This data release supports the analysis of the recurrence interval of post-fire debris-flow generating rainfall in the southwestern United States. We define the recurrence interval of the peak 15-, 30-, and 60-minute rainfall intensities for 316 observations of post-fire debris-flow occurrence in 18 burn areas, 5 U.S. states, and 7 climate types (as defined by Beck, H. E., Zimmermann, N. E., McVicar, T. R., Vergopolan, N., Berg, A., & Wood, E. F. (2018). Present and future Köppen-Geiger climate classification maps at 1-km resolution. Scientific Data, 5(1), 180214. doi:10.1038/sdata.2018.214).

FluidHarmony is an algorithmic method for defining a hierarchical harmonic lexicon in equal temperaments. It utilizes an enharmonic weighted Fourier transform space to represent pitch class set (pcsets) relations. The method ranks pcsets based on user-defined constraints: the importance of interval classes (ICs) and a reference pcset. Evaluation of 5,184 Western musical pieces from the 16th to 20th centuries shows FluidHarmony captures 8% of the corpus's harmony in its top pcsets. This highlights the role of ICs and a reference pcset in regulating harmony in Western tonal music while enabling systematic approaches to define hierarchies and establish metrics beyond 12-TET.

Parameters and values, simulation study of bias due to early death and loss-to-follow up events occurring during a hypothetical post-treatment initiation sputum collection interval among participants missing a pre-treatment sputum culture.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, for 2020, the 2020 Census provides the official counts of the population and housing units for the nation, states, counties, cities, and towns. For 2016 to 2019, the Population Estimates Program provides estimates of the population for the nation, states, counties, cities, and towns and intercensal housing unit estimates for the nation, states, and counties..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.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..Source: U.S. Census Bureau, 2016-2020 American Community Survey 5-Year Estimates.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). The effect of nonsampling error is not represented in these tables..For more information on understanding race and Hispanic origin data, please see the Census 2010 Brief entitled, Overview of Race and Hispanic Origin: 2010, issued March 2011. (pdf format).The Hispanic origin and race codes were updated in 2020. For more information on the Hispanic origin and race code changes, please visit the American Community Survey Technical Documentation website..The 2016-2020 American Community Survey (ACS) data generally reflect the September 2018 Office of Management and Budget (OMB) delineations of metropolitan and micropolitan statistical areas. In certain instances, the names, codes, and boundaries of the principal cities shown in ACS tables may differ from the OMB delineation lists due to differences in the effective dates of the geographic entities..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on Census 2010 data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

Although the American Community Survey (ACS) produces population, demographic and housing unit estimates, the decennial census is the official source of population totals for April 1st of each decennial year. In between censuses, the Census Bureau's Population Estimates Program produces and disseminates the official estimates of the population for the nation, states, counties, cities, and towns and estimates of housing units and the group quarters population for states and counties..Information about the American Community Survey (ACS) can be found on the ACS website. Supporting documentation including code lists, subject definitions, data accuracy, and statistical testing, and a full list of ACS tables and table shells (without estimates) can be found on the Technical Documentation section of the ACS website.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..Source: U.S. Census Bureau, 2023 American Community Survey 1-Year Estimates.ACS data generally reflect the geographic boundaries of legal and statistical areas as of January 1 of the estimate year. For more information, see Geography Boundaries by Year..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). The effect of nonsampling error is not represented in these tables..Users must consider potential differences in geographic boundaries, questionnaire content or coding, or other methodological issues when comparing ACS data from different years. Statistically significant differences shown in ACS Comparison Profiles, or in data users' own analysis, may be the result of these differences and thus might not necessarily reflect changes to the social, economic, housing, or demographic characteristics being compared. For more information, see Comparing ACS Data..The age dependency ratio is derived by dividing the combined under-18 and 65-and-over populations by the 18-to-64 population and multiplying by 100..The old-age dependency ratio is derived by dividing the population 65 and over by the 18-to-64 population and multiplying by 100..The child dependency ratio is derived by dividing the population under 18 by the 18-to-64 population and multiplying by 100..When information is missing or inconsistent, the Census Bureau logically assigns an acceptable value using the response to a related question or questions. If a logical assignment is not possible, data are filled using a statistical process called allocation, which uses a similar individual or household to provide a donor value. The "Allocated" section is the number of respondents who received an allocated value for a particular subject..Estimates of urban and rural populations, housing units, and characteristics reflect boundaries of urban areas defined based on 2020 Census data. As a result, data for urban and rural areas from the ACS do not necessarily reflect the results of ongoing urbanization..Explanation of Symbols:- The estimate could not be computed because there were an insufficient number of sample observations. For a ratio of medians estimate, one or both of the median estimates falls in the lowest interval or highest interval of an open-ended distribution. For a 5-year median estimate, the margin of error associated with a median was larger than the median itself.N The estimate or margin of error cannot be displayed because there were an insufficient number of sample cases in the selected geographic area. (X) The estimate or margin of error is not applicable or not available.median- The median falls in the lowest interval of an open-ended distribution (for example "2,500-")median+ The median falls in the highest interval of an open-ended distribution (for example "250,000+").** The margin of error could not be computed because there were an insufficient number of sample observations.*** The margin of error could not be computed because the median falls in the lowest interval or highest interval of an open-ended distribution.***** A margin of error is not appropriate because the corresponding estimate is controlled to an independent population or housing estimate. Effectively, the corresponding estimate has no sampling error and the margin of error may be treated as zero.

