A layout is a composition of one or more maps along with supporting elements, such as a title, a legend, and descriptive text. Some layouts include more than one map. For example, a layout may have a main map and an overview map to show the main map in a larger geographic context.Estimated time: 45 minutesSoftware requirements: ArcGIS Pro

Bedrock is the solid rock at or below the land surface. Over much of Ireland, the bedrock is covered by materials such as soil and gravel. The Bedrock map shows what the land surface of Ireland would be made up of if these materials were removed. As the bedrock is commonly covered, bedrock maps are an interpretation of the available data. Geologists map and record information on the composition and structure of rock outcrops (rock which can be seen on the land surface) and boreholes (a deep narrow round hole drilled in the ground). Areas are drawn on a map to show the distribution of rocks. To produce this dataset, the Geological Survey Ireland (GSI) bedrock geology 1:500,000 and 1:100,000 maps were generalised. The Northern Irish data was generalised using the Geological Survey of Northern Ireland (GSNI) 1:250,000 bedrock geology map. The data was created for the OneGeology project. The offshore data was produced for the EMODnet project using data collected by our marine section as part of the INFOMAR project. This map is to the scale 1:1,000,000. When printed at that scale 1cm on the map relates to a distance of 10km.The map is intended to be used as a teaching resource.

This ArcGIS Map Package contains information on brook trout occupancy in the southern portion of the brook trout range (PA and south). Fish sample data from a number of state and federal agencies/organizations were used to define patches for brook trout as groups of occupied contiguous catchment polygons from the National Hydrography Dataset Plus Version 1 (NHDPlusV1) catchment GIS layer. After defining patches, NHDPlusV1 catchments were assigned occupancy codes. Then state and federal agencies reviewed patches and codes to verify data accuracy. A similar effort is currently being conducted by the Eastern Brook Trout Joint Venture to develop occupancy data for the remainder of the brook trout range including states of New York, Maine, New Hampshire, Connecticut, Vermont, Massachusetts, Rhode Island, and Ohio. This ArcGIS Map Package contains data for the entire southern portion of the brook trout range with preset symbology that displays brook trout occupancy. The Map Package also includes the same information clipped into seperate layers for each state. State information is provided for the convenience of users that are interested in data for only a particular state. Additional layers displaying state boundaries, quadrangle maps, and the brook trout range are also included as spatial references.

