2013-2023 Virginia Median Household Income based on the past 12 months by Census Block Group. Contains estimates and margins of error.

Special data considerations: Large negative values do exist (more detail below) and should be addressed prior to graphing or aggregating the data.

A value of -666,666,666 in the estimate column indicates that either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.

A value of -222,222,222 in the margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.

U.S. Census Bureau; American Community Survey, American Community Survey 5-Year Estimates, Table B19013 Data accessed from: Census Bureau's API for American Community Survey (https://www.census.gov/data/developers/data-sets.html)

The United States Census Bureau's American Community Survey (ACS): -What is the American Community Survey? (https://www.census.gov/programs-surveys/acs/about.html) -Geography & ACS (https://www.census.gov/programs-surveys/acs/geography-acs.html) -Technical Documentation (https://www.census.gov/programs-surveys/acs/technical-documentation.html)

Supporting documentation on code lists, subject definitions, data accuracy, and statistical testing can be found on the American Community Survey website in the Technical Documentation section. (https://www.census.gov/programs-surveys/acs/technical-documentation/code-lists.html)

Sample size and data quality measures (including coverage rates, allocation rates, and response rates) can be found on the American Community Survey website in the Methodology section. (https://www.census.gov/acs/www/methodology/sample_size_and_data_quality/)

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, it is the Census Bureau's Population Estimates Program that produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units for states and counties.

Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted roughly as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see ACS Technical Documentation https://www.census.gov/programs-surveys/acs/technical-documentation.html). The effect of nonsampling error is not represented in these tables.

Annotation values are character representations of estimates and have values when non-integer information needs to be represented. Below are a few examples. Complete information is available on the ACS website under Notes on ACS Estimate and Annotation Values. (https://www.census.gov/data/developers/data-sets/acs-1year/notes-on-acs-estimate-and-annotation-values.html).

PurposeTo investigate the effect of the optional biometric parameters lens thickness (LT) and center corneal thickness (CCT) in the Kane formula on intraocular lens (IOL) power calculation.MethodsA cross-sectional study included consecutive cataract patients who received uncomplicated cataract surgery with IOL implantation from May to September 2022 were enrolled. The ocular biometric parameters were obtained using IOLMaster 700 and then inputted into online Kane formula calculator. The IOL power was calculated for targeting emmetropia and compared between groups: not omitting (NO) group, omitting LT and CCT (OLC) group, omitting LT (OL) group and omitting CCT (OC) group. Further, according to the axial length (AL), anterior chamber depth (ACD), and mean keratometry (Km), the eyes were divided into three subgroups, respectively.Results1005 eyes of 1005 consecutive patients were included. There was no significant difference in IOL power between NO group and OC group (P = 0.064), and the median absolute difference (MedAD) was 0.05D. The IOL power in NO group showed significant differences from OLC group and OL group respectively (P < 0.001), and both MedAD values were 0.18D. Among AL subgroups, MedAD ranged from 0.06D to 0.35D in short eyes. Among ACD subgroups, the above values ranged from 0.06D to 0.23D in shallow ACD subgroup. Among Km subgroups, these values ranged from 0.05D to 0.31D in steep Km subgroup.ConclusionThe optional biometric parameter CCT has no effect on the calculation results of the Kane formula, whereas the parameter LT has a great influence on the Kane formula results for the IOL power calculation in cataract patients with short AL, shallow ACD and steep Km.