In 1999, the U.S. Geological Survey (USGS), in partnership with the South Carolina Sea Grant Consortium, began a study to investigate processes affecting shoreline change along the northern coast of South Carolina, focusing on the Grand Strand region. Previous work along the U.S. Atlantic coast shows that the structure and composition of older geologic strata located seaward of the coast heavily influences the coastal behavior of areas with limited sediment supply, such as the Grand Strand. By defining this geologic framework and identifying the transport pathways and sinks of sediment, geoscientists are developing conceptual models of the present-day physical processes shaping the South Carolina coast. The primary objectives of this research effort are: 1) to provide a regional synthesis of the shallow geologic framework underlying the coastal upland, shoreface and inner continental shelf, and define its role in coastal evolution and modern beach behavior; 2) to identify and model the physical processes affecting coastal ocean circulation and sediment transport, and to define their role in shaping the modern shoreline; and 3) to identify sediment sources and transport pathways; leading to construction of a regional sediment budget.

This web map visualizes the prevalence of households in a given geography that do not own a computer, smartphone, or tablet. Data are shown by tract, county, and state boundaries -- zoom out to see data visualized for larger geographies. The map also displays the boundary lines for the jurisdiction of Rochester, NY (visible when viewing the tract level data), as this map was created for a Rochester audience.This web map draws from an Esri Demographics service that is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. 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: 2014-2018ACS Table(s): B28001, B28002 (Not all lines of ACS table B28002 are available in this feature layer)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 19, 2019National 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. 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. 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 clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. 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., -555555...) have been set to null. 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. NOTE: any calculated percentages or counts that contain estimates that have null margins of error yield null margins of error for the calculated fields.

This map reveals the most popular alternatives to driving alone to work. This includes workers who commuted to work by:WalkingWorking from homePublic transportation (excluding taxi)CarpoolingThis map is based on workers' place of residence by mode of commute. This is shown by tract, county, and state boundaries. 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. 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: 2015-2019ACS Table(s): B08301 (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 10, 2020National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis map can also 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. 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. 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 clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. 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.

Sputum culture conversion and early death and loss-to-follow up events among participants missing a sputum culture in the specified interval before (-) and after (+) treatment initiation, endTB observational cohort.

Vertical Profiles of Urban wind speed, wind direction and turbulence measured by LiDAR on campus of University College Cork, Ireland

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README version 1.3, 21/07/2022

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Contact info:

Paul Leahy, University College Cork

paul.leahy@ucc.ie | +353 21 4902017

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Contents

1. Measurement location and time period

2. What is measured (brief description)

3. Instrumentation

4. CSV file detailed descriptions

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1. Measurement location and time period:

North roof of Kane Building, University College Cork (UCC), Ireland.

Lat 51 d 53 m 34 s N.

Long 8 d 29 m 39 s W.

Roof is c. 39 m above sea level, and c. 26 m above ground level (ground level reference point is the car park West of the UCC Kane Building).

The measurements were taken over a time period of several months in the years 2013 / 2014.

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1. What is measured (brief description):

• LiDAR Wind speed (horizontal and vertical), wind direction, turbulence intensity at 5 altitudes; reference point (0 m) for these altitudes is the top of the LiDAR instrument c. 1.2 m above roof level.

