The arrival of ArcGIS Pro has brought a challenge to ArcMap users. The new software is sufficiently different in architecture and layout that switching from the old to the new is not a simple process. In some ways, Pro is harder to learn for ArcMap users than for new GIS users, because some workflows have to be unlearned, or at least heavily modified. Current ArcMap users are pressed for time, trying to learn the new software while still completing their daily tasks, so a book that teaches Pro from the start is not an efficient method.Switching to ArcGIS Pro from ArcMap aims to quickly transition ArcMap users to ArcGIS Pro. Rather than teaching Pro from the start, as for a novice user, this book focuses on how Pro is different from ArcMap. Covering the most common and important workflows required for most GIS work, it leverages the user’s prior experience to enable a more rapid adjustment to Pro.AUDIENCEProfessional and scholarly; College/higher education; General/trade.AUTHOR BIOMaribeth H. Price, PhD, South Dakota School of Mines and Technology, has been using Esri products since 1991, teaching college GIS since 1995 and writing textbooks utilizing Esri’s software since 2001. She has extensive familiarity with both ArcMap/ArcCatalog and Pro, both as a user and in the classroom, as well as long experience writing about GIS concepts and developing software tutorials. She teaches GIS workshops, having offered more than 100 workshops to over 1,200 participants since 2000.Pub Date: Print: 2/14/2019 Digital: 1/28/2019 Format: PaperbackISBN: Print: 9781589485440 Digital: 9781589485457 Trim: 8 x 10 in.Price: Print: $49.99 USD Digital: $49.99 USD Pages: 172Table of ContentsPreface1 Contemplating the switch to ArcGIS ProBackgroundSystem requirementsLicensingCapabilities of ArcGIS ProWhen should I switch?Time to exploreObjective 1.1: Downloading the data for these exercisesObjective 1.2: Starting ArcGIS Pro, signing in, creating a project, and exploring the interfaceObjective 1.3: Accessing maps and data from ArcGIS OnlineObjective 1.4: Arranging the windows and panesObjective 1.5: Accessing the helpObjective 1.6: Importing a map document2 Unpacking the GUIBackgroundThe ribbon and tabsPanesViewsTime to exploreObjective 2.1: Getting familiar with the Contents paneObjective 2.2: Learning to work with objects and tabsObjective 2.3: Exploring the Catalog pane3 The projectBackgroundWhat is a project?Items stored in a projectPaths in projectsRenaming projectsTime to exploreObjective 3.1: Exploring different elements of a projectObjective 3.2: Accessing properties of projects, maps, and other items4 Navigating and exploring mapsBackgroundExploring maps2D and 3D navigationTime to exploreObjective 4.1: Learning to use the Map toolsObjective 4.2: Exploring 3D scenes and linking views5 Symbolizing mapsBackgroundAccessing the symbol settings for layersAccessing the labeling propertiesSymbolizing rastersTime to exploreObjective 5.1: Modifying single symbolsObjective 5.2: Creating maps from attributesObjective 5.3: Creating labelsObjective 5.4: Managing labelsObjective 5.5: Symbolizing rasters6 GeoprocessingBackgroundWhat’s differentAnalysis buttons and toolsTool licensingTime to exploreObjective 6.1: Getting familiar with the geoprocessing interfaceObjective 6.2: Performing interactive selectionsObjective 6.3: Performing selections based on attributesObjective 6.4: Performing selections based on locationObjective 6.5: Practicing geoprocessing7 TablesBackgroundGeneral table characteristicsJoining and relating tablesMaking chartsTime to exploreObjective 7.1: Managing table viewsObjective 7.2: Creating and managing properties of a chartObjective 7.3: Calculating statistics for tablesObjective 7.4: Calculating and editing in tables8 LayoutsBackgroundLayouts and map framesLayout editing proceduresImporting map documents and templatesTime to exploreObjective 8.1: Creating the maps for the layoutObjective 8.2: Setting up a layout page with map framesObjective 8.3: Setting map frame extent and scaleObjective 8.4: Formatting the map frameObjective 8.5: Creating and formatting map elementsObjective 8.6: Fine-tuning the legendObjective 8.7: Accessing and copying layouts9 Managing dataBackgroundData modelsManaging the geodatabase schemaCreating domainsManaging data from diverse sourcesProject longevityManaging shared data for work groupsTime to exploreObjective 9.1: Creating a project and exporting data to itObjective 9.2: Creating feature classesObjective 9.3: Creating and managing metadataObjective 9.4: Creating fields and domainsObjective 9.5: Modifying the table schemaObjective 9.6: Sharing data using ArcGIS Online10 EditingBackgroundBasic editing functionsCreating featuresModifying existing featuresCreating and editing annotationTime to exploreObjective 10.1: Understanding the editing tools in ArcGIS ProObjective 10.2: Creating pointsObjective 10.3: Creating linesObjective 10.4: Creating polygonsObjective 10.5: Modifying existing featuresObjective 10.6: Creating an annotation feature classObjective 10.7: Editing annotationObjective 10.8: Creating annotation features11 Moving forwardData sourcesIndex

