped into four major themesIndex value calculated for each county for the 15 social factors, four major themes, and the overall rankWhat is CDC Social Vulnerability Index?ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) has created a tool to help emergency response planners and public health officials identify and map the communities that will most likely need support before, during, and after a hazardous event.The Social Vulnerability Index (SVI) uses U.S. Census data to determine the social vulnerability of every county and tract. CDC SVI ranks each county and tract on 15 social factors, including poverty, lack of vehicle access, and crowded housing, and groups them into four related themes:SocioeconomicHousing Composition and DisabilityMinority Status and LanguageHousing and Transportation VariablesFor a detailed description of variable uses, please refer to the full SVI 2018 documentation.RankingsWe ranked counties and tracts for the entire United States against one another. This feature layer can be used for mapping and analysis of relative vulnerability of counties in multiple states, or across the U.S. as a whole. Rankings are based on percentiles. Percentile ranking values range from 0 to 1, with higher values indicating greater vulnerability. For each county and tract, we generated its percentile rank among all counties and tracts for 1) the fifteen individual variables, 2) the four themes, and 3) its overall position. Overall Rankings:We totaled the sums for each theme, ordered the counties, and then calculated overall percentile rankings. Please note: taking the sum of the sums for each theme is the same as summing individual variable rankings.The overall tract summary ranking variable is RPL_THEMES. Theme rankings:For each of the four themes, we summed the percentiles for the variables comprising each theme. We ordered the summed percentiles for each theme to determine theme-specific percentile rankings. The four summary theme ranking variables are: Socioeconomic theme - RPL_THEME1Housing Composition and Disability - RPL_THEME2Minority Status & Language - RPL_THEME3Housing & Transportation - RPL_THEME4FlagsCounties in the top 10%, i.e., at the 90th percentile of values, are given a value of 1 to indicate high vulnerability. Counties below the 90th percentile are given a value of 0. For a theme, the flag value is the number of flags for variables comprising the theme. We calculated the overall flag value for each county as the total number of all variable flags. SVI Informational VideosIntroduction to CDC Social Vulnerability Index (SVI)Methods for CDC Social Vulnerability Index (SVI)More Questions?CDC SVI 2018 Full DocumentationSVI Home PageContact the SVI Coordinator

This processed data represents the estimated percentile level of traffic density. The data is from the 2020 Traffic Monitoring Annual Average Daily Traffic Report, CT Department of Transportation. The census block data was converted into census tract data by the mean of the census blocks within a tract comprising the data associated with each tract. From there the percentile and the rank were calculated. A percentile is a score indicating the value below which a given percentage of observations in a group of observations fall. It indicates the relative position of a particular value within a dataset. For example, the 20th percentile is the value below which 20% of the observations may be found. The rank refers to a process of arranging percentiles in descending order, starting from the highest percentile and ending with the lowest percentile. Once the percentiles are ranked, a normalization step is performed to rescale the rank values between 0 and 10. A rank value of 10 represents the highest percentile, while a rank value of 0 corresponds to the lowest percentile in the dataset. The normalized rank provides a relative assessment of the position of each percentile within the distribution, making it simpler to understand the relative magnitude of differences between percentiles. Normalization between 0 and 10 ensures that the rank values are standardized and uniformly distributed within the specified range. This normalization allows for easier interpretation and comparison of the rank values, as they are now on a consistent scale.For detailed methods, go to connecticut-environmental-justice.circa.uconn.edu.

This feature layer visualizes the 2014 overall SVI for U.S. census tractsSocial Vulnerability Index (SVI) indicates the relative vulnerability of every U.S. census tract15 social factors grouped into four major themesIndex value calculated for each census tract for the 15 social factors, four major themes, and the overall rankWhat is CDC's Social Vulnerability Index?ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) has created a tool to help emergency response planners and public health officials identify and map the communities that will most likely need support before, during, and after a hazardous event.The Social Vulnerability Index (SVI) uses U.S. Census data to determine the social vulnerability of every census tract. The SVI ranks each census tract on 15 social factors, including poverty, lack of vehicle access, and crowded housing, and groups them into four related themes:SocioeconomicHousing Composition and DisabilityMinority Status and LanguageHousing and Transportation VariablesFor a detailed description of variable uses, please refer to the full 2014 SVI documentation.RankingsWe ranked census tracts for the entire United States against one another. This feature layer can be used for mapping and analysis of relative vulnerability of census tracts in multiple states, or across the U.S. as a whole. Census tract rankings are based on percentiles. Percentile ranking values range from 0 to 1, with higher values indicating greater vulnerability. For each census tract, we generated its percentile rank among all census tracts for 1) the fifteen individual variables, 2) the four themes, and 3) Its overall position. Overall Rankings:We summed the sums for each theme, ordered the census tracts, and then calculated overall percentile rankings. Please note; taking the sum of the sums for each theme is the same as summing individual variable rankings.The overall tract summary ranking variable is RPL_THEMES. Theme rankings:For each of the four themes, we summed the percentiles for the variables comprising each theme. We ordered the summed percentiles for each theme to determine theme-specific percentile rankings. The four summary theme ranking variables are: Socioeconomic theme - RPL_THEME1Housing Composition and Disability - RPL_THEME2Minority Status & Language - RPL_THEME3Housing & Transportation - RPL_THEME4FlagsCensus tracts in the top 10%, i.e., at the 90th percentile of values, are given a value of 1 to indicate high vulnerability. Census tracts below the 90th percentile are given a value of 0. For a theme, the flag value is the number of flags for variables comprising the theme. We calculated the overall flag value for each census tract as the number of all variable flags. SVI Informational VideosIntroduction to CDC’s Social Vulnerability Index (SVI)Methods for CDC’s Social Vulnerability Index (SVI)More Questions?2014 SVI Full DocumentationSVI Home PageContact the SVI Coordinator