• Air temperature, atmospheric pressure, relative humidity.

• Wind speed and direction from an ultrasonic anemometer mounted on top of the instrument (c. 1.2 m above roof level).

• 10-minute average values (2 files) and high-resolution (c. 23 sec) data (1 file) are provided.

See 'CSV file detailed description' below for detailed information.

• Diagnostic information.

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2.1 Surrounding terrain:

Surrounding area is urban/suburban. The aspect is northerly.

To the West: 2-5 storey buildings, open spaces, suburban.

To the South: 2-3 storey buildings, open spaces, trees, river.

To the East: 2-3 storey buildings, open spaces.

To the North: A higher section of the Kane Building roof (47 m asl), 1-3 storey buildings, suburban.

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1. Instrumentation:

ZephIR 175 continuous wave wind profiling LiDAR with integrated sonic anemometer, temperature, humidity, air temperature pressure sensors and GPS.

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1. CSV files detailed description:

4.1 Data on 10-minute averages:

Filename 05092013-03122013_10min_res.csv contains:

10 minute averaged data from 05/09/2013 to 03/12/2013.

Measurement altitudes: 148 m, 90 m, 69 m, 44 m, 19m above instrument level.

Filename 03122013-07082014_10min_res.csv contains:

10 minute averaged data from: 03/12/2013 to 07/08/2014.

Measurement altitudes: 148 m, 90 m, 50 m, 35 m, 15 m above instrument level.

Note: from 19/06/2014 onwards, LiDAR data missing (MET data continues).

The first two rows contain header information.

Row 1 contains location information (GPS record)) and the measurement altitudes for wind speeds.

Sample GPS record: N51535775W8296590 = 51 d 53.5775 m North; 8 d 29.6590 m West.

Row 2 contains the data column headers including units.

Wind speeds at each altitude are recorded:

No of Packets (= number of scan units averaged over) []

Wind direction (mean) [deg]

Horizontal wind speed (mean) & standard deviation [m/s]

Vertical wind speed (mean) & standard deviation [m/s]

Horizontal variance [m^2/s^2]

Horizontal min [m/s]

Horizontal max [m/s]

TI (turbulence intensity) []

Other meteorological data:

Air temperature [oC]

Pressure [mbar]

Rel. Humidity [%]

Rain indicator [unitless] Higher values indicate more rain during the averaging interval.

Wind Speed m/s

Wind direction deg.

Other housekeeping and diagnostic data:

Instrument tilt [deg]

Instrument bearing [deg]

GPS data [degrees N, degrees W]

Battery voltage [V]

Optics, electronics and battery temperature [oC]

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4.2 Data with high time resolution (~23 s):

Filename 05092013-11112013_23s_res.csv contains:

High resolution data from 05/09/2013 to 11/11/2013

Measurement altitudes: 148 m, 90 m, 69 m, 44 m, 19m.

Note on time resolution:

The time resolution of processed wind measurements is c. 3 seconds per wind level, and around 8 seconds to reset to the first level. A full wind profile measurement at 5 altitudes therefore takes around (5 x 3) + 8 = 23 s to complete.

The raw scanning resolution of the instrument is higher than this, as each wind measurement is an average of several values.

Row 1 contains location information (lat, long) and the vertical measurement levels for wind speeds.

Row 2 contains the data column headers including units.

Wind speeds at each altitude are recorded:

No of Packets (= scan units averaged over) []

Wind direction (mean) [deg]

Horizontal wind speed (mean) & standard deviation [m/s]

Vertical wind speed (mean) & standard deviation [m/s]

Horizontal variance [m^2/s^2] not defined as measurement interval is too short.

Horizontal min [m/s] not defined as measurement interval is too short.

Horizontal max [m/s] not defined as measurement interval is too short.

TI (turbulence intensity) [] not defined as measurement interval is too short.

Other meteorological data:

Air temperature [oC]

Pressure [mbar]

Rel. Humidity [%]

Rain indicator [unitless] Higher values indicate more rain during the scanning interval.

Wind Speed m/s

Wind direction [deg] (column 'MET Direction' measured at the top of the instrument by the ultrasonic anemometer.

