This web map features a vector basemap of OpenStreetMap (OSM) data created and hosted by Esri. Esri produced this vector tile basemap in ArcGIS Pro from a live replica of OSM data, hosted by Esri, and rendered using a creative cartographic style emulating a blueprint technical drawing. The vector tiles are updated every few weeks with the latest OSM data. This vector basemap is freely available for any user or developer to build into their web map or web mapping apps.OpenStreetMap (OSM) is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap site: www.OpenStreetMap.org. Esri is a supporter of the OSM project and is excited to make this new vector basemap available available to the OSM, GIS, and Developer communities.

World Countries Generalized represents generalized boundaries for the countries of the world as of August 2022. The generalized political boundaries improve draw performance and effectiveness at a global or continental level. This layer is best viewed out beyond a scale of 1:5,000,000.This layer's geography was developed by Esri and sourced from Garmin International, Inc., the U.S. Central Intelligence Agency (The World Factbook), and the National Geographic Society for use as a world basemap. It is updated annually as country names or significant borders change.

U.S. Rivers and Streams represents detailed rivers and streams in the United States. Due to the very large number of features in this dataset, it has a minimum draw scale of 1:400,000.To download the data for this layer as a layer package for use in ArcGIS desktop applications, refer to USA Detailed Rivers and Streams.

The Minnesota DNR Toolbox and Hydro Tools provide a number of convenience geoprocessing tools used regularly by MNDNR staff. Many of these may be useful to the wider public. However, some tools may rely on data that is not available outside of the DNR. All tools require at least ArcGIS 10+.

If you create a GDRS using GDRS Manager and include this toolbox resource and MNDNR Quick Layers, the DNR toolboxes will automatically be added to the ArcToolbox window whenever Quick Layers GDRS Location is set to the GDRS location that has the toolboxes.

Toolsets included in MNDNR Tools V10:
- Analysis Tools
- Conversion Tools
- Division Tools
- General Tools
- Hydrology Tools
- LiDAR and DEM Tools
- Raster Tools
- Sampling Tools

These toolboxes are provided free of charge and are not warrantied for any specific use. We do not provide support or assistance in downloading or using these tools. We do, however, strive to produce high-quality tools and appreciate comments you have about them.

This layer displays a global map of land use/land cover (LULC) derived from ESA Sentinel-2 imagery at 10m resolution. Each year is generated with Impact Observatory’s deep learning AI land classification model, trained using billions of human-labeled image pixels from the National Geographic Society. The global maps are produced by applying this model to the Sentinel-2 Level-2A image collection on Microsoft’s Planetary Computer, processing over 400,000 Earth observations per year.The algorithm generates LULC predictions for nine classes, described in detail below. The year 2017 has a land cover class assigned for every pixel, but its class is based upon fewer images than the other years. The years 2018-2024 are based upon a more complete set of imagery. For this reason, the year 2017 may have less accurate land cover class assignments than the years 2018-2024.Variable mapped: Land use/land cover in 2017, 2018, 2019, 2020, 2021, 2022, 2023, 2024Source Data Coordinate System: Universal Transverse Mercator (UTM) WGS84Service Coordinate System: Web Mercator Auxiliary Sphere WGS84 (EPSG:3857)Extent: GlobalSource imagery: Sentinel-2 L2ACell Size: 10-metersType: ThematicAttribution: Esri, Impact ObservatoryWhat can you do with this layer?Global land use/land cover maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land use/land cover anywhere on Earth. This layer can also be used in analyses that require land use/land cover input. For example, the Zonal toolset allows a user to understand the composition of a specified area by reporting the total estimates for each of the classes. NOTE: Land use focus does not provide the spatial detail of a land cover map. As such, for the built area classification, yards, parks, and groves will appear as built area rather than trees or rangeland classes.Class definitionsValueNameDescription1WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2TreesAny significant clustering of tall (~15 feet or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation, clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields.10CloudsNo land cover information due to persistent cloud cover.11RangelandOpen areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.Classification ProcessThese maps include Version 003 of the global Sentinel-2 land use/land cover data product. It is produced by a deep learning model trained using over five billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world.The underlying deep learning model uses 6-bands of Sentinel-2 L2A surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map for each year.The input Sentinel-2 L2A data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch.CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.

