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TwitterThe pathway representation consists of segments and intersection elements. A segment is a linear graphic element that represents a continuous physical travel path terminated by path end (dead end) or physical intersection with other travel paths. Segments have one street name, one address range and one set of segment characteristics. A segment may have none or multiple alias street names. Segment types included are Freeways, Highways, Streets, Alleys (named only), Railroads, Walkways, and Bike lanes. SNDSEG_PV is a linear feature class representing the SND Segment Feature, with attributes for Street name, Address Range, Alias Street name and segment Characteristics objects. Part of the Address Range and all of Street name objects are logically shared with the Discrete Address Point-Master Address File layer. Appropriate uses include: Cartography - Used to depict the City's transportation network location and connections, typically on smaller scaled maps or images where a single line representation is appropriate. Used to depict specific classifications of roadway use, also typically at smaller scales. Used to label transportation network feature names typically on larger scaled maps. Used to label address ranges with associated transportation network features typically on larger scaled maps. Geocode reference - Used as a source for derived reference data for address validation and theoretical address location Address Range data repository - This data store is the City's address range repository defining address ranges in association with transportation network features. Polygon boundary reference - Used to define various area boundaries is other feature classes where coincident with the transportation network. Does not contain polygon features. Address based extracts - Used to create flat-file extracts typically indexed by address with reference to business data typically associated with transportation network features. Thematic linear location reference - By providing unique, stable identifiers for each linear feature, thematic data is associated to specific transportation network features via these identifiers. Thematic intersection location reference - By providing unique, stable identifiers for each intersection feature, thematic data is associated to specific transportation network features via these identifiers. Network route tracing - Used as source for derived reference data used to determine point to point travel paths or determine optimal stop allocation along a travel path. Topological connections with segments - Used to provide a specific definition of location for each transportation network feature. Also provides a specific definition of connection between each transportation network feature. (defines where the streets are and the relationship between them ie. 4th Ave is west of 5th Ave and 4th Ave does intersect with Cherry St) Event location reference - Used as source for derived reference data used to locate event and linear referencing.Data source is TRANSPO.SNDSEG_PV. Updated weekly.
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TwitterThe National Hydrography Dataset Plus (NHDplus) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US EPA Office of Water and the US Geological Survey, the NHDPlus provides mean annual and monthly flow estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses. For more information on the NHDPlus dataset see the NHDPlus v2 User Guide.Dataset SummaryPhenomenon Mapped: Surface waters and related features of the United States and associated territories not including Alaska.Coordinate System: Web Mercator Auxiliary Sphere Extent: The United States not including Alaska, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American Samoa Visible Scale: Visible at all scales but layer draws best at scales larger than 1:1,000,000Resolution/Tolerance: 1 meter/2 meters Number of Features: 3,035,617 flowlines, 473,936 waterbodies, 16,658 sinksFeature Request Limit: 5,000Source: EPA and USGSPublication Date: March 13, 2019ArcGIS Server URL: https://services.arcgis.com/P3ePLMYs2RVChkJx/arcgis/rest/services/NHDPlusV21/FeatureServerPrior to publication, the NHDPlus network and non-network flowline feature classes were combined into a single flowline layer. Similarly, the NHDPlus Area and Waterbody feature classes were merged under a single schema.Attribute fields were added to the flowline and waterbody layers to simplify symbology and enhance the layer's pop-ups. Fields added include Pop-up Title, Pop-up Subtitle, On or Off Network (flowlines only), Esri Symbology (waterbodies only), and Feature Code Description. All other attributes are from the original NHDPlus dataset. No data values -9999 and -9998 were converted to Null values for many of the flowline fields.What can you do with this Feature Layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer is limited to scales of approximately 1:1,000,000 or larger but a vector tile layer created from the same data can be used at smaller scales to produce a webmap that displays across the full range of scales. The layer or a map containing it can be used in an application. Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Apply filters. For example you can set a filter to show larger streams and rivers using the mean annual flow attribute or the stream order attribute. Change the layer’s style and symbologyAdd labels and set their propertiesCustomize the pop-upUse as an input to the ArcGIS Online analysis tools. This layer works well as a reference layer with the trace downstream and watershed tools. The buffer tool can be used to draw protective boundaries around streams and the extract data tool can be used to create copies of portions of the data.ArcGIS ProAdd this layer to a 2d or 3d map. Use as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class. Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the ArcGIS Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.
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TwitterThe National Hydrography Dataset Plus High Resolution (NHDplus High Resolution) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US Geological Survey, NHDPlus High Resolution provides mean annual flow and velocity estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses.For more information on the NHDPlus High Resolution dataset see the User’s Guide for the National Hydrography Dataset Plus (NHDPlus) High Resolution.Dataset SummaryPhenomenon Mapped: Surface waters and related features of the United States and associated territoriesGeographic Extent: The Contiguous United States, Hawaii, portions of Alaska, Puerto Rico, Guam, US Virgin Islands, Northern Marianas Islands, and American SamoaProjection: Web Mercator Auxiliary Sphere Visible Scale: Visible at all scales but layer draws best at scales larger than 1:1,000,000Source: USGSUpdate Frequency: AnnualPublication Date: July 2022This layer was symbolized in the ArcGIS Map Viewer and while the features will draw in the Classic Map Viewer the advanced symbology will not. Prior to publication, the network and non-network flowline feature classes were combined into a single flowline layer. Similarly, the Area and Waterbody feature classes were merged under a single schema.Attribute fields were added to the flowline and waterbody layers to simplify symbology and enhance the layer's pop-ups. Fields added include Pop-up Title, Pop-up Subtitle, Esri Symbology (waterbodies only), and Feature Code Description. All other attributes are from the original dataset. No data values -9999 and -9998 were converted to Null values.What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer or a map containing it can be used in an application. Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Apply filters. For example you can set a filter to show larger streams and rivers using the mean annual flow attribute or the stream order attribute.Change the layer’s style and symbologyAdd labels and set their propertiesCustomize the pop-upUse as an input to the ArcGIS Online analysis tools. This layer works well as a reference layer with the trace downstream and watershed tools. The buffer tool can be used to draw protective boundaries around streams and the extract data tool can be used to create copies of portions of the data.ArcGIS ProAdd this layer to a 2d or 3d map.Use as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the ArcGIS Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.
