MapLegendExtraction

This dataset contains high-resolution geological maps annotated with the bounding boxes of their embedded map legends, along with structured JSON representations of legend content. Designed for training models in legend detection, legend parsing, and map understanding.

The scientifically sound soil mapping with the issuance of soil maps is the most important basis for soil use, soil protection and soil history research. These are area data of soil mapping for the distribution and properties of the soils. They are the basis for the soil map 1: 25 000. The soil maps of North Rhine-Westphalia 1: 25000 (BK 25) represent the distribution of soils combined into soil units in the leaf area. For each soil unit, the map legend contains information on soil species stratification up to 2 m depth, soil types and initial geological rocks. In a special column, the values of the soil estimate, the suitability of use, the yieldability and workability as well as the water conditions of the soils are given. To the sheets of the ground maps 1: 25000 There are explanatory booklets in which the soils, including their chemical and physical properties, are described in detail. The soil maps form an important document for tasks of agriculture and forestry, land planning, state maintenance, water management and nature conservation, as well as for research, teaching and teaching. On a scale 1: Between 1964 and 1980, 25000 were published by the then Geological State Office of North Rhine-Westphalia. The map was then in favor of the ground map on a scale of 1: 50000 discontinued. The map sheets depicting the soil units (soil types and soil species stratification) and the water conditions (groundwater, slope water, slopes and dams) are provided with a detailed explanatory booklet, including list of writings and maps. In addition to a geological-morphological overview and the description of the soil forming factors, the soil units shown on the map are explained in detail. Tables with soil analyses as well as descriptions of agricultural and forestry land use and plant societies of arable and grassland complete the chapters on soil history. Some hands are attached as an attachment to usability cards or natural yield cards. The following soil maps of North Rhine-Westphalia 1: 25000 (BK 25) are available: Sheet No. Leaf name Year of publication ISBN Special features [old name] Map Allowed. 3516 Lemförde 1967 1970 3-86029-510-1 3517 Rahden 1965 1968 3-86029-511-X U 3617 Lübbecke 1968 1971 3-86029-512-8 GE 3618 3618 Hille [Hartum] 1971 3-86029-512-8 GE 3617 4107 Borken 1973 1973 3-86029-514-4 4116 Rietberg 1977 1980 3-86029-515-2 4117 Verl 1977 1980 3-Ω-516-0 4206 Brünen 1971 3-Ω-517-9 4207 Raesfeld 1973 3-Ω-518-7 4216 Mastholte 1970 1970 3- and 524-1 GE 4603 5003 Linnich 1972 3-Ω-525-X U 4703 Schwalmtal [Waldniel] 1968 3-Ω-524-1 GE 4703 5003 Linnich 1972 3-Ω-525-X U 4703 Schwalmtal [Waldniel] 1968 3-Ω-524-1 GE 4603 5003 Linnich 1972 5004 Jülich 1971 1972 3-Ω-528-4 5104 Düren 1965 1968 3-Ω-529-2 U GE joint explanation with... U card only unfolded (plano) available

Use the Interactive Legend template to allow users to filter layers in your map by toggling the visibility of features based categories and ranges in the legend. Choose from paired feature-specific effects, such as bloom and blur, to distinguish between selected items in the legend and the remaining data. Choose from several options to emphasize selected items in the legend while other items remain on the map in muted colors. Examples: Form a better understanding of the spatial relationship between map features by changing the visibility of the content. Present economic data relevant to numerical range values of interest during a seminar. Analyze crime data to facilitate decision making of law enforcement distribution pertaining to specific crime categories. Data requirements The Interactive Legend template requires a feature layer to use all of its capabilities. The following drawing styles are supported: Location (Single Symbol) Types (Unique symbols) Counts and amounts (Size) - Classify Data Checked Counts and Amounts (Color) - Classify Data Checked Relationship Relationship and Size (Partially Interactive) Predominant Category Predominant Category and Size (Partially interactive) Types and Size (Partially interactive) Key app capabilities Layer effects - Use layer effects to differentiate between features included and excluded in a filter, and specify how features are emphasized and de-emphasized when a filter is applied using the legend. Zoom to button - Allow users to zoom to features selected in the legend. Feature count - Include a feature count for items that are selected in the legend Export - Capture an image (PDF, JPG, or PNG) from the app that a user can save. Time filter - Filter features in the map using time enabled layers Language switcher - Provide translations for custom text and create a multilingual app. Home, Zoom controls, Legend, Layer List, Search Supportability This web app is designed responsively to be used in browsers on desktops, mobile phones, and tablets. We are committed to ongoing efforts towards making our apps as accessible as possible. Please feel free to leave a comment on how we can improve the accessibility of our apps for those who use assistive technologies.