Other housekeeping and diagnostic data:

Instrument tilt [deg]

Instrument bearing [deg]

GPS data [degrees N, degrees W]

Battery voltage [V]

Optics, electronics and battery temperature [oC]

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4.3 Quality control indicators:

9998 atmospheric conditions which adversely affect LiDAR wind speed measurements e.g. fog

9999 high quality wind speed measurement not possible e.g. very low wind speed or obscuration of optical path

Status Flag 'Green' => good

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This layer shows median household income by race and by age of householder. This is shown by tract, county, and state boundaries. 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. Median income and income source is based on income in past 12 months of survey. This layer is symbolized to show median household income. 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): B19013B, B19013C, B19013D, B19013E, B19013F, B19013G, B19013H, B19013I, B19049, B19053Data 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.

This layer shows disability status by sex and age group. This is shown by tract, county, and state boundaries. 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. This layer is symbolized to show the percentage of elderly (65+) with a disability. 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: 2015-2019ACS Table(s): B18101Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 10, 2020National 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. 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 clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. 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.

Data has been processed by NODC to the NODC standard Bathythermograph (XBT Aircraft) (C118), Bathythermograph (XBT) (C116), Bathythermograph XBT Selected Depths (SBT) (C125), and High-Resolution CTD/STD (F022) formats. The C116/C118 format contains temperature-depth profile data obtained using expendable bathythermograph (XBT) instruments. Cruise information, position, date and time were reported for each observation. The data record was comprised of pairs of temperature-depth values. Unlike the MBT Data File, in which temperature values were recorded at uniform 5 m intervals, the XBT data files contained temperature values at non-uniform depths. These depths were recorded at the minimum number of points ("inflection points") required to accurately define the temperature curve. Standard XBTs can obtain profiles to depths of either 450 or 760 m. With special instruments, measurements can be obtained to 1830 m. Prior to July 1994, XBT data were routinely processed to one of these standard types. XBT data are now processed and loaded directly in to the NODC Ocean Profile Data Base (OPDB). Historic data from these two data types were loaded into the OPDB. The C116/C118 format contains temperature-depth profile data obtained using expendable bathythermograph (XBT) instruments. Cruise information, position, date and time were reported for each observation. The data record was comprised of pairs of temperature-depth values. Unlike the MBT Data File, in which temperature values were recorded at uniform 5 m intervals, the XBT data files contained temperature values at non-uniform depths. These depths were recorded at the minimum number of points ("inflection points") required to accurately define the temperature curve. Standard XBTs can obtain profiles to depths of either 450 or 760 m. With special instruments, measurements can be obtained to 1830 m. Prior to July 1994, XBT data were routinely processed to one of these standard types. XBT data are now processed and loaded directly in to the NODC Ocean Profile Data Base (OPDB). Historic data from these two data types were loaded into the OPDB. The UBT (C125) format contains temperature-depth profile data obtained using expendable bathythermograph (XBT) instruments. Cruise information, position, date and time were reported for each observation. The data records are comprised of pairs of temperature-depth values. Depths are selected by the originator - usually at standard horizons or some fixed interval. Standard XBTs can obtain profiles to depths of either 450 or 760 m. Special instruments permitted measurements to be obtained to 1830 m. The F022 format contains high-resolution data collected using CTD (conductivity-temperature-depth) and STD (salinity-temperature-depth) instruments. As they are lowered and raised in the oceans, these electronic devices provide nearly continuous profiles of temperature, salinity, and other parameters. Data values may be subject to averaging or filtering or obtained by interpolation and may be reported at depth intervals as fine as 1m. Cruise and instrument information, position, date, time and sampling interval are reported for each station. Environmental data at the time of the cast (meteorological and sea surface conditions) may also be reported. The data record comprises values of temperature, salinity or conductivity, density (computed sigma-t), and possibly dissolved oxygen or transmissivity at specified depth or pressure levels. Data may be reported at either equally or unequally spaced depth or pressure intervals. A text record is available for comments.