This series of products from MODIS represents the only daily global composites available and is suitable for use at global and regional levels. This True Color band composition (Bands 1 4 3 | Red, Green, Blue) most accurately shows how we see the earth’s surface with our own eyes. It is a natural looking image that is useful for land surface, oceanic and atmospheric analysis. There are four True Color products in total. For each satellite (Aqua and Terra) there is a 250 meter corrected reflectance product and a 500 meter surface reflectance product. Although the resolution is coarser than other satellites, this allows for a global collection of imagery on a daily basis, which is made available in near real-time. In contrast, Landsat needs 16 days to collect a global composite. Besides the maximum resolution difference, the surface and corrected reflectance products also differ in the algorithm used for atmospheric correction.NASA Global Imagery Browse Services (GIBS)This image layer provides access to a subset of the NASA Global Imagery Browse Services (GIBS), which are a set of standard services to deliver global, full-resolution satellite imagery. The GIBS goal is to enable interactive exploration of NASA's Earth imagery for a broad range of users. The purpose of this image layer, and the other GIBS image services hosted by Esri, is to enable convenient access to this beautiful and useful satellite imagery for users of ArcGIS. The source data used by this image layer is a finished image; it is not recommended for quantitative analysis.Several full resolution, global imagery products are built and served by GIBS in near real-time (usually within 3.5 hours of observation). These products are built from NASA Earth Observing System satellites data courtesy of LANCE data providers and other sources. The MODIS instrument aboard Terra and Aqua satellites, the AIRS instrument aboard Aqua, and the OMI instrument aboard Aura are used as sources. Several of the MODIS global products are made available on this Esri hosted service.This image layer hosted by Esri provides direct access to one of the GIBS image products. The Esri servers do not store any of this data itself. Instead, for each received data request, multiple image tiles are retrieved from GIBS, which are then processed and assembled into the proper image for the response. This processing takes place on-the-fly, for each and every request. This ensures that any update to the GIBS data is immediately available in the Esri mosaic service.Note on Time: The image service supporting this map is time enabled, but time has been disabled on this image layer so that the most recent imagery displays by default. If you would like to view imagery over time, you can update the layer properties to enable time animation and configure time settings. The results can be saved in a web map to use later or share with others.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. Our final map product is a geographic information system (GIS) database of vegetation structure and composition across the Crater Lake National Park terrestrial landscape, including wetlands. The database includes photos we took at all relevé, validation, and accuracy assessment plots, as well as the plots that were done in the previous wetlands inventory. We conducted an accuracy assessment of the map by evaluating 698 stratified random accuracy assessment plots throughout the project area. We intersected these field data with the vegetation map, resulting in an overall thematic accuracy of 86.2 %. The accuracy of the Cliff, Scree & Rock Vegetation map unit was difficult to assess, as only 9% of this vegetation type was available for sampling due to lack of access. In addition, fires that occurred during the 2017 accuracy assessment field season affected our sample design and may have had a small influence on the accuracy. Our geodatabase contains the locations where particular associations are found at 600 relevé plots, 698 accuracy assessment plots, and 803 validation plots.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for Olympic National Park. The vegetation map is a geotiff raster, and at 67MB may be difficult to download. An ArcGIS file geodatabase contains plot data and lookup tables that relate map class units to mapping associations. The geodatabase includes a vegetation Feature dataset with the park boundary and project boundary used in the map. The map development process was organized around the random forests machine learning algorithm. The modeling used 2,519 plots representing 150 vegetation associations and 50 map classes. Imagery from the National Agriculture Imagery Program and the Sentinel-2 and Landsat 8 satellites, airborne lidar bare earth and canopy height data, elevation data from the U.S. Geological Survey 3D Elevation Program, and climate normals from the PRISM Climate Group were used to develop a variety of predictor metrics. The predictors and the map class calls at each plot were input to a process in which each map class was modeled against every other map class in a factorial random forests scheme. We used the plot-level modeling outcomes and species composition data to adjust the crosswalk between association and map class so that floristic consistency and model accuracy were jointly optimized across all classes. The map was produced by predicting the factorial models and selecting the overall best-performing class at each 3-meter pixel. The final vegetation map, including a buffer surrounding the park, contains 43 natural vegetated classes, seven mostly unvegetated natural classes, and four classes representing burned areas or anthropogenic disturbance.

Learn more about the City of Santa Monica's Multi-Hazard Plan at https://www.smgov.net/Departments/OEM/Preparedness/Multi-Hazard_Plan.aspx