We compared the ability of two legend designs on a soil-landscape map to efficiently and effectively support map reading tasks with the goal of better understanding how the design choices affect user performance. Developing such knowledge is essential to design effective interfaces for digital earth systems. One of the two legends contained an alphabetical ordering of categories, while the other used a perceptual grouping based on the Munsell color space. We tested the two legends for 4 tasks with 20 experts (in geography-related domains). We analyzed traditional usability metrics and participants’ eye movements to identify the possible reasons behind their success and failure in the experimental tasks. Surprisingly, an overwhelming majority of the participants failed to arrive at the correct responses for two of the four tasks, irrespective of the legend design. Furthermore, participants’ prior knowledge of soils and map interpretation abilities led to interesting performance differences between the two legend types. We discuss how participant background might have played a role in performance and why some tasks were particularly hard to solve despite participants’ relatively high levels of experience in map reading. Based on our observations, we caution soil cartographers to be aware of the perceptual complexity of soil-landscape maps.

In order to use the standard color legend for Romanian soil type maps in the ESRI ArcMap-10 electronic format, a dataset consisting a shapefile set (.dbf, .shp, .shx, .sbn, and .sbx files), four different .lyr files, and three different .style files have been prepared (ESRI, 2016). The shapefile set is not a “real” georeferenced layer/coverage; it is designed only to handle all the instants of soil types from the standard legend. This legend contains 67 standard items: 63 proper colors (different color hues, each of them having, generally, 2 - 4 degrees of lightness and/or chroma, four shades of grey, and white color), and four hatching patterns on white background (ESRI, 2016). The “color difference DE*ab” between any two legend colors, calculated with the color perceptually-uniform model CIELAB , is greater than 10 units, thus ensuring acceptably-distinguishable colors in the legend. The 67 standard items are assigned to 60 main soils existing in Romania, four main nonsoils, and three special cases of unsurveyed land. The soils are specified in terms of the current Romanian system of soil taxonomy, SRTS-2012+, and of the international soil classification system WRB-2014. The four different .lyr files presented here are: legend_soilcode_srts_wrb.lyr, legend_soilcode_wrb.lyr, legend_colourcode_srts_wrb.lyr, and legend_colourcode_wrb.lyr. The first two of them are built using as value field the ‘Soil_codes’ field, and as labels (explanation texts) the ‘Soil_name’ field (storing the soil types according to SRTS/WRB classification), respectively, the ‘WRB’ field (the soil type according to WRB classification), while the last two .lyr files are built using as value field the ‘colour_code’ field (storing the color codes) and as labels the soil name in SRTS and WRB, respectively, in WRB classification. In order to exemplify how the legend is displayed, two .jpg files are also presented: legend_soil_srts_wrb.jpg and legend_colour_wrb.jpg. The first displays the legend (symbols and labels) according to the SRTS classification order, the second according to the WRB classification. The three different .style files presented here are: soil_symbols.style, wrb_codes.style, and colour_codes.style. They use as name the soil acronym in SRTS classification, soil acronym in WRB classification, and, respectively, the color code.

The ArcGIS Online US Geological Survey (USGS) topographic map collection now contains over 177,000 historical quadrangle maps dating from 1882 to 2006. The USGS Historical Topographic Map Explorer app brings these maps to life through an interface that guides users through the steps for exploring the map collection:Find a location of interest.View the maps.Compare the maps.Download and share the maps or open them in ArcGIS Desktop (ArcGIS Pro or ArcMap) where places will appear in their correct geographic location. Save the maps in an ArcGIS Online web map.

Finding the maps of interest is simple. Users can see a footprint of the map in the map view before they decide to add it to the display, and thumbnails of the maps are shown in pop-ups on the timeline. The timeline also helps users find maps because they can zoom and pan, and maps at select scales can be turned on or off by using the legend boxes to the left of the timeline. Once maps have been added to the display, users can reorder them by dragging them. Users can also download maps as zipped GeoTIFF images. Users can also share the current state of the app through a hyperlink or social media. This ArcWatch article guides you through each of these steps: https://www.esri.com/esri-news/arcwatch/1014/envisioning-the-past.Once signed in, users can create a web map with the current map view and any maps they have selected. The web map will open in ArcGIS Online. The title of the web map will be the same as the top map on the side panel of the app. All historical maps that were selected in the app will appear in the Contents section of the web map with the earliest at the top and the latest at the bottom. Turning the historical maps on and off or setting the transparency on the layers allows users to compare the historical maps over time. Also, the web map can be opened in ArcGIS Desktop (ArcGIS Pro or ArcMap) and used for exploration or data capture.Users can find out more about the USGS topograhic map collection and the app by clicking on the information button at the upper right. This opens a pop-up with information about the maps and app. The pop-up includes a useful link to a USGS web page that provides access to documents with keys explaining the symbols on historic and current USGS topographic maps. The pop-up also has a link to send Esri questions or comments about the map collection or the app.We have shared the updated app on GitHub, so users can download it and configure it to work with their own map collections.

In order to use the Romanian color standard for soil type map legends, a dataset of ESRI ArcMap-10 files, consisting of a shapefile set (.dbf, .shp, .shx, .sbn, and .sbx files), four different .lyr files, and three different .style files (https://desktop.arcgis.com/en/arcmap/10.3/map/ : saving-layers-and-layer-packages, about-creating-new-symbols, what-are-symbols-and-styles-), have been prepared. The shapefile set is not a “real” georeferenced layer/coverage; it is designed only to handle all the instants of soil types from the standard legend.