WWF developed a global analysis of the world's most important deforestation areas or deforestation fronts in 2015. This assessment was revised in 2020 as part of the WWF Deforestation Fronts Report.Emerging Hotspots analysisThe goal of this analysis was to assess the presence of deforestation fronts: areas where deforestation is significantly increasing and is threatening remaining forests. We selected the emerging hotspots analysis to assess spatio-temporal trends of deforestation in the pan-tropics.Spatial UnitWe selected hexagons as the spatial unit for the hotspots analysis for several reasons. They have a low perimeter-to-area ratio, straightforward neighbor relationships, and reduced distortion due to curvature of the earth. For the hexagon size we decided on a unit of 1,000 ha, based on the resolution of the deforestation data (250m) meant that we could aggregate several deforestation events inside units over time. Hexagons that are closer to or equal to the size of a deforestation event means there could only be one event before the forest is gone and limit statistical analysis.We processed over 13 million hexagons for this analysis and limited the emerging hotspots analysis to only hexagons with at least 15% forest cover remaining (from the all-evidence forest map). This prevented including hotspots in agricultural areas or where all forest has been converted.OutputsThis analysis uses the Getis-Ord and Mann-Kendall statistics to identify spatial clusters of deforestation which have a non-parametric significant trend across a time series. The spatial clusters are defined by the spatial unit and a temporal neighborhood parameter. We use a neighborhood parameter of 5km to include spatial neighbors in the hotspots assessment and time slices for each country described below. Deforestation events are summarized by a spatial unit (hexagons described below) and the results comprise a trends assessment which defines increasing or decreasing deforestation in the units determined at 3 different confidence intervals (90%, 95% and 99%) and the spatio-temporal analysis classifying areas into 8 hot unique or cold spot categories. Our analysis identified 7 hotspot categories:Hotspot TypeDefinitionNewA location with a statistically significant increasing hotspots only in the final time stepConsecutiveAn uninterrupted run of statistically significant hotspot in the final time-steps IntensifyingA statistically significant hotspot for >90% of the bins, including the final time stepPersistentA statistically significant hotspot for >90% of the bins with no upward or downward trend in clustering intensityDiminishingA statistically significant hotspot for >90% of the time steps, with where the clustering is decreasing, or the most recent time step is not hot.SporadicA on-again then off-again hotspot where <90% of the time-step intervals have been statistically significant hot spots and none have been statistically significant cold spots.HistoricalAt least ninety percent of the time-step intervals have been statistically significant hot spots, with the exception of the final time steps..For the evaluation of spatio-temporal trends of tropical deforestation we selected the Terra-i deforestation dataset to define the temporal deforestation patterns. Terra-i is a freely available monitoring system derived from the analysis of MODIS (NVDI) and TRMM (rainfall) data which are used to assess forest cover changes due to anthropic interventions at a 250 m resolution [ref]. It was first developed for Latin American countries in 2012, and then expanded to pan-tropical countries around the world. Terra-i has generated maps of vegetation loss every 16 days, since January 2004. This relatively high temporal resolution of twice monthly observations allows for a more detailed emerging hotspots analysis, increasing the number of time steps or bins available for assessing spatio-temporal patterns relative to annual datasets. Next, the spatial resolution of 250m is more relevant for detecting forest loss than changes in individual tree cover or canopies and is better adapted to process trends on large scales. Finally, the added value of the Terra-i algorithm is that it employs an additional neural network machine learning to identify vegetation loss that is due to anthropic causes as opposed to natural events or other causes. Our dataset comprised all Terra-i deforestation events observed between 2004 and 2017. Temporal unitThe temporal unit or time slice was selected for each country according to the distribution of data. The deforestation data comprised 16-day periods between 2004 and 2017 for a total of 312 potential observation time periods. These were aggregated to time bins to overcome any seasonality in the detection of deforestation events (due to clouds). The temporal unit is combined with the spatial parameter (i.e. 5km) to create the space-time bins for hotspot analysis. For dense time series or countries with a lot of deforestation events (i.e. Brazil) a smaller time slice was used (i.e. 3 months, n=54) with a neighborhood interval of 8 months, meaning that the previous year and next year together were combined to assess statistical trends relative to the global variables together. The rule we employed was that the time slice x neighborhood interval was equal to 24 months, or 2 years, in order to look at general trends over the entire time period and prevent the hotspots analysis from being biased to short time intervals of a few months.Deforestation FrontsFinally, using trends and hotpots we identify 24 major deforestation fronts, areas of significantly increasing deforestation and the focus of WWF's call for action to slow deforestation.

2013-2023 Virginia Non-Single Occupancy Vehicle (SOV) Travel Percent by Census Urban Area. Contains estimates. Workers 16 years and over, commuting to work, who are NOT using a car, truck, or van when driving alone.