Download 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.

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 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.

WorldClim 2.1 provides downscaled estimates of climate variables as monthly means over the period of 1970-2000 based on interpolated station measurements. Here we provide analytical image services of precipitation for each month along with an annual mean. Each time step is accessible from a processing template.Time Extent: Monthly/Annual 1970-2000Units: mm/monthCell Size: 2.5 minutes (~5 km)Source Type: StretchedPixel Type: 16 Bit IntegerData Projection: GCS WGS84Mosaic Projection: GCS WGS84Extent: GlobalSource: WorldClim v2.1Using Processing Templates to Access TimeThere are 13 processing templates applied to this service, each providing access to the 12 monthly and 1 annual mean precipitation layers. To apply these in ArcGIS Online, select the Image Display options on the layer. Then pull down the list of variables from the Renderer options. Click Apply and Close. In ArcGIS Pro, go into the Layer Properties. Select Processing Templates from the left-hand menu. From the Processing Template pull down menu, select the version to display.What can you do with this layer?This layer may be added to maps to visualize and quickly interrogate each pixel value. The pop-up provides a graph of the time series along with the calculated annual mean value.This layer can be used in analysis. For example, the layer may be added to ArcGIS Pro and an area count of precipitation may be produced for a feature dataset using the zonal statistics tool. Statistics may be compared with the statistics from month to month to show seasonal patterns.To calculate precipitation by land area, or any other analysis, be sure to use an equal area projection, such as Albers or Equal Earth.Source Data: The datasets behind this layer were extracted from GeoTIF files produced by WorldClim at 2.5 minutes resolution. The mean of the 12 GeoTIFs was calculated (annual), and the 13 rasters were converted to Cloud Optimized GeoTIFF format and added to a mosaic dataset.Citation: Fick, S.E. and R.J. Hijmans, 2017. WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. International Journal of Climatology 37 (12): 4302-4315.

This map presents transportation data, including highways, roads, railroads, and airports for the world.

The map was developed by Esri using Esri highway data; Garmin basemap layers; HERE street data for North America, Europe, Australia, New Zealand, South America and Central America, India, most of the Middle East and Asia, and select countries in Africa. Data for Pacific Island nations and the remaining countries of Africa was sourced from OpenStreetMap contributors. Specific country list and documentation of Esri's process for including OSM data is available to view.

Lesson 1. An Introduction to working with multispectral satellite data in ArcGIS Pro In which we learn: • How to unpack tar and gz files from USGS EROS • The basic map interface in ArcGIS • How to add image files • What each individual band of Landsat spectral data looks like • The difference between: o Analysis-ready data: surface reflectance and surface temperature o Landsat Collection 1 Level 3 data: burned area and dynamic surface water o Sentinel2data o ISRO AWiFS and LISS-3 data Lesson 2. Basic image preprocessing In which we learn: • How to composite using the composite band tool • How to represent composite images • All about band combinations • How to composite using raster functions • How to subset data into a rectangle • How to clip to a polygon Lesson 3. Working with mosaic datasets In which we learn: o How to prepare an empty mosaic dataset o How to add images to a mosaic dataset o How to change symbology in a mosaic dataset o How to add a time attribute o How to add a time dimension to the mosaic dataset o How to view time series data in a mosaic dataset Lesson 4. Working with and creating derived datasets In which we learn: • How to visualize Landsat ARD surface temperature • How to calculate F° from K° using ARD surface temperature • How to generate and apply .lyrx files • How to calculate an NDVI raster using ISRO LISS-3 data • How to visualize burned areas using Landsat Level 3 data • How to visualize dynamic surface water extent using Landsat Level 3 data

Click anywhere on earth to see how the water balance is changing over time. This app is based on data from GLDAS version 2.1, which uses weather observations like temperature, humidity, and rainfall to run the Noah land surface model. This model estimates how much of the rain becomes runoff, how much evaporates, and how much infiltrates into the soil. These output variables, calculated every three hours, are aggregated into monthly averages, giving us a record of the hydrologic cycle going all the way back to January 2000. Because the model is run with 0.25 degree spatial resolution (~30 km), these data should only be used for regional analysis. A specific farm or other small area might experience very different conditions than the region around it, especially because human influences like irrigation are not included.This app can also be seen as a useful template for sharing other climate datasets. If you would like to customize it for your own organization, or use it as a starting point for your own scientific application, the source code is available on github for anyone to use.