This legend contains 67 standard items: 63 proper colors (different color hues, each of them having, generally, 2 - 4 degrees of lightness and/or chroma, four shades of grey, and white color), and four hatching patterns on white background. The “color difference DE*ab” between any two legend colors, calculated with the color perceptually-uniform model CIELAB, is greater than 10 units, thus ensuring acceptably-distinguishable colors in the legend. The 67 standard items are assigned to 60 main soils existing in Romania, four main nonsoils, and three special cases of unsurveyed land. The soils are specified in terms of the current Romanian system of soil taxonomy, SRTS-2012+, and of the international system WRB-2014.

The four different .lyr files presented here are: legend_soilcode_srts_wrb.lyr, legend_soilcode_wrb.lyr, legend_colorcode_srts_wrb.lyr, and legend_colorcode_wrb.lyr. The first two of them are built using as value field the “Soil_codes” field, and as labels (explanation texts) the “Soil_name” field (storing the soil types according to SRTS/WRB classification), respectively, the “WRB” field (the soil type according to WRB classification), while the last two .lyr files are built using as value field the “color_code” field (storing the color codes) and as labels the soil name in SRTS and WRB, respectively, in WRB classification.

In order to exemplify how the legend is displayed, two .jpg files are also presented: legend_soil_srts_wrb.jpg and legend_color_wrb.jpg. The first displays the legend (symbols and labels) according to the SRTS classification order, the second according to the WRB classification.

The three different .style files presented here are: soil_symbols.style, wrb_codes.style, and color_codes.style. They use as name the soil acronym in SRTS classification, soil acronym in WRB classification, and, respectively, the color code.

The presented file set may be used to directly implement the Romanian color standard in digital soil type map legends, or may be adjusted/modified to other specific requirements.

Author: J. Cain, educator, Minnesota Alliance for Geographic EducationGrade/Audience: grade 4Resource type: lessonSubject topic(s): mapsRegion: united statesStandards: Minnesota Social Studies Standards

Standard 2: People use geographic representations and geospatial technologies to acquire, process and report information within a spatial context. Objectives: Students will be able to:

1. Explore a variety of maps.

2. Become acquainted with the elements of maps referred to as TODALS:

3. Title

4. Orientation

5. Date

6. Author

7. Legend (Key)

8. Scale

9. Locate and interpret TODALS from a variety of maps.

10. Compare and contrast elements of given maps while looking for bias.

11. Reflect on the importance of knowing TODALS when understanding and interpreting maps. Summary: Basic mapping terminology is essential for understanding and interpreting various types of maps. Knowing where to find these essential elements, and interpreting their meaning, are critical to the development of a 4th grader’s knowledge of geography.

Download this ZIP collection and extract the folder to see all map files. Each file"s name reflects the print size.For the 2026 Ward shapefile, please visit the City of Cleveland Wards (2026) dataset.CLICK TO DOWNLOAD ALL MAPSThis folder contains the 2026 Ward Maps in both landscape and portrait orientations. These maps display the updated ward boundaries that will go into effect in 2026, ensuring users have access to a clear and comprehensive view of each ward’s layout.Each map provides:Clearly marked ward boundaries with labels or numbers.Key streets, landmarks, and natural features to assist with orientation.A legend explaining any colors, line styles, or symbols used.A north arrow for direction.A scale bar for approximate distance reference.The mixed orientations (landscape and portrait) accommodate different use cases—landscape for wider ward shapes and portrait for taller or more compact wards—ensuring flexibility in viewing and printing. These maps are an essential GIS resource for planners, policymakers, and community members involved in redistricting, demographic analysis, and community planning for the 2026 ward implementation.Update FrequencyStaticDepartment ContactCleveland City Planning Commission

Potential distribution of land cover classes (Potential Natural Vegetation) at 250 m spatial resolution based on a compilation of data sets (Biome6000k, Geo-Wiki, LandPKS, mangroves soil database, and from various literature sources; total of about 65,000 training points). We used a comparable thematic legend used to produce the Dynamic Land Cover 100m: Version 2. Copernicus Global Land Operations product (Buchhorn et al. 2019), which is based on the UN FAO Land Cover Classification System (LCCS), so that users can compare actual (https://lcviewer.vito.be/) vs potential (this data set) land cover. Two classes not available in the LCCS were added: "subtropical/tropical mangrove vegetation" and "sub-polar or polar barren-lichen-moss, grassland". The map was created using relief and climate variables representing conditions the climate for the last 20+ years and predicted at 250 m globally using an Ensemble Machine Learning approach as implemented in the mlr package for R. Processing steps are described in detail here. Maps with "_sd_" contain estimated model errors per class. Antarctica is not included.

Produced for the needs of the NatureMap which is project run by the International Institute for Applied Systems Analysis (IIASA), the International Institute for Sustainability (IIS), the UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC), and the UN Sustainable Development Solutions Network (SDSN). NatureMap is funded by Norway’s International Climate Initiative (NICFI).

