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TwitterCounty local coordinates and Monument Records for the Public Land Survey System monuments that serve as the foundation of the County GIS program in compliance with Wisconsin Administrative Code SS 7.08 and Wisconsin State Statute SS 59.45(1)2
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TwitterUse the app to find the downloadable area within Jackson County - 2 Foot Contour MapThe 2-foot Contour Map shows contours that were derived from several different LiDAR projects in the Rogue Valley over the last 10 years. The map can be used to both download and view the contour data. To use the map, search or zoom in to an address. When zoomed in to a specific scale, the map will change from the downloadable areas layer to 2-foot interval contour lines. The LiDAR Project Dates layer can be used to identify the date when the elevation was collected in an area. Please note that data is available only for the valley floor areas at this time.The 2ft contours were created from 1-meter pixel DEM and then cleaned to remove very small elevation changes and to create a smooth contour line. This information should not be used to create topographic surveys or other applications where the precise elevation of a location is required. For additional information on LiDAR in Oregon or to download the source data, please visit the DOGAMI Lidar Viewer.The downloadable data is a zipped ESRI Shapefile and is projected to Oregon State Plane South (Intl Feet) with NAD 1983 datum.
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TwitterRailroad Lines dataset current as of 2001. This is an ESRI feature class of Railroads within Rock County, Wisconsin..
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TwitterU.S. Government Workshttps://www.usa.gov/government-works
License information was derived automatically
This U.S. Geological Survey (USGS) data release provides a digital geospatial database for the geologic map of the White Rock Canyon quadrangle, Carbon County, Wyoming (Hyden and others, 1968). Attribute tables and geospatial features (points, lines and polygons) conform to the Geologic Map Schema (USGS NCGMP, 2020) and represent the geologic map as published in USGS Geologic Quadrangle Map GQ-789. The 35,758-acre map area represents the geology at a publication scale of 1:24,000.
References: Hyden, H.J., Houston, R.S., and King, J.S., 1968, Geologic map of the White Rock Canyon quadrangle, Carbon County, Wyoming: U.S. Geological Survey, Geologic Quadrangle Map GQ-789, scale 1:24,000, https://doi.org/10.3133/gq789.
U.S. Geological Survey National Cooperative Geologic Mapping Program, 2020, GeMS (Geologic Map Schema) - A standard format for the digital publication of geologic maps: U.S. Geological Survey Techniques and Methods, book 11, chap. B10, 74 p., https://doi.org//10.3133 ...
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TwitterElectric Substations dataset current as of 2006. This is a ESRI feature class of American Transmission Company's Rock County electrical sub station sites.
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TwitterTransmission Lines or Poles, Electric dataset current as of 2006. This is an ESRI feature class of American Transmission Company's Rock County electrical lines..
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TwitterU.S. Government Workshttps://www.usa.gov/government-works
License information was derived automatically
This U.S. Geological Survey (USGS) data release provides a digital geospatial database for the geologic map of Precambrian metasedimentary rocks of the Medicine Bow Mountains, Albany and Carbon Counties, Wyoming (Houston and Karlstrom, 1992). Attribute tables and geospatial features (points, lines and polygons) conform to the Geologic Map Schema (USGS NCGMP, 2020) and represent the geologic map plates as published at a scale of 1:50,000. The 358,697-acre map area includes the geologically complex Medicine Bow Mountains located 30 miles (48 kilometers) west of Laramie in southeastern Wyoming.
References: Houston, R.S., and Karlstrom, K.E., 1992, Geologic map of Precambrian metasedimentary rocks of the Medicine Bow Mountains, Albany and Carbon Counties, Wyoming: U.S. Geological Survey, Miscellaneous Investigations Series Map I-2280, scale 1:50,000, https://doi.org/10.3133/i2280. U.S. Geological Survey National Cooperative Geologic Mapping Program, 2020, GeMS (Geologic Map Schema) ...
