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.

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

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

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.

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

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/

The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making.

   BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions.


   Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself.


   For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise.


   Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. 


   This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery.


   See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt


   See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

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 10 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/

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.

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

Pedestrian and Other Transportation in Dunwoody. Includes MARTA Ridership from 8/2016 to 12/2016

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).

Arc Resistant Switchgear Market Outlook

The global arc resistant switchgear market size was valued at approximately USD 2.97 billion in 2023 and is projected to reach around USD 4.81 billion by 2032, growing at a compound annual growth rate (CAGR) of 5.5% during the forecast period. The key factors driving this market include increasing industrialization, rising demand for electrical safety, and stringent regulations on workplace safety. Additionally, the advancement in technology and infrastructure development, especially in emerging economies, are providing a significant boost to the market.

One of the primary growth factors for the arc resistant switchgear market is the increasing emphasis on workplace safety. Arc resistance in switchgear significantly reduces the risk of electrical accidents, thus ensuring a safer environment for workers. Governments and regulatory bodies worldwide are implementing stringent safety standards, pushing industries to adopt arc resistant switchgear. Another growth driver is the rising demand for uninterrupted power supply in various sectors such as healthcare, manufacturing, and data centers. This demand necessitates the installation of reliable and advanced switchgear systems, further propelling the market growth.

Technological advancements in the electrical equipment sector are also influencing the market positively. Innovations such as smart grid technology and the integration of Internet of Things (IoT) in switchgear systems have improved the functionality and efficiency of arc resistant switchgear. These innovations not only enhance operational safety but also offer predictive maintenance features, reducing downtime and operational costs. Moreover, the ongoing urbanization and industrial development in emerging economies are leading to increased investments in electrical infrastructure, thereby creating a lucrative market for arc resistant switchgear.

Additionally, the renewable energy sector is creating new opportunities for the arc resistant switchgear market. The shift towards renewable energy sources such as wind and solar power requires robust electrical equipment to handle fluctuating loads and ensure grid stability. Arc resistant switchgear is particularly beneficial in such applications due to its ability to contain and extinguish electrical arcs, thus preventing potential damage to the system. This demand from the renewable energy sector is expected to contribute significantly to the market growth during the forecast period.

From a regional perspective, North America and Europe are the leading markets for arc resistant switchgear, attributed to stringent safety regulations and high industrialization levels. However, the Asia Pacific region is anticipated to witness the highest growth rate due to rapid urbanization, expanding industrial base, and increasing investments in power infrastructure. Countries like China and India are focusing on upgrading their electrical infrastructure to meet the growing energy demands, which is expected to drive the market in this region.

Type Analysis

The arc resistant switchgear market can be segmented by type into Type 1, Type 2, Type 2B, and Type 2C. Type 1 arc resistant switchgear is designed to withstand the effects of internal arcing faults and is primarily used in applications where personnel safety is paramount. This type is gaining popularity in industries where there is a high risk of electrical faults, such as manufacturing and utilities. The growing awareness about workplace safety and the increasing stringency of safety regulations are driving the demand for Type 1 arc resistant switchgear.

Type 2 arc resistant switchgear, on the other hand, provides enhanced protection by containing the arc within the switchgear enclosure, thereby preventing it from reaching personnel and equipment. This type is widely used in industrial and commercial applications where higher levels of safety and reliability are required. The market for Type 2 arc resistant switchgear is expected to grow significantly due to its superior safety features and the increasing adoption of advanced electrical systems in various industries.

Type 2B arc resistant switchgear offers additional protection by preventing the arc from reaching the rear of the equipment, making it suitable for applications where the switchgear is accessible from the back. This type is particularly beneficial in utilities and data centers where space constraints and accessibility issues are common. The increasing demand for reliable and safe electrical systems in these sectors is driving the gro

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.

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.

Arc Fault Detection Market Outlook

The global Arc Fault Detection market size was estimated to be USD 3.2 billion in 2023 and is expected to reach USD 6.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 8.5% from 2024 to 2032. This growth is driven by increasing safety concerns and stringent government regulations to prevent electrical fires.

One of the primary growth factors for the Arc Fault Detection market is the rising incidence of electrical fires, which has led to an increased focus on safety measures across residential, commercial, and industrial sectors. Governments and regulatory bodies around the world are implementing stringent safety standards that mandate the use of arc fault detection devices. The National Electrical Code (NEC) in the United States, for example, has made the installation of Arc Fault Circuit Interrupters (AFCIs) mandatory in many types of new construction, which has significantly boosted market demand.

Technological advancements are another crucial driver for market growth. The integration of smart technologies and Internet of Things (IoT) in arc fault detection systems has improved the efficiency and reliability of these devices. Smart AFCIs can now be monitored and controlled remotely, providing real-time data and diagnostics that further enhance safety measures. Such advancements are attracting investments from both established players and new entrants, accelerating market expansion.

Economic development in emerging markets, particularly in Asia Pacific and Latin America, is also contributing significantly to the growth of the Arc Fault Detection market. The rapid urbanization and industrialization in these regions are leading to increased construction activities, which in turn is driving the demand for arc fault detection devices. Governments in these regions are also becoming more aware of the importance of electrical safety, leading to the adoption of stricter regulations and standards, thereby fueling market growth.

The Faulted Circuit Indicating FCI System Sales have been gaining traction as a vital component in enhancing electrical safety and reliability. These systems are designed to quickly identify and locate faults in electrical circuits, thereby minimizing downtime and preventing potential hazards. With the increasing complexity of electrical grids and the growing emphasis on smart grid technologies, the demand for advanced FCI systems is on the rise. These systems not only improve operational efficiency but also contribute to the overall safety of electrical infrastructures. As utilities and industries strive to modernize their electrical networks, the adoption of FCI systems is expected to see significant growth, further driving market expansion.

Regionally, North America and Europe are currently the leading markets for arc fault detection, owing to the stringent safety regulations and high awareness levels. However, the Asia Pacific region is expected to witness the highest growth over the forecast period, driven by rapid urbanization, increasing construction activities, and growing awareness about electrical safety. Latin America and the Middle East & Africa are also expected to show substantial growth, albeit at a slower pace compared to Asia Pacific.

Product Type Analysis

The product type segment of the Arc Fault Detection market includes Combination Arc Fault Circuit Interrupters (CAFCI), Branch/Feeder Arc Fault Circuit Interrupters (BCAFCI), and Outlet Circuit Arc Fault Circuit Interrupters (OCAFCI). Each of these product types serves distinct applications and has unique advantages that cater to specific needs in the market.

Combination Arc Fault Circuit Interrupters (CAFCI) are designed to provide protection against both parallel and series arc faults. These devices are highly versatile and are increasingly being installed in residential and commercial buildings. The increasing preference for CAFCIs is attributed to their dual protection capability, which significantly reduces the risk of electrical fires. The growing awareness about electrical safety in residential areas is expected to drive the demand for CAFCIs, making them one of the fastest-growing segments in the market.

Branch/Feeder Arc Fault Circuit Interrupters (BCAFCI) are specifically designed to protect branch circuits and feeders from arc faults. These devices are commonly used in commercial a