This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

The Viewshed analysis layer is used to identify visible areas. You specify the places you are interested in, either from a file or interactively, and the Viewshed service combines this with Esri-curated elevation data to create output polygons of visible areas. Some questions you can answer with the Viewshed task include:What areas can I see from this location? What areas can see me?Can I see the proposed wind farm?What areas can be seen from the proposed fire tower?The maximum number of input features is 1000.Viewshed has the following optional parameters:Maximum Distance: The maximum distance to calculate the viewshed.Maximum Distance Units: The units for the Maximum Distance parameter. The default is meters.DEM Resolution: The source elevation data; the default is 90m resolution SRTM. Other options include 30m, 24m, 10m, and Finest.Observer Height: The height above the surface of the observer. The default value of 1.75 meters is an average height of a person. If you are looking from an elevation location such as an observation tower or a tall building, use that height instead.Observer Height Units: The units for the Observer Height parameter. The default is meters.Surface Offset: The height above the surface of the object you are trying to see. The default value is 0. If you are trying to see buildings or wind turbines add their height here.Surface Offset Units: The units for the Surface Offset parameter. The default is meters.Generalize Viewshed Polygons: Determine if the viewshed polygons are to be generalized or not. The viewshed calculation is based upon a raster elevation model which creates a result with stair-stepped edges. To create a more pleasing appearance, and improve performance, the default behavior is to generalize the polygons. This generalization will not change the accuracy of the result for any location more than one half of the DEM's resolution.By default, this tool currently works worldwide between 60 degrees north and 56 degrees south based on the 3 arc-second (approximately 90 meter) resolution SRTM dataset. Depending upon the DEM resolution pick by the user, different data sources will be used by the tool. For 24m, tool will use global dataset WorldDEM4Ortho (excluding the counties of Azerbaijan, DR Congo and Ukraine) 0.8 arc-second (approximately 24 meter) from Airbus Defence and Space GmbH. For 30m, tool will use 1 arc-second resolution data in North America (Canada, United States, and Mexico) from the USGS National Elevation Dataset (NED), SRTM DEM-S dataset from Geoscience Australia in Australia and SRTM data between 60 degrees north and 56 degrees south in the remaining parts of the world (Africa, South America, most of Europe and continental Asia, the East Indies, New Zealand, and islands of the western Pacific). For 10m, tool will use 1/3 arc-second resolution data in the continental United States from USGS National Elevation Dataset (NED) and approximately 10 meter data covering Netherlands, Norway, Finland, Denmark, Austria, Spain, Japan Estonia, Latvia, Lithuania, Slovakia, Italy, Northern Ireland, Switzerland and Liechtenstein from various authoritative sources.To learn more, read the developer documentation for Viewshed or follow the Learn ArcGIS exercise called I Can See for Miles and Miles. To use this Geoprocessing service in ArcGIS Desktop 10.2.1 and higher, you can either connect to the Ready-to-Use Services, or create an ArcGIS Server connection. Connect to the Ready-to-Use Services by first signing in to your ArcGIS Online Organizational Account:Once you are signed in, the Ready-to-Use Services will appear in the Ready-to-Use Services folder or the Catalog window:If you would like to add a direct connection to the Elevation ArcGIS Server in ArcGIS for Desktop or ArcGIS Pro, use this URL to connect: https://elevation.arcgis.com/arcgis/services. You will also need to provide your account credentials. ArcGIS for Desktop:ArcGIS Pro:The ArcGIS help has additional information about how to do this:Learn how to make a ArcGIS Server Connection in ArcGIS Desktop. Learn more about using geoprocessing services in ArcGIS Desktop.This tool is part of a larger collection of elevation layers that you can use to perform a variety of mapping analysis tasks.

This Guide is designed to assist you with using ArcGIS Online (AGOL)'s Map Viewer.An ArcGIS web map is an interactive display of geographic information. Web maps are made by adding and combining layers. The layers are made from data, they are logical collections of geographic data.Map Viewer can be used to view, explore and create web maps in ArcGIS Online.

The terms of use for community users setting up ARC Survey Hub ArcGIS Online accounts.

This image was uploaded for use as your organization's Shared Theme logo.

Electric Pocket Lighter Market was valued at $680.5 Mn in 2023, and is projected to reach $USD 951.9 Mn by 2032, at a CAGR of 3.80% from 2023 to 2032.