River basins or hydrologic units are often the spatial unit used for aggregating and analyzing components of the water cycle such as precipitation, runoff, riverine discharge, etc. The hydroSHEDS dataset, derived from the Shuttle Radar Topography Mission, are the most commonly used global hydrologic unit for these analyses. But when planning water use or gaps, political boundaries need to be considered. Water provinces (Straatsma et al 2020) provide a much more realistic hydrologic unit for such purposes.Esri’s World Administration Divisions (2011) defines 3,300 subnational units. Areas less than 150,000 sq km were aggregated into 1,099 regions. The water provinces were then calculated by overlaying these regions with the major basins from hydroSHEDS. After sliver polygons were removed, the result was 1,604 unique units based on river basins but constrained by political boundaries. These water provinces provide a suitable unit for longterm water use planning, especially at local scales.A more detailed description can be accessed here.

This layer features special areas of interest (AOIs) that have been contributed to Esri Community Maps using the new Community Maps Editor app. The data that is accepted by Esri will be included in selected Esri basemaps, including our suite of Esri Vector Basemaps, and made available through this layer to export and use offline. Export DataThe contributed data is also available for contributors and other users to export (or extract) and re-use for their own purposes. Users can export the full layer from the ArcGIS Online item details page by clicking the Export Data button and selecting one of the supported formats (e.g. shapefile, or file geodatabase (FGDB)). User can extract selected layers for an area of interest by opening in Map Viewer, clicking the Analysis button, viewing the Manage Data tools, and using the Extract Data tool. To display this data with proper symbology and metadata in ArcGIS Pro, you can download and use this layer file.Data UsageThe data contributed through the Community Maps Editor app is primarily intended for use in the Esri Basemaps. Esri staff will periodically (e.g. weekly) review the contents of the contributed data and either accept or reject the data for use in the basemaps. Accepted features will be added to the Esri basemaps in a subsequent update and will remain in the app for the contributor or others to edit over time. Rejected features will be removed from the app.Esri Community Maps Contributors and other ArcGIS Online users can download accepted features from this layer for their internal use or map publishing, subject to the terms of use below.

This dataset represents cellular tower locations as recorded by the Federal Communications Commission This feature class serves as base information for use in GIS systems for general planning, analytical, and research purposes. It is not intended for engineering work or to legally define FCC licensee data or FCC market boundaries. The material in these data and text files are provided as-is. The FCC disclaims all warranties with regard to the contents of these files, including their fitness. In no event shall the FCC be liable for any special, indirect, or consequential damages whatsoever resulting from loss or use, data or profits, whether in connection with the use or performance of the contents of these files, action of contract, negligence, or other action arising out of, or in connection with the use of the contents of these files. It is known that there are some errors in the licensing information - Latitude, Longitude and Ground Elevation data as well as frequency assignment data from which these MapInfo files were generated.

The U.S. Geological Survey has been forecasting sea-level rise impacts on the landscape to evaluate where coastal land will be available for future use. The purpose of this project is to develop a spatially explicit, probabilistic model of coastal response for the Northeastern U.S. to a variety of sea-level scenarios that take into account the variable nature of the coast and provides outputs at spatial and temporal scales suitable for decision support. Model results provide predictions of adjusted land elevation ranges (AE) with respect to forecast sea-levels, a likelihood estimate of this outcome (PAE), and a probability of coastal response (CR) characterized as either static or dynamic. The predictions span the coastal zone vertically from -12 meters (m) to 10 m above mean high water (MHW). Results are produced at a horizontal resolution of 30 meters for four decades (the 2020s, 2030s, 2050s and 2080s). Adjusted elevations and their respective probabilities are generated using regional geospatial datasets of current sea-level forecasts, vertical land movement rates, and current elevation data. Coastal response type predictions incorporate adjusted elevation predictions with land cover data and expert knowledge to determine the likelihood that an area will be able to accommodate or adapt to water level increases and maintain its initial land class state or transition to a new non-submerged state (dynamic) or become submerged (static). Intended users of these data include scientific researchers, coastal planners, and natural resource management communities.