Connecticut Quaternary Geology Long Island Submerged Marine Fluvial-Estuarine, Channel-Fill Deposits identifies early postglacial, channel-fill deposits submerged in Long Island Sound and Fishers Island Sound. This information appears on Sheet 1 of the The Quaternary Geologic Map of Connecticut and Long Island Sound Basin (Stone and others, 2005). The Connecticut Quaternary Geology digital spatial data combines the information portrayed on the on-land portion of the Quaternary Geologic Map of Connecticut and Long Island Sound Basin (Stone and others 2005) with the information portrayed on its sister map, the Surficial Materials Map of Connecticut (Stone and others, 1992). When used together, these maps provide a three dimensional context for understanding and predicting the internal composition, resource potential and hydrologic character of Connecticut's glacial and postglacial deposits. Both were compiled at 1:24,000 scale, and published at 1:125,000 scale. The Quaternary Geologic Map of Connecticut and Long Island Sound Basin (Stone and others, 2005) portrays the glacial and postglacial deposits of Connecticut (including Long Island Sound) with an emphasis on where and how they were emplaced. Glacial Ice-Laid Deposits (thin till, thick till, and deposits of individual end moraines), Early Postglacial Deposits (Late Wisconsinan to Early Holocene stream terrace and inland dune deposits) and Holocene Postglacial Deposits (alluvium, swamp deposits, marsh deposits, beach and dune deposits, talus, and artificial fill) are differentiated from Glacial Meltwater Deposits. This mapping is based on the concept of systematic northward retreat of the Late Wisconsinan glacier. Meltwater deposits are divided into six depositional system categories (Deposits of Major Ice-Dammed Lakes, Deposits of Major Sediment-Dammed Lakes, Deposits of Related Series of Ice-Dammed Ponds, Deposits of Related Series of Sediment-Dammed Ponds, Deposits of Proximal Meltwater Streams, and Deposits of Distal Meltwater Streams) based on the arrangement and character of the groupings of sedimentary facies (morphosequences). The Surficial Materials Map of Connecticut (Stone and others, 1992) portrays the glacial and postglacial deposits of Connecticut in terms of their aerial extent and subsurface textural relationships. Glacial Ice-Laid Deposits (thin till, thick till, end moraine deposits) and Postglacial Deposits (alluvium, swamp deposits, marsh deposits, beach deposits, talus, and artificial fill) are differentiated from Glacial Meltwater Deposits. The meltwater deposits are further characterized using four texturally-based map units (g = gravel, sg = sand and gravel, s = sand, and f = fines). In many places a single map unit (e.g. sand) is sufficient to describe the entire meltwater section. Where more complex stratigraphic relationships exist, "stacked" map units are used to characterize the subsurface (e.g. sg/s/f - sand and gravel overlying sand overlying fines). Where postglacial deposits overlie meltwater deposits, this relationship is also described (e.g. alluvium overlying sand). Map unit definitions (Surficial Materials Polygon Code definitions, found in the metadata) provide a short description of the inferred depositional environment for each of the glacial meltwater map units. The geologic contacts between till and meltwater deposits coincide on both the Quaternary and Surficial Materials maps, as do the boundaries of polygons that define areas of thick till, alluvium, swamp deposits, marsh deposits, beach and dune deposits, talus, and artificial fill. Within the meltwater deposits, a Quaternary map unit (deposit) may contain several Surficial Materials textural units (akin to facies within a delta, for example). Combining the textural and vertical stacking information from the Surficial Materials map with the orderly portrayal of morphosequence relationships, up and down valley, that can be gleaned from the Quaternary map provides a three dimensional predictive context for relating the geologic setting of Connecticut's glacial meltwater deposits to their behavior as aquifers and/or transmitters of contaminants. Since this data layer is a polygon and line feature representation of the two maps combined, each map unit's depiction and description could provide information as to its aerial extent, subsurface textural characteristics, depositional and paleogeographic settings, and facies composition in a morphosequence context. Therefore, a typical meltwater polygon would have a combination of Quaternary (e.g. Deposit of Major Sediment-Dammed Lake; Glacial Lake Middletown Cromwell Deltaic Deposit) and Surficial Materials (e.g. sand and gravel overlying sand overlying fine) map attributes. Additional polygon features are incorporated to define surface water areas for streams, lakes, ponds, bays, and estuaries greater than 5 acres in size. Line features describe the type of boundary between individual geologic or textural units such as a geologic contact line between two different geologic units or a linear shoreline feature between a textural unit and an adjacent waterbody. The data have been updated to reflect minor changes in map unit name (QUPOLY_COD) for consistency with the 2005 publication of the Quaternary Geologic Map of Connecticut and Long Island Sound Basin. Previously distributed versions of CTQSGEOM were consistent with the 1998 Open-file Report for the same map. It is important to note that this data layer represents only the on-land portion of the Quaternary Geologic Map of Connecticut and Long Island Sound Basin (Stone and others, 2005). The off-shore geologic units are organized in separate data layers (LISQMOR, LISQFAN, LISQLAKE, LISQCHAN, LISQMARD) which can be used in conjunction with this data layer. These Long Island Sound layers have been mapped at 1:80,000 scale using seismic reflection data. The CTQSGEOM data layer should be used as the geologic base for Connecticut Quaternary Geology / Surficial Materials Features (CTQSFEAT) data layer which represents features such as eskers, meltwater channels, spillways, and locations of radio-carbon dated samples.