When rain falls over land, a portion of it runs off into stream channels and storm water systems while the remainder infiltrates into the soil or returns to the atmosphere directly through evaporation.Physical properties of soil affect the rate that water is absorbed and the amount of runoff produced by a storm. Hydrologic soil group provides an index of the rate that water infiltrates a soil and is an input to rainfall-runoff models that are used to predict potential stream flow.For more information on using hydrologic soil group in hydrologic modeling see the publication Urban Hydrology for Small Watersheds (Natural Resources Conservation Service, United States Department of Agriculture, Technical Release–55).Dataset SummaryPhenomenon Mapped: Soil hydrologic groupUnits: ClassesCell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerData Coordinate System: USA Contiguous Albers Equal Area Conic USGS version (contiguous US, Puerto Rico, US Virgin Islands), WGS 1984 Albers (Alaska), Hawaii Albers Equal Area Conic (Hawaii), Western Pacific Albers Equal Area Conic (Guam, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American Samoa)Mosaic Projection: Web Mercator Auxiliary SphereExtent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American SamoaSource: Natural Resources Conservation ServicePublication Date: December 2021ArcGIS Server URL: https://landscape11.arcgis.com/arcgis/Data from the gNATSGO database was used to create the layer for the contiguous United States, Alaska, Puerto Rico, and the U.S. Virgin Islands. The remaining areas were created with the gSSURGO database (Hawaii, Guam, Marshall Islands, Northern Marianas Islands, Palau, Federated States of Micronesia, and American Samoa).This layer is derived from the 30m (contiguous U.S.) and 10m rasters (all other regions) produced by the Natural Resources Conservation Service (NRCS). The value for hydrologic group is derived from the gSSURGO map unit aggregated attribute table field Hydrologic Group - Dominant Conditions (hydgrpdcd).The seven classes of hydrologic soil group followed by definitions:Group A - Group A soils consist of deep, well drained sands or gravelly sands with high infiltration and low runoff rates.Group B - Group B soils consist of deep well drained soils with a moderately fine to moderately coarse texture and a moderate rate of infiltration and runoff.Group C - Group C consists of soils with a layer that impedes the downward movement of water or fine textured soils and a slow rate of infiltration.Group D - Group D consists of soils with a very slow infiltration rate and high runoff potential. This group is composed of clays that have a high shrink-swell potential, soils with a high water table, soils that have a clay pan or clay layer at or near the surface, and soils that are shallow over nearly impervious material.Group A/D - Group A/D soils naturally have a very slow infiltration rate due to a high water table but will have high infiltration and low runoff rates if drained.Group B/D - Group B/D soils naturally have a very slow infiltration rate due to a high water table but will have a moderate rate of infiltration and runoff if drained.Group C/D - Group C/D soils naturally have a very slow infiltration rate due to a high water table but will have a slow rate of infiltration if drained.What can you do with this Layer? This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "soil hydrologic group" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "soil hydrologic group" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.Online you can filter the layer to show subsets of the data using the filter button and the layer's built-in raster functions.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.

Map InformationThis nowCOAST time-enabled map service provides maps depicting the NWS Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimate mosaics for 1-, 3-, 6-, 12-, 24-, 48-, and 72-hr time periods at a 1 km (0.6 miles) horizontal resolution for CONUS and southern part of Canada. The precipitation estimates are based only on radar data. The total precipitation amount is indicated by different colors at 0.01, 0.10, 0.25 and then at 1/4 inch intervals up to 4.0 inches (e.g. 0.50, 0.75, 1.00, 1.25, etc.), at 1-inch intervals from 4 to 10 inches and then at 2-inch intervals up to 14 inches. The increments from 0.01 to 1.00 or 2.00 inches are similar to what are used on NCEP's Weather Prediction Center QPF products and the NWS River Forecast Center (RFC) daily precipitation analysis. The 1-hr mosaic is updated every 4 minutes with a latency on nowCOAST of about 6-7 minutes from valid time. The 3-, 6-, 12-, and 24-hr QPEs are updated on nowCOAST every hour for the period ending at the top of the hour. The 48- and 72-hr QPEs are generated daily for the period ending at 12 UTC (i.e. 7AM EST) and available on nowCOAST shortly afterwards. For more detailed information about the update schedule.Background InformationThe NWS Multi-Radar Multi-Sensor System (MRMS)/Q3 QPEs are radar-only based quantitative precipitation analyses. The 1-h precipitation accumulation is obtained by aggregating 12 instantaneous rate fields. Missing rate fields are filled with the neighboring rate fields if the data gap is not significantly large (e.g.<=15 minutes). The instantaneous rate is computed from the hybrid scan reflectivity and the precipitation flag fields. (Both are 2-D derivative products from the National 3-D Reflectivity Mosaic grid which has a 1-km horizontal resolution, 31 vertical levels and a 5-minute update cycle). The instantaneous rate currently uses four Z-R relationships (i.e. tropical, convective, stratiform, or snow). The particular ZR relationship used in any grid cell depends on precipitation type which is indicated by the precipitation flag. The other accumulation products are derived by aggregating the hourly accumulations. The 1-hr QPE are generated every 4 minutes, while the 3-,6-,12-, and 24-hr accumulations are generated every hour at the top of the hour. The 48- and 72-hr QPEs are updated daily at approximately 12 UTC. MRMS was developed by NOAA/OAR/National Severe Storms Laboratory and migrated into NWS operations at NOAA Integrated Dissemination Program.Time InformationThis map is time-enabled, meaning that each individual layer contains time-varying data and can be utilized by clients capable of making map requests that include a time component.This particular service can be queried with or without the use of a time component. If the time parameter is specified in a request, the data or imagery most relevant to the provided time value, if any, will be returned. If the time parameter is not specified in a request, the latest data or imagery valid for the present system time will be returned to the client. If the time parameter is not specified and no data or imagery is available for the present time, no data will be returned.In addition to ArcGIS Server REST access, time-enabled OGC WMS 1.3.0 access is also provided by this service.Due to software limitations, the time extent of the service and map layers displayed below does not provide the most up-to-date start and end times of available data. Instead, users have three options for determining the latest time information about the service:Issue a returnUpdates=true request for an individual layer or for the service itself, which will return the current start and end times of available data, in epoch time format (milliseconds since 00:00 January 1, 1970). To see an example, click on the "Return Updates" link at the bottom of this page under "Supported Operations". Refer to the ArcGIS REST API Map Service Documentation for more information.Issue an Identify (ArcGIS REST) or GetFeatureInfo (WMS) request against the proper layer corresponding with the target dataset. For raster data, this would be the "Image Footprints with Time Attributes" layer in the same group as the target "Image" layer being displayed. For vector (point, line, or polygon) data, the target layer can be queried directly. In either case, the attributes returned for the matching raster(s) or vector feature(s) will include the following:validtime: Valid timestamp.starttime: Display start time.endtime: Display end time.reftime: Reference time (sometimes reffered to as issuance time, cycle time, or initialization time).projmins: Number of minutes from reference time to valid time.desigreftime: Designated reference time; used as a common reference time for all items when individual reference times do not match.desigprojmins: Number of minutes from designated reference time to valid time.Query the nowCOAST LayerInfo web service, which has been created to provide additional information about each data layer in a service, including a list of all available "time stops" (i.e. "valid times"), individual timestamps, or the valid time of a layer's latest available data (i.e. "Product Time"). For more information about the LayerInfo web service, including examples of various types of requests, refer to the nowCOAST help documentation.References For more information about the MRMS/Q3 system.