These are simulated data without any identifying information or informative birth-level covariates. We also standardize the pollution exposures on each week by subtracting off the median exposure amount on a given week and dividing by the interquartile range (IQR) (as in the actual application to the true NC birth records data). The dataset that we provide includes weekly average pregnancy exposures that have already been standardized in this way while the medians and IQRs are not given. This further protects identifiability of the spatial locations used in the analysis. 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: File format: R workspace file; “Simulated_Dataset.RData”. Metadata (including data dictionary) • y: Vector of binary responses (1: adverse outcome, 0: control) • x: Matrix of covariates; one row for each simulated individual • z: Matrix of standardized pollution exposures • n: Number of simulated individuals • m: Number of exposure time periods (e.g., weeks of pregnancy) • p: Number of columns in the covariate design matrix • alpha_true: Vector of “true” critical window locations/magnitudes (i.e., the ground truth that we want to estimate) Code Abstract We provide R statistical software code (“CWVS_LMC.txt”) to fit the linear model of coregionalization (LMC) version of the Critical Window Variable Selection (CWVS) method developed in the manuscript. We also provide R code (“Results_Summary.txt”) to summarize/plot the estimated critical windows and posterior marginal inclusion probabilities. Description “CWVS_LMC.txt”: This code is delivered to the user in the form of a .txt file that contains R statistical software code. Once the “Simulated_Dataset.RData” workspace has been loaded into R, the text in the file can be used to identify/estimate critical windows of susceptibility and posterior marginal inclusion probabilities. “Results_Summary.txt”: This code is also delivered to the user in the form of a .txt file that contains R statistical software code. Once the “CWVS_LMC.txt” code is applied to the simulated dataset and the program has completed, this code can be used to summarize and plot the identified/estimated critical windows and posterior marginal inclusion probabilities (similar to the plots shown in the manuscript). Optional Information (complete as necessary) Required R packages: • For running “CWVS_LMC.txt”: • msm: Sampling from the truncated normal distribution • mnormt: Sampling from the multivariate normal distribution • BayesLogit: Sampling from the Polya-Gamma distribution • For running “Results_Summary.txt”: • plotrix: Plotting the posterior means and credible intervals Instructions for Use Reproducibility (Mandatory) What can be reproduced: The data and code can be used to identify/estimate critical windows from one of the actual simulated datasets generated under setting E4 from the presented simulation study. How to use the information: • Load the “Simulated_Dataset.RData” workspace • Run the code contained in “CWVS_LMC.txt” • Once the “CWVS_LMC.txt” code is complete, run “Results_Summary.txt”. Format: Below is the replication procedure for the attached data set for the portion of the analyses using a simulated data set: Data The data used in the application section of the manuscript consist of geocoded birth records from the North Carolina State Center for Health Statistics, 2005-2008. In the simulation study section of the manuscript, we simulate synthetic data that closely match some of the key features of the birth certificate data while maintaining confidentiality of any actual pregnant women. Availability Due to the highly sensitive and identifying information contained in the birth certificate data (including latitude/longitude and address of residence at delivery), we are unable to make the data from the application section publically available. However, we will make one of the simulated datasets available for any reader interested in applying the method to realistic simulated birth records data. This will also allow the user to become familiar with the required inputs of the model, how the data should be structured, and what type of output is obtained. While we cannot provide the application data here, access to the North Carolina birth records can be requested through the North Carolina State Center for Health Statistics, and requires an appropriate data use agreement. Description Permissions: These are simulated data without any identifying information or informative birth-level covariates. We also standardize the pollution exposures on each week by subtracting off the median exposure amount on a given week and dividing by the interquartile range (IQR) (as in the actual application to the true NC birth records data). The dataset that we provide includes weekly average pregnancy exposures that have already been standardized in this way while the medians and IQRs are not given. This further protects identifiability of the spatial locations used in the analysis. This dataset is associated with the following publication: Warren, J., W. Kong, T. Luben, and H. Chang. Critical Window Variable Selection: Estimating the Impact of Air Pollution on Very Preterm Birth. Biostatistics. Oxford University Press, OXFORD, UK, 1-30, (2019).

These data represent mean annual precipitation in the Louisiana StreamStats study area for the period of 1971-2000.

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in United States. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in United States. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in United States, householders within the 45 to 64 years age group have the highest median household income at $94,847, followed by those in the 25 to 44 years age group with an income of $87,575. Meanwhile householders within the 65 years and over age group report the second lowest median household income of $57,108. Notably, householders within the under 25 years age group, had the lowest median household income at $43,534.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for United States median household income by age. You can refer the same here

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Craig County. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Craig County. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Craig County, householders within the 45 to 64 years age group have the highest median household income at $61,163, followed by those in the 25 to 44 years age group with an income of $51,042. Meanwhile householders within the 65 years and over age group report the second lowest median household income of $44,122. Notably, householders within the under 25 years age group, had the lowest median household income at $34,395.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Craig County median household income by age. You can refer the same here

Median time in port (days) – All ships: The median time vessels spent within port limits (in days).

1000 simulated data sets stored in a list of R dataframes used in support of Reisetter et al. (submitted) 'Mixture model normalization for non-targeted gas chromatography / mass spectrometry metabolomics data'. These are results after normalization using median scaling as described in Reisetter et al.