Connecticut Quaternary Geology Long Island Submerged Marine Deltaic Deposits identifies early postglacial, marine deltaic deposits submerged in Long Island Sound. This information appears on Sheet 1 of the The Quaternary Geologic Map of Connecticut and Long Island Sound Basin (Stone and others, 2005). The Connecticut Quaternary Geology digital spatial data combines the information portrayed on the on-land portion of the Quaternary Geologic Map of Connecticut and Long Island Sound Basin (Stone and others 2005) with the information portrayed on its sister map, the Surficial Materials Map of Connecticut (Stone and others, 1992). When used together, these maps provide a three dimensional context for understanding and predicting the internal composition, resource potential and hydrologic character of Connecticut's glacial and postglacial deposits. Both were compiled at 1:24,000 scale, and published at 1:125,000 scale. The Quaternary Geologic Map of Connecticut and Long Island Sound Basin (Stone and others, 2005) portrays the glacial and postglacial deposits of Connecticut (including Long Island Sound) with an emphasis on where and how they were emplaced. Glacial Ice-Laid Deposits (thin till, thick till, and deposits of individual end moraines), Early Postglacial Deposits (Late Wisconsinan to Early Holocene stream terrace and inland dune deposits) and Holocene Postglacial Deposits (alluvium, swamp deposits, marsh deposits, beach and dune deposits, talus, and artificial fill) are differentiated from Glacial Meltwater Deposits. This mapping is based on the concept of systematic northward retreat of the Late Wisconsinan glacier. Meltwater deposits are divided into six depositional system categories (Deposits of Major Ice-Dammed Lakes, Deposits of Major Sediment-Dammed Lakes, Deposits of Related Series of Ice-Dammed Ponds, Deposits of Related Series of Sediment-Dammed Ponds, Deposits of Proximal Meltwater Streams, and Deposits of Distal Meltwater Streams) based on the arrangement and character of the groupings of sedimentary facies (morphosequences). The Surficial Materials Map of Connecticut (Stone and others, 1992) portrays the glacial and postglacial deposits of Connecticut in terms of their aerial extent and subsurface textural relationships. Glacial Ice-Laid Deposits (thin till, thick till, end moraine deposits) and Postglacial Deposits (alluvium, swamp deposits, marsh deposits, beach deposits, talus, and artificial fill) are differentiated from Glacial Meltwater Deposits. The meltwater deposits are further characterized using four texturally-based map units (g = gravel, sg = sand and gravel, s = sand, and f = fines). In many places a single map unit (e.g. sand) is sufficient to describe the entire meltwater section. Where more complex stratigraphic relationships exist, "stacked" map units are used to characterize the subsurface (e.g. sg/s/f - sand and gravel overlying sand overlying fines). Where postglacial deposits overlie meltwater deposits, this relationship is also described (e.g. alluvium overlying sand). Map unit definitions (Surficial Materials Polygon Code definitions, found in the metadata) provide a short description of the inferred depositional environment for each of the glacial meltwater map units. The geologic contacts between till and meltwater deposits coincide on both the Quaternary and Surficial Materials maps, as do the boundaries of polygons that define areas of thick till, alluvium, swamp deposits, marsh deposits, beach and dune deposits, talus, and artificial fill. Within the meltwater deposits, a Quaternary map unit (deposit) may contain several Surficial Materials textural units (akin to facies within a delta, for example). Combining the textural and vertical stacking information from the Surficial Materials map with the orderly portrayal of

1H NMR profiling is nowadays a consolidated technique for the identification of geographical origin of food samples. The common approach consists in correlating NMR spectra of food samples to their territorial origin by multivariate classification statistical algorithms. In the present work we illustrate an alternative perspective to exploit territorial information, contained in the NMR spectra, which is based on the implementation of a Geographic Information System (GIS). NMR spectra are used to build a GIS map permitting the identification of territorial regions having strong similarities in the chemical content of the produced food (terroir units). These terroir units can, in turn, be used as input for labeling samples to be analyzed by traditional classification methods. In this work we describe the methods and the algorithms which permits to produce GIS maps from NMR profiles and apply the described method to the analysis of the geographical distribution of olive oils in an Italian region. In particular, we analyzed by 1H NMR up to 98 georeferenced olive oil samples produced in the Abruzzo Italian region. By using the first principal component of the NMR variables selected according to the Moran test, we produced a GIS map, in which we identified two regions incidentally corresponding to the provinces of Teramo and Pescara. We then labeled the samples according to the province of provenience and built a LDA model which provides a classification ability up to 99 % . A comparison between the variables selected in the geostatistics and classification steps is finally performed.