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TwitterThis data release includes the data used to generate histograms that compared total watershed pollutant removal efficiency (TWPRE) in the two study watersheds Crystal Rock (traditional watershed) and Tributary (Trib.) 104 low impact development (LID watershed) to determine if LID BMP design offered an improved water quality benefit. Input/calibrants data used in the model (Monte Carlo) are described in the manuscript as mentioned in the list below: -BMP Name and Type: references in the manuscript -BMP Connectivity: Proprietary (derived from Montgomery County GIS Data) -BMP Drainage Areas: Proprietary (derived from Montgomery County GIS Data) -BMP Efficiency Ranges: referenced in manuscript -Baseline Pollutant Loadings: referenced in manuscript Stormwater runoff and associated pollutants from urban areas in the Chesapeake Bay Watershed represent a serious impairment to local streams and downstream ecosystems, despite urbanized land comprising only 7% of the Bay watershed area. Excess nitrogen, phosphorus, and sediment affect local streams in the Bay watershed by causing problems ranging from eutrophication and toxic algal blooms to reduced oxygen levels and loss of biodiversity. Traditional management of urban stormwater has primarily focused on directing runoff away from developed areas as quickly as possible. More recently, stormwater best management practices (BMPs) have been implemented in a low impact development (LID) manner on the landscape to treat stormwater runoff closer to its source.The objective of this research was to use a modeling approach to compare total watershed pollutant removal efficiency (TWPRE) of two watersheds with differing spatial patterns of SW BMP design (traditional and LID), and determine if LID SW BMP design offered an improved water quality benefit.
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TwitterThis data release includes the data used to generate sewershed "bubble plots" that compared pollutant removal efficiency (PRE) in each sewershed in the two study watersheds Crystal Rock (traditional watershed) and Tributary (Trib.) 104 low impact development (LID watershed) to determine if LID BMP design offered an improved water quality benefit as compared on a sewershed basis. Input/calibrants data used in the model (Monte Carlo) are described in the manuscript as mentioned in the list below: -BMP Name and Type: references in the manuscript -BMP Connectivity: Proprietary (derived from Montgomery County GIS Data) -BMP Drainage Areas: Proprietary (derived from Montgomery County GIS Data) -BMP Efficiency Ranges: referenced in manuscript -Baseline Pollutant Loadings: referenced in manuscript Stormwater runoff and associated pollutants from urban areas in the Chesapeake Bay Watershed represent a serious impairment to local streams and downstream ecosystems, despite urbanized land comprising only 7% of the Bay watershed area. Excess nitrogen, phosphorus, and sediment affect local streams in the Bay watershed by causing problems ranging from eutrophication and toxic algal blooms to reduced oxygen levels and loss of biodiversity. Traditional management of urban stormwater has primarily focused on directing runoff away from developed areas as quickly as possible. More recently, stormwater best management practices (BMPs) have been implemented in a low impact development (LID) manner on the landscape to treat stormwater runoff closer to its source.The objective of this research was to use a modeling approach to compare total watershed pollutant removal efficiency (TWPRE) of two watersheds with differing spatial patterns of SW BMP design (traditional and LID), and determine if LID SW BMP design offered an improved water quality benefit.
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TwitterThis dataset is a compilation of road centerline data from Minnesota suppliers that have opted-in for their road centerline data to be included in this dataset.
It includes the following 45 suppliers that have opted-in to share their data openly as of the publication date of this dataset: Metropolitan Emergency Services Board (10 counties), Aitkin County, Becker County, Benton County, Cass County, Chippewa County, Clay County, Cook County, Crow Wing County, Douglas County, Fillmore County, Grant County, Houston County, Itasca County, Koochiching County, Lac qui Parle County, Lake County, Le Sueur County, Lyon County, Marshall County, McLeod County, Morrison County, Mower County, Murray County, Otter Tail County, Pipestone County, Polk County, Pope County, Renville County, Rock County, Saint Louis County, Stearns County, Stevens County, Waseca County, Winona County, Wright County, and Yellow Medicine County.
The two sources of road centerline data are the Minnesota Next Generation 9-1-1 (NG9-1-1) Program, in collaboration with local data suppliers, and the MetroGIS Road Centerlines (Geospatial Advisory Council Schema) which is on the Minnesota Geospatial Commons:
The Minnesota NG9-1-1 Program enterprise database provides the data outside of the Metro Region which is provide by the suppliers. The data have been aggregated into a single dataset which implements the MN NG9-1-1 GIS Data Model (https://ng911gis-minnesota.hub.arcgis.com/documents/79beb1f9bde84e84a0fa9b74950f7589/about ).
Only data which have meet the requirements for supporting NG9-1-1 are in the statewide aggregate GIS data. MnGeo extracts the available data, applies domain translations, and transforms it to UTM Zone 15 to comply with the GAC road centerline attribute schema: https://www.mngeo.state.mn.us/committee/standards/roadcenterline/index.html.