Cancer Rates for Lake County Illinois. Explanation of field attributes: Colorectal Cancer - Cancer that develops in the colon (the longest part of the large intestine) and/or the rectum (the last several inches of the large intestine). This is a rate per 100,000. Lung Cancer – Cancer that forms in tissues of the lung, usually in the cells lining air passages. This is a rate per 100,000. Breast Cancer – Cancer that forms in tissues of the breast. This is a rate per 100,000. Prostate Cancer – Cancer that forms in tissues of the prostate. This is a rate per 100,000. Urinary System Cancer – Cancer that forms in the organs of the body that produce and discharge urine. These include the kidneys, ureters, bladder, and urethra. This is a rate per 100,000. All Cancer – All cancers including, but not limited to: colorectal cancer, lung cancer, breast cancer, prostate cancer, and cancer of the urinary system. This is a rate per 100,000.

This feature service includes change areas for city boundaries and county line adjustments filed in accordance with Government Code 54900. The boundaries in this map are based on the State Board of Equalization's tax rate area maps for the assessment roll year specified in the COFILE field. The information is updated regularly within 10 business days of the most recent BOE acknowledgement date. Some differences may occur between actual recorded boundaries and boundary placement in the tax rate area GIS map. Tax rate area boundaries are representations of taxing jurisdictions for the purpose of determining property tax assessments and should not be used to determine precise city or county boundary line locations. BOE_CityAnx Data Dictionary: COFILE = county number - assessment roll year - file number; CHANGE = affected city, unincorporated county, or boundary correction; EFFECTIVE = date the change was effective by resolution or ordinance; RECEIVED = date the change was received at the BOE; ACKNOWLEDGED = date the BOE accepted the filing for inclusion into the tax rate area system; NOTES: additional clarifying information about the action. BOE_CityCounty Data Dictionary: COUNTY = county name; CITY = city name or unincorporated territory; COPRI = county number followed by the 3-digit city primary number used in the BOE's 6-digit tax rate area numbering system (for the purpose of this map, unincorporated areas are assigned 000 to indicate that the area is not within a city).

The interactive map creation tools market is experiencing robust growth, driven by increasing demand for visually engaging data representation across diverse sectors. The market, estimated at $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $8 billion by 2033. This expansion is fueled by several key factors. The rising adoption of cloud-based solutions and the proliferation of readily available geospatial data are lowering the barrier to entry for both individual and corporate users. Furthermore, advancements in mapping technologies, such as 3D mapping capabilities and improved user interfaces, are enhancing the overall user experience and driving wider adoption. The increasing need for effective data visualization in fields like real estate, urban planning, environmental monitoring, and marketing is further bolstering market growth. Segmentation reveals a significant portion of the market is attributed to paid use licenses, reflecting the advanced features and support provided by premium tools. However, the free-use segment is also growing rapidly, driven by the availability of user-friendly open-source tools and freemium models offered by major players. Corporate users constitute a larger portion of the market compared to individual users, primarily due to their higher budget allocations for data visualization and analysis tools. Geographic distribution reveals a concentration of market share in North America and Europe, largely due to higher technological adoption and a well-established digital infrastructure. However, rapid growth is anticipated in Asia Pacific regions like China and India, driven by increasing urbanization and government initiatives promoting digital transformation. Market restraints include the high cost of advanced mapping software, the need for specialized technical skills for complex projects, and the potential for data security and privacy concerns. Nevertheless, ongoing technological innovation, coupled with the increasing accessibility of data and analytical tools, is anticipated to mitigate these challenges and continue to drive significant market expansion throughout the forecast period. Key players like Mapbox, ArcGIS StoryMaps, and Google are actively shaping the market landscape through continuous product development and strategic partnerships, fostering innovation and competitive pricing strategies.

