38950 New_York_Bathymetry_Model_Uncertainty Data Set Published / External 37192 ny_spatialplan Project Completed 2012-05 This dataset represents depth uncertainty predictions from a bathymetric model developed for the New York offshore spatial planning area. The model also includes spatially-explicit depth estimates represented in another raster dataset. Uncertainty is defined as the standard error of depth estimates from model output. The model used to derive depth estimates and corresponding uncertainty builds on previous predictive bathymetric modeling approaches in the region (e.g. Calder, 2006), provides a continuous gridded bathymetric surface, and allows users to view and explore spatial variation in the vertical accuracy of depth predictions. The spatial resolution of the model is identical to the National Oceanic and Atmospheric Administration's (NOAA) Coastal Relief Model (CRM; horizontal resolution approximately 83.8 m) in the study area and was built from the same database of hydrographic survey points. These depth uncertainty predictions were developed to be viewed along with model depth estimates. Together depth predictions and uncertainty estimates were made to prioritize locations for new bathymetric surveys with high uncertainty and better understand the reliability of depth predictions and derived spatial layers (e.g., benthic habitats, positions of depth contours). 566 Theme ISO 19115 Topic Category environment Theme ISO 19115 Topic Category oceans Theme Geospatial Platform OceanCommunity Theme NOS Data Explorer Topic Category Bathymetry/Topography Theme New York Theme Seafloor Theme bathymetry/topography Theme depth Theme environment Theme prediction Theme spatial planning Theme uncertainty Temporal Long-term average determined by input data Spatial Long Island Spatial Mid-Atlantic Spatial New York Bight Spatial New York Offshore Planing Area Spatial Northwest Atlantic Ocean National Centers for Coastal Ocean Science Silver Spring MD Data Set None Planned raster digital data Raster values correspond to estimates of depth uncertainty below MLLW. Estimates are based on a bathymetric geostatistical model developed using NOS hydrographic surveys. Uncertainty is defined by the standard error of model output. For more information on the values represented in the raster see the following reference: Menza, C., B.P. Kinlan, D.S. Dorfman, M. Poti and C. Caldow (eds.). 2012. A Biogeographic Assessment of Seabirds, Deep Sea Corals and Ocean Habitats of the New York Bight: Science to Support Offshore Spatial Planning. NOAA Technical Memorandum NOS NCCOS 141. Silver Spring, MD. 224 pp. These data were prepared by an agency of the United States Government. Neither the United States Government nor any agency thereof, nor any of their employees, make any warranty, expressed or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed in this report, or represents that its use would not infringe privately owned rights. Reference therein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof. Any views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. Although all data have been used by NOAA, no warranty, expressed or implied, is made by NOAA as to the accuracy of the data and/or related materials. The act of distribution shall not constitute any such warranty, and no responsibility is assumed by NOAA in the use of these data or related materials. CCMA credits these people for deriving this dataset: M. Poti, B.P. Kinlan, and Menza, C. 40996 Estimated depth uncertainty Published / External Completed Estimated uncertainty of depth below MLLW | Description Source: NOAA Converted from FGDC using 'fgdc_to_inport_xml_entity.pl' script. Data Steward 2012-05 Position NCCOS Scientific Data Coordinator NCCOS.data@noaa.gov Distributor 2012-05 Position NCCOS Scientific Data Coordinator NCCOS.data@noaa.gov Metadata Contact 2012-05 Position NCCOS Scientific Data Coordinator NCCOS.data@noaa.gov Point of Contact 2012-05 Position NCCOS Scientific Data Coordinator NCCOS.data@noaa.gov Principal Investigator 2012-05 Person Menza, Charles Charles.menza@noaa.gov 1305 East-West Hwy Silver Spring MD 20910 240-533-0372 301-713-4388 201205 -75 -69 42 37 Discrete 2012-05 Yes 3 Area No Row 5975 Column 5335 Vertical 1 Unclassified Contact Distributor for instructions on how to acquire dataset.; Data not yet available online. Please reference