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Twitter*This dataset is authored by ESRI and is being shared as a direct link to the feature service by Pend Oreille County. NHD is a primary hydrologic reference used by our organization.The National Hydrography Dataset Plus High Resolution (NHDplus High Resolution) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US Geological Survey, NHDPlus High Resolution provides mean annual flow and velocity estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses.For more information on the NHDPlus High Resolution dataset see the User’s Guide for the National Hydrography Dataset Plus (NHDPlus) High Resolution.Dataset SummaryPhenomenon Mapped: Surface waters and related features of the United States and associated territoriesCoordinate System: Web Mercator Auxiliary Sphere Extent: The Contiguous United States, Hawaii, portions of Alaska, Puerto Rico, Guam, US Virgin Islands, Northern Marianas Islands, and American Samoa Visible Scale: Visible at all scales but layer draws best at scales larger than 1:1,000,000Source: USGSPublication Date: July 2022This layer was symbolized in the ArcGIS Map Viewer and while the features will draw in the Classic Map Viewer the advanced symbology will not.Prior to publication, the network and non-network flowline feature classes were combined into a single flowline layer. Similarly, the Area and Waterbody feature classes were merged under a single schema.Attribute fields were added to the flowline and waterbody layers to simplify symbology and enhance the layer's pop-ups. Fields added include Pop-up Title, Pop-up Subtitle, Esri Symbology (waterbodies only), and Feature Code Description. All other attributes are from the original dataset. No data values -9999 and -9998 were converted to Null values.What can you do with this Feature Layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer or a map containing it can be used in an application. Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Apply filters. For example you can set a filter to show larger streams and rivers using the mean annual flow attribute or the stream order attribute.Change the layer’s style and symbologyAdd labels and set their propertiesCustomize the pop-upUse as an input to the ArcGIS Online analysis tools. This layer works well as a reference layer with the trace downstream and watershed tools. The buffer tool can be used to draw protective boundaries around streams and the extract data tool can be used to create copies of portions of the data.ArcGIS ProAdd this layer to a 2d or 3d map.Use as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the ArcGIS Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.
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TwitterDownload In State Plane Projection Here. ** The Street Centerline feature class now follows the NG911/State of Illinois data specifications including a StreetNameAlias table. The download hyperlink above also contains a full network topology for use with the Esri Network Analyst extension ** These street centerlines were developed for a myriad of uses including E-911, as a cartographic base, and for use in spatial analysis. This coverage should include all public and selected private roads within Lake County, Illinois. Roads are initially entered using recorded documents and then later adjusted using current aerial photography. This dataset should satisfy National Map Accuracy Standards for a 1:1200 product. These centerlines have been provided to the United States Census Bureau and were used to conflate the TIGER road features for Lake County. The Census Bureau evaluated these centerlines and, based on field survey of 109 intersections, determined that there is a 95% confidence level that the coordinate positions in the centerline dataset fall within 1.9 meters of their true ground position. The fields PRE_DIR, ST_NAME, ST_TYPE and SUF_DIR are formatted according to United States Postal Service standards. Update Frequency: This dataset is updated on a weekly basis.
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TwitterApache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
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Basic Information:
Number of entries: 374,661 Number of features: 19 Data Types:
15 integer columns 3 float columns 1 object column (label) Column Names:
id, Time, Is_CH, who CH, Dist_To_CH, ADV_S, ADV_R, JOIN_S, JOIN_R, SCH_S, SCH_R, Rank, DATA_S, DATA_R, Data_Sent_To_BS, dist_CH_To_BS, send_code, Consumed Energy, label Explore the Dataset First Five Rows:
id Time Is_CH who CH Dist_To_CH ADV_S ADV_R JOIN_S JOIN_R SCH_S SCH_R Rank DATA_S DATA_R Data_Sent_To_BS dist_CH_To_BS send_code Consumed Energy label 0 101000 50 1 101000 0.00000 1 0 0 25 1 0 0 0 1200 48 0.00000 1 0.00000 Attack 1 101001 50 0 101044 75.32345 0 4 1 0 0 1 2 38 0 0 0.00000 1 0.09797 Normal 2 101002 50 0 101010 46.95453 0 4 1 0 0 1 19 41 0 0 0.00000 1 0.09797 Normal 3 101003 50 0 101044 64.85231 0 4 1 0 0 1 16 38 0 0 0.00000 1 0.09797 Normal 4 101004 50 0 101010 4.83341 0 4 1 0 0 1 0 41 0 0 0.00000 1 0.09797 Normal Missing Values: No missing values detected in the dataset.
Statistical Summary:
The dataset includes various features related to network operations such as Time, Is_CH, who CH, Dist_To_CH, and more. The target variable label contains categorical values such as "Attack" and "Normal". Analyze Class Distribution Let's analyze the distribution of the classes within the dataset.
python Copy code class_distribution = dataset['label'].value_counts() class_distribution Handle Class Imbalance If a class imbalance is identified, appropriate techniques such as undersampling, oversampling, or SMOTE (Synthetic Minority Over-sampling Technique) will be applied to balance the dataset.
Next Steps:
Identify the class distribution. Apply balancing techniques if necessary. Continue with data preprocessing and feature engineering. We will perform the class distribution analysis and balancing in the subsequent step.
i have found some duplicate values and drop them dataset.duplicated().sum() dataset.drop_duplicates(inplace=True)
ChatGPT Duplicate Handling Initial Duplicate Count Duplicates found: 8,873 Action Taken Duplicates dropped: The dataset has been cleaned by removing all duplicate entries. Verification Duplicates after cleaning: 0 The dataset is now free of duplicate entries. We can proceed with the analysis of the class distribution and any necessary steps to handle class imbalance.
Analyze Class Distribution Let's analyze the distribution of the label column to understand the balance between the classes.
python Copy code class_distribution = dataset['label'].value_counts() class_distribution I'll compute this now.
Class Distribution Analysis The distribution of the classes within the dataset is as follows:
Normal: 332,040 Grayhole: 13,909 Blackhole: 10,049 TDMA: 6,633 Flooding: 3,157 Observations There is a significant class imbalance, with the majority class being "Normal" (332,040 instances). Minority classes include "Grayhole", "Blackhole", "TDMA", and "Flooding".