WARNING: This is a pre-release dataset and its fields names and data structures are subject to change. It should be considered pre-release until the end of 2024. Expected changes:Metadata is missing or incomplete for some layers at this time and will be continuously improved.We expect to update this layer roughly in line with CDTFA at some point, but will increase the update cadence over time as we are able to automate the final pieces of the process.This dataset is continuously updated as the source data from CDTFA is updated, as often as many times a month. If you require unchanging point-in-time data, export a copy for your own use rather than using the service directly in your applications.PurposeCounty and incorporated place (city) boundaries along with third party identifiers used to join in external data. Boundaries are from the authoritative source the California Department of Tax and Fee Administration (CDTFA), altered to show the counties as one polygon. This layer displays the city polygons on top of the County polygons so the area isn"t interrupted. The GEOID attribute information is added from the US Census. GEOID is based on merged State and County FIPS codes for the Counties. Abbreviations for Counties and Cities were added from Caltrans Division of Local Assistance (DLA) data. Place Type was populated with information extracted from the Census. Names and IDs from the US Board on Geographic Names (BGN), the authoritative source of place names as published in the Geographic Name Information System (GNIS), are attached as well. Finally, the coastline is used to separate coastal buffers from the land-based portions of jurisdictions. This feature layer is for public use.Related LayersThis dataset is part of a grouping of many datasets:Cities: Only the city boundaries and attributes, without any unincorporated areasWith Coastal BuffersWithout Coastal BuffersCounties: Full county boundaries and attributes, including all cities within as a single polygonWith Coastal BuffersWithout Coastal BuffersCities and Full Counties: A merge of the other two layers, so polygons overlap within city boundaries. Some customers require this behavior, so we provide it as a separate service.With Coastal Buffers (this dataset)Without Coastal BuffersPlace AbbreviationsUnincorporated Areas (Coming Soon)Census Designated Places (Coming Soon)Cartographic CoastlinePolygonLine source (Coming Soon)Working with Coastal BuffersThe dataset you are currently viewing includes the coastal buffers for cities and counties that have them in the authoritative source data from CDTFA. In the versions where they are included, they remain as a second polygon on cities or counties that have them, with all the same identifiers, and a value in the COASTAL field indicating if it"s an ocean or a bay buffer. If you wish to have a single polygon per jurisdiction that includes the coastal buffers, you can run a Dissolve on the version that has the coastal buffers on all the fields except COASTAL, Area_SqMi, Shape_Area, and Shape_Length to get a version with the correct identifiers.Point of ContactCalifornia Department of Technology, Office of Digital Services, odsdataservices@state.ca.govField and Abbreviation DefinitionsCOPRI: county number followed by the 3-digit city primary number used in the Board of Equalization"s 6-digit tax rate area numbering systemPlace Name: CDTFA incorporated (city) or county nameCounty: CDTFA county name. For counties, this will be the name of the polygon itself. For cities, it is the name of the county the city polygon is within.Legal Place Name: Board on Geographic Names authorized nomenclature for area names published in the Geographic Name Information SystemGNIS_ID: The numeric identifier from the Board on Geographic Names that can be used to join these boundaries to other datasets utilizing this identifier.GEOID: numeric geographic identifiers from the US Census Bureau Place Type: Board on Geographic Names authorized nomenclature for boundary type published in the Geographic Name Information SystemPlace Abbr: CalTrans Division of Local Assistance abbreviations of incorporated area namesCNTY Abbr: CalTrans Division of Local Assistance abbreviations of county namesArea_SqMi: The area of the administrative unit (city or county) in square miles, calculated in EPSG 3310 California Teale Albers.COASTAL: Indicates if the polygon is a coastal buffer. Null for land polygons. Additional values include "ocean" and "bay".GlobalID: While all of the layers we provide in this dataset include a GlobalID field with unique values, we do not recommend you make any use of it. The GlobalID field exists to support offline sync, but is not persistent, so data keyed to it will be orphaned at our next update. Use one of the other persistent identifiers, such as GNIS_ID or GEOID instead.AccuracyCDTFA"s source data notes the following about accuracy:City boundary changes and county boundary line adjustments filed with the Board of Equalization per Government Code 54900. This GIS layer contains the boundaries of the unincorporated county and incorporated cities within the state of California. The initial dataset was created in March of 2015 and was based on the State Board of Equalization tax rate area boundaries. As of April 1, 2024, the maintenance of this dataset is provided by the California Department of Tax and Fee Administration for the purpose of determining sales and use tax rates. The boundaries are continuously being revised to align with aerial imagery when areas of conflict are discovered between the original boundary provided by the California State Board of Equalization and the boundary made publicly available by local, state, and federal government. Some differences may occur between actual recorded boundaries and the boundaries used for sales and use tax purposes. The boundaries in this map are representations of taxing jurisdictions for the purpose of determining sales and use tax rates and should not be used to determine precise city or county boundary line locations. COUNTY = county name; CITY = city name or unincorporated territory; COPRI = county number followed by the 3-digit city primary number used in the California State Board of Equalization"s 6-digit tax rate area numbering system (for the purpose of this map, unincorporated areas are assigned 000 to indicate that the area is not within a city).Boundary ProcessingThese data make a structural change from the source data. While the full boundaries provided by CDTFA include coastal buffers of varying sizes, many users need boundaries to end at the shoreline of the ocean or a bay. As a result, after examining existing city and county boundary layers, these datasets provide a coastline cut generally along the ocean facing coastline. For county boundaries in northern California, the cut runs near the Golden Gate Bridge, while for cities, we cut along the bay shoreline and into the edge of the Delta at the boundaries of Solano, Contra Costa, and Sacramento counties.In the services linked above, the versions that include the coastal buffers contain them as a second (or third) polygon for the city or county, with the value in the COASTAL field set to whether it"s a bay or ocean polygon. These can be processed back into a single polygon by dissolving on all the fields you wish to keep, since the attributes, other than the COASTAL field and geometry attributes (like areas) remain the same between the polygons for this purpose.SliversIn cases where a city or county"s boundary ends near a coastline, our coastline data may cross back and forth many times while roughly paralleling the jurisdiction"s boundary, resulting in many polygon slivers. We post-process the data to remove these slivers using a city/county boundary priority algorithm. That is, when the data run parallel to each other, we discard the coastline cut and keep the CDTFA-provided boundary, even if it extends into the ocean a small amount. This processing supports consistent boundaries for Fort Bragg, Point Arena, San Francisco, Pacifica, Half Moon Bay, and Capitola, in addition to others. More information on this algorithm will be provided soon.Coastline CaveatsSome cities have buffers extending into water bodies that we do not cut at the shoreline. These include South Lake Tahoe and Folsom, which extend into neighboring lakes, and San Diego and surrounding cities that extend into San Diego Bay, which our shoreline encloses. If you have feedback on the exclusion of these items, or others, from the shoreline cuts, please reach out using the contact information above.Offline UseThis service is fully enabled for sync and export using Esri Field Maps or other similar tools. Importantly, the GlobalID field exists only to support that use case and should not be used for any other purpose (see note in field descriptions).Updates and Date of ProcessingConcurrent with CDTFA updates, approximately every two weeks, Last Processed: 12/17/2024 by Nick Santos using code path at https://github.com/CDT-ODS-DevSecOps/cdt-ods-gis-city-county/ at commit 0bf269d24464c14c9cf4f7dea876aa562984db63. It incorporates updates from CDTFA as of 12/12/2024. Future updates will include improvements to metadata and update frequency.

The CDTFA Mobile app will enable you to find a sales and use tax rate, locate and contact our field offices, and conveniently access our website and online services. FEATURES • California Sales and Use Tax Rates: Find a tax rate by address, city, or your current location • CDTFA Field Offices: Get an office's address and other details, and with the tap of a button call an office or open its location in the Maps application to get driving directions • Website: View our website right within the app • Online Services: Access our online services directly with the tap of a button • And more features will be coming soon!

