The 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.Geographic 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 SamoaProjection: Web Mercator Auxiliary Sphere Visible Scale: Visible at all scales but layer draws best at scales larger than 1:1,000,000Source: EPA and USGSUpdate Frequency: There is new new data since this 2019 version, so no updates planned in the futurePublication Date: March 13, 2019Prior 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 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.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.

A series of annual geochemical models were created by RockWare utilizing RockWorks v2021 which were interpolated based on the 1,4-dioxane levels that were measured during 1986 through 2020. In cases where the same intervals were samples on more than one occasion during a given year, the highest 1,4-dioxane values were used. The extent of each annual model were limited to polygons based on only the wells that were sampled during the associated year to eliminate interpolating in areas where data is not present. The annual geochemical models were then filtered based on lithology to eliminate any voxels within the areas deemed impermeable based on lithology. The models were further constrained by utilizing the maximum historical water level surface (MHWLS) grid model to further restrict the interpolation from areas lacking measured data. Finally, the voxel models were converted to annual grid models, in which the cell values are based on the highest value within the corresponding column of voxels.The 2020 plume presented here was created from the RockWorks project database files on June 09, 2021 (Gelman3.sqlite v2021-04-29). The grid file titled 2020-01-01_to_2020-12-31.RwGrd (v20210710) was converted by The Mannik and Smith Group (MSG) to a raster file compatible in ArcGIS and a custom color scheme was applied with the shades becoming darker as concentrations increase. Iso-concentration lines were then generated at the following concentrations: 4 ppb, 7.2 ppb, 85 ppb, 150 ppb, 280 ppb, 500 ppb, 1000 ppb, 1900 ppb, 3000 ppb, and 5000 ppb. The 7.2 ppb lines were created because it represents the current EGLE Part 201 generic residential cleanup criterion (GRCC). The 85 ppb lines were created to represent the Consent Judgement 3 (CJ3) drinking water criteria. The 280 ppb lines were created because that is the new EGLE groundwater-surface water interface (GSI) criterion, and 1900 ppb is the Vapor Intrusion criteria. EGLE is contouring the 4 ppb level because that could become a new trigger for response if detected in sentinel wells if the proposed 4th Consent Judgment is approved.To host the plume files on EGLE's ArcGIS Online, MSG prepared the raster file, contour layer, and the input points used as the input for the specified year model in ArcGIS Pro. The points were labeled using three levels of detail. When zoomed out beyond 1:5000 no labels appear at the points because it would be too dense to read and cover the underlying plume. When zoomed in between 1:5000 and 1:1200, the bore name and maximum 1,4-dioxane at that well in 2020 appear. When zoomed in closer than 1:1200, the labels show the boring name, sample depth interval, and maximum 1,4-dioxane at that interval for 2020. The plume layer was set to 7.5% transparency (this can be adjusted later) and shared as a web tile layer using the ArcGIS Online / Bing Maps / Google Maps tiling scheme for levels of detail 12 – 19.This is a previous version of the data. The newest vintage is available at: Gelman Site of 1,4-Dioxane Contamination - Dioxane Plume (2023 Data).This data is used in the Gelman Site of 1,4-Dioxane Contamination web map (item details). If you have questions regarding the Gelman Sciences, Inc site of contamination contact Chris Svoboda at 517-256-2849 or svobodac@michigan.gov. Report problems or data functionality suggestions to EGLE-Maps@Michigan.gov.