The dataset used in the experiments on the paper "Modeling citation worthiness by using attention‑based bidirectional long short‑term memory networks and interpretable models"For the pre-processing of the dataset, please refer to the paper Bonab et al., 2018 (http://doi.org/10.1145/3209978.3210162)We downloaded a copy of that dataset, adjusted some fields. The data are stored in jsonl format (each row is an json object), we list a couple of rows as example:{"cur_sent":"the nespole uses a client server architecture to allow a common user who is initially browsing through the web pages of a service provider on the internet to connect seamlessly to a human agent of the service provider who speaks another language and provides speech to speech translation service between the two parties","cur_scaled_len_features":{"type":1,"values":[0.06936542669584245,0.07202216066481995]},"cur_has_citation":1}
For the code using this dataset to modeling citation worthiness, please refer to https://github.com/sciosci/cite-worthiness

This layer was developed by the Research & Analytics Group of the Atlanta Regional Commission, using data from the U.S. Census Bureau’s American Community Survey 5-year estimates for 2013-2017, to show populations with computer and internet access by ARC 20 County in the Atlanta region.

The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent.

The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2013-2017). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available.

For further explanation of ACS estimates and margin of error, visit Census ACS website.

Naming conventions:

Prefixes:

None

Count

p

Percent

r

Rate

m

Median

a

Mean (average)

t

Aggregate (total)

ch

Change in absolute terms (value in t2 - value in t1)

pch

Percent change ((value in t2 - value in t1) / value in t1)

chp

Change in percent (percent in t2 - percent in t1)

Suffixes:

None

Change over two periods

_e

Estimate from most recent ACS

_m

Margin of Error from most recent ACS

_00

Decennial 2000

Attributes:

SumLevel

Summary level of geographic unit (e.g., County, Tract, NSA, NPU, DSNI, SuperDistrict, etc)

GEOID

Census tract Federal Information Processing Series (FIPS) code

NAME

Name of geographic unit

Planning_Region

Planning region designation for ARC purposes

Acres

Total area within the tract (in acres)

SqMi

Total area within the tract (in square miles)

County

County identifier (combination of Federal Information Processing Series (FIPS) codes for state and county)

CountyName

County Name

TotalHH_e

# Total households, 2017

TotalHH_m

# Total households, 2017 (MOE)

WithAComputer_e

# Households with a computer, 2017

WithAComputer_m

# Households with a computer, 2017 (MOE)

pWithAComputer_e

% Households with a computer, 2017

pWithAComputer_m

% Households with a computer, 2017 (MOE)

WithBroadband_e

# Households with broadband Internet, 2017

WithBroadband_m

# Households with broadband Internet, 2017 (MOE)

pWithBroadband_e

% Households with broadband Internet, 2017

pWithBroadband_m

% Households with broadband Internet, 2017 (MOE)

last_edited_date

Last date the feature was edited by ARC

Source: U.S. Census Bureau, Atlanta Regional Commission

Date: 2013-2017

For additional information, please visit the Census ACS website.

The Pedon Vault form will record soil exposures across the country for agency and citizen scientists to provide a means of population for a national database of sites to visit in the field for good exposures of soil profiles to be used by educators, scientists, interested public, and universities classes and soil judging teams. It will require that users have an ArcGIS Online account. It’s a great first step toward citizen science involvement with the national soils program.

Probabilistic seismic-hazard maps were prepared for the conterminous United States portraying peak horizontal acceleration and horizontal spectral response acceleration for 0.2- and 1.0-second periods with probabilities of exceedance of 10 percent in 50 years and 2 percent in 50 years. This particular data set is for peak horizontal acceleration with a 2 percent probability of exceedance in 50 years. All of the maps were prepared by combining the hazard derived from spatially smoothed historic seismicity with the hazard from fault-specific sources. The acceleration values contoured are the random horizontal component. The reference site condition is firm rock, defined as having an average shear-wave velocity of 760 m/sec in the top 30 meters corresponding to the boundary between NEHRP (National Earthquake Hazards Reduction program) site classes B and C.

For more information online visit: http://pubs.usgs.gov/sim/3195/, http://pubs.usgs.gov/of/2008/1128/, and http://earthquake.usgs.gov/hazards/

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

Interpretation of the extent of the Arckaringa Basin at time of issue.