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TwitterThis layer represents modeled stream temperatures derived from the NorWeST point feature class (NorWest_TemperaturePoints). NorWeST summer stream temperature scenarios were developed for all rivers and streams in the western U.S. from the > 20,000 stream sites in the NorWeST database where mean August stream temperatures were recorded. The resulting dataset includes stream lines (NorWeST_PredictedStreams) and associated mid-points NorWest_TemperaturePoints) representing 1 kilometer intervals along the stream network. Stream lines were derived from the 1:100,000 scale NHDPlus dataset (USEPA and USGS 2010; McKay et al. 2012). Shapefile extents correspond to NorWeST processing units, which generally relate to 6 digit (3rd code) hydrologic unit codes (HUCs) or in some instances closely correspond to state borders. The line and point shapefiles contain identical modeled stream temperature results. The two feature classes are meant to complement one another for use in different applications. In addition, spatial and temporal covariates used to generate the modeled temperatures are included in the attribute tables at https://www.fs.usda.gov/rm/boise/AWAE/projects/NorWeST/ModeledStreamTemperatureScenarioMaps.shtml. The NorWeST NHDPlusV1 processing units include: Salmon, Clearwater, Spokoot, Missouri Headwaters, Snake-Bear, MidSnake, MidColumbia, Oregon Coast, South-Central Oregon, Upper Columbia-Yakima, Washington Coast, Upper Yellowstone-Bighorn, Upper Missouri-Marias, and Upper Green-North Platte. The NorWeST NHDPlusV2 processing units include: Lahontan Basin, Northern California-Coastal Klamath, Utah, Coastal California, Central California, Colorado, New Mexico, Arizona, and Black Hills.
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TwitterThe Los Angeles County Storm Drain System is a geometric network model representing the storm drain infrastructure within Los Angeles County. The long term goal of this network is to seamlessly integrate the countywide drainage infrastructure, regardless of ownership or jurisdiction. Current uses by the Department of Public Works (DPW) include asset inventory, operational maintenance, and compliance with environmental regulations.
GIS DATA DOWNLOADS: (More information is in the table below)
File geodatabase: A limited set of feature classes comprise the majority of this geometric network. These nine feature classes are available in one file geodatabase (.gdb). ArcMap versions compatible with the .gdb are 10.1 and later. Read-only access is provided by the open-source software QGIS. Instructions on opening a .gdb file are available here, and a QGIS plugin can be downloaded here.
Acronyms and Definitions (pdf) are provided to better understand terms used.
ONLINE VIEWING: Use your PC’s browser to search for drains by street address or drain name and download engineering drawings. The Web Viewer link is: https://dpw.lacounty.gov/fcd/stormdrain/
MOBILE GIS: This storm drain system can also be viewed on mobile devices as well as your PC via ArcGIS Online. (As-built plans are not available with this mobile option.)
More About these Downloads All data added or updated by Public Works is contained in nine feature classes, with definitions listed below. The file geodatabase (.gdb) download contains these eleven feature classes without network connectivity. Feature classes include attributes with unabbreviated field names and domains.
ArcMap versions compatible with the .gdb are 10.1 and later.
Feature Class Download Description
CatchBasin In .gdb Catch basins collect urban runoff from gutters
Culvert In .gdb A relatively short conduit that conveys storm water runoff underneath a road or embankment. Typical materials include reinforced concrete pipe (RCP) and corrugated metal pipe (CMP). Typical shapes are circular, rectangular, elliptical, or arched.
ForceMain In .gdb Force mains carry stormwater uphill from pump stations into gravity mains and open channels.
GravityMain In .gdb Underground pipes and channels.
LateralLine In .gdb Laterals connect catch basins to underground gravity mains or open channels.
MaintenanceHole In .gdb The top opening to an underground gravity main used for inspection and maintenance.
NaturalDrainage In .gdb Streams and rivers that flow through natural creek beds
OpenChannel In .gdb Concrete lined stormwater channels.
PumpStation In .gdb Where terrain causes accumulation, lift stations are used to pump stormwater to where it can once again flow towards the ocean
Data Field Descriptions
Most of the feature classes in this storm drain geometric network share the same GIS table schema. Only the most critical attributes are listed here per LACFCD operations.
Attribute Description
ASBDATE The date the design plans were approved “as-built” or accepted as “final records”.
CROSS_SECTIN_SHAPE The cross-sectional shape of the pipe or channel. Examples include round, square, trapezoidal, arch, etc.
DIAMETER_HEIGHT The diameter of a round pipe or the height of an underground box or open channel.
DWGNO Drain Plan Drawing Number per LACFCD Nomenclature
EQNUM Asset No. assigned by the Department of Public Works’ (in Maximo Database).
MAINTAINED_BY Identifies, to the best of LAFCD’s knowledge, the agency responsible for maintaining the structure.
MOD_DATE Date the GIS features were last modified.
NAME Name of the individual drainage infrastructure.
OWNER Agency that owns the drainage infrastructure in question.
Q_DESIGN The peak storm water runoff used for the design of the drainage infrastructure.
SOFT_BOTTOM For open channels, indicates whether the channel invert is in its natural state (not lined).
SUBTYPE Most feature classes in this drainage geometric nature contain multiple subtypes.
UPDATED_BY The person who last updated the GIS feature.
WIDTH Width of a channel in feet.