This layer presents the U.S. Census County (or County Equivalent) boundaries of the United States in the 50 states and the District of Columbia, sourced from 2023 Census TIGER/Line data and includes the estimated annual population total of each County.This layer is updated annually. The geography is sourced from U.S. Census Bureau 2023 TIGER FGDB (National Sub-State) and edited using TIGER Hydrography to add a detailed coastline for cartographic purposes. Attribute fields include 2023 estimated total population from the Esri demographics team.This ready-to-use layer can be used in ArcGIS Pro and in ArcGIS Online and its configurable apps, dashboards, StoryMaps, custom apps, and mobile apps. The data can also be exported for offline workflows. Cite the 'U.S. Census Bureau' when using this data.

SCAG has developed and maintained its regional geospatial dataset of land use information at parcel-level—approximately five million parcels in the SCAG Region. The parcel-based land use dataset is developed (1) to aid in SCAG’s regional transportation planning, scenario planning and growth forecasting, (2) facilitate policy discussion on various planning issues, and (3) enhance information database to better serve SCAG member jurisdictions, research institutes, universities, developers, general public, etc. After the successful release of SCAG’s 2016 regional land use dataset for the development of the Connect SoCal (the 2020 RTP/SCS), SCAG has initiated a process to annually update its regional land use information at the parcel-level (the Annual Land Use Update). For the Annual Land Use Update process, SCAG collected county assessor’s tax roll records (including parcel polygons and property information) from county assessor’s offices, plus other reference layers including California Protected Areas Database (CPAD), California School Campus Database (CSCD), Farmland Mapping and Monitoring Program (FMMP)'s Important Farmland, U.S. Department of Defense's Military Installations, Ranges, and Training Areas (MIRTA) as well as SCAG's regional geospatial datasets, such as airport polygons and water body polygons.Note: This dataset is intended for planning purposes only, and SCAG shall incur no responsibility or liability as to the completeness, currentness, or accuracy of this information. SCAG assumes no responsibility arising from use of this information by individuals, businesses, or other public entities. The information is provided with no warranty of any kind, expressed or implied, including but not limited to the implied warranties of merchantability and fitness for a particular purpose. Users should consult with each local jurisdiction directly to obtain the official land use information.Data DescriptionFIELD_NAMEDESCRIPTIONPID202020 SCAG's unique parcel identifierAPN202020 Assessor Parcel NumberAPN20_P2020 Assessor Parcel Number - Parent Parcel (if applicable)COUNTYCounty nameCOUNTY_IDCounty FIPS codeCITYCity nameCITY_IDCity FIPS codeMULTIPARTMultipart feature (the number of multipart polygons; '1' = singlepart feature)STACKDuplicate geometry (the number of stacked polygons; '1' = no duplicate polygons)ACRESParcel area (in acres)SLOPESlope information1GEOID202020 Census Block GEOIDAPN_DUPDuplicate APN (the number of multiple tax roll property records; '0' = no duplicate APN)IL_RATIORatio of improvements assessed value to land assessed valueALU202020 Existing Land UseALU20_SRC2020 Existing Land Use Source2GP19_CITY2019 Jurisdiction’s general plan land use designationGP19_SCAG2019 SCAG general plan land use codeSP19_CITY2019 Jurisdiction’s specific plan land use designationSP19_SCAG2019 SCAG specific plan land use codeZN19_CITY2019 Jurisdiction’s zoning codeZN19_SCAG2019 SCAG zoning codeSP19_INDEX2019 Specific Plan Index ('0' = outside specific plan area; '1' = inside specific plan area)DC_BLTDecade built of existing structure (example: year built between 1960-1969 is '1960s')3BF_SQFT Building footprint area (in square feet)4PUB_OWNPublic-owned land index ('1' = owned by public agency)PUB_TYPEType of public agency5ADU_STATEThis field is a rudimentary estimate of which parcels have adequate physical space to accommodate a typical detached Accessory Dwelling Unit (ADU)6, (1 = ADU eligible parcel, 0 = Not ADU eligible parcel)SF_UNBUILTDifference between parcel land area and building footprint area expressed in square feetFLOODParcel intersects with flood areas delineated by the Federal Emergency Management Agency (FEMA), obtained from the Digital Flood Insurance Rate Map from FEMA in August 2017. FIREParcel intersects with CalFire State Responsibility Areas Fire Hazard Severity zones (high and very high severity), dated 9/29/2023 and implemented 4/1/2024. WUIParcel intersects with Wildland-Urban Interface or Intermix zones, utilized from CAL FIRE’s Fire and Resource Assessment Program (FRAP), Wildland-Urban Interface (WUI) and Wildland-Urban Intermix (2020). See CAL FIRE for details. SEARISE36Parcel intersects with USGS Coastal Storm Modeling System (CoSMos) One-Meter Sea Level Rise inundation areas for Southern California (v3.0, Phase 2, 2018)WETLANDParcel intersects a wetland or deepwater habitat, obtained from the US Fish and Wildlife Services National Wetlands Inventory Data (2020)HABITATParcel intersects with habitat connectivity corridors. Data is obtained from the California Department of