NOAA/NOS/NCCOS/CCMA/Biogeography Branch when utilizing these data in a report or peer reviewed publication. Additionally, knowledge of how this dataset has been of use and which organizations are utilizing it is of great benefit for ensuring this information continues to meet the needs of the management and research communities. Therefore, it is requested but not mandatory, that any user of this data supply this information to the Project Manager: Charles Menza (charles.menza@noaa.gov) https://coastalscience.noaa.gov/projects/download.aspx?fpath=D%3a%5cWebsites%5cNCCOS%5cprojects-attachments%5c80%5cNY_assessment_data_package.zip Offline Data zip file Zip https://coastalscience.noaa.gov/projects/detail?key=80 Online Resource 2015-05-27 Date that the source FGDC record was last modified. 2017-04-05 Converted from FGDC Content Standard for Digital Geospatial Metadata (version FGDC-STD-001-1998) using 'fgdc_to_inport_xml.pl' script. Contact Tyler Christensen (NOS) for details. 2017-09-13 Partial upload of Spatial Info section only. 2017-11-01 Replaced entire Lineage section to populate new Source Contribution field. 2018-02-08 Partial upload of Positional Accuracy fields only. The accuracy of this raster depends on the source and resolution of the data samples. See the following report for more information horizontal positional accuracy: Menza, C., B.P. Kinlan, D.S. Dorfman, M. Poti and C. Caldow (eds.). 2012. A Biogeographic Assessment of Seabirds, Deep Sea Corals and Ocean Habitats of the New York Bight: Science to Support Offshore Spatial Planning. NOAA Technical Memorandum NOS NCCOS 141. Silver Spring, MD. 224 pp. The accuracy of this surface depends on the source and resolution of the data samples. See the following report for more information: Menza, C., B.P. Kinlan, D.S. Dorfman, M. Poti and C. Caldow (eds.). 2012. A Biogeographic Assessment of Seabirds, Deep Sea Corals and Ocean Habitats of the New York Bight: Science to Support Offshore Spatial Planning. NOAA Technical Memorandum NOS NCCOS 141. Silver Spring, MD. 224 pp. The following reference provides information on omissions, selection criteria, generalization, definitions used, and other rules used to derive the data set: Menza, C., B.P. Kinlan, D.S. Dorfman, M. Poti and C. Caldow (eds.). 2012. A Biogeographic Assessment of Seabirds, Deep Sea Corals and Ocean Habitats of the New York Bight: Science to Support Offshore Spatial Planning. NOAA Technical Memorandum NOS NCCOS 141. Silver Spring, MD. 224 pp. All users should independently analyze the dataset according to their own needs and standards to determine data usability. National Ocean Service (NOS) Hydrographic Survey Data Department of Commerce (DOC), National Oceanic and Atmospheric Administration (NOAA), National Ocean Service (NOS), National Centers for Coastal Ocean Science (NCCOS), Center for Coastal Monitoring and Assessment (CCMA), Biogeography Branch 2011-04-21 Discrete 2011-04-21 https://www.ngdc.noaa.gov/mgg/bathymetry/hydro.html Hydrographic survey data was used as input to a geostatistical model. This raster dataset is output from the geostatistical model. | Source Geospatial Form: vector digital data | Type of Source Media: online 1 A geostatistical modeling approach was used to predict a continuous, gridded bathymetric surface from scattered sounding points and to generate corresponding spatially-explicit uncertainty estimates. Geostatistical methods are based on the premise that neighboring samples are more similar than samples farther away (Tobler, 1970), a phenomenon known as spatial autocorrelation. Spatial autocorrelation was detected, quantified and modeled by semivariogram analysis, and used to make predictions at locations that have not been measured. See the following report for more information on this layer's lineage. Menza, C., B.P. Kinlan, D.S. Dorfman, M. Poti and C. Caldow (eds.). 2012. A Biogeographic Assessment of Seabirds, Deep Sea Corals and Ocean Habitats of the New York Bight: Science to Support Offshore Spatial Planning. NOAA Technical Memorandum NOS NCCOS 141. Silver Spring, MD. 224 pp. 2012-05-01T00:00:00 National Ocean Service (NOS) Hydrographic Survey Data 40996 Entity gov.noaa.nmfs.inport:38950 Tyler Christensen 2017-04-05T12:50:02 SysAdmin InPortAdmin 2025-05-15T19:16:14 2018-02-08 National Centers for Coastal Ocean Science NCCOS 1002 Public No No 2018-02-08 1 Year 2019-02-08