Graph and download economic data for Other Financial Information: Estimated Market Value of Owned Home by Deciles of Income Before Taxes: Ninth 10 Percent (81st to 90th Percentile) (CXU800721LB1510M) from 2014 to 2023 about owned, market value, information, percentile, tax, financial, income, housing, estimate, and USA.

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 0.4 mg/kg for sand, 1.0 mg/kg for clay, silt and very silty sand and 1.5 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.

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 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.

Official GMAT Focus Edition section scores (Quantitative, Verbal, and Data Insights) to percentile conversion tables for scores ranging from 60 to 90

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 20 mg/kg for sand, 40 mg/kg for clay, silt and very silty sand and 60 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.

In the analysis ensembles of 7-year SEM simulations were performed for 100 assessments for different scenarios and substances. For each assessment, an ensemble of 365 simulations was performed with varying dates of substance application, covering every day of the year. For each simulation the following postprocessing was performed on the daily substance emission (g.m-2.d-1) from the greenhouse and its 10-day moving average:

* Determine the of the annual maximum for each of the 7 simulation years.

* Calculate the 50th and 90th percentiles over the 7 annual maxima (referred to as PEC50 and the PEC90, respectively).

This results in four PEC values (PEC50--daily, PEC90--daily, PEC50--10-day-average, PEC90--10-day-average) for each of the 100x365 simulations. Next, for each of the 100 assessments, the results of the 365 simulations were processed as follows:

* Calculate the 90th percentile over 365 values for the four PEC values--this is referred to as the "true" 90th percentile.

* Remove 5 simulations for application dates 7-Feb, 21-Apr, 3-Jul, 14-Sep and 26-Nov, resulting in a set of 360 simulations. This is done because 360 has more divisors than 365.

Subsequently, processing was performed on subsamples of different sizes N, taken from the 360 simulations. The following subsample sizes were considered: 12, 15, 18, 20, 24, 30, 36, 40, 45, and 60. For each subsample size N, M_N = 360/N sets of subsamples were taken with application date evenly spread over the year. For example, for N=12, M_12=30 sets of application dates were selected, with each set one day offset to the next. This results in 10 sets of subsamples of varying size. For each set N, the following processing was performed:

* For each M_N values for the four PECs, calculate the relative difference compared to the true 90th percentile (based on the full 365 set of simulations; see above) as follows: RD = (PEC_est-PEC_365)/PEC_365.

* Calculate the 10th percentile over the M_N relative differences for each of the four PECs; this is referred to as the 90th percentile underestimation

* For each M_N values for the four PECs, calculate the multiplication factor relative to the true 90th percentile as follows: MF = PEC_est/PEC_365.

* Calculate the 90th percentile over the M_N multiplication factors for each of the four PECs.

This results in 4000 values for the relative difference and multiplication factor for each combination of assessment (100), subsample size N (10), and PEC quantity (4). The relative underestimations form the data underlying Figure 13.3 in Braakhekke et al. (2024). The multiplication factors for N=12 form the data underlying table 13.1 in Braakhekke et al. (2024).

90th percentile calculated during the productive period of the WFD (March-October) from 2003 to 2009, from the MODIS Chl-a algorithm processed by Ifremer OC5.

Graph and download economic data for Other Financial Information: Estimated Monthly Rental Value of Owned Home by Deciles of Income Before Taxes: Ninth 10 Percent (81st to 90th Percentile) (CXU910050LB1510M) from 2014 to 2023 about owned, information, percentile, rent, tax, financial, income, housing, estimate, and USA.

Annual number of days when the night temperature > 90th percentile. The baseline is calculated for 2001–2020, with projections for 2021–2040 and 2041–2060 under two climate scenarios: RCP 4.5 (moderate emissions) and RCP 8.5 (high emissions).

90th percentile of turbidity calculated during the productive period of the WFD (March-October) from 2015 to 2020, from the MODIS algorithm processed by OC5 IFREMER/ARGANS (Gohin et al 2002, Gohin 2011).

