“For data visualizations” datasets

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Digital Geologic-GIS Map of Great Basin National Park and Vicinity, Nevada (NPS, GRD, GRI, GRBA, GRBA digital map) adapted from Stanford University and the Stanford Geological Survey unpublished digital data by Miller and the Stanford Geological Survey (2007)
National Park ServiceUpdated 12 Mar 2025
… Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS …
- PDF
- ZIP
data.gov.au
SLATS NSW Woody Vegetation Change - 2009-2014 - SPOT5
NSW Department of Climate Change, Energy, the Environment and WaterUpdated 12 Jun 2026
… The woody change data is also used for vegetation compliance analysis. Change statistics are available for all change …
- PDF
- GEOTIFF
Data.gov Catalog
Digital Geologic-GIS Map of Yukon-Charley Rivers National Preserve and Vicinity, Alaska (NPS, GRD, GRI, YUCH, YUCH digital map) adapted from U.S. Geological Survey Scientific Investigations Map by Wilson an others (2015), and U.S. Geological Survey Open-File Report maps by Rombach, Freeman, Schaefer, Cameron, Werdon and others (1998, 1999, 2000, 2004 and 2008)
National Park ServiceUpdated 18 Mar 2021
… Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS …
- PDF
- ZIP
Data.gov Catalog
Digital Geologic-GIS Map of Guadalupe Mountains National Park and Vicinity, Texas (NPS, GRD, GRI, GUMO, GUMO digital map) adapted from a New Mexico Bureau of Geology and Mineral Resources Open-File Report map by Skotnicki (2022)
National Park ServiceUpdated 11 Apr 2022
… Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS …
- PDF
- ZIP
Data.gov Catalog
Digital Geologic-GIS Map of Everglades National Park and Vicinity, Florida (NPS, GRD, GRI, EVER, EVER digital map) adapted from Florida Geological Survey Open File Map Series maps by Green, Campbell, Scott, Means and Arthur (1995, 1996, 1997, 1998 and 1999), and Open-File Report map by Scott (2001), and U.S. Geological Survey Bulletin map by Bergendahl (1956), Open-File Report map by McCartan and Moy (1995), and Water-Resources maps by Causaras, Reese and Cunningham (1985, 1986 and 2000)
National Park ServiceUpdated 23 Jul 2021
… Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS …
- PDF
- ZIP
data.gov.au
Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared
Geoscience Australia DataUpdated 17 Oct 2025
In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of …
- PDF
- HTML
data.gov.au
Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared
Geoscience Australia DataUpdated 20 Jan 2026
In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of …
- PDF
- HTML
data.gov.au
Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared
Geoscience Australia DataUpdated 2 Mar 2026
In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of …
- PDF
- HTML
data.gov.au
Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared
Geoscience Australia DataUpdated 7 Mar 2026
In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of …
- PDF
- HTML
data.gov.au
Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared
Geoscience Australia DataUpdated 25 Mar 2026
In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of …
- PDF
- HTML
data.gov.au
Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared
Geoscience Australia DataUpdated 20 Apr 2026
In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of …
- PDF
- HTML
data.gov.au
Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared
Geoscience Australia DataUpdated 30 Apr 2026
In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of …
- PDF
- HTML
data.gov.au
Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared
Geoscience Australia DataUpdated 14 May 2026
In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of …
- PDF
- HTML
data.gov.au
NSW Woody Vegetation Extent 2011
NSW Department of Climate Change, Energy, the Environment and WaterUpdated 17 Mar 2026
… Lidar datasets (see lineage for more information), and 88% when compared to 6670 visually derived validation points. Although …
- PDF
- ZIP
- URL
Data.gov Catalog
Digital Quaternary Surficial Geologic-GIS Map of Anacapa Island, California (NPS, GRD, GRI, CHIS, ANAC_surficial digital map) adapted from a U.S. Geological Survey Scientific Investigations Map by Schmidt, Minor and Bedford (2018)
National Park ServiceUpdated 22 Sept 2020
… Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS …
- PDF
- ZIP
data.gov.au
Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared
Australian Ocean Data NetworkUpdated 11 Jan 2026
In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of …
- PDF
- HTML
data.gov.au
Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared
Australian Ocean Data NetworkUpdated 23 Jan 2026
In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of …
- PDF
- HTML
data.gov.au
Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared
Australian Ocean Data NetworkUpdated 12 Feb 2026
In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of …
- PDF
- HTML
data.gov.au
Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared
Australian Ocean Data NetworkUpdated 5 Mar 2026
In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of …
- PDF
- HTML
data.gov.au
Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared
Australian Ocean Data NetworkUpdated 16 Mar 2026
In 2008, the performance of 14 statistical and mathematical methods for spatial interpolation was compared using samples of …
- PDF
- HTML

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Digital Geologic-GIS Map of Great Basin National Park and Vicinity, Nevada (NPS, GRD, GRI, GRBA, GRBA digital map) adapted from Stanford University and the Stanford Geological Survey unpublished digital data by Miller and the Stanford Geological Survey (2007)

SLATS NSW Woody Vegetation Change - 2009-2014 - SPOT5

Digital Geologic-GIS Map of Guadalupe Mountains National Park and Vicinity, Texas (NPS, GRD, GRI, GUMO, GUMO digital map) adapted from a New Mexico Bureau of Geology and Mineral Resources Open-File Report map by Skotnicki (2022)

Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared

Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared

Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared

Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared

Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared

Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared

Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared

Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared

NSW Woody Vegetation Extent 2011

Digital Quaternary Surficial Geologic-GIS Map of Anacapa Island, California (NPS, GRD, GRI, CHIS, ANAC_surficial digital map) adapted from a U.S. Geological Survey Scientific Investigations Map by Schmidt, Minor and Bedford (2018)

Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared

Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared

Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared

Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared

Predicting Seabed Mud Content across the Australian Margin: Performance of Machine Learning Methods and their combinations with Ordinary Kriging and Inverse Distance Squared

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