One important reason for performing GIS analysis is to determine proximity. Often, this type of analysis is done using vector data and possibly the Buffer or Near tools. In this course, you will learn how to calculate distance using raster datasets as inputs in order to assign cells a value based on distance to the nearest source (e.g., city, campground). You will also learn how to allocate cells to a particular source and to determine the compass direction from a cell in a raster to a source.What if you don't want to just measure the straight line from one place to another? What if you need to determine the best route to a destination, taking speed limits, slope, terrain, and road conditions into consideration? In cases like this, you could use the cost distance tools in order to assign a cost (such as time) to each raster cell based on factors like slope and speed limit. From these calculations, you could create a least-cost path from one place to another. Because these tools account for variables that could affect travel, they can help you determine that the shortest path may not always be the best path.After completing this course, you will be able to:Create straight-line distance, direction, and allocation surfaces.Determine when to use Euclidean and weighted distance tools.Perform a least-cost path analysis.

Feature layer generated from running the Create Buffers analysis tool.

This layer is a 45-meter growth buffer surrounding the maximum extent of eelgrass (green layer called "SF Bay Eelgrass") surveyed in San Francisco Bay. Eelgrass beds are highly dynamic and the exact location and extent of eelgrass beds can change across seasons and years. Thus, the purpose of the 45-meter growth buffer, as described in the National Marine Fisheries Service's LTMS Programmatic Essential Fish Habitat consultation is to account for areas between eelgrass patches, temporal variation in bed extent, and potential bed expansion. In cases where a dredge project intersects with the 45-meter growth buffer direct impacts to eelgrass may occur and therefore assessment, minimization, and mitigation measures may be required on a project-by-project basis. A pre-dredge eelgrass area and density survey is required 30 days prior to the start of dredging and should be submitted to the LTMS permitting agencies. Methods for creating this layer are as follows: Downloaded Baywide Eelgrass Surveys for 2003, 2009, and 2014 by Merkel & Associates, Inc. (Merkel) from San Francisco Estuary Institute (SFEI) website. Obtained Richardson Bay 2019 eelgrass survey from Merkel. Loaded all layers into ArcGIS Pro © ESRI and re-projected all data to NAD 1983 UTM Zone 10N. Used Buffer tool to develop a single multipart shapefile with a 45-meter buffer of the input layers. Imported the Pacific Marine and Estuarine Fish Habitat Partnership (PMEP) Estuary Extent layer and clipped the 45-meter buffer over terrestrial areas based on the PEMP Estuary Extent. Some minor adjustments were made where the buffer layer resulted in fragments on land or behind levees.

The National Hydrography Dataset Plus High Resolution (NHDplus High Resolution) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US Geological Survey, NHDPlus High Resolution provides mean annual flow and velocity estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses.For more information on the NHDPlus High Resolution dataset see the User’s Guide for the National Hydrography Dataset Plus (NHDPlus) High Resolution.Dataset SummaryPhenomenon Mapped: Surface waters and related features of the United States and associated territoriesGeographic Extent: The Contiguous United States, Hawaii, portions of Alaska, Puerto Rico, Guam, US Virgin Islands, Northern Marianas Islands, and American SamoaProjection: Web Mercator Auxiliary Sphere Visible Scale: Visible at all scales but layer draws best at scales larger than 1:1,000,000Source: USGSUpdate Frequency: AnnualPublication Date: July 2022This layer was symbolized in the ArcGIS Map Viewer and while the features will draw in the Classic Map Viewer the advanced symbology will not. Prior to publication, the network and non-network flowline feature classes were combined into a single flowline layer. Similarly, the Area and Waterbody feature classes were merged under a single schema.Attribute fields were added to the flowline and waterbody layers to simplify symbology and enhance the layer's pop-ups. Fields added include Pop-up Title, Pop-up Subtitle, Esri Symbology (waterbodies only), and Feature Code Description. All other attributes are from the original dataset. No data values -9999 and -9998 were converted to Null values.What can you do with this layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer or a map containing it can be used in an application. Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Apply filters. For example you can set a filter to show larger streams and rivers using the mean annual flow attribute or the stream order attribute.Change the layer’s style and symbologyAdd labels and set their propertiesCustomize the pop-upUse as an input to the ArcGIS Online analysis tools. This layer works well as a reference layer with the trace downstream and watershed tools. The buffer tool can be used to draw protective boundaries around streams and the extract data tool can be used to create copies of portions of the data.ArcGIS ProAdd this layer to a 2d or 3d map.Use as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the ArcGIS Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Questions?Please leave a comment below if you have a question about this layer, and we will get back to you as soon as possible.

