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Forest Retention Index classes for the southeastern United States at 2040 were processed using the Forest Retention Decision Tree and rendered on a 30-meter by 30-meter grid. The Forest Retention Index is used only for current forestland, identified using National Land Cover Database 2011. Many datasets were used as inputs for the Forest Retention Decision Tree, and they can be grouped into five broad categories: Protected, Tier 1 Priority, Tier 2 Priority, Threats to Forest Retention, and Socio-Economic Value of Forests. Protected datasets include Protected Areas Database-United States, National Conservation Easement Database, state-maintained databases, and private datasets volunteered by conservation partners....
Categories: Data;
Types: ArcGIS REST Map Service,
ArcGIS Service Definition,
Downloadable,
Map Service;
Tags: Alabama,
Arkansas,
Florida,
Georgia,
Illinois,
Digital flood-inundation maps for a 3.4-mile reach of Fourmile Creek at Silver Grove, Kentucky (Ky.), were created by the U.S. Geological Survey (USGS) in cooperation with the City of Silver Grove and the U.S. Army Corps of Engineers Louisville District. Because the City of Silver Grove is subject to flooding from Fourmile Creek and the Ohio River (backwater flooding up Fourmile Creek), a set of flood-inundation maps was created for each flooding source independently and for combinations of possible flooding scenarios. The flood-inundation maps depict estimates of the areal extent and depth of flooding corresponding to a range of different gage heights (gage height is commonly referred to as “stage,” or the water-surface...
The Geographic Names Information System (GNIS) is the Federal standard for geographic nomenclature. The U.S. Geological Survey developed the GNIS for the U.S. Board on Geographic Names, a Federal inter-agency body chartered by public law to maintain uniform feature name usage throughout the Government and to promulgate standard names to the public. The GNIS is the official repository of domestic geographic names data; the official vehicle for geographic names use by all departments of the Federal Government; and the source for applying geographic names to Federal electronic and printed products of all types.
The Geographic Names Information System (GNIS) is the Federal standard for geographic nomenclature. The U.S. Geological Survey developed the GNIS for the U.S. Board on Geographic Names, a Federal inter-agency body chartered by public law to maintain uniform feature name usage throughout the Government and to promulgate standard names to the public. The GNIS is the official repository of domestic geographic names data; the official vehicle for geographic names use by all departments of the Federal Government; and the source for applying geographic names to Federal electronic and printed products of all types.
These data were released prior to the October 1, 2016 effective date for the USGS’s policy dictating the review, approval, and release of scientific data as referenced in USGS Survey Manual Chapter 502.8 Fundamental Science Practices: Review and Approval of Scientific Data for Release. This data set represents the extent of the Southeastern Coastal Plain aquifer system in Kentucky, Tennessee, Mississippi, Alabama, Georgia, and South Carolina.
Layers of geospatial data include contours, boundaries, land cover, hydrography, roads, transportation, geographic names, structures, and other selected map features.
Layers of geospatial data include contours, boundaries, land cover, hydrography, roads, transportation, geographic names, structures, and other selected map features.
Layers of geospatial data include contours, boundaries, land cover, hydrography, roads, transportation, geographic names, structures, and other selected map features.
Layers of geospatial data include contours, boundaries, land cover, hydrography, roads, transportation, geographic names, structures, and other selected map features.
Layers of geospatial data include contours, boundaries, land cover, hydrography, roads, transportation, geographic names, structures, and other selected map features.
This child item describes Python code used to estimate average yearly and monthly tourism per 1000 residents within public-supply water service areas. Increases in population due to tourism may impact amounts of water used by public-supply water systems. This data release contains model input datasets, Python code used to develop the tourism information, and output estimates of tourism. This dataset is part of a larger data release using machine learning to predict public supply water use for 12-digit hydrologic units from 2000-2020. Output from this code was used as an input feature in the public supply delivery and water use machine learning models. This page includes the following files: tourism_input_data.zip...
This child item describes a public supply delivery machine learning model that was developed to estimate public-supply deliveries. Publicly supplied water may be delivered to domestic users or to commercial, industrial, institutional, and irrigation (CII) users. This model predicts total, domestic, and CII per capita rates for public-supply water service areas within the conterminous United States for 2009-2020. This child item contains model input datasets, code used to build the delivery machine learning model, and national predictions. This dataset is part of a larger data release using machine learning to predict public-supply water use for 12-digit hydrologic units from 2000-2020. This page includes the following...
This child item describes Python code used to query census data from the TigerWeb Representational State Transfer (REST) services and the U.S. Census Bureau Application Programming Interface (API). These data were needed as input feature variables for a machine learning model to predict public supply water use for the conterminous United States. Census data were retrieved for public-supply water service areas, but the census data collector could be used to retrieve data for other areas of interest. This dataset is part of a larger data release using machine learning to predict public supply water use for 12-digit hydrologic units from 2000-2020. Data retrieved by the census data collector code were used as input...
Layers of geospatial data include contours, boundaries, land cover, hydrography, roads, transportation, geographic names, structures, and other selected map features.
Layers of geospatial data include contours, boundaries, land cover, hydrography, roads, transportation, geographic names, structures, and other selected map features.
Types: Citation;
Tags: 7.5 x 7.5 minute,
7_5_Min,
Combined Vector,
Combined Vector,
Combined Vector,
Layers of geospatial data include contours, boundaries, land cover, hydrography, roads, transportation, geographic names, structures, and other selected map features.
Layers of geospatial data include contours, boundaries, land cover, hydrography, roads, transportation, geographic names, structures, and other selected map features.
This map layer shows major ports in the United States, Puerto Rico, and the U.S. Virgin Islands. A port is a city, town, or urban area with a harbor where ships load or unload. This is a revised version of the July 2012 map layer.
Categories: Data;
Types: Downloadable,
Map Service,
OGC WFS Layer,
OGC WMS Layer,
Shapefile;
Tags: Academics & scientific researchers,
Alabama,
Arizona,
Arkansas,
California,
This data set represents the extent, approximate location and type of wetlands and deepwater habitats in the United States and its Territories. These data delineate the areal extent of wetlands and surface waters as defined by Cowardin et al. (1979). Certain wetland habitats are excluded from the National mapping program because of the limitations of aerial imagery as the primary data source used to detect wetlands. These habitats include seagrasses or submerged aquatic vegetation that are found in the intertidal and subtidal zones of estuaries and near shore coastal waters. Some deepwater reef communities (coral or tuberficid worm reefs) have also been excluded from the inventory. These habitats, because of their...
Categories: Data;
Types: Map Service,
OGC WFS Layer,
OGC WMS Layer,
OGC WMS Service;
Tags: Academics & scientific researchers,
Alabama,
Alabama,
Alaska,
Arizona,
This dataset is a point shapefile of wells measured for the potentiometric surface maps of the Mississippi River Valley alluvial aquifer (MRVA) in Spring 2016, 2018, and 2020. The data provided for each well considered in the applicable potentiometric surface map are the water-level date, altitude [relative to the North American vertical datum of 1988 (NAVD88)], a useYYYY code (which is positive if the water level was used in the potentiometric surface map for that year), a use comment (which is populated for water levels not used), and the water-level change values, for 2016-18, 2018-20, and 2016-20 for water levels with positive useYYYY codes for the applicable years. The data provided for each streamgage considered...
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