The MetroGIS Road Centerlines data was created by a joint collaborative project involving the technical and managerial GIS staff from the the Metropolitan Counties (Anoka, Carver, Chisago, Dakota, Hennepin, Isanti, Ramsey, Scott, Sherburne, and Washington), the Metropolitan Emergency Services Board, MetroGIS and the Metropolitan Council. The data are pulled from the Minnesota Geospatial Commons: https://gisdata.mn.gov/dataset/us-mn-state-metrogis-trans-road-centerlines-gac
‘Supplier’ is a term used throughout this document. A supplier will typically be a county, but it could also be a public safety answering point (PSAP), region, or tribal nation. The supplier is the agency which provides the individual datasets for the aggregated dataset. The trans_road_centerlines_open_metadata feature layer will contain the geometry/shape of the supplier boundaries, supplier name, supplier type, and feature count.
Aggregation Process:
1. Extract NG9-1-1 data from the Department of Public Safety (DPS) Enterprise database.
2. Download the latest MetroGIS data from the Geospatial Commons.
3. Extract, Translate, and Load (ETL) the DPS data to the GAC schema.
4. Combine NG9-1-1 data with MetroGIS data.
5. Filter the data for the Opt-In Open data counties
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TwitterThis Zoning feature class is an element of the Oregon GIS Framework statewide, Zoning spatial data. This version is authorized for public use. Attributes include zoning districts that have been generalized to state classes. As of June 30, 2023, this feature class contains zoning data from 229 local jurisdictions. DLCD plans to continue adding to and updating this statewide zoning dataset as they receive zoning information from the local jurisdictions. Jurisdictions included in the latest version of the statewide zoning geodatabase:
Cities: Adams, Adrian, Albany, Amity, Antelope, Ashland, Astoria, Athena, Aurora, Banks, Barlow, Bay City, Beaverton, Bend, Boardman, Bonanza, Brookings, Brownsville, Burns, Butte Falls, Canby, Cannon Beach, Carlton, Cascade Locks, Cave Junction, Central Point, Chiloquin, Coburg, Columbia City, Coos Bay, Cornelius, Corvallis, Cottage Grove, Creswell, Culver, Dayton, Detroit, Donald, Drain, Dufur, Dundee, Dunes City, Durham, Eagle Point, Echo, Enterprise, Estacada, Eugene, Fairview, Falls City, Florence, Forest Grove, Fossil, Garibaldi, Gaston, Gates, Gearhart, Gervais, Gladstone, Gold Beach, Gold Hill, Grants Pass, Grass Valley, Gresham, Halsey, Happy Valley, Harrisburg, Helix, Hermiston, Hillsboro, Hines, Hood River, Hubbard, Idanha, Independence, Jacksonville, Jefferson, Johnson City, Jordan Valley, Junction City, Keizer, King City, Klamath Falls, La Grande, La Pine, Lafayette, Lake Oswego, Lebanon, Lincoln City, Lowell, Lyons, Madras, Malin, Manzanita, Maupin, Maywood Park, McMinnville, Medford, Merrill, Metolius, Mill City, Millersburg, Milton-Freewater, Milwaukie, Mitchell, Molalla, Monmouth, Moro, Mosier, Mount Angel, Myrtle Creek, Myrtle Point, Nehalem, Newberg, Newport, North Bend, North Plains, Nyssa, Oakridge, Ontario, Oregon City, Pendleton, Philomath, Phoenix, Pilot Rock, Port Orford, Portland, Prescott, Prineville, Rainier, Redmond, Reedsport, Rivergrove, Rockaway Beach, Rogue River, Roseburg, Rufus, Saint Helens, Salem, Sandy, Scappoose, Scio, Scotts Mills, Seaside, Shady Cove, Shaniko, Sheridan, Sherwood, Silverton, Sisters, Sodaville, Spray, Springfield, Stanfield, Stayton, Sublimity, Sutherlin, Sweet Home, Talent, Tangent, The Dalles, Tigard, Tillamook, Toledo, Troutdale, Tualatin, Turner, Ukiah, Umatilla, Vale, Veneta, Vernonia, Warrenton, Wasco, Waterloo, West Linn, Westfir, Weston, Wheeler, Willamina, Wilsonville, Winston, Wood Village, Woodburn, Yamhill.
Counties: Baker County, Benton County, Clackamas County, Clatsop County, Columbia County, Coos County, Crook County, Curry County, Deschutes County, Douglas County, Harney County, Hood River County, Jackson County, Jefferson County, Josephine County, Klamath County, Lane County, Lincoln County, Linn County, Malheur County, Marion County, Multnomah County, Polk County, Sherman County, Tillamook County, Umatilla County, Union County, Wasco County, Washington County, Wheeler County, Yamhill County.