Malaysia HIBAS: Monthly Gross Income: Median: Top 20%: Bumiputera data was reported at 11,819.000 MYR in 2016. This records an increase from the previous number of 10,301.000 MYR for 2014. Malaysia HIBAS: Monthly Gross Income: Median: Top 20%: Bumiputera data is updated yearly, averaging 11,060.000 MYR from Dec 2014 (Median) to 2016, with 2 observations. The data reached an all-time high of 11,819.000 MYR in 2016 and a record low of 10,301.000 MYR in 2014. Malaysia HIBAS: Monthly Gross Income: Median: Top 20%: Bumiputera data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Malaysia – Table MY.H033: Household Income and Basic Amenities Survey: Monthly Gross Income: Household Group: Median and Mean: by Ethnic Group.

This layer shows median earnings by occupational group broken down by sex. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Only full-time year-round workers included. Median earnings is based on earnings in past 12 months of survey. Occupation Groups based on Bureau of Labor Statistics (BLS)' Standard Occupation Classification (SOC). This layer is symbolized to show median earnings of the full-time, year-round civilian employed population. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B24022 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Portugal PT: Proportion of People Living Below 50 Percent Of Median Income: % data was reported at 10.500 % in 2021. This records a decrease from the previous number of 12.300 % for 2020. Portugal PT: Proportion of People Living Below 50 Percent Of Median Income: % data is updated yearly, averaging 12.200 % from Dec 2003 (Median) to 2021, with 19 observations. The data reached an all-time high of 14.400 % in 2013 and a record low of 10.500 % in 2021. Portugal PT: Proportion of People Living Below 50 Percent Of Median Income: % data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Portugal – Table PT.World Bank.WDI: Social: Poverty and Inequality. The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.;World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.;;The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

GeoMAD is the Digital Earth Africa (DE Africa) surface reflectance geomedian and triple Median Absolute Deviation data service. It is a cloud-free composite of satellite data compiled over specific timeframes. This service is ideal for longer-term time series analysis, cloudless imagery and statistical accuracy.

GeoMAD has two main components: Geomedian and Median Absolute Deviations (MADs).

The geomedian component combines measurements collected over the specified timeframe to produce one representative, multispectral measurement for every pixel unit of the African continent. The end result is a comprehensive dataset that can be used to generate true-colour images for visual inspection of anthropogenic or natural landmarks. The full spectral dataset can be used to develop more complex algorithms.

For each pixel, invalid data is discarded, and remaining observations are mathematically summarised using the geomedian statistic. Flyover coverage provided by collecting data over a period of time also helps scope intermittently cloudy areas.