Fish and Wildlife Habitat Essential Connectivity Project (2010).CONSERVParcel intersects with Areas of Conservation Emphasis (ACEIIv2), obtained from California Department of Fish and Wildlife Areas of Conservation Emphasis (2015)SOARParcel intersects with publicly owned open space identified by the County of Ventura Save Our Agricultural Resources (SOAR, 2017), which consist of a series of voter initiatives that require a majority vote of the people before agricultural land or open space areas can be rezoned for developmentCPADParcel intersects with publicly owned protected open space lands in the State of California through fee ownership as identified in the 2021 California Protected Areas Database (CPAD)CCEDParcel intersects with lands protected under conservation easements as identified in the 2021 California Conservation Easement Database (CCED)TRIBALParcel intersects with the tribal lands for the 16 Federally Recognized Tribal entities in the SCAG region, obtained from the American Indian Reservations/ Federally Recognized Tribal Entities dataset (2021)MILITARYParcel intersects with military lands managed by the US Department of Defense as of 2018FARMLANDParcel intersects with farmlands as identified in the Farmland Mapping and Monitoring Program (FMMP) in the Division of Land Resource Protection in the California Department of Conservation (2018)GRRA_INDEXThe number of Green Region Rresource Areas (GRRAs) that the parcel intersects with. GRRAs are areas where climate hazard zones, environmental sensitivities, and administrative areas where growth would generally not advance SB 375 objectives. See Connect SoCal 2024 Land Use & Communities Technical Report for details. UAZParcel centroid lies within Caltrans 2020 Adjusted Urbanized Area TCAC_2024The opportunity/resource level in the 2024 CTCAC/HCD Opportunity Map SB535_INDEXField takes a value of 1 if parcel intersects with SB 535 Disadvantaged Communities. See Connect SoCal 2024 Equity Analysis Technical Report for details. PEC_INDEXField takes a value of 1 if parcel's block falls within Priority Equity Communities. See Connect SoCal 2024 Equity Analysis Technical Report for details. PDA_INDEXThe number of Priority Development Areas (PDAs) that the parcel's largest overlapping area falls in. PDAs in Connect SoCal 2024 include Neighborhood Mobility Areas (NMAs), Transit Priority Areas (TPAs), Livable Corridors and Spheres of Influence (SOIs) (in unincorporated areas only). See Connect SoCal 2024 for details. PDA_NMAField takes a value of 1 if the parcel's largest overlapping area falls within Neighborhood Mobility Areas. See Connect SoCal 2024 for details. PDA_LCField takes a value of 1 if the parcel's largest overlapping area falls within Livable Corridors. See Connect SoCal 2024 for details. PDA_SOIField takes a value of 1 if the parcel's largest overlapping area falls within Spheres of Influence (SOIs) (in unincorporated areas only). See Connect SoCal 2024 for details. PDA_TPAField takes a value of 1 if the parcel's largest overlapping area falls within Transit Priority Areas. See Connect SoCal 2024 for details. APPAREL1MIThe number of apparel stores within a 1-mile drive7EDUC1MIThe number of educational institutions within a 1-mile drive7GROCERY1MIThe number of grocery stores within a 1-mile drive7HOSPIT1MIThe number of hospitals within a 1-mile drive7RESTAUR1MIThe number of restaurants within a 1-mile drive7JOBS_30MINThe number of the region's jobs accessible within a 30-minute commute by car during morning peak hour (6-9am) in 2050 based on Connect SoCal 2024 travel demand modeling. See Equity Technical Report for details. VMT_TOTAverage daily vehicle miles traveled (VMT) per average resident in the parcel’s transportation analysis zone (TAZ) in 2019, rounded to the nearest mile. This field contains results derived from Connect SoCal 2024’s activity-based travel demand model and do not reflect survey data, do not reflect VMT in any particular parcel within a TAZ, and are not validated at the TAZ-level. SCAG assumes no liability arising from the use of this data.8VMT_WORKAverage daily vehicle miles traveled (VMT) per average resident for work purposes in the parcel’s transportation analysis zone (TAZ) in 2019, rounded to the nearest mile. This field contains results derived from Connect SoCal 2024’s activity-based travel demand model and do not reflect survey data, do not reflect VMT in any particular parcel within a TAZ, and are not validated at the TAZ-level. SCAG assumes no liability arising from the use of this data.8JURIS_PLUSSub-jurisdictional geography in Los Angeles City (Community Plan Areas) and unincorporated areas of Los Angeles County (Planning Areas)YEARDataset YearShape_LengthLength of feature in internal unitsShape_AreaArea of feature in internal units squared1. Slope: '0' - 0~4 percent; '5' - 5~9 percent; '10' - 10~14 percent; '15' = 15~19 percent; '20' - 20~24 percent; '25' = 25 percent or greater.2. ASSESSOR- Assessor's 2020 tax roll records; CPAD- California Protected Areas Database (version 2020b; released in December 2020); CSCD- California School Campus Database (version 2021; released in March 2020); FMMP- Farmland Mapping and Monitoring Program's Important Farmland GIS data (v.2020 for Imperial County and Riverside County, v.2018 for the other counties, assessed in April 2024); MIRTA- U.S. Department of