This dataset was developed by the Research & Analytics Group at the Atlanta Regional Commission using data from the U.S. Census Bureau.For a deep dive into the data model including every specific metric, see the Infrastructure Manifest. The manifest details ARC-defined naming conventions, field names/descriptions and topics, summary levels; source tables; notes and so forth for all metrics.Naming conventions:Prefixes: None Countp Percentr Ratem Mediana Mean (average)t Aggregate (total)ch Change in absolute terms (value in t2 - value in t1)pch Percent change ((value in t2 - value in t1) / value in t1)chp Change in percent (percent in t2 - percent in t1)s Significance flag for change: 1 = statistically significant with a 90% CI, 0 = not statistically significant, blank = cannot be computed Suffixes: _e19 Estimate from 2014-19 ACS_m19 Margin of Error from 2014-19 ACS_00_v19 Decennial 2000, re-estimated to 2019 geography_00_19 Change, 2000-19_e10_v19 2006-10 ACS, re-estimated to 2019 geography_m10_v19 Margin of Error from 2006-10 ACS, re-estimated to 2019 geography_e10_19 Change, 2010-19The user should note that American Community Survey data represent estimates derived from a surveyed sample of the population, which creates some level of uncertainty, as opposed to an exact measure of the entire population (the full census count is only conducted once every 10 years and does not cover as many detailed characteristics of the population). Therefore, any measure reported by ACS should not be taken as an exact number – this is why a corresponding margin of error (MOE) is also given for ACS measures. The size of the MOE relative to its corresponding estimate value provides an indication of confidence in the accuracy of each estimate. Each MOE is expressed in the same units as its corresponding measure; for example, if the estimate value is expressed as a number, then its MOE will also be a number; if the estimate value is expressed as a percent, then its MOE will also be a percent. The user should also note that for relatively small geographic areas, such as census tracts shown here, ACS only releases combined 5-year estimates, meaning these estimates represent rolling averages of survey results that were collected over a 5-year span (in this case 2015-2019). Therefore, these data do not represent any one specific point in time or even one specific year. For geographic areas with larger populations, 3-year and 1-year estimates are also available. For further explanation of ACS estimates and margin of error, visit Census ACS website.Source: U.S. Census Bureau, Atlanta Regional CommissionDate: 2015-2019Data License: Creative Commons Attribution 4.0 International (CC by 4.0)Link to the manifest: https://www.arcgis.com/sharing/rest/content/items/3d489c725bb24f52a987b302147c46ee/data

You will learn how to combine layout composition, color, symbology, and text to design a map that clearly communicates your intended message.

This data set comprises the Environmental Sensitivity Index (ESI) data for Rhode Island, Connecticut, and the New York - New Jersey Metropolitan Area from 1999 to 2001. ESI data characterize estuarine environments and wildlife by their sensitivity to spilled oil. The ESI data include information for three main components: shoreline habitats, sensitive biological resources, and human-use resources. This atlas was developed to be utilized within desktop GIS systems and contains GIS files and related D-base files. Associated files include MOSS (Multiple Overlay Statistical System) export files, .PDF maps, and detailed user guides and metadata.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. the draft final map was subjected to a heads-up screen digitizing edit using the most recent aerial photography. Accordingly, we accessed recent imagery through 2012 Microsoft Corporation Bing Imagery, available via ESRI ArcGis 10.0. As with all Bing imagery, the exact image date is not provided, but a search of the Digital Globe library indicates three possible dates: 2009-01-13, 2011-11-18, 2012-01-09, or a combination thereof. We think that it is not likely that the 2012 imagery had been posted to Bing, and that the 2011 imagery is the most likely candidate. We were also able to bring directly in additional 2009 New Mexico county imagery, and 2005 NAIP color-infrared and natural-color imagery at 1 m resolution. During the final edit, the thematic composition and number of Level 1 and 2 map units were finalized and the final map product produced using NPS cartographic standards. While the minimum mapping requirements were at 1:24,000 scale with map unit delineations or polygons at 0.5 ha or larger, most of the final line work was completed at an operational scale between 1:12,000 and 1:3,000. Hence, polygons down to 0.25 ha were often maintained. For final map production, adjacent polygons of the same class were merged. Final map products included the geodatabase and a 1:44,000 poster map at Level 1 and 2.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for Mount Rainier National Park. The vegetation map is a geotiff raster, and at 67MB may be difficult to download. An ArcGIS file geodatabase contains plot data and lookup tables that relate map class units to mapping associations. The geodatabase includes a vegetation Feature dataset with the park boundary and project boundary used in the map. The map development process was organized around the random forests machine learning algorithm. The modeling used 1,900 plots representing 124 vegetation associations and 37 map classes. Imagery from the National Agriculture Imagery Program and the Sentinel-2 and Landsat 8 satellites, airborne lidar bare earth and canopy height data, elevation data from the U.S. Geological Survey 3D Elevation Program, and climate normals from the PRISM Climate Group were used to develop a variety of predictor metrics. The predictors and the map class calls at each plot were input to a process in which each map class was modeled against every other map class in a factorial random forests scheme. We used the plot-level modeling outcomes and species composition data to adjust the crosswalk between association and map class so that floristic consistency and model accuracy were jointly optimized across all classes. The map was produced by predicting the factorial models and selecting the overall best-performing class at each 3-meter pixel. The final vegetation map, including a buffer surrounding the park, contains 33 natural vegetated classes, five mostly unvegetated natural classes, and four classes representing burned areas or anthropogenic disturbance