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TwitterNOTE: A more current version of the Protected Areas Database of the United States (PAD-US) is available: PAD-US 3.0 https://doi.org/10.5066/P9Q9LQ4B. The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public land and voluntarily provided private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastre Theme (https://communities.geoplatform.gov/ngda-cadastre/). The PAD-US is an ongoing project with several published versions of a spatial database including areas dedicated to the preservation of biological diversity, and other natural (including extraction), recreational, or cultural uses, managed for these purposes through legal or other effective means. The database was originally designed to support biodiversity assessments; however, its scope expanded in recent years to include all public and nonprofit lands and waters. Most are public lands owned in fee (the owner of the property has full and irrevocable ownership of the land); however, long-term easements, leases, agreements, Congressional (e.g. 'Wilderness Area'), Executive (e.g. 'National Monument'), and administrative designations (e.g. 'Area of Critical Environmental Concern') documented in agency management plans are also included. The PAD-US strives to be a complete inventory of public land and other protected areas, compiling “best available” data provided by managing agencies and organizations. The PAD-US geodatabase maps and describes areas using over twenty-five attributes and five feature classes representing the U.S. protected areas network in separate feature classes: Fee (ownership parcels), Designation, Easement, Marine, Proclamation and Other Planning Boundaries. Five additional feature classes include various combinations of the primary layers (for example, Combined_Fee_Easement) to support data management, queries, web mapping services, and analyses. This PAD-US Version 2.1 dataset includes a variety of updates and new data from the previous Version 2.0 dataset (USGS, 2018 https://doi.org/10.5066/P955KPLE ), achieving the primary goal to "Complete the PAD-US Inventory by 2020" (https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/science/pad-us-vision) by addressing known data gaps with newly available data. The following list summarizes the integration of "best available" spatial data to ensure public lands and other protected areas from all jurisdictions are represented in PAD-US, along with continued improvements and regular maintenance of the federal theme. Completing the PAD-US Inventory: 1) Integration of over 75,000 city parks in all 50 States (and the District of Columbia) from The Trust for Public Land's (TPL) ParkServe data development initiative (https://parkserve.tpl.org/) added nearly 2.7 million acres of protected area and significantly reduced the primary known data gap in previous PAD-US versions (local government lands). 2) First-time integration of the Census American Indian/Alaskan Native Areas (AIA) dataset (https://www2.census.gov/geo/tiger/TIGER2019/AIANNH) representing the boundaries for federally recognized American Indian reservations and off-reservation trust lands across the nation (as of January 1, 2020, as reported by the federally recognized tribal governments through the Census Bureau's Boundary and Annexation Survey) addressed another major PAD-US data gap. 3) Aggregation of nearly 5,000 protected areas owned by local land trusts in 13 states, aggregated by Ducks Unlimited through data calls for easements to update the National Conservation Easement Database (https://www.conservationeasement.us/), increased PAD-US protected areas by over 350,000 acres. Maintaining regular Federal updates: 1) Major update of the Federal estate (fee ownership parcels, easement interest, and management designations), including authoritative data from 8 agencies: Bureau of Land Management (BLM), U.S. Census Bureau (Census), Department of Defense (DOD), U.S. Fish and Wildlife Service (FWS), National Park Service (NPS), Natural Resources Conservation Service (NRCS), U.S. Forest Service (USFS), National Oceanic and Atmospheric Administration (NOAA). The federal theme in PAD-US is developed in close collaboration with the Federal Geographic Data Committee (FGDC) Federal Lands Working Group (FLWG, https://communities.geoplatform.gov/ngda-govunits/federal-lands-workgroup/); 2) Complete National Marine Protected Areas (MPA) update: from the National Oceanic and Atmospheric Administration (NOAA) MPA Inventory, including conservation measure ('GAP Status Code', 'IUCN Category') review by NOAA; Other changes: 1) PAD-US field name change - The "Public Access" field name changed from 'Access' to 'Pub_Access' to avoid unintended scripting errors associated with the script command 'access'. 2) Additional field - The "Feature Class" (FeatClass) field was added to all layers within PAD-US 2.1 (only included in the "Combined" layers of PAD-US 2.0 to describe which feature class data originated from). 3) Categorical GAP Status Code default changes - National Monuments are categorically assigned GAP Status Code = 2 (previously GAP 3), in the absence of other information, to better represent biodiversity protection restrictions associated with the designation. The Bureau of Land Management Areas of Environmental Concern (ACECs) are categorically assigned GAP Status Code = 3 (previously GAP 2) as the areas are administratively protected, not permanent. More information is available upon request. 4) Agency Name (FWS) geodatabase domain description changed to U.S. Fish and Wildlife Service (previously U.S. Fish & Wildlife Service). 5) Select areas in the provisional PAD-US 2.1 Proclamation feature class were removed following a consultation with the data-steward (Census Bureau). Tribal designated statistical areas are purely a geographic area for providing Census statistics with no land base. Most affected areas are relatively small; however, 4,341,120 acres and 37 records were removed in total. Contact Mason Croft (masoncroft@boisestate) for more information about how to identify these records. For more information regarding the PAD-US dataset please visit, https://usgs.gov/gapanalysis/PAD-US/. For more information about data aggregation please review the Online PAD-US Data Manual available at https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-manual .
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TwitterODC Public Domain Dedication and Licence (PDDL) v1.0http://www.opendatacommons.org/licenses/pddl/1.0/
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A. SUMMARY This dataset lists intersections that have at least one continental crosswalk and meet the following criteria:
Continental crosswalks are marked with bold, wide stripes to indicate safe places for pedestrians to cross the road. Their high-visibility design helps alert drivers and cyclists to watch for people crossing.
B. HOW THE DATASET IS CREATED Locations of continental crosswalks collected at the intersection level. Pre-2015 data was collected in the summer of 2019 as a one-time effort to locate every intersection with continental crosswalks on the city's High Injury Network. Crosswalks painted post-2015 are collected as part of Vision Zero data reporting. "Shops reports" are used as the data source. Shops reports include data citywide. Crosswalks marked "UNDETERMINED" in the "CONTINENTAL" field may or may not have continental crosswalks and require additional scrutiny. These two data sources were joined with an intersection nodes layer to create the feature class.
The dataset is made available by SFMTA via their ArcGIS server/ feature server.
C. UPDATE PROCESS The dataset is updated by MTA quarterly and published to the Open Data Portal automatically.
D. HOW TO USE THIS DATASET This dataset includes: (1) all continental crosswalks citywide that were installed after 1/1/2015, and (2) all continental crosswalks that were installed before 12/31/2014 on the High Injury Network. It does not include continental crosswalks off the High Injury Network that were painted before 2015.