Maps will also be made available via: OpenLandMap.org. These are initial predictions for testing purposes only. A publication explaining all processing steps is pending.

If you discover a bug, artifact or inconsistency in the predictions, or if you have a question please use some of the following channels:

• Technical issues and questions about the code: https://github.com/Envirometrix/PNVmaps/issues

All files internally compressed using "COMPRESS=DEFLATE" creation option in GDAL. File naming convention:

• pnv = theme: potential natural vegetation,
• potential.landcover = variable: potential land cover type (e.g. "open forest, evergreen needleleaf"),
• probav.lc100 = classification model: ProbaV-based land cover mapping legend (LCCS),
• c = factor,
• 250m = spatial resolution / block support: 250 m,
• b0..0cm = vertical reference: land surface,
• 2000..2017 = time reference: period 2000-2017,
• v0.1 = version number: 0.1,

Land cover maps are the basic data layer required for understanding and modeling ecological patterns and processes. The Circumpolar Arctic Vegetation Map (CAVM), produced in 2003, has been widely used as a base map for studies in the arctic tundra biome. However, the relatively coarse resolution and vector format of the map were not compatible with many other data sets. We present a new version of the CAVM, building on the strengths of the original map, while providing a finer spatial resolution, raster format, and improved mapping. The Raster CAVM uses the legend, extent and projection of the original CAVM. The legend has 16 vegetation types, glacier, saline water, freshwater, and non-arctic land. The Raster CAVM divides the original rock-water-vegetation complex map unit that mapped the Canadian Shield into two map units, distinguishing between areas with lichen- and shrub-dominated vegetation. In contrast to the original hand-drawn CAVM, the new map is based on unsupervised classifications of seventeen geographic/floristic sub-sections of the Arctic, using the Advanced Very High Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) data (reflectance and Normalized Difference Vegetation Index (NDVI)) and elevation data. The units resulting from the classification were modeled to the CAVM types using a wide variety of ancillary data. The map was reviewed by experts familiar with their particular region, including many of the original authors of the CAVM from Canada, Greenland (Denmark), Iceland, Norway (including Svalbard), Russia, and the United States (U.S.). The analysis presented here summarizes the area, geographical distribution, elevation, summer temperatures, and NDVI of the map units. The greater spatial resolution of the Raster CAVM allowed more detailed mapping of water-bodies and mountainous areas. It portrays coastal-inland gradients, and better reflects the heterogeneity of vegetation type distribution than the original CAVM. Accuracy assessment of random 1-kilometer (km) pixels interpreted from 6 Landsat scenes showed an average of 70 percent (%) accuracy, up from 39 % for the original CAVM. The distribution of shrub-dominated types changed the most, with more prostrate shrub tundra mapped in mountainous areas, and less low shrub tundra in lowland areas. This improved mapping is important for quantifying existing and potential changes to land cover, a key environmental indicator for modeling and monitoring ecosystems. The Raster CAVM was released in 2019. Raster map data are available for download from Menedeley Data (DOI: 10.17632/c4xj5rv6kv.2). This data record contains PDFs a 36x36-inch print version of the map at at 1:7,000,000. The print map is illustrated with photographs of representative plant communities and species for each of the 16 map units, data on the area of each unit, and information on the making of raster CAVM. The press-quality version includes a 1/8-inch bleed on all sides to allow the map to printed at 36.25 square inches and trimmed to produce a "full bleed" map with color extending to the edge on all sides.

ABSTRACT Reliable environmental monitoring and evaluation require high-quality maps of land use and land cover. For the Amazon biome, the TerraClass and MapBiomas projects apply different methodologies to create these maps. We evaluated the agreement between land cover and land use maps generated by TerraClass and MapBiomas (Collections 2 and 3) for the Brazilian Amazon biome, from 2004 to 2014. Specifically, we: (1) described both project legends based on the LCCS (Land Cover Classification System); (2) analyzed the differences between their classes; and (3) compared the mapping differences among the Brazilian states that are totally or partially covered by the Amazon biome. We compared the classifications with a per-pixel approach and performed an evaluation based on agreement matrices. The overall agreement between the projects was 87.4% (TerraClass x MapBiomas 2) and 92.0% (TerraClass x MapBiomas 3). We analyzed methodological differences to explain the disagreements in class identification. We conclude that using these maps together without a properly adapted legend is not recommended for the analysis of land use and land cover change. Depending on the application, one mapping system may be more suitable than the other.

Database application of digital map archive contains a large collection of geoscientific maps and manuscript documents from the Czech Republic and the world. Map documents are mainly produced by CGS and its predecessors. It is possible to carry out detailed searches using map applications by means of which previews of digitized documents and other detailed information can be obtained (explanation, legend, geological cross sections, etc). For most of the map is available on-line thumbnails.

A collection of geospatial files, map images, publication documentation, and informational resources in support of the Geologic Map of North America.