Purpose

The data file is used to indicate the location and understood outer extent of the Arckaringa Basin at the time of issue. Basement highs (or inliers) that puncture the extent of Arckaringa Basin sediments are not displayed in this file.

Dataset History

The outer extent of the Arckaringa Basin has been interpreted from outcrop geology mapping as provided by the South Australian Government Department of State Development (DSD) from there online SARIG database, seismic data that was either collected by or provided to the DSD for record-keeping and regulatory requirements, as well as vetted downhole geological logging data as stored in the South Australian Government managed geoscientific database SA_Geodata.

Dataset Citation

SA Department of Environment, Water and Natural Resources (2015) Arckaringa basin - ARC. Bioregional Assessment Source Dataset. Viewed 26 May 2016, http://data.bioregionalassessments.gov.au/dataset/ddb8400d-1b76-43b1-8c1a-427fe1297884.

NOAA's National Geophysical Data Center (NGDC) is building high-resolution digital elevation models (DEMs) to support individual coastal States as part of the National Tsunami Hazard Mitigation Program's (NTHMP) efforts to improve community preparedness and hazard mitigation. These integrated bathymetric-topographic DEMs are used to support tsunami and coastal inundation mapping. Bathymetric, topographic, and shoreline data used in DEM compilation are obtained from various sources, including NGDC, the U.S. National Ocean Service (NOS), the U.S. Geological Survey (USGS), the U.S. Army Corps of Engineers (USACE), the Federal Emergency Management Agency (FEMA), and other federal, state, and local government agencies, academic institutions, and private companies. DEMs are referenced to various vertical and horizontal datums depending on the specific modeling requirements of each State. For specific datum information on each DEM, refer to the appropriate DEM documentation. Cell sizes also vary depending on the specification required by modelers in each State, but typically range from 8/15 arc-second (~16 meters) to 8 arc-seconds (~240 meters).This is an ArcGIS image service showing color shaded relief visualizations of high-resolution digital elevation models (DEMs) of U.S. coastal regions. NOAA's National Geophysical Data Center (NGDC) builds and distributes high-resolution coastal digital elevation models (DEMs) that integrate ocean bathymetry and land topography to support NOAA's mission to understand and predict changes in Earth's environment, and conserve and manage coastal and marine resources to meet our Nation's economic, social, and environmental needs. They can be used for modeling of coastal processes (tsunami inundation, storm surge, sea-level rise, contaminant dispersal, etc.), ecosystems management and habitat research, coastal and marine spatial planning, and hazard mitigation and community preparedness. DEMs included in this visualization: High-resolution DEMs of select U.S. coastal communities and surrounding areas. Most are at a resolution of 1/3 to 1 arc-second (approx 10-30 m); U.S. Coastal Relief Model: A 3 arc-second (approx 90 m) comprehensive view of the conterminous U.S. coastal zone, Puerto Rico, and Hawaii; Southern Alaska Coastal Relief Model: A 24 arc-second (approx. 500 m) model of Southern Alaska, spanning the Bering Sea, Aleutian Islands, and Gulf of Alaska. This map service can be used as a basemap. It has a transparent background, so it can also be shown as a layer on top of a different basemap. Please see NGDC's corresponding DEM Footprints map service for polygon footprints and more information about the individual DEMs used to create this composite view.A map service showing the location and coverage of land and seafloor digital elevation models (DEMs) available from NOAA's National Geophysical Data Center. NOAA's National Geophysical Data Center (NGDC) builds and distributes high-resolution, coastal digital elevation models (DEMs) that integrate ocean bathymetry and land topography to support NOAA's mission to understand and predict changes in Earth's environment, and conserve and manage coastal and marine resources to meet our Nation's economic, social, and environmental needs. They can be used for modeling of coastal processes (tsunami inundation, storm surge, sea-level rise, contaminant dispersal, etc.), ecosystems management and habitat research, coastal and marine spatial planning, and hazard mitigation and community preparedness. Layers available in the map service: Layers 1-4: DEMs by Category (includes various DEMs, both hosted at NGDC, and elsewhere on the web); Layers 6-11: NGDC DEM Projects (DEMs hosted at NGDC, color-coded by project); Layer 12: All NGDC Bathymetry DEMs (All bathymetry or bathy-topo DEMs hosted at NGDC).