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A series of pedestrian networks in the Denver metropolitan area All files are uploaded in a zip archive file format- they will need to be extracted before opening in ArcGIS, QGIS, or similar programs. In general we recommend using the gdb source rather than shp sources for data due to some data loss and field truncation in the shape file archives. Projected Coordinate System NAD 1983 HARN StatePlane Colorado Central FIPS 0502 (US Feet) WKID: 2877 Sources DRCOG Sidewalk Centerlines 2022 (link) Paved sidewalk centerlines delineated from 2022 aerial imagery, along with network features such as crosswalks and likely missing sidewalks. This has been produced every two years since 2014. It includes a type field that differentiates pedestrian path types: Sidewalk: a paved path for pedestrians; most often on the side of the road Crosswalk: a marked (painted) part of the paved road where pedestrians have right of way to cross. Crosswalks within parking lots are not included. Possible missing sidewalk: an area on the side of a road where a sidewalk appears to be missing. An indication of a missing sidewalk would be existing sidewalks in the surrounding blocks. This feature should not be paved and should not cross the road. Segments with this attribution can be delineated up to 650 feet to maintain connectivity in the layer. Possible missing crosswalk: a crossing of a major street that doesn’t currently have crosswalk paint markings. Major streets are indicated by road surface markings. Segments with this attribution can be delineated up to the width of the street to maintain connectivity in the layer. Other path: is a line segment that doesn’t meet criteria for the other types but needs to be included in the data set to maintain connectivity in the layer. An example would be a sidewalk or possible missing sidewalk that breaks at a parking apron. The section that covers the parking apron would be an “other path.” Segments with this attribution can be delineated up to 650 feet to maintain connectivity in the layer. Best‑fit line: a straight feature drawn through a decorative sidewalk pattern (e.g. on a school campus). It indicates that a sidewalk is there but does not trace all pedestrian possibilities. Harvard Streets (link) A national public street network dataset, clipped to a ½ mile buffered RTD boundary and excluding streets with the type motorway, motorway link (ramps), and motorway & motorway link. Edits from source file performed by Karlyn Russell‑Carlson. Caveats While the sidewalk data is provided by DRCOG, not all areas within the DRCOG boundary had sidewalk data collected. Please reference the sidewalk_extent_2022 feature class for a polygon representing the extent of the sidewalk data. The sidewalk data does “not include private sidewalks (i.e. serving individual residences, contained within campuses, malls, apartment complexes, mobile home parks, or commercial complexes) except where the sidewalk maintains connectivity with the public sidewalk network” (quote from DRCOG documentation). Additionally, it does not include crosswalks in parking lots. Due to this, the below datasets may not have a complete representation of the pedestrian network in large parking lots. For example, below is a screenshot of the ideal network in green surrounding the Target and PetSmart commercial development near Colorado Boulevard and Alameda Ave: While sidewalk data has been produced every two years, a longitudinal study may be difficult. Data in the 2014 and 2016 versions appeared to lack many sidewalks and were primarily public trails upon a brief review. Feature Layers with Field Descriptions ada_sidewalk_network Minimum accessible sidewalk network (≥ 3 feet wide). createdate: Date the attribute was created createuser: Vendor who created the feature (Kucera International, Inc.) comments: Explanation or notes (null for all values) update_sta: Change status from previous dataset (A, M, NC) width_feet: Width in feet Shape_Length: Length in feet GlobalID: Unique ID for the feature class complete_sidewalk_network Entire sidewalk network regardless of width or type. createdate: From DRCOG createuser: From DRCOG comments: From DRCOG (null for all) update_sta: From DRCOG width_feet: From DRCOG Shape_Length: Length in feet GlobalID: Unique ID for the feature class ideal_network Broadest network including sidewalks and street segments; allows walking in the streets. access: OSM tag for legal accessibility bridge: OSM tag for bridges from_: OSM node reference highway: OSM tag for road type junction: OSM tag for junction type key: OSM node reference lanes: Number of lanes maxspeed: Speed limit name: Roadway name oneway: One-way flag osmid: OSM unique ID ref: OSM reference number to: OSM node reference tunnel: Whether it's a tunnel service: Whether it's a service road createdate: From DRCOG createuser: From DRCOG comments: From DRCOG (null) update_sta: From DRCOG width_feet: From DRCOG Shape_Length: Length in feet Source: DRCOG...
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TwitterThe Transboundary Geospatial Fabric (TGF) is a dataset of spatial modeling units consistent with the Geospatial Fabric for National Hydrologic Modeling (abbreviated within this document as GFv1, Viger and Bock, 2014). These features were derived from National Hydrography Dataset Plus High Resolution data (NHDPlus HR, U.S. Geological Survey [USGS], 2018) in the following conterminous United States (CONUS) - Canada transboundary four-digit Hydrologic Units (HUC4): 0101, 0105, 0108, 0901, 0902, 0903, 0904, 1005, 1006, 1701, 1702, and 1711. The data described here include the following vector feature classes: points of interest (POIs), a stream network (nsegment), major waterbodies (waterbodies), and hydrologic response units (nhru). These feature classes are contained within the Environmental Systems Research Institute (ESRI) geodatabase format (TGF.gdb).
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This dataset is one of several segments of a regional high detailed stream flowpath dataset. The data was separated using the TOPO 50 map series extents.The stream network was originally created for the purpose of high detailed work along rivers and streams in the Wellington region. It was started as a pilot study for the Mangatarere subcatchment of the Waiohine River for the Environmental Sciences department who was attempting to measure riparian vegetation. The data was sourced from a modelled stream network created using the 2013 LiDAR digital elevation model. Once the Mangatarere was complete the process was expanded to cover the entire region on an as needed basis for each whaitua. This dataset is one of several that shows the finished stream datasets for the Wairarapa region.The base stream network was created using a mixture of tools found in ArcGIS Spatial Analyst under Hydrology along with processes located in the Arc Hydro downloadable add-on for ArcGIS. The initial workflow for the data was based on the information derived from the help files provided at the Esri ArcGIS 10.1 online help files. The updated process uses the core Spatial Analyst tools to generate the streamlines while digital dams are corrected using the DEM Reconditioning tool provided by the Arc Hydro toolset. The whaitua were too large for processing separated into smaller units according to the subcatchments within it. In select cases like the Taueru subcatchment of the Ruamahanga these subcatchments need to be further defined to allow processing. The catchment boundaries available are not as precise as the LiDAR information which causes overland flows that are on edges of the catchments to become disjointed from each other and required manual correction.Attributes were added to the stream network using the River Environment Classification (REC) stream network from NIWA. The Spatial Join tool in Arcmap was used to add the Reach ID to each segment of the generated flow path. This ID was used to join a table which had been created by intersecting stream names (generated from a point feature class available from LINZ) with the REC subcatchment dataset. Both of the REC datasets are available from NIWA's website.
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TwitterAs part of the basemap data layers, the street centerline map layer is an integral part of the Lexington Fayette-Urban County Government Geographic Information System. Basemap data layers are accessed by personnel in most LFUCG divisions for basic applications such as viewing, querying, and map output production. More advanced user applications may focus on thematic mapping, summarization of data by geography, or planning purposes (including defining boundaries, managing assets and facilities, integrating attribute databases with geographic features, spatial analysis, network analysis, geocoding, and presentation output).This dataset is designed to represent and locate the street centerlines in Lexington-Fayette County. The street centerlines are broken down into line segments that run from intersection to intersect and include street maintance, ownership, address ranges, and name information. The centerlines are updates through a variety of methods. Most commonly new streets are added from georeferenced plats, but corrections are alwo made by GPS collection and referencing to aerial photos.The data is in ESRI feature class format, but can be exported to any number of supported formats, including shapefile and dxf. The native projection for the data is Kentucky State Plane North (NAD83), but may have been reprojected for use in other applications. Please check metadata to determine current projection.