The way to access Layers Quickly.

Quick Layers is an Add-In for ArcGIS Pro 3 that allows rapid access to the DNR's Geospatial Data Resource Site (GDRS). The GDRS is a data structure that serves core geospatial dataset and applications for not only DNR, but many state agencies, and supports the Minnesota Geospatial Commons. Data added from Quick Layers is pre-symbolized, helping to standardize visualization and map production. Current version: 3.11

To use Quick Layers with the GDRS, there's no need to download QuickLayers from this location. Instead, download a full copy or a custom subset of the public GDRS (including Quick Layers for ArcGIS Pro 3) using GDRS Manager.

Quick Layers also allows users to save and share their own pre-symbolized layers, thus increasing efficiency and consistency across the enterprise.

Installation:

After using GDRS Manager to create a GDRS, including Quick Layers, add the path to the Quick Layers addin to the list of shared folders:
1. Open ArcGIS Pro
2. Project -> Add-In Manager -> Options
3. Click add folder, and enter the location of the Quick Layers Pro app. For example, if your GDRS is mapped to the V drive, the path would be V:\gdrs\apps\pub\us_mn_state_dnr\quick_layers_pro3
4. After you do this, the Quick Layers ribbon will be available. To also add Quick Layers to the Quick Access Toolbar at the top, right click Quick Layers, and select Add to Quick Access Toolbar

The link below is only for those who are using Quick Layers without a GDRS. To get the most functionality out of Quick Layers, don't install it separately, but instead download it as part of a GDRS build using GDRS Manager.