Western Foundation of Vertebrate Zoology contracted Stillwater Sciences in 2018 to create a fine-scale vegetation map of portions of the Santa Clara River. The mapping study area, consists of approximately 16,370 acres of Ventura County. Work was performed on the project during the summer and fall of 2018. The projects main goal was to address the need for detailed up-to-date vegetation information in support of identifying and modeling habitat for southwestern willow flycatcher, yellow-billed cuckoo, and least Bell's vireo. Funding for the project was provided by an Endangered Species Act Section 6 grant from the United States Fish and Wildlife Service to the California Department of Fish and Wildlife. This project builds off a prior mapping project that was conducted by Stillwater Sciences and URS, which was funded by the California State Coastal Conservancy and the Santa Clara River Trustee Council, in 2007. Species composition data collected in the field was compiled and reviewed in the office to assign the appropriate MCV alliance to each sampled location. In cases where the species present were best described by an MCV association (a sub-category of the broader MCV alliance), one was assigned. For field sampled locations with unique species composition not currently represented by an existing MCV alliance or association, a provisional alliance or association was created. In addition, some areas were classified into broader land cover types (e.g., agriculture, developed, riverwash). The vegetation map was produced applying digital aerial imagery (natural color, 2-foot resolution) from the National Agricultural Imagery Program (NAIP) (USDA-FSA 2016) flown in May, June, and July 2016. The minimum mapping unit (MMU) is 0.5 acres for most types and 0.1 for more unusual types that were discernable from areal photography and/or documented in the field. Once the map was made photointerpretation of the NAIP imagery took place in order to identify vegetation types. Field mapping took place after to refine the vegetation type definitions, CNPS vegetation reconnaissance field forms were used in the field. There was a total of 91 mapping classes. There was no accuracy assessment was done for this project. More information can be found in the project report, which is bundled with the vegetation map published for BIOs here: https://filelib.wildlife.ca.gov/Public/BDB/GIS/BIOS/Public_Datasets/2900_2999/ds2961.zip

This data set consists of general soil association units. It was developed by the National Cooperative Soil Survey and supersedes the State Soil Geographic (STATSGO) data set published in 2006. It consists of a broad based inventory of soils and nonsoil areas that occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. The data set was created by generalizing more detailed soil survey maps. Where more detailed soil survey maps were not available, data on geology, topography, vegetation, and climate were assembled, together with Land Remote Sensing Satellite (LANDSAT) images. Soils of like areas were studied, and the probable classification and extent of the soils were determined.

Map unit composition was determined by transecting or sampling areas on the more detailed maps and expanding the data statistically to characterize the whole map unit.

This data set consists of georeferenced vector digital data and tabular digital data. The map data were collected in 1-by 2-degree topographic quadrangle units and merged into a seamless national data set. It is distributed in state/territory and national extents. The soil map units are linked to attributes in the National Soil Information System data base which gives the proportionate extent of the component soils and their properties.

This mapping completed the Larsemann Hills photogrammetric mapping project. The project was commenced on 14 December 2001 and completed in April 2003. It includes the integration of newly mapped data with dataset gis136. (Larsemann Hills - Mapping from Landsat 7 imagery captured January 2000)

A report on the project is available at the url given below.