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TwitterThe USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public land and voluntarily provided private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastre Theme ( https://communities.geoplatform.gov/ngda-cadastre/ ). The PAD-US is an ongoing project with several published versions of a spatial database including areas dedicated to the preservation of biological diversity, and other natural (including extraction), recreational, or cultural uses, managed for these purposes through legal or other effective means. The database was originally designed to support biodiversity assessments; however, its scope expanded in recent years to include all open space public and nonprofit lands and waters. Most are public lands owned in fee (the owner of the property has full and irrevocable ownership of the land); however, permanent and long-term easements, leases, agreements, Congressional (e.g. 'Wilderness Area'), Executive (e.g. 'National Monument'), and administrative designations (e.g. 'Area of Critical Environmental Concern') documented in agency management plans are also included. The PAD-US strives to be a complete inventory of U.S. public land and other protected areas, compiling “best available” data provided by managing agencies and organizations. The PAD-US geodatabase maps and describes areas using thirty-six attributes and five separate feature classes representing the U.S. protected areas network: Fee (ownership parcels), Designation, Easement, Marine, Proclamation and Other Planning Boundaries. An additional Combined feature class includes the full PAD-US inventory to support data management, queries, web mapping services, and analyses. The Feature Class (FeatClass) field in the Combined layer allows users to extract data types as needed. A Federal Data Reference file geodatabase lookup table (PADUS3_0Combined_Federal_Data_References) facilitates the extraction of authoritative federal data provided or recommended by managing agencies from the Combined PAD-US inventory. This PAD-US Version 3.0 dataset includes a variety of updates from the previous Version 2.1 dataset (USGS, 2020, https://doi.org/10.5066/P92QM3NT ), achieving goals to: 1) Annually update and improve spatial data representing the federal estate for PAD-US applications; 2) Update state and local lands data as state data-steward and PAD-US Team resources allow; and 3) Automate data translation efforts to increase PAD-US update efficiency. The following list summarizes the integration of "best available" spatial data to ensure public lands and other protected areas from all jurisdictions are represented in the PAD-US (other data were transferred from PAD-US 2.1). Federal updates - The USGS remains committed to updating federal fee owned lands data and major designation changes in annual PAD-US updates, where authoritative data provided directly by managing agencies are available or alternative data sources are recommended. The following is a list of updates or revisions associated with the federal estate: 1) Major update of the Federal estate (fee ownership parcels, easement interest, and management designations where available), including authoritative data from 8 agencies: Bureau of Land Management (BLM), U.S. Census Bureau (Census Bureau), Department of Defense (DOD), U.S. Fish and Wildlife Service (FWS), National Park Service (NPS), Natural Resources Conservation Service (NRCS), U.S. Forest Service (USFS), and National Oceanic and Atmospheric Administration (NOAA). The federal theme in PAD-US is developed in close collaboration with the Federal Geographic Data Committee (FGDC) Federal Lands Working Group (FLWG, https://communities.geoplatform.gov/ngda-govunits/federal-lands-workgroup/ ). 2) Improved the representation (boundaries and attributes) of the National Park Service, U.S. Forest Service, Bureau of Land Management, and U.S. Fish and Wildlife Service lands, in collaboration with agency data-stewards, in response to feedback from the PAD-US Team and stakeholders. 3) Added a Federal Data Reference file geodatabase lookup table (PADUS3_0Combined_Federal_Data_References) to the PAD-US 3.0 geodatabase to facilitate the extraction (by Data Provider, Dataset Name, and/or Aggregator Source) of authoritative data provided directly (or recommended) by federal managing agencies from the full PAD-US inventory. A summary of the number of records (Frequency) and calculated GIS Acres (vs Documented Acres) associated with features provided by each Aggregator Source is included; however, the number of records may vary from source data as the "State Name" standard is applied to national files. The Feature Class (FeatClass) field in the table and geodatabase describe the data type to highlight overlapping features in the full inventory (e.g. Designation features often overlap Fee features) and to assist users in building queries for applications as needed. 4) Scripted the translation of the Department of Defense, Census Bureau, and Natural Resource Conservation Service source data into the PAD-US format to increase update efficiency. 5) Revised conservation measures (GAP Status Code, IUCN Category) to more accurately represent protected and conserved areas. For example, Fish and Wildlife Service (FWS) Waterfowl Production Area Wetland Easements changed from GAP Status Code 2 to 4 as spatial data currently represents the complete parcel (about 10.54 million acres primarily in North Dakota and South Dakota). Only aliquot parts of these parcels are documented under wetland easement (1.64 million acres). These acreages are provided by the U.S. Fish and Wildlife Service and are referenced in the PAD-US geodatabase Easement feature class 'Comments' field. State updates - The USGS is committed to building capacity in the state data-steward network and the PAD-US Team to increase the frequency of state land updates, as resources allow. The USGS supported efforts to significantly increase state inventory completeness with the integration of local parks data in the PAD-US 2.1, and developed a state-to-PAD-US data translation script during PAD-US 3.0 development to pilot in future updates. Additional efforts are in progress to support the technical and organizational strategies needed to increase the frequency of state updates. The PAD-US 3.0 included major updates to the following three states: 1) California - added or updated state, regional, local, and nonprofit lands data from the California Protected Areas Database (CPAD), managed by GreenInfo Network, and integrated conservation and recreation measure changes following review coordinated by the data-steward with state managing agencies. Developed a data translation Python script (see Process Step 2 Source Data Documentation) in collaboration with the data-steward to increase the accuracy and efficiency of future PAD-US updates from CPAD. 2) Virginia - added or updated state, local, and nonprofit protected areas data (and removed legacy data) from the Virginia Conservation Lands Database, provided by the Virginia Department of Conservation and Recreation's Natural Heritage Program, and integrated conservation and recreation measure changes following review by the data-steward. 3) West Virginia - added or updated state, local, and nonprofit protected areas data provided by the West Virginia University, GIS Technical Center. For more information regarding the PAD-US dataset please visit, https://www.usgs.gov/gapanalysis/PAD-US/. For more information about data aggregation please review the PAD-US Data Manual available at https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-manual . A version history of PAD-US updates is summarized below (See https://www.usgs.gov/core-science-systems/science-analytics-and-synthesis/gap/pad-us-data-history for more information): 1) First posted - April 2009 (Version 1.0 - available from the PAD-US: Team pad-us@usgs.gov). 2) Revised - May 2010 (Version 1.1 - available from the PAD-US: Team pad-us@usgs.gov). 3) Revised - April 2011 (Version 1.2 - available from the PAD-US: Team pad-us@usgs.gov). 4) Revised - November 2012 (Version 1.3) https://doi.org/10.5066/F79Z92XD 5) Revised - May 2016 (Version 1.4) https://doi.org/10.5066/F7G73BSZ 6) Revised - September 2018 (Version 2.0) https://doi.org/10.5066/P955KPLE 7) Revised - September 2020 (Version 2.1) https://doi.org/10.5066/P92QM3NT 8) Revised - January 2022 (Version 3.0) https://doi.org/10.5066/P9Q9LQ4B Comparing protected area trends between PAD-US versions is not recommended without consultation with USGS as many changes reflect improvements to agency and organization GIS systems, or conservation and recreation measure classification, rather than actual changes in protected area acquisition on the ground.