Online Service für Wasserstandsüberwachung in den USA, CA, BE, NL, UK, IE, DE, AT, CH und Südtirol.

Uniform-hazard ground motion maps and their underlying GIS data were prepared for PGA and horizontal spectral accelerations at 0.2, 1.0, and 5.0 second period, with a probability of exceedance of 2%, 5% and 10% in 50 years, for NEHRP soil site classes B/C and D (VS30 equal to 760 and 260 m/s, respectively).Full dataset can be downloaded from: 04. Uniform-hazard ground motion maps for the conterminous U.S., Alaska, and Hawaii - ScienceBase-Catalog

NOAA's National Geophysical Data Center (NGDC) is building high-resolution digital elevation models (DEMs) for select U.S. coastal regions in the Gulf of Mexico. These integrated bathymetric-topographic DEMs were developed for NOAA Coastal Survey Development Laboratory (CSDL) through the American Recovery and Reinvestment Act (ARRA) of 2009 to evaluate the utility of the Vertical Datum Transformation tool (VDatum), developed jointly by NOAA's Office of Coast Survey (OCS), National Geodetic Survey (NGS), and Center for Operational Oceanographic Products and Services (CO-OPS). Bathymetric, topographic, and shoreline data used in DEM compilation are obtained from various sources, including NGDC, the U.S. Coastal Services Center (CSC), the U.S. Office of Coast Survey (OCS), the U.S. Army Corps of Engineers (USACE), and other federal, state, and local government agencies, academic institutions, and private companies. DEMs are referenced to the vertical tidal datum of North American Vertical Datum of 1988 (NAVD 88), Mean High Water (MHW) or Mean Lower Low Water (MLLW) and horizontal datum of North American Datum of 1983 (NAD 83). Cell size ranges from 1/3 arc-second (~10 meters) to 1 arc-second (~30 meters). The NOAA VDatum DEM Project was funded by the American Recovery and Reinvestment Act (ARRA) of 2009 (http://www.recovery.gov/).The DEM Global Mosaic is an image service providing access to bathymetric/topographic digital elevation models stewarded at NOAA's National Centers for Environmental Information (NCEI), along with the global GEBCO_2014 grid: http://www.gebco.net/data_and_products/gridded_bathymetry_data. NCEI builds and distributes high-resolution, coastal digital elevation models (DEMs) that integrate ocean bathymetry and land topography to support NOAA's mission to understand and predict changes in Earth's environment, and conserve and manage coastal and marine resources to meet our Nation's economic, social, and environmental needs. They can be used for modeling of coastal processes (tsunami inundation, storm surge, sea-level rise, contaminant dispersal, etc.), ecosystems management and habitat research, coastal and marine spatial planning, and hazard mitigation and community preparedness. This service is a general-purpose global, seamless bathymetry/topography mosaic. It combines DEMs from a variety of near sea-level vertical datums, such as mean high water (MHW), mean sea level (MSL), and North American Vertical Datum of 1988 (NAVD88). Elevation values have been rounded to the nearest meter, with DEM cell sizes going down to 1 arc-second. Higher-resolution DEMs, with greater elevation precision, are available in the companion NAVD88: http://noaa.maps.arcgis.com/home/item.html?id=e9ba2e7afb7d46cd878b34aa3bfce042 and MHW: http://noaa.maps.arcgis.com/home/item.html?id=3bc7611c1d904a5eaf90ecbec88fa799 mosaics. By default, the DEMs are drawn in order of cell size, with higher-resolution grids displayed on top of lower-resolution grids. If overlapping DEMs have the same resolution, the newer one is shown. Please see NCEI's corresponding DEM Footprints map service: http://noaa.maps.arcgis.com/home/item.html?id=d41f39c8a6684c54b62c8f1ab731d5ad for polygon footprints and more information about the individual DEMs used to create this composite view. In this visualization, the elevations/depths are displayed using this color ramp: http://gis.ngdc.noaa.gov/viewers/images/dem_color_scale.png.A map service showing the location and coverage of land and seafloor digital elevation models (DEMs) available from NOAA's National Centers for Environmental Information (NCEI). NCEI builds and distributes high-resolution, coastal digital elevation models (DEMs) that integrate ocean bathymetry and land topography to support