Bedrock is the solid rock at or below the land surface. Over much of Ireland, the bedrock is covered by materials such as soil and gravel. The Bedrock map shows what the land surface of Ireland would be made up of if these materials were removed. As the bedrock is commonly covered, bedrock maps are an interpretation of the available data. Geologists map and record information on the composition and structure of rock outcrops (rock which can be seen on the land surface) and boreholes (a deep narrow round hole drilled in the ground). Areas are drawn on a map to show the distribution of rocks. To produce this dataset, the twenty one 1:100,000 paper maps covering Ireland were digitised and borders and overlaps between map sheets were removed. We collect new data to update our map and also use data made available from other sources. This map is to the scale 1:100,000. This means it should be viewed at that scale. When printed at that scale 1cm on the map relates to a distance of 1km.It is a vector dataset. Vector data portray the world using points, lines, and polygons (areas).The bedrock data is shown as polygons. Each polygon holds information on the rock unit name, its description, stratigraphy code (rock layers with age profile), lithology code (rock type) and map sheet number. Each polygon is linked to the bedrock lexicon table which has more detailed information such as a definition of the rock unit, rock types, age, thickness and other comments.

Bedrock is the solid rock at or below the land surface. Over much of Ireland, the bedrock is covered by materials such as soil and gravel. The Bedrock map shows what the land surface of Ireland would be made up of if these materials were removed. As the bedrock is commonly covered, bedrock maps are an interpretation of the available data. Geologists map and record information on the composition and structure of rock outcrops (rock which can be seen on the land surface) and boreholes (a deep narrow round hole drilled in the ground). Areas are drawn on a map to show the distribution of rocks. The Geological Lines layer shows the details of the structural geology; faults, folds and unconformities. Faults and folds are the result of great pressure being applied to rock across a whole continent or more. These rocks will either break under the pressure, forming faults, or they will bend to form folds. Faults are recorded in the Geological Lines layer as lines where the break in the rock meets the surface. Folds are shown only using the lines of their axes, synclinal (where the rock folds downwards) and anticlinal (where the rock folds upwards). Unconformities are where there is a gap in the rock record, typically where rock has been eroded away in the past and a new rock deposited on top.Geologists map and record information on the structural geology. Lines are drawn on a map to show the location and extent of these structures. The structural symbols layer is used to describe the geology of an area through dip and strike information. Dip and strike describe the behaviour of the rock bedding plane. To describe a geometric plane two values are required; the angle from horizontal that it is dipping and the direction that it is dipping. Geologists describe the dip direction by the strike value; this is the azimuth perpendicular to the steepest dip of the plane.The measurements that this layer contains give information about the geometry of the rock units under the ground. These measurements are the only way to see if the rocks are folded and faulted and how. With this information we can also start to see why the rocks have the shapes that they do.To produce this dataset, the twenty one 1:100,000 paper maps covering Ireland were digitised and borders and overlaps between map sheets were removed. We collect new data to update our map and also use data made available from other sources. This map is to the scale 1:100,000. This means it should be viewed at that scale. When printed at that scale 1cm on the map relates to a distance of 1km.It is a vector dataset. Vector data portray the world using points, lines, and polygons (areas).The bedrock data is shown as polygons. Each polygon holds information on the rock unit name, its description, stratigraphy code (rock layers with age profile), lithology code (rock type) and map sheet number. Each polygon is linked to the bedrock lexicon table which has more detailed information such as a definition of the rock unit, rock types, age, thickness and other comments.The geological line data is shown as lines. Each line holds information on: description of the line, bedrock 100k map sheet number, style and label information. Other information if relevant such as name, stratigraphy code (rock layers with age profile) & lithology code (rock type). Each line is linked to the bedrock linework lexicon table which has more detailed information such as a definition of the rock unit, rock types, age, thickness and other comments. The structural symbols data is shown as points. Each point holds information on: the dip angle and direction, the strike angle and a description.The outcrop data is shown as polygons.

This data set consists of general soil association units. It was develped by the National Cooperative Soil Survey and supersedes the State Soil Geographic (STATSGO) data set published in 1994. It consists of a broad based inventory of soils and nonsoil areas that occur in a repeatable pattern on the landscape and that can be cartographically shown at the scale mapped. The data set was created by generalizing more detailed soil survey maps. Where more detailed soil survey maps were not available, data on geology, topography, vegetation, and climate were assembled, together with Land Remote Sensing Satellite (LANDSAT) images. Soils of like areas were studied, and the probable classification and extent of the soils were determined. Map unit composition was determined by transecting or sampling areas on the more detailed maps and expanding the data statistically to characterize the whole map unit. This data set consists of georeferenced vector digital data and tabular digital data. The map data were collected in 1-by 2-degree topographic quadrangle units and merged into a seamless national data set. It is distributed in state/territory and national extents. The soil map units are linked to attributes in the National Soil Information System data base which gives the proportionate extent of the component soils and their properties.