In this feature class, the number of structures within a park parcel is given by four fields described below:Count_of_Buildings_In_Parcel: This field counts the total number of structures contained in a particular park parcel.Count_of_Buildings_Outside_FZ: This field counts the number of structures contained in a particular park parcel that do not fall within the 100-year or 500-year flood zones.Count_of_Buildings_in_500yr_FZ: This field counts the number of structures contained in a particular park parcel that also intersect flood zone X per the 2022 preliminary Digital Flood Insurance Rate Map.Count_of_Buildings_in_100yr_FZ: This field counts the number of structures contained in a particular park parcel that also intersect the FEMA special flood hazard area per the 2022 preliminary Digital Flood Insurance Rate Map.The building structures counted in this feature class include Residential, Commercial, Institutional, Garages, Other structures, Buildings under Construction, Water Towers, Storage Tanks, and Silos, as provided by Baltimore County.

This layer shows housing occupancy, tenure, and median rent/housing value. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Homeownership rate on Census Bureau's website is owner-occupied housing unit rate (called B25003_calc_pctOwnE in this layer). This layer is symbolized by the overall homeownership rate. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B25002, B25003, B25058, B25077, B25057, B25059, B25076, B25078Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

WARNING: This is a pre-release dataset and its fields names and data structures are subject to change. It should be considered pre-release until the end of March 2025. The schema changed in February 2025 - please see below. We will post a roadmap of upcoming changes, but service URLs and schema are now stable. For deployment status of new services in February 2025, see https://gis.data.ca.gov/pages/city-and-county-boundary-data-status. Additional roadmap and status links at the bottom of this metadata.This dataset is continuously updated as the source data from CDTFA is updated, as often as many times a month. If you require unchanging point-in-time data, export a copy for your own use rather than using the service directly in your applications.PurposeCity boundaries along with third party identifiers used to join in external data. Boundaries are from the California Department of Tax and Fee Administration (CDTFA). These boundaries are the best available statewide data source in that CDTFA receives changes in incorporation and boundary lines from the Board of Equalization, who receives them from local jurisdictions for tax purposes. Boundary accuracy is not guaranteed, and though CDTFA works to align boundaries based on historical records and local changes, errors will exist. If you require a legal assessment of boundary location, contact a licensed surveyor.This dataset joins in multiple attributes and identifiers from the US Census Bureau and Board on Geographic Names to facilitate adding additional third party data sources. In addition, we attach attributes of our own to ease and reduce common processing needs and questions. Finally, coastal buffers are separated into separate polygons, leaving the land-based portions of jurisdictions and coastal buffers in adjacent polygons. This feature layer is for public use.Related LayersThis dataset is part of a grouping of many datasets:Cities: Only the city boundaries and attributes, without any unincorporated areasWith Coastal BuffersWithout Coastal Buffers (this dataset)Counties: Full county boundaries and attributes, including all cities within as a single polygonWith Coastal BuffersWithout Coastal BuffersCities and Full Counties: A merge of the other two layers, so polygons overlap within city boundaries. Some customers require this behavior, so we provide it as a separate service.With Coastal BuffersWithout Coastal BuffersCity and County AbbreviationsUnincorporated Areas (Coming Soon)Census Designated PlacesCartographic CoastlinePolygonLine source (Coming Soon)Working with Coastal BuffersThe dataset you are currently viewing excludes the coastal buffers for cities and counties that have them in the source data from CDTFA. In the versions where they are included, they remain as a second polygon on cities or counties that have them, with all the same identifiers, and a value in the COASTAL field indicating if it"s an ocean or a bay buffer. If you wish to have a single polygon per jurisdiction that includes the coastal buffers, you can run a Dissolve on the version that has the coastal buffers on all the fields except OFFSHORE and AREA_SQMI to get a version with the correct identifiers.Point of ContactCalifornia Department of Technology, Office of Digital Services, odsdataservices@state.ca.govField and Abbreviation DefinitionsCDTFA_CITY: CDTFA incorporated city nameCDTFA_COUNTY: CDTFA county name. For counties, this will be the name of the polygon itself. For cities, it is the name of the county the city polygon is within.CDTFA_COPRI: county number followed by the 3-digit city primary number used in the Board of Equalization"s 6-digit tax rate area numbering system. The boundary data originate with CDTFA's teams managing tax rate information, so this field is preserved and flows into this dataset.CENSUS_GEOID: numeric geographic identifiers from the US Census BureauCENSUS_PLACE_TYPE: City, County, or Town, stripped off the census name for identification purpose.GNIS_PLACE_NAME: Board on Geographic Names authorized nomenclature for area names published in the Geographic Name Information SystemGNIS_ID: The numeric identifier from the Board on Geographic Names that can be used to join these boundaries to other datasets utilizing this identifier.CDT_CITY_ABBR: Abbreviations of incorporated area names - originally derived from CalTrans Division of Local Assistance and now managed by CDT. Abbreviations are 4 characters. Not present in the county-specific layers.CDT_COUNTY_ABBR: Abbreviations of county names - originally derived from CalTrans Division of Local Assistance and now managed by CDT. Abbreviations are 3 characters.CDT_NAME_SHORT: The name of the jurisdiction (city or county) with the word "City" or "County" stripped off the end. Some changes may come to how we process this value to make it more consistent.AREA_SQMI: The area of the administrative unit (city or county) in square miles, calculated in EPSG 3310 California Teale Albers.OFFSHORE: Indicates if the polygon is a coastal buffer. Null for land polygons. Additional values include "ocean" and "bay".PRIMARY_DOMAIN: Currently empty/null for all records. Placeholder field for official URL of the city or countyCENSUS_POPULATION: Currently null for all records. In the future, it will include the most recent US Census population estimate for the jurisdiction.GlobalID: While all of the layers we provide in this dataset include a GlobalID field with unique values, we do not recommend you make any use of it. The GlobalID field exists to support offline sync, but is not persistent, so data keyed to it will be orphaned at our next update. Use one of the other persistent identifiers, such as GNIS_ID or GEOID instead.Boundary AccuracyCounty boundaries were originally derived from a 1:24,000 accuracy dataset, with improvements made in some places to boundary alignments based on research into historical records and boundary changes as CDTFA learns of them. City boundary data are derived from pre-GIS tax maps, digitized at BOE and CDTFA, with adjustments made directly in GIS for new annexations, detachments, and corrections. Boundary accuracy within the dataset varies. While CDTFA strives to correctly include or exclude parcels from jurisdictions for accurate tax assessment, this dataset does not guarantee that a parcel is placed in the correct jurisdiction. When a parcel is in the correct jurisdiction, this dataset cannot guarantee accurate placement of boundary lines within or between parcels or rights of way. This dataset also provides no information on parcel boundaries. For exact jurisdictional or parcel boundary locations, please consult the county assessor's office and a licensed surveyor.CDTFA's data is used as the best available source because BOE and CDTFA receive information about changes in jurisdictions which otherwise need to be collected independently by an agency or company to compile into usable map boundaries. CDTFA maintains the best available statewide boundary information.CDTFA's source data notes the following about accuracy:City boundary changes and county boundary line adjustments filed with the Board of Equalization per Government Code 54900. This GIS layer contains the boundaries of the unincorporated county and incorporated cities within the state of California. The initial dataset was created in March of 2015 and was based on the State Board of Equalization tax rate area boundaries. As of April 1, 2024, the maintenance of this dataset is provided by the California Department of Tax and Fee Administration for the purpose of determining sales and use tax rates. The boundaries are continuously being revised to align with aerial imagery when areas of conflict are discovered between the original boundary provided by the California State Board of Equalization and the boundary made publicly available by local, state, and federal government. Some differences may occur between actual recorded boundaries and the boundaries used for sales and use tax purposes. The boundaries in this map are representations of taxing jurisdictions for the purpose of determining sales and use tax rates and should not be used to determine precise city or county boundary line locations. Boundary ProcessingThese data make a structural change from the source data. While the full boundaries provided by CDTFA include coastal buffers of varying sizes, many users need boundaries to end at the shoreline of the ocean or a bay. As a result, after examining existing city and county boundary layers, these datasets provide a coastline cut generally along the ocean facing coastline. For county boundaries in northern California, the cut runs near the Golden Gate Bridge, while for cities, we cut along the bay shoreline and into the edge of the Delta at the boundaries of Solano, Contra Costa, and Sacramento counties.In the services linked above, the versions that include the coastal buffers contain them as a second (or third) polygon for the city or county, with the value in the COASTAL field set to whether it"s a bay or ocean polygon. These can be processed back into a single polygon by dissolving on all the fields you wish to keep, since the attributes, other than the COASTAL field and geometry attributes (like areas) remain the same between the polygons for this purpose.SliversIn cases where a city or county"s boundary ends near a coastline, our coastline data may cross back and forth many times while roughly paralleling the jurisdiction"s boundary, resulting in many polygon slivers. We post-process the data to remove these slivers using a city/county boundary priority algorithm. That is, when the data run parallel to each other, we discard the coastline cut and keep the CDTFA-provided boundary, even if it extends into the ocean a small amount. This processing supports consistent boundaries for Fort Bragg, Point Arena, San