Please note that this dataset is not an official City of Toronto land use dataset. It was created for personal and academic use using City of Toronto Land Use Maps (2019) found on the City of Toronto Official Plan website at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/official-plan-maps-copy, along with the City of Toronto parcel fabric (Property Boundaries) found at https://open.toronto.ca/dataset/property-boundaries/ and Statistics Canada Census Dissemination Blocks level boundary files (2016). The property boundaries used were dated November 11, 2021. Further detail about the City of Toronto's Official Plan, consolidation of the information presented in its online form, and considerations for its interpretation can be found at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/ Data Creation Documentation and Procedures Software Used The spatial vector data were created using ArcGIS Pro 2.9.0 in December 2021. PDF File Conversions Using Adobe Acrobat Pro DC software, the following downloaded PDF map images were converted to TIF format. 9028-cp-official-plan-Map-14_LandUse_AODA.pdf 9042-cp-official-plan-Map-22_LandUse_AODA.pdf 9070-cp-official-plan-Map-20_LandUse_AODA.pdf 908a-cp-official-plan-Map-13_LandUse_AODA.pdf 978e-cp-official-plan-Map-17_LandUse_AODA.pdf 97cc-cp-official-plan-Map-15_LandUse_AODA.pdf 97d4-cp-official-plan-Map-23_LandUse_AODA.pdf 97f2-cp-official-plan-Map-19_LandUse_AODA.pdf 97fe-cp-official-plan-Map-18_LandUse_AODA.pdf 9811-cp-official-plan-Map-16_LandUse_AODA.pdf 982d-cp-official-plan-Map-21_LandUse_AODA.pdf Georeferencing and Reprojecting Data Files The original projection of the PDF maps is unknown but were most likely published using MTM Zone 10 EPSG 2019 as per many of the City of Toronto's many datasets. They could also have possibly been published in UTM Zone 17 EPSG 26917 The TIF images were georeferenced in ArcGIS Pro using this projection with very good results. The images were matched against the City of Toronto's Centreline dataset found here The resulting TIF files and their supporting spatial files include: TOLandUseMap13.tfwx TOLandUseMap13.tif TOLandUseMap13.tif.aux.xml TOLandUseMap13.tif.ovr TOLandUseMap14.tfwx TOLandUseMap14.tif TOLandUseMap14.tif.aux.xml TOLandUseMap14.tif.ovr TOLandUseMap15.tfwx TOLandUseMap15.tif TOLandUseMap15.tif.aux.xml TOLandUseMap15.tif.ovr TOLandUseMap16.tfwx TOLandUseMap16.tif TOLandUseMap16.tif.aux.xml TOLandUseMap16.tif.ovr TOLandUseMap17.tfwx TOLandUseMap17.tif TOLandUseMap17.tif.aux.xml TOLandUseMap17.tif.ovr TOLandUseMap18.tfwx TOLandUseMap18.tif TOLandUseMap18.tif.aux.xml TOLandUseMap18.tif.ovr TOLandUseMap19.tif TOLandUseMap19.tif.aux.xml TOLandUseMap19.tif.ovr TOLandUseMap20.tfwx TOLandUseMap20.tif TOLandUseMap20.tif.aux.xml TOLandUseMap20.tif.ovr TOLandUseMap21.tfwx TOLandUseMap21.tif TOLandUseMap21.tif.aux.xml TOLandUseMap21.tif.ovr TOLandUseMap22.tfwx TOLandUseMap22.tif TOLandUseMap22.tif.aux.xml TOLandUseMap22.tif.ovr TOLandUseMap23.tfwx TOLandUseMap23.tif TOLandUseMap23.tif.aux.xml TOLandUseMap23.tif.ov Ground control points were saved for all georeferenced images. The files are the following: map13.txt map14.txt map15.txt map16.txt map17.txt map18.txt map19.txt map21.txt map22.txt map23.txt The City of Toronto's Property Boundaries shapefile, "property_bnds_gcc_wgs84.zip" were unzipped and also reprojected to EPSG 26917 (UTM Zone 17) into a new shapefile, "Property_Boundaries_UTM.shp" Mosaicing Images Once georeferenced, all images were then mosaiced into one image file, "LandUseMosaic20211220v01", within the project-generated Geodatabase, "Landuse.gdb" and exported TIF, "LandUseMosaic20211220.tif" Reclassifying Images Because the original images were of low quality and the conversion to TIF made the image colours even more inconsistent, a method was required to reclassify the images so that different land use classes could be identified. Using Deep learning Objects, the images were re-classified into useful consistent colours. Deep Learning Objects and Training The resulting mosaic was then prepared for reclassification using the Label Objects for Deep Learning tool in ArcGIS Pro. A training sample, "LandUseTrainingSamples20211220", was created in the geodatabase for all land use types as follows: Neighbourhoods Insitutional Natural Areas Core Employment Areas Mixed Use Areas Apartment Neighbourhoods Parks Roads Utility Corridors Other Open Spaces General Employment Areas Regeneration Areas Lettering (not a land use type, but an image colour (black), used to label streets). By identifying the letters, it then made the reclassification and vectorization results easier to clean up of unnecessary clutter caused by the labels of streets. Reclassification Once the... Visit https://dataone.org/datasets/sha256%3A3e3f055bf6281f979484f847d0ed5eeb96143a369592149328c370fe5776742b for complete metadata about this dataset.

This Deep Learning Package (DLPK) acts as a bridge between ArcGIS Pro and vision language models from OpenAI and Meta. Vision-language models are renowned for their advanced capabilities in natural language processing and understanding, as well as their ability to interpret and generate human-like text. The integration of these models into a DLPK enhances their utility by enabling them to process images and perform zero-shot classification of objects in imagery.Use this deep learning package to leverage the power of large vision language models to perform object classification on images and rasters within ArcGIS Pro. This DLPK allows for flexibility in classifying objects, as it is not restricted to predefined classes; users can specify custom class labels at the time of running the tool. This capability opens up new avenues for analysis and interpretation of spatial data, making it easier for professionals in fields such as environmental science, urban planning, and remote sensing to extract meaningful insights from imagery. These models can support disaster response and recovery efforts.Note: This model requires internet connection to work. The data used for classification, including the imagery and possible class labels, will be shared with OpenAI when using the GPT models. However, if you are using the Llama Vision model, it operates locally and does not require an internet connection, ensuring that your data remains on your machine without being shared externally. This model is not supported in ArcGIS Online.Using the modelFollow the guide to use the model. Before using the Llama vision model, ensure that the supported deep learning libraries are installed. For more details, check the Deep Learning Libraries Installer for ArcGIS. OpenAI models do not require deep learning libraries to be installed.Fine-tuning the modelThis model cannot be fine-tuned using ArcGIS.Input8-bit RGB imagery.OutputFeature class with classification of features in the imagery.Applicable geographiesThis model is expected to work well globally.Model architectureThe implementation uses OpenAI's vision language models or Llama Vision models.Sample resultsHere are a few sample results from the model.

This vector web map features outline maps of the World. The maps can be used for coloring and other fun activities by budding cartographers. These outline maps are great for teaching children about our World. Have them color and label countries, regions and bodies of water. Limited labels appear on the map at large scales. After coloring the city maps, children can do further research to learn more about these places. These maps are also available in a printable PDF format. See this blog with more details on how to work with the vector maps in ArcGIS Pro.For other creatively designed Esri vector basemaps, see the ArcGIS Living Atlas of the World gallery.