gov.noaa.nmfs.inport:38960
eng
UTF8
dataset
National Centers for Coastal Ocean Science
resourceProvider
NCCOS Scientific Data Coordinator
NCCOS.data@noaa.gov
pointOfContact
2024-02-29T00:00:00
ISO 19115-2 Geographic Information - Metadata Part 2 Extensions for imagery and gridded data
ISO 19115-2:2009(E)
complex
3586
NOAA ESRI Shapefile - sediment composition class predictions in New York offshore planning area from Biogeography Branch
New_York_Sediment_Classes
2012-05
publication
NOAA/NMFS/EDM
38960
https://www.fisheries.noaa.gov/inport/item/38960
WWW:LINK-1.0-http--link
Full Metadata Record
View the complete metadata record on InPort for more information about this dataset.
information
Menza, Charles
240-533-0372
301-713-4388
1305 East-West Hwy
Silver Spring
MD
20910
Charles.menza@noaa.gov
principalInvestigator
http://coastalscience.noaa.gov/projects/detail?key=80
WWW:LINK-1.0-http--link
Citation URL
Online Resource
download
This dataset represents sediment composition class predictions from a sediment spatial model developed for the New York offshore spatial planning area. The predictive spatial model of mean grain size was developed building upon the data compilation and analytical framework laid out by Goff et al. (2008) and Poppe et al. (2005).
Mapping seafloor features, including sediment characteristics and distribution, provides crucial information for a number of coastal and marine spatial planning applications. Seafloor maps can be used to help identify critical habitat areas for benthic organisms (e.g., clams, corals, demersal fish), select appropriate offshore construction sites, and plan sand/gravel mining operations.
CCMA credits these people for deriving this dataset: M. Poti, B.P. Kinlan and C. Menza
completed
NCCOS Scientific Data Coordinator
NCCOS.data@noaa.gov
pointOfContact
NCCOS Scientific Data Coordinator
NCCOS.data@noaa.gov
custodian
notPlanned
OceanCommunity
theme
Geospatial Platform
New York
Seafloor
bathymetry/topography
environment
grain size
prediction
sediment
spatial planning
uncertainty
theme
Long-term average determined by input data
temporal
Long Island
Mid-Atlantic
New York Bight
New York Offshore Planing Area
Northwest Atlantic Ocean
place
DOC/NOAA/NOS/NCCOS > National Centers for Coastal Ocean Science, National Ocean Service, NOAA, U.S. Department of Commerce
dataCentre
Global Change Master Directory (GCMD) Data Center Keywords
2017-04-24
publication
8.5
ny_spatialplan
project
InPort
otherRestrictions
Cite As: National Centers for Coastal Ocean Science, [Date of Access]: NOAA ESRI Shapefile - sediment composition class predictions in New York offshore planning area from Biogeography Branch [Data Date Range], https://www.fisheries.noaa.gov/inport/item/38960.
NOAA provides no warranty, nor accepts any liability occurring from any incomplete, incorrect, or misleading data, or from any incorrect, incomplete, or misleading use of the data. It is the responsibility of the user to determine whether or not the data is suitable for the intended purpose.
otherRestrictions
Access Constraints: Data not yet available online.
otherRestrictions
Use Constraints: 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)
otherRestrictions
Distribution Liability: 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.
unclassified
NOAA Data Management Plan (DMP)
NOAA/NMFS/EDM
38960
https://www.fisheries.noaa.gov/inportserve/waf/noaa/nos/nccos/dmp/pdf/38960.pdf
WWW:LINK-1.0-http--link
NOAA Data Management Plan (DMP)
NOAA Data Management Plan for this record on InPort.
information
crossReference
vector
eng; US
environment
oceans
-75
-69
37
42
| Currentness: 201205
2012-05-01
false
eng
false
For more information see 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.
zip file
Zip
NCCOS Scientific Data Coordinator
NCCOS.data@noaa.gov
distributor
http://coastalscience.noaa.gov/projects/download.aspx?fpath=D%3a%5cWebsites%5cNCCOS%5cprojects-attachments%5c80%5cNY_assessment_data_package.zip
WWW:LINK-1.0-http--link
http://coastalscience.noaa.gov/projects/download.aspx?fpath=D%3a%5cWebsites%5cNCCOS%5cprojects-attachments%5c80%5cNY_assessment_data_package.zip
Offline Data
download
dataset
Horizontal Positional Accuracy
The accuracy of this raster depends on the source and resolution of the data samples. See the following report for more information on the horizontal 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.
Completeness Report
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.
Conceptual Consistency
All users should independently analyze the dataset according to their own needs and standards to determine data usability.
A geostatistical modeling approach was used to predict a continuous, gridded sediment size prediction surface from scattered sediment sample 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
Source Contribution: Sediment size data was used as input to a geostatistical model. | Source Geospatial Form: vector digital data | Type of Source Media: online
Parsed and extracted database of mean sediment grain size
2011-04-21
publication
Dr. John Goff, University of Texas at Austin
2011-04-21