Crawford County, Pennsylvania address points. Location and information of address points in Crawford County,Pennsylvania. This feature service has restricted fields available for this open data version. Certain fields have been redacted. The full dataset (as seen via the GIS mapping applications) is available via cost by contacting Crawford County. The full dataset available at cost provides all records that are not redacted by law. Data is public use and unrestricted.

The Federal Emergency Management Agency (FEMA) produces Flood Insurance Rate maps and identifies Special Flood Hazard Areas as part of the National Flood Insurance Program's floodplain management. Special Flood Hazard Areas have regulations that include the mandatory purchase of flood insurance for holders of federally regulated mortgages. In addition, this layer can help planners and firms avoid areas of flood risk and also avoid additional cost to carry insurance for certain planned activities.Dataset SummaryPhenomenon Mapped: Flood Hazard AreasGeographic Extent: Contiguous United States, Alaska, Hawaii, Puerto Rico, Guam, US Virgin Islands, Northern Mariana Islands and American Samoa.Projection: Web Mercator Auxiliary SphereData Coordinate System: USA Contiguous Albers Equal Area Conic USGS version (contiguous US, Puerto Rico, US Virgin Islands), WGS 1984 Albers (Alaska), Hawaii Albers Equal Area Conic (Hawaii), Western Pacific Albers Equal Area Conic (Guam, Northern Mariana Islands, and American Samoa)Cell Sizes: 10 meters (default), 30 meters, and 90 metersUnits: NoneSource Type: ThematicPixel Type: Unsigned integerSource: Federal Emergency Management Agency (FEMA)Update Frequency: AnnualPublication Date: December 18, 2024This layer is derived from the December 18, 2024 version Flood Insurance Rate Map feature class S_FLD_HAZ_AR. The vector data were then flagged with an index of 88 classes, representing a unique combination of values displayed by three renderers. (In three resolutions the three renderers make nine processing templates.) Repair Geometry was run on the set of features, then the features were rasterized using the 88 class index at a resolutions of 10, 30, and 90 meters, using the Polygon to Raster tool and the "MAXIMUM_COMBINED_AREA" option. Not every part of the United States is covered by flood rate maps. This layer compiles all the flood insurance maps available at the time of publication. To make analysis easier, areas that were NOT mapped by FEMA for flood insurance rates no longer are served as NODATA but are filled in with a value of 250, representing any unmapped areas which appear in the US Census' boundary of the USA states and territories. The attribute table corresponding to value 250 will indicate that the area was not mapped.What can you do with this layer?This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "flood hazard areas" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expand Portal if necessary, then select Living Atlas. Type "flood hazard areas" in the search box, browse to the layer then click OK.In ArcGIS Pro you can use the built-in raster functions to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.Processing TemplatesCartographic Renderer - The default. These are meaningful classes grouped by FEMA which group its own Flood Zone Type and Subtype fields. This renderer uses FEMA's own cartographic interpretations of its flood zone and zone subtype fields to help you identify and assess risk. Flood Zone Type Renderer - Specifically renders FEMA FLD_ZONE (flood zone) attribute, which distinguishes the original, broadest categories of flood zones. This renderer displays high level categories of flood zones, and is less nuanced than the Cartographic Renderer. For example, a fld_zone value of X can either have moderate or low risk depending on location. This renderer will simply render fld_zone X as its own color without identifying "500 year" flood zones within that category.Flood Insurance Requirement Renderer - Shows Special Flood Hazard Area (SFHA) true-false status. This may be helpful if you want to show just the places where flood insurance is required. A value of True means flood insurance is mandatory in a majority of the area covered by each 10m pixel.Each of these three renderers have templates at three different raster resolutions depending on your analysis needs. To include the layer in web maps to serve maps and queries, the 10 meter renderers are the preferred option. These are served with overviews and render at all resolutions. However, when doing analysis of larger areas, we now offer two coarser resolutions of 30 and 90 meters in processing templates for added convenience and time savings.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.