NOAA's mission to understand and predict changes in Earth's environment, and conserve and manage coastal and marine resources to meet our Nation's economic, social, and environmental needs. They can be used for modeling of coastal processes (tsunami inundation, storm surge, sea-level rise, contaminant dispersal, etc.), ecosystems management and habitat research, coastal and marine spatial planning, and hazard mitigation and community preparedness. Layers available in the map service: Layers 1-4: DEMs by Category (includes various DEMs, both hosted at NCEI, and elsewhere on the web); Layers 6-11: NCEI DEM Projects (DEMs hosted at NCEI, color-coded by project); Layer 12: All NCEI Bathymetry DEMs (All bathymetry or bathy-topo DEMs hosted at NCEI).This is an image service providing access to bathymetric/topographic digital elevation models stewarded at NOAA's National Centers for Environmental Information (NCEI), with vertical units referenced to mean high water (NAVD88). NCEI builds and distributes high-resolution, coastal digital elevation models (DEMs) that integrate ocean bathymetry and land topography to support NOAA's mission to understand and predict changes in Earth's environment, and conserve and manage coastal and marine resources to meet our Nation's economic, social, and environmental needs. They can be used for modeling of coastal processes (tsunami inundation, storm surge, sea-level rise, contaminant dispersal, etc.), ecosystems management and habitat research, coastal and marine spatial planning, and hazard mitigation and community preparedness. This service provides data from many individual DEMs combined together as a mosaic. By default, the rasters are drawn in order of cell size, with higher-resolution grids displayed on top of lower-resolution grids. If overlapping DEMs have the same resolution, the newer one is shown. Alternatively, a single DEM or group of DEMs can be isolated using a filter/definition query or using the 'Lock Raster 'mosaic method in ArcMap. This is one of three services displaying collections of DEMs that are referenced to common vertical datums: North American Vertical Datum of 1988 (NAVD88): http://noaa.maps.arcgis.com/home/item.html?id=e9ba2e7afb7d46cd878b34aa3bfce042, Mean High Water (MHW): http://noaa.maps.arcgis.com/home/item.html?id=3bc7611c1d904a5eaf90ecbec88fa799, and Mean Higher High Water: http://noaa.maps.arcgis.com/home/item.html?id=9471f8d4f43e48109de6275522856696. In addition, the DEM Global Mosaic is a general-purpose global, seamless bathymetry/topography mosaic containing all the DEMs together. Two services are available: http://noaa.maps.arcgis.com/home/item.html?id=c876e3c96a8642ab8557646a3b4fa0ff Elevation Values: http://noaa.maps.arcgis.com/home/item.html?id=c876e3c96a8642ab8557646a3b4fa0ff and Color Shaded Relief: http://noaa.maps.arcgis.com/home/item.html?id=feb3c625dc094112bb5281c17679c769. Please see the corresponding DEM Footprints map service: http://noaa.maps.arcgis.com/home/item.html?id=d41f39c8a6684c54b62c8f1ab731d5ad for polygon footprints and more information about the individual DEMs used to create this composite view. This service has several server-side functions available. These can be selected in the ArcGIS Online layer using 'Image Display ', or in ArcMap under 'Processing Templates '. None: The default. Provides elevation/depth values in meters relative to the NAVD88 vertical datum. ColorHillshade: An elevation-tinted hillshade visualization. The depths are displayed using this color ramp: http://gis.ngdc.noaa.gov/viewers/images/dem_color_scale.png. GrayscaleHillshade: A simple grayscale hillshade visualization. SlopeMapRGB: Slope in degrees, visualized using these colors: http://downloads.esri.com/esri_content_doc/landscape/SlopeMapLegend_V7b.png. SlopeNumericValues: Slope in degrees, returning the actual numeric values. AspectMapRGB: Orientation of the terrain (0-360 degrees), visualized using these colors: http://downloads.esri.com/esri_content_doc/landscape/AspectMapLegendPie_V7b.png. AspectNumericValues: Aspect in degrees, returning the actual numeric values.

Right of Way maintenance check log

Abstract

This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied.

Earth impact structure location.

Purpose

Understanding the geological complexity in the vicinity.