This metadata record describes the observed and estimated hydrologic metrics for the 1980 to 2019 period for U.S. Geological Survey streamgage locations across the Conterminous United States. The datasets are arranged in four files: (1) CONUS_Observed_Estimated_HMs_Annual_Monthly.csv, (2) CONUS_Bootstrap_Validations_for_Models.csv, (3) CONUS_Streamflow_Gages_for_Models.csv, and (4) Data_Dictionary_Flow_Metrics.csv. The CONUS_Observed_Estimated_HMs_Annual_Monthly.csv file contains the following six attributes: (1) the U.S. Geological Survey streamgage identification number, (2) calendar year, (3) observed hydrologic metric, (4) estimated hydrologic metric, (5) hydrologic metric abbreviation, and (6) aggregated level 2 ecoregion. The observed hydrologic metrics were calculated using collected streamflow daily values from U.S. Geological Survey streamflow gaging stations (U.S. Geological Survey National Water Information System, http://dx.doi.org/10.5066/F7P55KJN), and the estimated hydrologic metrics were estimated by cross-sectional time series random forest modeling methods by Miller, M.P., Carlisle, D.M., Wolock, D.M., and Wieczorek, M., 2018, A database of natural monthly streamflow estimates from 1950 to 2015 for the conterminous United States: Journal of the American Water Resources Association, 54(6), 1258-1269 [Also available at https://doi.org/10.1111/1752-1688.12685]. Forty-seven hydrologic metrics representing magnitude, frequency, duration, and timing were calculated. The hydrologic metric abbreviations, definitions, units, and citations are detailed in the Data_Dictionary_Flow_Metrics.csv file. The low- and high-flow magnitudes were calculated from the 10th and 90th percentile non-exceedence streamflows divided by the drainage area, respectively. The low- and high-flow frequencies were calculated as the number of pulses below the 10th and above the 90th percentile values, respectively. The low- and high-flow durations were calculated from the length of time (in days) that the streamflow was below the 10th percentile or above the 90th percentile, respectively. The low- and high-flow seasonality values were calculated based on frequency of occurrence in different seasons (for more details, please see Eng, K., Carlisle, D.M., Grantham, T.E., Wolock, D.M., and Eng, R.L., 2019, Severity and extent of alterations to natural streamflow regimes based on hydrologic metrics in the conterminous United States, 1980-2014: U.S. Geological Survey Scientific Investigations Report 2019-5001, 25 p. [Also available at https://doi.org/10.3133/sir20195001]. The CONUS_Streamflow_Gages_for_Models.csv file contains the U.S. Geological Survey list of streamflow gaging stations used in cross-sectional time series random forest models. The CONUS_Bootstrap_Validations_for_Models.csv file lists the U.S. Geological Survey streamflow gaging stations used in the bootstrapped validation data sets used to assess model performance. In addition, bootstrap validation also assesses model robustness by testing various calibration configurations. These bootstrap validation data sets may contain random amounts of observations that are outside the range of the observations used in the calibration, and/or observations that are not independent from one another. There are no missing values in any of the files. The three data files are in a comma separated value text format.

What is CDC Social Vulnerability Index?ATSDR’s Geospatial Research, Analysis & Services Program (GRASP) created the Social Vulnerability Index (SVI) 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.SVI uses U.S Census Data to determine the social vulnerability of every county and tract. CDC SVI ranks each county and tract on 16 social factors, including poverty, lack of vehicle access, and crowded housing, and groups them into four related themes:Theme 1 - Socioeconomic StatusTheme 2 - Household CharacteristicsTheme 3 - Racial & Ethnic Minority StatusTheme 4 - Housing Type & Transportation VariablesFor a detailed description of variable uses, please refer to the full SVI 2020 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 sixteen 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 Status - RPL_THEME1Household Characteristics - RPL_THEME2Racial & Ethnic Minority Status - RPL_THEME3Housing Type & Transportation - RPL_THEME4FlagsCounties and tracts in the top 10%, i.e., at the 90th percentile of values, are given a value of 1 to indicate high vulnerability. Counties and 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 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 2020 Full DocumentationSVI Home PageContact the SVI Coordinator

Annual number of days when the day temperature > 90th percentile. The baseline is calculated for 2001–2020, with projections for 2021–2040 and 2041–2060 under two climate scenarios: RCP 4.5 (moderate emissions) and RCP 8.5 (high emissions).

The 90th percentile of a data collective is to be understood as the 90th P background value. It is the level at which 90% of the cases observed to date have lower levels. The calculation is performed after the data group has been cleaned of outliers. The 90th percentile is often used as the upper limit of the background range to define 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 BBodSchV (1999) does not specify any precautionary values ​​for manganese. According to LABO (2003), a sample number of >=20 is required for the calculation of background values. However, groups with a number of samples >= 10 are also shown on the map. This information is then only informal and not representative.

90th percentile of non-algal suspended matter calculated during the productive period of the WFD (March-October) from 2015 to 2020, from the MODIS algorithm processed by OC5 IFREMER/ARGANS (Gohin et al 2002, Gohin 2011).

The Chlorophyll a daily anomaly occurrences at a given pixel and the total number of days that valid data was collected. the relative frequency will be calculated as follows: Relative Frequency of Pixel Chlorophyll a Anomalies = the number of days with a moderate (in the 90th percentile), high (in the 95th percentile) or extreme (in the 99th percentiles) anomaly divided by the number of days valid observations

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

90th percentile calculated during the productive period of the WFD (March-October) from 2015 to 2020, from the MODIS Chl-a algorithm processed by OC5 IFREMER/ARGANS (Gohin et al 2002, Gohin 2011).

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