This layer was created as part of Esri’s Green Infrastructure Initiative and is one of five newly generated companion datasets that can be used for Green Infrastructure (GI) planning at national, regional, and more local scales. If used together, these layers should have corresponding date-based suffixes (YYYYMMDD). The corresponding layer names are: Intact Habitat Cores, Habitat Connectors, Habitat Fragments, Habitat Cost Surface, and Intact Habitat Cores by Betweeness. These Esri derived data, and additional data central to GI planning from other authoritative sources, are also available as Map Packages for each U.S. State and can be downloaded from the Green Infrastructure Data Gallery.This layer represents the modeled Least Cost Paths (LCPs) among neighboring Intact Habitat Cores. Least cost paths reflect the route of least resistance between neighboring habitat core edges, and by extension, represent possible paths of wildlife movement. Esri generated this comprehensive network of LCPs using the Cost Connectivity tool which was introduced in ArcGIS 10.4 and ArcGISPro in 1.3. The Habitat Cost Surface layer was used as the input computational surface. The resulting network was also utilized to compute Betweenness Centrality attribution for the Intact Habitat Cores by Betweenness layer, denoting a measure of the Core’s connectivity importance compared to all others in the network.The PathCost field represents the non-directional cumulative cost of this route. Cost is not accrued for movement within habitat cores, thus the portion of each path that falls within a core’s boundary should be considered schematic only. These paths can be used to create a network dataset for use in additional analysis. If a network dataset is created, it should be cost-based, rather than length-based due to the schematic and costless nature of traveling within a core. The PathCost, LowCoreValue, and HighCoreValue fields were used to generate a network graph.While least cost paths are useful for illuminating the discrete path of least resistance from one location to another, they should not be interpreted as least cost corridors. Least cost corridors expand least cost paths to encompass functionally larger areas that may facilitate species movement.

This nowCOAST™ time-enabled map service provides maps depicting the NWS Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimate mosaics for 1-, 3-, 6-, 12-, 24-, 48-, and 72-hr time periods at a 1 km (0.6 miles) horizontal resolution for CONUS and southern part of Canada. The precipitation estimates are based only on radar data. The total precipitation amount is indicated by different colors at 0.01, 0.10, 0.25 inches and then at 1/4 inch intervals up to 4.0 inches (e.g. 0.50, 0.75, 1.00, 1.25, etc.), at 1-inch intervals from 4 to 10 inches and then at 2-inch intervals up to 14+ inches. The increments from 0.01 to 1.00 or 2.00 inches are similar to what are used on NCEP's Weather Prediction Center QPF products and the NWS River Forecast Center (RFC) daily precipitation analysis. The 1-hr mosaic is updated every 4 minutes with a latency on nowCOAST™ of about 6-7 minutes from valid time. The 3-, 6-, 12-, and 24-hr QPEs are updated on nowCOAST™ every hour for the period ending at the top of the hour. The 48- and 72-hr QPEs are generated daily for the period ending at 12 UTC (i.e. 7AM EST) and available on nowCOAST™ shortly afterwards. For more detailed information about layer update frequency and timing, please reference the nowCOAST™ Dataset Update Schedule.The NWS Multi-Radar Multi-Sensor System (MRMS)/Q3 QPEs are radar-only based quantitative precipitation analyses. The 1-hr precipitation accumulation is obtained by aggregating 12 instantaneous rate fields. Missing rate fields are filled with the neighboring rate fields if the data gap is not significantly large (e.g.<=15 minutes). The instantaneous rate is computed from the hybrid scan reflectivity and the precipitation flag fields (both are 2-D derivative products from the National 3-D Reflectivity Mosaic grid which has a 1-km horizontal resolution, 31 vertical levels and a 5-minute update cycle). The instantaneous rate currently uses four Z-R relationships (i.e. tropical, convective, stratiform, or snow). The particular ZR relationship used in any grid cell depends on precipitation type which is indicated by the precipitation flag. The other accumulation products are derived by aggregating the hourly accumulations. The 1-hr QPE are generated every 4 minutes, while the 3-, 6-, 12-, and 24-hr accumulations are generated every hour at the top of the hour. The 48- and 72-hr QPEs are updated daily at approximately 12 UTC. MRMS was developed by NOAA/OAR/National Severe Storms Laboratory and migrated into NWS operations at NOAA Integrated Dissemination Program.This map service is time-enabled, meaning that each individual layer contains time-varying data and can be utilized by clients capable of making map requests that include a time component.In addition to ArcGIS Server REST access, time-enabled OGC WMS 1.3.0 access is also provided by this service.This particular service can be queried with or without the use of a time component. If the time parameter is specified in a request, the data or imagery most relevant to the provided time value, if any, will be returned. If the time parameter is not specified in a request, the latest data or imagery valid for the present system time will be returned to the client. If the time parameter is not specified and no data or imagery is available for the present time, no data will be returned.This service is configured with time coverage support, meaning that the service will always return the most relevant available data, if any, to the specified time value. For example, if the service contains data valid today at 12:00 and 12:10 UTC, but a map request specifies a time value of today at 12:07 UTC, the data valid at 12:10 UTC will be returned to the user. This behavior allows more flexibility for users, especially when displaying multiple time-enabled layers together despite slight differences in temporal resolution or update frequency.When interacting with this time-enabled service, only a single instantaneous time value should be specified in each request. If instead a time range is specified in a request (i.e. separate start time and end time values are given), the data returned may be different than what was intended.Care must be taken to ensure the time value specified in each request falls within the current time coverage of the service. Because this service is frequently updated as new data becomes available, the user must periodically determine the service's time extent. However, due to software limitations, the time extent of the service and map layers as advertised by ArcGIS Server does not always provide the most up-to-date start and end times of available data. Instead, users have three options for determining the latest time extent of the service:Issue a returnUpdates=true request (ArcGIS REST protocol only) for an individual layer or for the service itself, which will return the current start and end times of available data, in epoch time format (milliseconds since 00:00 January 1, 1970). To see an example, click on the "Return Updates" link at the bottom of the REST Service page under "Supported Operations". Refer to the ArcGIS REST API Map Service Documentation for more information.Issue an Identify (ArcGIS REST) or GetFeatureInfo (WMS) request against the proper layer corresponding with the target dataset. For raster data, this would be the "Image Footprints with Time Attributes" layer in the same group as the target "Image" layer being displayed. For vector (point, line, or polygon) data, the target layer can be queried directly. In either case, the attributes returned for the matching raster(s) or vector feature(s) will include the following:validtime: Valid timestamp.starttime: Display start time.endtime: Display end time.reftime: Reference time (sometimes referred to as issuance time, cycle time, or initialization time).projmins: Number of minutes from reference time to valid time.desigreftime: Designated reference time; used as a common reference time for all items when individual reference times do not match.desigprojmins: Number of minutes from designated reference time to valid time.Query the nowCOAST™ LayerInfo web service, which has been created to provide additional information about each data layer in a service, including a list of all available "time stops" (i.e. "valid times"), individual timestamps, or the valid time of a layer's latest available data (i.e. "Product Time"). For more information about the LayerInfo web service, including examples of various types of requests, refer to the nowCOAST™ LayerInfo Help DocumentationReferencesFor more information about the MRMS/Q3 system, please see http://nmq.ou.edu and http://www.nssl.noaa.gov/projects/mrms.