Authors: Martha K. Raynolds, Donald A. Walker, Andrew Balser, Christian Bay, Mitch Campbell, Mikhail M. Cherosov, Fred J. A. Daniëls, Pernille Bronken Eidesen, Ksenia A. Ermokhina, Gerald V. Frost, Birgit Jedrzejek, M. Torre Jorgenson, Blair E. Kennedy, Sergei S. Kholod, Igor A. Lavrinenko, Olga V. Lavrinenko, Borgþór Magnússon, Nadezhda V. Matveyeva, Sigmar Metúsalemsson, Lennart Nilsen, Ian Olthof, Igor N. Pospelov, Elena B. Pospelova, Darren Pouliot, Vladimir Razzhivin, Gabriela Schaepman-Strub, Jozef Šibík, Mikhail Yu. Telyatnikov, Elena Troeva.

Land cover maps are the basic data layer required for understanding and modeling ecological patterns and processes. The Circumpolar Arctic Vegetation Map (CAVM), produced in 2003, has been widely used as a base map for studies in the arctic tundra biome. However, the relatively coarse resolution and vector format of the map were not compatible with many other data sets. We present a new version of the CAVM, building on the strengths of the original map, while providing a finer spatial resolution, raster format, and improved mapping. The Raster CAVM uses the legend, extent and projection of the original CAVM. The legend has 16 vegetation types, glacier, saline water, freshwater, and non-arctic land. The Raster CAVM divides the original rock-water-vegetation complex map unit that mapped the Canadian Shield into two map units, distinguishing between areas with lichen- and shrub-dominated vegetation. In contrast to the original hand-drawn CAVM, the new map is based on unsupervised classifications of seventeen geographic/floristic sub-sections of the Arctic, using AVHRR and MODIS data (reflectance and NDVI) and elevation data. The units resulting from the classification were modeled to the CAVM types using a wide variety of ancillary data. The map was reviewed by experts familiar with their particular region, including many of the original authors of the CAVM from Canada, Greenland (Denmark), Iceland, Norway (including Svalbard), Russia, and the U.S. The analysis presented here summarizes the area, geographical distribution, elevation, summer temperatures, and NDVI of the map units. The greater spatial resolution of the Raster CAVM allowed more detailed mapping of water-bodies and mountainous areas. It portrays coastal-inland gradients, and better reflects the heterogeneity of vegetation type distribution than the original CAVM. Accuracy assessment of random 1-km pixels interpreted from 6 Landsat scenes showed an average of 70 % accuracy, up from 39 % for the original CAVM. The distribution of shrub-dominated types changed the most, with more prostrate shrub tundra mapped in mountainous areas, and less low shrub tundra in lowland areas. This improved mapping is important for quantifying existing and potential changes to land cover, a key environmental indicator for modeling and monitoring ecosystems.

The map identifies surficial materials and associated landforms left by the retreat of the last glaciers and non glacial environments. The surficial geology is based on compilation of existing maps. This work provides new geological knowledge and improves our understanding ofthe distribution, nature and glacial history of surficial materials. It contributes to resource assessments and effective land use management. This new surficial geology map product represents the conversion of the map "Surficial Materials of Canada" (Fulton, 1995) and its legend, using the Geological Survey of Canada's Surficial Data Model (SDM version 2.0) which can be found in Open File 7631 (Deblonde et al.,2014). All geoscience knowledge and information from map 1880A that conformed to the current SDM were maintained during the conversion process. However, only terrestrial units are depicted on this map. Map units below modern sea level or major lake levels are not shown but are maintained in the digital data of this publication. Where additional information was required in certain regions of the Arctic and Cordillera, legacy geology map data were used. These maps are listed in the digital "Map Information" document. All other source maps used in map 1880A are not relisted here. The purpose of converting legacy map data to a common science language and common legend is to enable and facilitate the efficient digital compilation, interpretation, management and dissemination of geologic map information in a structured and consistent manner. This provides an effective knowledge management tool designed around a geo-database which can expand following the type of information to appear on new surficial geology maps.

The scientifically based soil mapping (soil inventory) with the publication of soil maps is the most important basis for soil use, soil protection and soil research. These are surface data of soil-based mapping for the distribution and characteristics of the soils. They are the basis for the ground map 1: 100 000. The Ground Map of North Rhine-Westphalia 1: 100 000 (BK 100) represent the distribution of soils grouped into soil units in the leafy area. The map legend contains for each floor unit information on the soil type stratification up to 2 m depth, the soil types and the geological base rock. A special column shows the values of soil estimation, suitability for use, yieldability and workability, as well as the water conditions of the soils. To the sheets of the ground cards 1: There are 100 000 explanatory booklets in which the soils, including their chemical and physical characteristics, are described in detail. The soil maps form an important basis for tasks in agriculture, forestry, land planning, land maintenance, water management and nature conservation, as well as for research, teaching and teaching. This work of charts was started in the 70s of the 20th century and after the publication of the two existing sheets in favor of the BK 50 no longer continued. The soil contents correspond to those of BK 50. The soil types (after initial substrate) and soil species layering up to 2 m depth are key parameters. Both maps have appeared with detailed explanations on the factors of soil formation and the different soil units including layers. The following maps of North Rhine-Westphalia 1: 100 000 (BK 100) are available: Year of publication ISBN C 4306 Recklinghausen 1975 3-86029-547-0 C 4314 Gütersloh 1979 3-86029-549-7