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The 3D Hydrography Program (3DHP) data is an integrated, National, 3D-enabled hydrologic dataset derived from the USGS 3D Elevation Program (3DEP) data. For areas where Elevation-derived Hydrography (EDH) has not yet been collected, 3DHP data is supplemented by hydrologic vector data from the National Hydrography Dataset (NHD). As further EDH data is collected, it will replace the NHD data in those areas. 3DHP data ingested from EDH sources includes ‘value added’ catchments and flowline network derivative attributes. All the data is open and non-proprietary. However, users should be aware that temporal changes may have occurred since this dataset was collected and that some parts of this data may no longer represent actual surface conditions. Users should not use this data for critical applications without a full awareness of its limitations. This dataset is not intended to be used for site-specific regulatory determinations. 3DHP datasets include a three-dimensional (3D) hydrography network generated from, and integrated with, elevation data from the 3DEP to better represent stream gradients and channel conditions, along with waterbodies, hydrologic units, hydrologically enhanced elevation and other surfaces, and more consistent and accurate attributes. This product is new in federal fiscal year 2025 (FY25), and consists only of vector data in a series of feature classes. The product represents the 3DHP dataset and the schema in which it is contained as of September 30, 2024 Future Annual Staged Product releases will reflect the schema at the time the product is generated and include more EDH-sourced data holdings.
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TwitterThe Stormwater Network consists of 11 features classes with related drawing number tables. Feature classes include: inflow points, outfall points, network structures, general points, storm drain lines, underdrains, trench slotted drains, channel swales, outfall protections, stormwater management (SWM) ponds, and SWM drainage areas. The SWM pond points and the SWM drainage areas are managed by the Department of Environmental Protection and Sustainability (EPS) while the remaining feature classes are managed by the Department of Public Works (DPW) with some overlap occurring in the outfall point feature class.
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TwitterThe Low Stress Network quantifies a roadway’s perceived stress using speed of adjacent traffic, presence or absence of on-street parking, separation from traffic, intersection approach, and the intersection control type itself. Intersection approach characteristics that are included in the analysis include posted speed of the road; presence or absence of left- and right-turn lanes; and required rider position and interaction with traffic. Perceived stress is quantified via the Low Traffic Stress (LTS) value. This line feature class includes all low stress network segments as defined by the TSP.Data is updated as changes are approved by City Council.
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TwitterThe Maryland Habitat Connectivity Network (HCN) provides high resolution, statewide data regarding the connected network of hubs, corridors, and gaps throughout the state. For the purposes of the MD HCN, hubs were defined as large contiguous blocks of forests and wetlands, corridors are defined as linear features connecting hubs that enable animals and plant propagules to move between hubs, and gaps are defined as areas within corridors that are not currently part of the optimal natural land use type(s).
The Maryland Habitat Connectivity Network (HCN) was formerly referred to as Maryland's Green Infrastructure Assessment (GIA). The original GIA was completed in 2003, and included mapping of hubs and corridors using 30m resolution Landsat landcover landuse data. GIA hubs were updated in 2010, using newer Landsat data, however corridors were not remapped at that time.
This current update to the MD HCN dataset leverages the Chesapeake Conservancy’s 2017/2018 1m Land Use Land Cover (LCLU) dataset. This update provides the most up to date, high resolution green infrastructure data possible for the state. This data set also contains several improvements, including a more detailed breakdown of 3 hub types (forest, wetland, aquatic), 2 corridor types (forest and aquatic), and 3 corridor and gap statuses (natural corridor, restorable gap, non-restorable gap).
***Please note, the MD Habitat Corridor Network is simply a renaming of the Maryland Green Infrastructure Assessment. The data represented in the HCN is the same data contained in the most recent update to the MD GIA.Maryland's Habitat Connectivity Network is a network of undeveloped lands that provide the bulk of the state's natural support system. Ecosystem services, such as cleaning the air, filtering water, storing and cycling nutrients, conserving soils, regulating climate, and maintaining hydrologic function, are all provided by the existing expanses of forests, wetlands, and other natural lands. These ecologically valuable lands also provide marketable goods and services, like forest products, fish and wildlife, and recreation. Maryland’s Green Infrastructure serves as vital habitat for wild species and contributes in many ways to the health and quality of life for Maryland residents. To identify and prioritize Maryland's green infrastructure, the Maryland Green Infrastructure Assessment (GIA) was developed. The GIA was developed using principles of landscape ecology and conservation biology, and provides a consistent approach to evaluating land conservation and restoration efforts in Maryland. It specifically attempts to recognize: a variety of natural resource values (as opposed to a single species of wildlife, for example), how a given place fits into a larger system, the ecological importance of natural open space in rural and developed areas, the importance of coordinating local, state and even interstate planning, and the need for a regional or landscape-level view for wildlife conservation. Maryland’s Green Infrastructure Assessment (GIA) provides high resolution, statewide data regarding the connected network of hubs, corridors, and gaps throughout the state. For the purposes of the MD GIA, hubs were defined as large contiguous blocks of forests and wetlands, corridors are defined as linear features connecting hubs that enable animals and plant propagules to move between hubs, and gaps are defined as areas within corridors that are not currently part of the optimal natural land use type(s). The original GIA was completed in 2003, and included mapping of hubs and corridors using 30m resolution Landsat landcover landuse data. GIA hubs were updated in 2010, using newer Landsat data, however corridors were not remapped at that time. This current update to the MD GIA dataset leverages the Chesapeake Conservancy’s 2017/2018 1m Land Use Land Cover (LCLU) dataset. This update provides the most up to date, high resolution green infrastructure data possible for the state. The Maryland GIA, includes mapping and differentiation of 3 types of hubs: forest, wetland, and aquatic. For the most recent update, forest hubs are defined as large contiguous blocks of forests that are a minimum of 50 acres in size and containing a minimum of 10 acres of contiguous interior forest. Wetlands hubs are defined as contiguous patches of wetlands that are a minimum of 50 acres in size. Aquatic hubs include waterways that meet specific ecological