Continuing the tradition of the best-selling Getting to Know series, Getting to Know ArcGIS Pro 2.6 teaches new and existing GIS users how to get started solving problems using ArcGIS Pro. Using ArcGIS Pro for these tasks allows you to understand complex data with the leading GIS software that many businesses and organizations use every day.Getting to Know ArcGIS Pro 2.6 introduces the basic tools and capabilities of ArcGIS Pro through practical project workflows that demonstrate best practices for productivity. Explore spatial relationships, building a geodatabase, 3D GIS, project presentation, and more. Learn how to navigate ArcGIS Pro and ArcGIS Online by visualizing, querying, creating, editing, analyzing, and presenting geospatial data in both 2D and 3D environments. Using figures to show each step, Getting to Know ArcGIS Pro 2.6 demystifies complicated process like developing a geoprocessing model, using Python to write a script tool, and the creation of space-time cubes. Cartographic techniques for both web and physical maps are included.Each chapter begins with a prompt using a real-world scenario in a different industry to help you explore how ArcGIS Pro can be applied for operational efficiency, analysis, and problem solving. A summary and glossary terms at the end of every chapter help reinforce the lessons and skills learned.Ideal for students, self-learners, and seasoned professionals looking to learn a new GIS product, Getting to Know ArcGIS Pro 2.6 is a broad textbook and desk reference designed to leave users feeling confident in using ArcGIS Pro on their own.AUDIENCEProfessional and scholarly. Higher education.AUTHOR BIOMichael Law is a cartographer and GIS professional with more than a decade of experience. He was a cartographer for Esri, where he developed cartography for books, edited and tested GIS workbooks, and was the editor of the Esri Map Book. He continues to work with GIS software, writing technical documentation, teaching training courses, and designing and optimizing user interfaces.Amy Collins is a writer and editor who has worked with GIS for over 16 years. She was a technical editor for Esri, where she honed her GIS skills and cultivated an interest in designing effective instructional materials. She continues to develop books on GIS education, among other projects.Pub Date: Print: 10/6/2020 Digital: 8/18/2020 ISBN: Print: 9781589486355 Digital: 9781589486362 Price: Print: $84.99 USD Digital: $84.99 USD Pages: 420 Trim: 7.5 x 9.25 in.Table of ContentsPrefaceChapter 1 Introducing GISExercise 1a: Explore ArcGIS OnlineChapter 2 A first look at ArcGIS Pro Exercise 2a: Learn some basics Exercise 2b: Go beyond the basics Exercise 2c: Experience 3D GISChapter 3 Exploring geospatial relationshipsExercise 3a: Extract part of a dataset Exercise 3b: Incorporate tabular data Exercise 3c: Calculate data statistics Exercise 3d: Connect spatial datasetsChapter 4 Creating and editing spatial data Exercise 4a: Build a geodatabase Exercise 4b: Create features Exercise 4c: Modify featuresChapter 5 Facilitating workflows Exercise 5a: Manage a repeatable workflow using tasks Exercise 5b: Create a geoprocessing model Exercise 5c: Run a Python command and script toolChapter 6 Collaborative mapping Exercise 6a: Prepare a database for data collection Exercise 6b: Prepare a map for data collection Exercise 6c: Collect data using ArcGIS CollectorChapter 7 Geoenabling your projectExercise 7a: Prepare project data Exercise 7b: Geocode location data Exercise 7c: Use geoprocessing tools to analyze vector dataChapter 8 Analyzing spatial and temporal patternsExercise 8a: Create a kernel density map Exercise 8b: Perform a hot spot analysis Exercise 8c: Explore the results in 3D Exercise 8d: Animate the dataChapter 9 Determining suitability Exercise 9a: Prepare project data Exercise 9b: Derive new surfaces Exercise 9c: Create a weighted suitability modelChapter 10 Presenting your project Exercise 10a: Apply detailed symbology Exercise 10b: Label features Exercise 10c: Create a page layout Exercise 10d: Share your projectAppendix Image and data source credits Data license agreement GlossaryGetting to Know ArcGIS Pro 2.6 | Official Trailer | 2020-08-10 | 00:57