Variations between the geomedian and the individual measurements are captured by the three Median Absolute Deviation (MAD) layers. These are higher-order statistical measurements calculating variation relative to the geomedian. The MAD layers can be used on their own or together with geomedian to gain insights about the land surface and understand change over time.Key PropertiesGeographic Coverage: Continental Africa - approximately 37° North to 35° SouthTemporal Coverage: 2013 – 2020*Spatial Resolution: 30 x 30 meterUpdate frequency: Annual from 2013 - 2020Product Type: Surface Reflectance (SR)Product Level: Analysis Ready (ARD)Number of Bands: 10 BandsParent Dataset: Landsat Collection 2 Level-2 Surface ReflectanceSource Data Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)Service Coordinate System: WGS 84 / NSIDC EASE-Grid 2.0 Global (EPSG:6933)*Time is enabled on this service using UTC – Coordinated Universal Time. To assure you are seeing the correct year for each annual slice of data, the time zone must be set specifically to UTC in the Map Viewer settings each time this layer is opened in a new map. More information on this setting can be found here: Set the map time zone.ApplicationsGeoMAD is the Digital Earth Africa (DE Africa) surface reflectance geomedian and triple Median Absolute Deviation data service. It is a cloud-free composite of satellite data compiled over specific timeframes. This service is ideal for:Longer-term time series analysisCloud-free imageryStatistical accuracyAvailable BandsBand IDDescriptionValue rangeData typeNo data valueSR_B2Geomedian SR_B2 (Blue)1 - 10000uint160SR_B3Geomedian SR_B3 (Green)1 - 10000uint160SR_B4Geomedian SR_B4 (Red)1 - 10000uint160SR_B5Geomedian SR_B5 (NIR)1 - 10000uint160SR_B6Geomedian SR_B6 (SWIR 1)1 - 10000uint160SR_B7Geomedian SR_B7 (SWIR 2)1 - 10000uint160SMADSpectral Median Absolute Deviation0 - 1float32NaNEMADEuclidean Median Absolute Deviation0 - 31623float32NaNBCMADBray-Curtis Median Absolute Deviation0 - 1float32NaNCOUNTNumber of clear observations1 - 65535uint160Bands have been subdivided as follows:Geomedian - 6 bands: The geomedian is calculated using the spectral bands of data collected during the specified time period. Surface reflectance values have been scaled between 1 and 10000 to allow for more efficient data storage as unsigned 16-bit integers (uint16). Note parent datasets often contain more bands, some of which are not used in GeoMAD.Median Absolute Deviations (MADs) - 3 bands: Deviations from the geomedian are quantified through median absolute deviation calculations. The GeoMAD service utilises three MADs, each stored in a separate band: Euclidean MAD (EMAD), spectral MAD (SMAD), and Bray-Curtis MAD (BCMAD). Each MAD is calculated using the same ten bands as in the geomedian. SMAD and BCMAD are normalized ratios, therefore they are unitless and their values always fall between 0 and 1. EMAD is a function of surface reflectance but is neither a ratio nor normalized, therefore its valid value range depends on the number of bands used in the geomedian calculation - ten in GeoMAD.Count - 1 band: The number of clear satellite measurements of a pixel for that calendar year. This is around 20 for Landsat 8 annually, but doubles at areas of overlap between scenes. “Count” is not incorporated in either the geomedian or MADs calculations. It is intended for metadata analysis and data validation.ProcessingAll clear observations for the given time period are collated from the parent dataset. Cloudy pixels are identified and excluded. The geomedian and MADs calculations are then performed by the hdstats package. Annual GeoMAD datasets for the period use hdstats version 0.2.Known LimitationsThe Landsat 8 (& 9) GeoMAD has a known issue with data quality over marine regions. The GeoMAD algorithm uses pixel quality information from the input data to identify and mask pixels with poor quality obervations. Landsat 8 & 9 analysis ready satellite images over the ocean often contain negative surface reflectance values, and the GeoMAD masking procedures remove pixels where any negative values occur. Thus, in regions where pixels are persistently negative throughout the year, the GeoMAD product will contain a no-data value. An example of this can be seen in Image 7 below where a shallow marine system contains no-data values in the GeoMAD because the NIR band values in the input data are persistently negative.More details on this dataset can be found here.

Median number of participants across active IXPs located in a country. Data is retrieved during the month of December in each year. Packet Clearing House (PCH) is an intergovernmental treaty organization responsible for providing operational support and security to critical Internet infrastructure, including Internet exchange points and the core of the domain name system. See: https://www.pch.net/ixp/data

This plan for the prevention of natural hazards (PPRN) concerns the municipality of St-Pardoult and was approved by prefectural decree of 28/06/1996. The different layers present in this data set are as follows: — scope of exposure to the risk of the PPRN; — regulatory zoning of the PPRN; — Existing and complementary information on the PPRN;

ensemble median global sea surface temperature (emsst) is a daily sst dataset constructed by nagoya university from an ensemble of 18 global sst products for the period from january 1, 1988 to february 28, 2019. the data set includes sst calculated as an ensemble median on each 0.25 degree by 0.25 degree grids over global ice-free oceans. the data set also includes an ensemble mean, standard deviation, minimum, maximum, number and kind of source products used.

HIBAS: Monthly Gross Income: Median: Top 20%: Johor data was reported at 12,304.000 MYR in 2016. HIBAS: Monthly Gross Income: Median: Top 20%: Johor data is updated yearly, averaging 12,304.000 MYR from Dec 2016 (Median) to 2016, with 1 observations. HIBAS: Monthly Gross Income: Median: Top 20%: Johor data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Malaysia – Table MY.H034: Household Income and Basic Amenities Survey: Monthly Gross Income: Household Group: Median and Mean: by State.

HIBAS: Monthly Gross Income: Mean: Middle 40%: Sabah data was reported at 5,037.000 MYR in 2016. HIBAS: Monthly Gross Income: Mean: Middle 40%: Sabah data is updated yearly, averaging 5,037.000 MYR from Dec 2016 (Median) to 2016, with 1 observations. HIBAS: Monthly Gross Income: Mean: Middle 40%: Sabah data remains active status in CEIC and is reported by Department of Statistics. The data is categorized under Global Database’s Malaysia – Table MY.H034: Household Income and Basic Amenities Survey: Monthly Gross Income: Household Group: Median and Mean: by State.

The median of the total annual number of hours required for the preparation, filing, and payment of all taxes (profit taxes, labor taxes, VAT, GST, or sales taxes) in a fiscal year.