criteria, including those located in Tier II catchments, HUC 12 watersheds with trout, or those with Anadromous fish spawning segments. This recent update also includes mapping and differentiation of both forest and aquatic corridors. Mapping of corridors was done in 3 major steps. First, forest and aquatic cost rasters were created based on various relevant ecological variables that represent the cost for wildlife to move through each pixel across the landscape. Then, the “Optimal Regions Tool” in ArcGIS was used to manually identify the shortest, least cost path between each set of hub areas. Finally, these least cost paths were buffered by 550 feet to create corridor areas. Corridors generally follow the best ecological or "most natural" routes between hubs. Typically these are streams with wide riparian buffers and healthy fish communities. Other good wildlife corridors include ridge lines or forested valleys. Developed areas, major roads, and other unsuitable features were avoided. Finally, this updated dataset provides a detailed breakdown of land within green infrastructure corridors. Forest and aquatic corridors are broken into 3 categories, natural corridors, restorable gaps, and non-restorable gaps. Natural corridors are defined as natural land use classes that provide the lowest cost for wildlife movement. Restorable gaps are land use classes that are not currently optimal for animal movement, but that could be good candidates for restoration, such as low vegetation and shrub scrub areas. Non-Restorable Corridors are land use classes wildlife avoid/pass through quickly, and that can not be easily restored, such as impervious surfaces, roads, or buildings. The Green Infrastructure Assessment was developed to provide decision support for Maryland's Department of Natural Resources land conservation programs. Methods used to identify and rank green infrastructure lands are intended solely for this use. Other applications are at the discretion of the user. The Maryland Department of Natural Resources is not responsible for any inaccuracies in the data and does not necessarily endorse any uses or products derived from the data other than those for which the data were originally intended. Please to the Green Infrastructure web site (https://dnr.maryland.gov/land/Pages/Green-Infrastructure.aspx) for additional information. More information can also be found on the DNR Greenprint Webmap (https://geodata.md.gov/greenprint/) Credits: DNR, Chesapeake Conservancy MD, iMAP, Rachel Marks (rachel.marks@maryland.gov)Subject: Habitat Connectivity Network - Hubs, Corridors, and Gapshttps://mdgeodata.md.gov/imap/rest/services/Biota/MD_HabitatConnectivityNetwork/FeatureServer/0
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TwitterThis layer indicates the location of the observed stream temperature records used for the NorWeST database summaries. NorWeST summer stream temperature scenarios were developed for all rivers and streams in the western U.S. from the greater than 20,000 stream sites in the NorWeST database where mean August stream temperatures were recorded. The resulting dataset includes stream lines (NorWeST_PredictedStreams) and associated mid-points NorWest_TemperaturePoints) representing 1 kilometer intervals along the stream network. Stream lines were derived from the 1:100,000 scale NHDPlus dataset (USEPA and USGS 2010; McKay et al. 2012). Shapefile extents correspond to NorWeST processing units, which generally relate to 6 digit (3rd code) hydrologic unit codes (HUCs) or in some instances closely correspond to state borders. The line and point shapefiles contain identical modeled stream temperature results. The two feature classes are meant to complement one another for use in different applications. In addition, spatial and temporal covariates used to generate the modeled temperatures are included in the attribute tables at https://www.fs.usda.gov/rm/boise/AWAE/projects/NorWeST/ModeledStreamTemperatureScenarioMaps.shtml. The NorWeST NHDPlusV1 processing units include: Salmon, Clearwater, Spokoot, Missouri Headwaters, Snake-Bear, MidSnake, MidColumbia, Oregon Coast, South-Central Oregon, Upper Columbia-Yakima, Washington Coast, Upper Yellowstone-Bighorn, Upper Missouri-Marias, and Upper Green-North Platte. The NorWeST NHDPlusV2 processing units include: Lahontan Basin, Northern California-Coastal Klamath, Utah, Coastal California, Central California, Colorado, New Mexico, Arizona, and Black Hills.
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TwitterThe pathway representation consists of segments and intersection elements. A segment is a linear graphic element that represents a continuous physical travel path terminated by path end (dead end) or physical intersection with other travel paths. Segments have one street name, one address range and one set of segment characteristics. A segment may have none or multiple alias street names. Segment types included are Freeways, Highways, Streets, Alleys (named only), Railroads, Walkways, and Bike lanes. SNDSEG_PV is a linear feature class representing the SND Segment Feature, with attributes for Street name, Address Range, Alias Street name and segment Characteristics objects. Part of the Address Range and all of Street name objects are logically shared with the Discrete Address Point-Master Address File layer. Appropriate uses include: Cartography - Used to depict the City's transportation network location and connections, typically on smaller scaled maps or images where a single line representation is appropriate. Used to depict specific classifications of roadway use, also typically at smaller scales. Used to label transportation network feature names typically on larger scaled maps. Used to label address ranges with associated transportation network features typically on larger scaled maps. Geocode reference - Used as a source for derived reference data for address validation and theoretical address location Address Range data repository - This data store is the City's address range repository defining address ranges in association with transportation network features. Polygon boundary reference - Used to define various area boundaries is other feature classes where coincident with the transportation network. Does not contain polygon features. Address based extracts - Used to create flat-file extracts typically indexed by address with reference to business data typically associated with transportation network features. Thematic linear location reference - By providing unique, stable identifiers for each linear feature, thematic data is associated to specific transportation network features via these identifiers. Thematic intersection location reference - By providing unique, stable identifiers for each intersection feature, thematic data is associated to specific transportation network features via these identifiers. Network route tracing - Used as source for derived reference data used to determine point to point travel paths or determine optimal stop allocation along a travel path. Topological connections with segments - Used to provide a specific definition of location for each transportation network feature. Also provides a specific definition of connection between each transportation network feature. (defines where the streets are and the relationship between them ie. 4th Ave is west of 5th Ave and 4th Ave does intersect with Cherry St) Event location reference - Used as source for derived reference data used to locate event and linear referencing.Data source is TRANSPO.SNDSEG_PV. Updated weekly.