This table contains weekly and monthly prices for different crops, based on weekly surveys of grain buyers' pricing, as well as other publicly available market sources. The table contains prices for the current and nine previous years. Each weekly dataset contains prices on the sales that occurred from Friday to Thursday. The monthly price represents a simple average of the corresponding weekly prices. For more information on major crops in Manitoba please visit ARD Livestock Markets and Statistics. This table is used in the Manitoba Crop Prices Historical dashboard. Fields included [Alias (Field name): Field description]

Year (Year): Year from the selection of last 10 years including current year Period (Period): Period of time to be presented on charts from the selection of Monthly and Weekly PeriodNo (PeriodNo): Serial number of period (1-12 for monthly presentation, 1-52 for weekly presentation). For weekly presentation, each week contains prices on sales that occurred from Friday to Thursday (e.g., Week 1 of 2021 represents sales between Friday, Jan. 1, 2021 and Thursday, Jan. 7, 2021, with the corresponding report published by the department on Friday, Jan. 8, 2021). For monthly presentation, each month contains simple average prices of weeks, which had more than two days of corresponding month in the period from Monday to Friday (e.g., if the first day of the month is Monday, Tuesday or Wednesday, monthly average includes this week; but if the first day of the month is Thursday or Friday, the weekly prices are included in monthly average prices of a previous month). Price (Price): Crop price for the corresponding period of time, in C$ per tonne Crop Category (Crop): Category of grains and oilseeds from the selection of:

Wheat, Northern Hard Red; Wheat, Western Red Spring; Wheat, Red Winter; Wheat, Special Purpose (Low Vomi); Barley, #1CW; Corn, #2; Oats, #2CW; Flaxseed, #1CW; Canola, #1CR; Canola Meal, 34%, Altona; Soybeans; Soymeal, 46%, Wpg; Peas, #2 Yellow.

Soil loss tolerance factor is the maximum rate of soil loss that will permit crop productivity to be sustained economically and indefinitely on a given soil. Soil loss tolerance is rated as 1, 2, 3, 4 or 5 tons per acre per year. The primary use for soil loss tolerance factor is evaluating the effectiveness of erosion control measures on farmland. Soil loss tolerance factor serves as a quantitative standard to compare to erosion rate estimates from models such as the Revised Universal Soil Loss Equation. Farmlands where soil loss tolerance factor is less than modeled erosion rates are considered unsustainable. Dataset SummaryPhenomenon Mapped: Soil loss toleranceUnits: tons/acre/yearCell Size: 30 metersSource Type: DiscretePixel Type: Unsigned integerData Coordinate System: WKID 5070 USA Contiguous Albers Equal Area Conic USGS version (contiguous US, Puerto Rico, US Virgin Islands), WKID 3338 WGS 1984 Albers (Alaska), WKID 4326 WGS 1984 Decimal Degrees (Guam, Republic of the Marshall Islands, Northern Mariana Islands, Republic of Palau, Federated States of Micronesia, American Samoa, and Hawaii).Projection: Web Mercator Auxiliary SphereSource: Natural Resources Conservation ServiceUpdate Frequency: AnnualPublication Date: December 2024 Data from the gNATSGO database was used to create the layer. This layer is derived from the 30m rasters produced by the Natural Resources Conservation Service (NRCS). The value for soil loss tolerance is derived from the gSSURGO component table field T (tfact). The value in this layer is the average value for all components of each map unit weighted by component percent (comppct_r). What can you do with this layer?This layer is suitable for both visualization and analysis acrossthe ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application. Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online, you can add this layer to a map by selecting AddthenBrowse Living Atlas Layers. A window will open. Type "loss tolerance" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro, open a map and selectAdd Datafrom the Map Tab. SelectDataat the top of the drop down menu. The Add Data dialog box will open on the left side of the box, expandPortalif necessary, then selectLiving Atlas. Type "loss tolerance" in the search box, browse to the layer then click OK. In ArcGIS Pro you can use the built-in raster functions or create your own to create custom extracts of the data. Imagery layers provide fast, powerful inputs to geoprocessing tools, models, or Python scripts in Pro. Online you can filter the layer to show subsets of the data using the filter button and the layer"s built-in raster functions. The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one. 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.