U.S. Census Bureau; American Community Survey, American Community Survey 5-Year Estimates, Table DP03, Column DP03_0019PE Data accessed from: Census Bureau's API for American Community Survey (https://www.census.gov/data/developers/data-sets.html)

Documentation of the method to calculate Non-SOV is provided by the Federal Highway Administration (https://www.fhwa.dot.gov/tpm/guidance/hif18024.pdf) page 38 explains the calculation of the Non-SOV Travel measure.

Urban areas with values of -666,666,666 or 0 have blanks calculated for Non-SOV values.

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.

ABSTRACT: Initial Maintenance Review Board Report (MRBR) uses in service operation experience as a reference to define maintenance tasks intervals. However, in general, there is no structured data to compare systems performance and provide useful information to the analysts' decision-making. Even when engineering judgment is based on certification process, structural design, components intrinsic reliability and so on, the analysts responsible for maintenance tasks definitions tend to choose rather conservative proposals. This article presents a method to optimize preventive maintenance tasks intervals and use structured data based on interval optimization process to define maintenance intervals to those of similar systems under development. The method has been applied in an aircraft manufacturing company using current operation database after regulatory authorities' approval. As a result, it has been feasible to propose to the selected system, a maintenance task interval 100% higher than the one applicable to a similar system under operation.

2013-2023 Virginia Population by Sex by Age by Census Block Group. Contains estimates and margins of error.

U.S. Census Bureau; American Community Survey, American Community Survey 5-Year Estimates, Table B01001 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.

Linear trend analysis of time series is standard procedure in many scientific disciplines. If the number of data is large, a trend may be statistically significant even if data are scattered far from the trend line. This study introduces and tests a quality criterion for time trends referred to as statistical meaningfulness, which is a stricter quality criterion for trends than high statistical significance. The time series is divided into intervals and interval mean values are calculated. Thereafter, r2 and p values are calculated from regressions concerning time and interval mean values. If r2≥0.65 at p≤0.05 in any of these regressions, then the trend is regarded as statistically meaningful. Out of ten investigated time series from different scientific disciplines, five displayed statistically meaningful trends. A Microsoft Excel application (add-in) was developed which can perform statistical meaningfulness tests and which may increase the operationality of the test. The presented method for distinguishing statistically meaningful trends should be reasonably uncomplicated for researchers with basic statistics skills and may thus be useful for determining which trends are worth analysing further, for instance with respect to causal factors. The method can also be used for determining which segments of a time trend may be particularly worthwhile to focus on.

Extracting regularities from ongoing stimulus streams to form predictions is crucialfor adaptive behavior. Such regularities exist in terms of the content of the stimuliand their timing, both of which are known to interactively modulate sensoryprocessing. In real-world stimulus streams such as music, regularities can occurat multiple levels, both in terms of contents (e.g., predictions relating to individualnotes vs. their more complex groups) and timing (e.g., pertaining to timingbetween intervals vs. the overall beat of a musical phrase). However, it is unknownwhether the brain integrates predictions in a manner that is mutually congruent(e.g., if “beat” timing predictions selectively interact with “what” predictions fallingon pulses which define the beat), and whether integrating predictions in differenttiming conditions relies on dissociable neural correlates. To address thesequestions, our study manipulated “what” and “when” predictions at different levels– (local) interval-defining and (global) beat-defining – within the same stimulusstream, while neural activity was recorded using electroencephalogram (EEG) inparticipants (N = 20) performing a repetition detection task. Our results revealthat temporal predictions based on beat or interval timing modulated mismatchresponses to violations of “what” predictions happening at the predicted timepoints, and that these modulations were shared between types of temporalpredictions in terms of the spatiotemporal distribution of EEG signals. Effectiveconnectivity analysis using dynamic causal modeling showed that the integrationof “what” and “when” predictions selectively increased connectivity at relativelylate cortical processing stages, between the superior temporal gyrus and thefronto-parietal network. Taken together, these results suggest that the brainintegrates different predictions with a high degree of mutual congruence, but in ashared and distributed cortical network. This finding contrasts with recent studiesindicating separable mechanisms for beat-based and memory-based predictiveprocessing.

2013-2023 Virginia Ratio of Income to Poverty Level in the Past 12 Months by Census Block Group. Contains estimates and margins of error.

U.S. Census Bureau; American Community Survey, American Community Survey 5-Year Estimates, Table C17002 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.