R emaining jurisdictions either chose not to share data to incorporate into the public, statewide dataset or did not respond to DLCD’s request for data. These jurisdictions’ attributes are designated “not shared” in the orZDesc field and “NS” in the orZCode field.
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TwitterThis map features highway-level and street-level data for the world. The map is intended to support the ArcGIS Online basemap gallery. For more details on the map, please visit the World Street Map.
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TwitterThis dataset was developed to provide a geologic map GIS of the Wallace 1x2 degree quadrangle for use in future spatial analysis by a variety of users.
This database is not meant to be used or displayed at any scale larger than 1:250,000 (e.g., 1:100,000 or 1:24,000)
This dataset was digitized by the U.S. Geological Survey EROS Data Center and U.S. Geological Survey Spokane Field Office for input into an Arc/Info geographic information system (GIS) The digital geologic map database can be queried in many ways to produce a variety of derivative geologic maps.
This GIS consists of two major and Arc/Info datasets: one line and polygon file (wal250k) containing geologic contacts and structures (lines) and geologic map rock units (polygons), and one point file (wal250bc) containing breccia outcrops.
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TwitterThe data set for the Corona North 7.5' quadrangle was prepared under the U.S. Geological Survey Southern California Areal Mapping Project (SCAMP) as part of an ongoing effort to develop a regional geologic framework of southern California, and to utilize a Geographic Information System (GIS) format to create regional digital geologic databases. These regional databases are being developed as contributions to the National Geologic Map Database of the National Cooperative Geologic Mapping Program of the USGS.
This data set maps and describes the geology of the Corona North 7.5' quadrangle, Riverside and San Bernardino Counties, California. Created using Environmental Systems Research Institute's ARC/INFO software, the data base consists of the following items: (1) a map coverage containing geologic contacts and units, (2) a coverage containing structural data, (3) a coverage containing geologic unit annotation and leaders, and (4) attribute tables for geologic units (polygons), contacts (arcs), and site-specific data (points). In addition, the data set includes the following graphic and text products: (1) a postscript graphic plot-file containing the geologic map, topography, cultural data, a Correlation of Map Units (CMU) diagram, a Description of Map Units (DMU), and a key for point and line symbols, and (2) PDF files of the Readme (including the metadata file as an appendix), and the graphic produced by the Postscript plot file.
The Corona North quadrangle is located near the northern end of the Peninsular Ranges Province. All but the southeastern tip of the quadrangle is within the Perris block, a relatively stable, rectangular in plan area located between the Elsinore and San Jacinto fault zones. The southeastern tip of the quadrangle is barely within the Elsinore fault zone.
The quadrangle is underlain by Cretaceous plutonic rocks that are part of the composite Peninsular Ranges batholith. These rocks are exposed in a triangular-shaped area bounded on the north by the Santa Ana River and on the south by Temescal Wash, a major tributary of the Santa Ana River. A variety of mostly silicic granitic rocks occur in the quadrangle, and are mainly of monzogranite and granodioritic composition, but range in composition from micropegmatitic granite to gabbro. Most rock units are massive and contain varying amounts of meso- and melanocratic equant-shaped inclusions. The most widespread granitic rock is monzogranite of the Cajalco pluton, a large pluton that extends some distance south of the quadrangle. North of Corona is a body of micropegmatite that appears to be unique in the batholith rocks.
Diagonally bisecting the quadrangle is the Santa Ana River. North of the Santa Ana River alluvial deposits are dominated by the distal parts of alluvial fans emanating from the San Gabriel Mountains north of the quadrangle. Widespread areas of the fan deposits are covered by a thin layer of wind blown sand.
Alluvial deposits in the triangular-shaped area between the Santa Ana River and Temescal Wash are quite varied, but consist principally of locally derived older alluvial fan deposits. These deposits rest on remnants of older, early Quaternary or late Tertiary age, nonmarine sedimentary deposits that were derived from both local sources and sources as far away as the San Bernardino Mountains. These deposits in part were deposited by an ancestral Santa Ana River. Older are a few scattered remnants of late Tertiary (Pliocene) marine sandstone that include some conglomerate lenses. Clasts in the conglomerate include siliceous volcanic rocks exotic to this part of southern California. This sandstone was deposited as the southeastern-most part of the Los Angeles sedimentary marine basin and was deposited along a rocky shoreline developed in the granitic rocks, much like the present day shoreline at Monterey, California. Most of the sandstone and granitic paleoshoreline features have been removed by quarrying and grading in the area of Porphyry north to Highway 91. Excellent exposures in highway road cuts still remain on the north side of Highway 91 just east of the 91-15 interchange and on the east side of U.S. 15 just north of the interchange.