Dataset History

Coordinate point taken from unknown source however Mount Toondina is a well known topographic feature and will be on any number of topographic and geologic map sheets. Given it is an astrobleme its co-ordinates will be on any number of meteorite impact crater web pages and books.

Dataset Citation

SA Department of Environment, Water and Natural Resources (2015) Mount Toondina Crater - ARC. Bioregional Assessment Source Dataset. Viewed 26 May 2016, http://data.bioregionalassessments.gov.au/dataset/4e160c80-2cbd-4acd-aef1-51c3a2813234.

The rivers of the Near East dataset is derived from the World Wildlife Fund's (WWF) HydroSHEDS drainage direction layer and a stream network layer. The source of the drainage direction layer was the 15-second Digital Elevation Model (DEM) from NASA's Shuttle Radar Topographic Mission (SRTM). The raster stream network was determined by using the HydroSHEDS flow accumulation grid, with a threshold of about 1000 km² upstream area.

The stream network dataset consists of the following information: the origin node of each arc in the network (FROM_NODE), the destination of each arc in the network (TO_NODE), the Strahler stream order of each arc in the network (STRAHLER), numerical code and name of the major basin that the arc falls within (MAJ_BAS and MAJ_NAME); - area of the major basin in square km that the arc falls within (MAJ_AREA); - numerical code and name of the sub-basin that the arc falls within (SUB_BAS and SUB_NAME); - area of the sub-basin in square km that the arc falls within (SUB_AREA); - numerical code of the sub-basin towards which the sub-basin flows that the arc falls within (TO_SUBBAS) (the codes -888 and -999 have been assigned respectively to internal sub-basins and to sub-basins draining into the sea). The attributes table now includes a field named "Regime" with tentative classification of perennial ("P") and intermittent ("I") streams.

Supplemental Information:

This dataset is developed as part of a GIS-based information system on water resources for the Near East. It has been published in the framework of the AQUASTAT - programme of the Land and Water Division of the Food and Agriculture Organization of the United Nations.

Contact points:

Metadata contact: AQUASTAT FAO-UN Land and Water Division

Contact: Jippe Hoogeveen FAO-UN Land and Water Division

Contact: Livia Peiser FAO-UN Land and Water Division

Data lineage:

The linework of the map was obtained by converting the stream network to a feature dataset with the Hydrology toolset in ESRI ArcGIS.The Flow Direction and Stream Order grids were derived from hydrologically corrected elevation data with a resolution of 15 arc-seconds.The elevation dataset was part of a mapping product, HydroSHEDS, developed by the Conservation Science Program of World Wildlife Fund.Original input data had been obtained during NASA's Shuttle Radar Topography Mission (SRTM).

Online resources:

Download - Rivers of the Near East (ESRI shapefile)

For general information regarding the HydroSHEDS data product

For HydroSHEDS dataset download and technical information

Hydrological basins in the Near East

Abstract Total marrow (lymph node) irradiation (TMI/TMLI) delivery requires more time than standard radiotherapy treatments. The patient's extremities, through the joints, can experience large movements. The reproducibility of TMI/TMLI patients' extremities was evaluated to find the best positioning and reduce unwanted movements. Eighty TMI/TMLI patients were selected (2013-2022). During treatment, a cone-beam computed tomography (CBCT) was performed for each isocenter to reposition the patient. CBCT-CT pairs were evaluated considering: (i) online vector shift (OVS) that matched the two series; (ii) residual vector shift (RVS) to reposition the patient's extremities; (iii) qualitative agreement (range 1-5). Patients were subdivided into (i) arms either leaning on the frame or above the body; (ii) with or without a personal cushion for foot positioning. The Mann-Whitney test was considered (p < 0.05 significant). Six-hundred-twenty-nine CBCTs were analyzed. The median OVS was 4.0 mm, with only 1.6% of cases ranked < 3, and 24% of RVS > 10 mm. Arms leaning on the frame had significantly smaller RVS than above the body (median: 8.0 mm/6.0 mm, p < 0.05). Using a personal cushion for the feet significantly improved the RVS than without cushions (median: 8.5 mm/1.8 mm, p < 0.01). The role and experience of the radiotherapy team are fundamental to optimizing the TMI/TMLI patient setup.

GIS Address Points, Road Centerlines, and Building Footprints provide base data information for many purposes, including call routing and emergency response within the New River Valley 911 Authority.