This map service displays Level III and Level IV Ecoregions of the United States and was created from ecoregion data obtained from the U.S. Environmental Protection Agency Office of Research and Development's Western Ecology Division. The original ecoregion data was projected from Albers to Web Mercator for this map service. To download shapefiles of ecoregion data (in Albers), please go to: https://gaftp.epa.gov/EPADataCommons/ORD/Ecoregions/. IMPORTANT NOTE ABOUT LEVEL IV POLYGON LEGEND DISPLAY IN ARCMAP: Due to the limitations of Graphical Device Interface (GDI) resources per application on Windows, ArcMap does not display the legend in the Table of Contents for the ArcGIS Server service layer if the legend has more than 100 items. As of December 2011, there are 968 unique legend items in the Level IV Ecoregion Polygon legend. Follow this link (http://support.esri.com/en/knowledgebase/techarticles/detail/33741) for instructions about how to increase the maximum number of ArcGIS Server service layer legend items allowed for display in ArcMap. Note the instructions at this link provide a slightly incorrect path to "Maximum Legend Count". The correct path is HKEY_CURRENT_USER > Software > ESRI > ArcMap > Server > MapServerLayer > Maximum Legend Count. When editing the "Maximum Legend Count", update the field, "Value data" to 1000. To download a PDF version of the Level IV ecoregion map and legend, go to https://gaftp.epa.gov/EPADataCommons/ORD/Ecoregions/us/Eco_Level_IV_US_pg.pdf. Ecoregions denote areas of general similarity in ecosystems and in the type, quality, and quantity of environmental resources. They are designed to serve as a spatial framework for the research, assessment, management, and monitoring of ecosystems and ecosystem components. These general purpose regions are critical for structuring and implementing ecosystem management strategies across federal agencies, state agencies, and nongovernment organizations that are responsible for different types of resources within the same geographical areas. The approach used to compile this map is based on the premise that ecological regions can be identified through the analysis of patterns of biotic and abiotic phenomena, including geology, physiography, vegetation, climate, soils, land use, wildlife, and hydrology. The relative importance of each characteristic varies from one ecological region to another. A Roman numeral hierarchical scheme has been adopted for different levels for ecological regions. Level I is the coarsest level, dividing North America into 15 ecological regions. Level II divides the continent into 52 regions (Commission for Environmental Cooperation Working Group, 1997). At Level III, the continental United States contains 104 regions whereas the conterminous United States has 85 (U.S. Environmental Protection Agency, 2005). Level IV ecoregions (n = 968) are further subdivisions of Level III ecoregions. Methods used to define the ecoregions are explained in Omernik (1995, 2004), Omernik and others (2000), and Gallant and others (1989). Literature cited: Commission for Environmental Cooperation Working Group, 1997, Ecological regions of North America- toward a common perspective: Montreal, Commission for Environmental Cooperation, 71 p. Gallant, A.L., Whittier, T.R., Larsen, D.P., Omernik, J.M., and Hughes, R.M., 1989, Regionalization as a tool for managing environmental resources: Corvallis, Oregon, U.S. Environmental Protection Agency, EPA/600/3-89/060, 152p. Omernik, J.M., 1995, Ecoregions - a framework for environmental management, in Davis, W.S. and Simon, T.P., eds., Biological assessment and criteria-tools for water resource planning and decision making: Boca Raton, Florida, Lewis Publishers, p.49-62. Omernik, J.M., Chapman, S.S., Lillie, R.A., and Dumke, R.T., 2000, Ecoregions of Wisconsin: Transactions of the Wisconsin Academy of Science, Arts, and Letters, v. 88, p. 77-103. Omernik, J.M., 2004, Perspectives on the nature and definitions of ecological regions: Environmental Management, v. 34, Supplement 1, p. s27-s38. Comments and questions regarding ecoregion development should be addressed to Glenn Griffith, Dynamac Corporation, c/o US EPA., 200 SW 35th Street, Corvallis, OR 97333, 541-754-4465, email:griffith.glenn@epa.gov Alternate: James Omernik, USGS, c/o US EPA, 200 SW 35th Street, Corvallis, OR 97333, 541-754-4458, email:omernik.james@epa.gov