South of Temescal Wash is a series of both younger and older alluvial fan deposits emanating from the Santa Ana Mountains to the southeast. In the immediate southwest corner of the quadrangle is a small exposure of sandstone and pebble conglomerate of the Sycamore Canyon member of the Puente Formation of early Pliocene and Miocene age and sandstone and conglomerate of undivided Sespe and Vaqueros Formations of early Miocene, Oligocene, and late Eocene age.
The geologic map data base contains original U.S. Geological Survey data generated by detailed field observation recorded on 1:24,000 scale aerial photographs. The map was created by transferring lines from the aerial photographs to a 1:24,000 scale topographic base. The map was digitized and lines, points, and polygons were subsequently edited using standard ARC/INFO commands. Digitizing and editing artifacts significant enough to display at a scale of 1:24,000 were corrected. Within the database, geologic contacts are represented as lines (arcs), geologic units are polygons, and site-specific data as points. Polygon, arc, and point attribute tables (.pat, .aat, and .pat, respectively) uniquely identify each geologic datum.
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TwitterThis layer contains the boundary of Williamson County in Central Texas. This layer is part of an original dataset provided and maintained by the City of Round Rock GIS/IT Department. The data in this layer are represented as a polyline. Williamson County (sometimes abbreviated as "Wilco") is a county in the U.S. state of Texas. As of the 2010 census, the population was 422,679. Its county seat is Georgetown. The county is named for Robert McAlpin Williamson (1804-1859), a community leader and a veteran of the Battle of San Jacinto. Williamson county is part of the Ausin-Round Rock, Texas Metropolitan Statistical Area. It was included with Austin in the Best Cities to Live in for 2009 by the Milken Institute. It is on both the Edwards Plateau to the west with rocky terrain and hills, and Texas Blackland Prairies in the each, with rich and fertile farming land. The two areas are roughly bisected by Interstate 35.You can read more about Williamson County here: https://en.wikipedia.org/wiki/Williamson_County,_Texas
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TwitterThis layer contains the data for public art for the Parks and Recreation department in the City of Round Rock, located in Williamson County, Texas. This layer is part of an original dataset provided and maintained by the City of Round Rock GIS/IT Department and the Planning and Development Services Department. The data in this layer are represented as points.Public art is defined as art created for the citizens of Round Rock to enjoy at no cost. The public art in this layer is represented as points and includes the names and addresses of the locations of the public art exhibits around town.
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TwitterThis map is designed to be used as a basemap by marine GIS professionals and as a reference map by anyone interested in ocean data. The map is intended to support the ArcGIS Online basemap gallery. For more details on the map, please visit the Ocean Basemap map service description.
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Twitterhis layer contains the city limit boundaries of the City of Round Rock, located in Williamson County, Texas. This layer is part of an original dataset provided and maintained by the City of Round Rock GIS/IT Department. The data in this layer are represented as polygons.A city limit is defined as the border or boundary of a city. The area within the city limit can be referred to as the city proper, while outlying areas are referred to as Extra-Territorial Jurisdictions (ETJs). This layer contains the city limits for the City of Round Rock, which has been isolated from the City Limits - Williamson County layer.
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TwitterThis layer contains the address points within Williamson County. This layer is part of an original dataset provided and maintained by the City of Round Rock GIS/IT Department. The data in this layer are represented as points. The entity responsible for updating each address point can be found in the Updating Agency field. Addresses within Round Rock city limits are maintained regularly by Planning and Developments Services.Update Frequency: Daily
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TwitterThis map features shaded relief imagery, bathymetry and coastal water features that provide neutral background with political boundaries and placenames for reference purposes. The map is intended to support the ArcGIS Online basemap gallery. For more details on the map, please visit the World Hillshade and Terrain with Labels.
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TwitterCounty local coordinates and Monument Records for the Public Land Survey System monuments that serve as the foundation of the County GIS program in compliance with Wisconsin Administrative Code SS 7.08 and Wisconsin State Statute SS 59.45(1)2