The documentation below is in reference to this items placement in the NM Supply Chain Data Hub. The documentation is of use to understanding the source of this item, and how to reproduce it for updatesTitle: Soil Survey Geographic Database (SSURGO) DownloaderItem Type: Web Mapping Application URLSummary: Download ready-to-use project packages with over 170 attributes derived from the SSURGO (Soil Survey Geographic Database) dataset.Notes: Prepared by: Uploaded by EMcRae_NMCDCSource: https://nmcdc.maps.arcgis.com/home/item.html?id=cdc49bd63ea54dd2977f3f2853e07fff link to Esri web mapping applicationFeature Service: https://nmcdc.maps.arcgis.com/home/item.html?id=305ef916da574a71877edb15c3f47f08#overviewUID: 26Data Requested: Ag CensusMethod of Acquisition: Esri web mapDate Acquired: 6/16/22Priority rank as Identified in 2022 (scale of 1 being the highest priority, to 11 being the lowest priority): 8Tags: PENDINGDOCUMENTATION FROM DATA SOURCE URL: This application provides quick access to ready-to-use project packages filled with useful soil data derived from the SSURGO dataset.To use this application, navigate to your study area and click the map. A pop-up window will open. Click download and the project package will be copied to your computer. Double click the downloaded package to open it in ArcGIS Pro. Alt + click on the layer in the table of contents to zoom to the subbasin.Soil map units are the basic geographic unit of the Soil Survey Geographic Database (SSURGO). The SSURGO dataset is a compilation of soils information collected over the last century by the Natural Resources Conservation Service (NRCS). Map units delineate the extent of different soils. Data for each map unit contains descriptions of the soil’s components, productivity, unique properties, and suitability interpretations.Each soil type has a unique combination of physical, chemical, nutrient and moisture properties. Soil type has ramifications for engineering and construction activities, natural hazards such as landslides, agricultural productivity, the distribution of native plant and animal life and hydrologic and other physical processes. Soil types in the context of climate and terrain can be used as a general indicator of engineering constraints, agriculture suitability, biological productivity and the natural distribution of plants and animals.Dataset SummaryThe map packages were created from the October 2021 SSURGO snapshot. The dataset covers the 48 contiguous United States plus Hawaii and portions of Alaska. Map packages are available for Puerto Rico and the US Virgin Islands. A project package for US Island Territories and associated states of the Pacific Ocean can be downloaded by clicking one of the included areas in the map. The Pacific Project Package includes: Guam, the Marshall Islands, the Northern Marianas Islands, Palau, the Federated States of Micronesia, and American Samoa.Not all areas within SSURGO have completed soil surveys and many attributes have areas with no data. The soil data in the packages is also available as a feature layer in the ArcGIS Living Atlas of the World.AttributesKey fields from nine commonly used SSURGO tables were compiled to create the 173 attribute fields in this layer. Some fields were joined directly to the SSURGO Map Unit polygon feature class while others required summarization and other processing to create a 1:1 relationship between the attributes and polygons prior to joining the tables. Attributes of this layer are listed below in their order of occurrence in the attribute table and are organized by the SSURGO table they originated from and the processing methods used on them.Map Unit Polygon Feature Class Attribute TableThe fields in this table are from the attribute table of the Map Unit polygon feature class which provides the geographic extent of the map units.Area SymbolSpatial VersionMap Unit SymbolMap Unit TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the table using the Map Unit Key field.Map Unit NameMap Unit KindFarmland ClassInterpretive FocusIntensity of MappingIowa Corn Suitability RatingLegend TableThis table has 1:1 relationship with the Map Unit table and was joined using the Legend Key field.Project ScaleSurvey Area Catalog TableThe fields in this table have a 1:1 relationship with the polygons and were joined to the Map Unit table using the Survey Area Catalog Key and Legend Key fields.Survey Area VersionTabular VersionMap Unit Aggregated Attribute TableThe fields in this table have a 1:1 relationship with the map unit polygons and were joined to the Map Unit attribute table using the Map Unit Key field.Slope Gradient - Dominant ComponentSlope Gradient - Weighted AverageBedrock Depth - MinimumWater Table Depth - Annual MinimumWater Table Depth - April to June MinimumFlooding Frequency - Dominant ConditionFlooding Frequency - MaximumPonding Frequency - PresenceAvailable Water Storage 0-25 cm - Weighted AverageAvailable Water Storage 0-50 cm - Weighted AverageAvailable Water Storage 0-100 cm - Weighted AverageAvailable Water Storage 0-150 cm - Weighted AverageDrainage Class - Dominant ConditionDrainage Class - WettestHydrologic Group - Dominant ConditionIrrigated Capability Class - Dominant ConditionIrrigated Capability Class - Proportion of Map Unit with Dominant ConditionNon-Irrigated Capability Class - Dominant ConditionNon-Irrigated Capability Class - Proportion of Map Unit with Dominant ConditionRating for Buildings without Basements - Dominant ConditionRating for Buildings with Basements - Dominant ConditionRating for Buildings with Basements - Least LimitingRating for Buildings with Basements - Most LimitingRating for Septic Tank Absorption Fields - Dominant ConditionRating for Septic Tank Absorption Fields - Least LimitingRating for Septic Tank Absorption Fields - Most LimitingRating for Sewage Lagoons - Dominant ConditionRating for Sewage Lagoons - Dominant ComponentRating for Roads and Streets - Dominant ConditionRating for Sand Source - Dominant ConditionRating for Sand Source - Most ProbableRating for Paths and Trails - Dominant ConditionRating for Paths and Trails - Weighted AverageErosion Hazard of Forest Roads and Trails - Dominant ComponentHydric Classification - PresenceRating for Manure and Food Processing Waste - Weighted AverageComponent Table – Dominant ComponentMap units have one or more components. To create a 1:1 join component data must be summarized by map unit. For these fields a custom script was used to select the component with the highest value for the Component Percentage Representative Value field (comppct_r). Ties were broken with the Slope Representative Value field (slope_r). Components with lower average slope were selected as dominant. If both soil order and slope were tied, the first value in the table was selected.Component Percentage - Low ValueComponent Percentage - Representative ValueComponent Percentage - High ValueComponent NameComponent KindOther Criteria Used to Identify ComponentsCriteria Used to Identify Components at the Local LevelRunoff ClassSoil loss tolerance factorWind Erodibility IndexWind Erodibility GroupErosion ClassEarth Cover 1Earth Cover 2Hydric ConditionHydric RatingAspect Range - Counter Clockwise LimitAspect - Representative ValueAspect Range - Clockwise LimitGeomorphic DescriptionNon-Irrigated Capability SubclassNon-Irrigated Unit Capability ClassIrrigated Capability SubclassIrrigated Unit Capability ClassConservation Tree Shrub GroupGrain Wildlife HabitatGrass Wildlife HabitatHerbaceous Wildlife HabitatShrub Wildlife HabitatConifer Wildlife HabitatHardwood Wildlife HabitatWetland Wildlife HabitatShallow Water Wildlife HabitatRangeland Wildlife HabitatOpenland Wildlife HabitatWoodland Wildlife HabitatWetland Wildlife HabitatSoil Slip PotentialSusceptibility to Frost HeavingConcrete CorrosionSteel CorrosionTaxonomic ClassTaxonomic OrderTaxonomic SuborderGreat GroupSubgroupParticle SizeParticle Size ModCation Exchange Activity ClassCarbonate ReactionTemperature ClassMoist SubclassSoil Temperature RegimeEdition of Keys to Soil Taxonomy Used to Classify SoilCalifornia Storie IndexComponent KeyComponent Table – Weighted AverageMap units may have one or more soil components. To create a 1:1 join, data from the Component table must be summarized by map unit. For these fields a custom script was used to calculate an average value for each map unit weighted by the Component Percentage Representative Value field (comppct_r).Slope Gradient - Low ValueSlope Gradient - Representative ValueSlope Gradient - High ValueSlope Length USLE - Low ValueSlope Length USLE - Representative ValueSlope Length USLE - High ValueElevation - Low ValueElevation - Representative ValueElevation - High ValueAlbedo - Low ValueAlbedo - Representative ValueAlbedo - High ValueMean Annual Air Temperature - Low ValueMean Annual Air Temperature - Representative ValueMean Annual Air Temperature - High ValueMean Annual Precipitation - Low ValueMean Annual Precipitation - Representative ValueMean Annual Precipitation - High ValueRelative Effective Annual Precipitation - Low ValueRelative Effective Annual Precipitation - Representative ValueRelative Effective Annual Precipitation - High ValueDays between Last and First Frost - Low ValueDays between Last and First Frost - Representative ValueDays between Last and First Frost - High ValueRange Forage Annual Potential Production - Low ValueRange Forage Annual Potential Production - Representative ValueRange Forage Annual Potential Production - High ValueInitial Subsidence - Low ValueInitial Subsidence - Representative ValueInitial Subsidence - High ValueTotal Subsidence - Low ValueTotal Subsidence - Representative ValueTotal Subsidence - High ValueCrop Productivity IndexEsri SymbologyThis field was created to provide symbology based on the Taxonomic Order field (taxorder). Because some map units have a null value for soil order, a

This dataset is available for use for non-commercial purposes only on request as AfA248 dataset Groundwater Vulnerability Maps (2017). For commercial use please contact the British Geological Survey. The Groundwater Vulnerability Maps show the vulnerability of groundwater to a pollutant discharged at ground level based on the hydrological, geological, hydrogeological and soil properties within a single square kilometre. The 2017 publication has updated the groundwater vulnerability maps to reflect improvements in data mapping, modelling capability and understanding of the factors affecting vulnerability Two map products are available: • The combined groundwater vulnerability map. This product is designed for technical specialists due to the complex nature of the legend which displays groundwater vulnerability (High, Medium, Low), the type of aquifer (bedrock and/or superficial) and aquifer designation status (Principal, Secondary, Unproductive). These maps require that the user is able to understand the vulnerability assessment and interpret the individual components of the legend. • The simplified groundwater vulnerability map. This was developed for non-specialists who need to know the overall risk to groundwater but do not have extensive hydrogeological knowledge or the time to interpret the underlying data. The map has five risk categories (High, Medium-High, Medium, Medium-Low and Low) based on the likelihood of a pollutant reaching the groundwater (i.e. the vulnerability), the types of aquifer present and the potential impact (i.e. the aquifer designation status). The two maps also identify areas where solution features that enable rapid movement of a pollutant may be present (identified as stippled areas) and areas where additional local information affecting vulnerability is held by the Environment Agency (identified as dashed areas). Attribution statement: © Environment Agency copyright and/or database right 2017. All rights reserved.Derived from 1:50k scale BGS Digital Data under Licence 2011/057 British Geological Survey. © NERC.