The purpose of this project is to determine the most suitable location for a wind farm in 50Kms radius of Calgary. Different criterion need to be considered for choosing the final site, ranging from distance from settlements, water bodies, proximity to power lines to slope and wind speed intensity of the region. Based on the literature review, the areas that did not have the potential for hosting wind turbines were excluded by using the buffer tool in ArcGIS Pro. Afterwards, the wind speed and slope of the remaining regions were analyzed to pick the location with the highest wind speed and the most suitable slope. As can be seen in the final map, the final site is located in the western side of Calgary.

The National Hydrography Dataset (NHD) is a feature-based database that interconnects and uniquely identifies the stream segments or reaches that make up the nation's surface water drainage system. NHD data was originally developed at 1:100,000-scale and exists at that scale for the whole country. This high-resolution NHD, generally developed at 1:24,000/1:12,000 scale, adds detail to the original 1:100,000-scale NHD. (Data for Alaska, Puerto Rico and the Virgin Islands was developed at high-resolution, not 1:100,000 scale.) Local resolution NHD is being developed where partners and data exist. The NHD contains reach codes for networked features, flow direction, names, and centerline representations for areal water bodies. Reaches are also defined on waterbodies and the approximate shorelines of the Great Lakes, the Atlantic and Pacific Oceans and the Gulf of Mexico. The NHD also incorporates the National Spatial Data Infrastructure framework criteria established by the Federal Geographic Data Committee. Mohawk River Watershed Processing: The original files were clipped to the Mohawk watershed. The data was re-projected to UTM 18N, NAD 83. NHDArea, NHDFlowline, NHDLine, and NHDPoint feature classes were buffered 50 feet using the Buffer Tool in ArcGIS v.10. The individual buffer files were merged and dissolved. View Dataset on the Gateway

Feature layer generated from running the Create Buffers analysis tool.

This file contains Hydrologic Unit boundaries and codes for the United States, Puerto Rico, and the U.S. Virgin Islands. The data is a seamless National representation of Hydrologic Unit Code (HUC) boundaries at HUC2 to HUC12 levels compiled from U.S. Geological Survey (USGS) National Hydrography Dataset (NHD) and U.S. Department of Agricultural (USDA) National Resources Conservation Services (NRCS) Watershed Boundary Dataset (WBD) sources. Mohawk River Watershed Processing: The original files were clipped to the Mohawk watershed. The data was re-projected to UTM 18N, NAD 83. NHDArea, NHDFlowline, NHDLine, and NHDPoint feature classes were buffered 150 feet using the Buffer Tool in ArcGIS v.10. The individual buffer files were merged and dissolved.View Dataset on the Gateway

This layer is displayed on the Water Catchments and Dual Reticulation overlay map in City Plan version 7 as 'Water Supply Buffer Area'. The layer is also available in Council’s City Plan interactive mapping tool. For further information on City Plan, please visit http://www.goldcoast.qld.gov.au/planning-and-building/city-plan-2015-19859.html

Feature layer generated from running the Create Buffers analysis tool.

Feature layer generated via the Create Drive Times tool. Inputs include private school point locations & a walking distance of 300 ft. to each school access point.

Analysis of ‘SF Bay Eelgrass 250m Buffer (BCDC 2021)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/4cf7910a-041e-449b-9d97-d9276a4b0be4 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

--- Original source retains full ownership of the source dataset ---

This map service is a one-stop location to view and explore Kentucky geologic map data and related-data (geologic outcrops, photos, and diagrams), Kentucky water wells and springs, Kentucky oil and gas wells. All features are provided by the Kentucky Geological Survey via ArcGIS Server services. This map service displays the 1:500,000-scale geologic map of Kentucky at scales smaller than 1:100,000, and 1:24,000-scale geological quadrangle data at larger scales. The 1:500,000-scale geologic map data were derived from the 1988 Geologic Map of Kentucky, which was compiled by Martin C. Noger (KGS) from the 1981 Geologic Map of Kentucky (Scale 1:250,000) by McDowell and others (USGS). The 1:24,000-scale geologic map data and the fault data were compiled from 707 Geological Survey 7.5-minute geologic quadrangle maps, which were digitized during the Kentucky Geological Survey Digital Mapping Program (1996-2006).The basemap data is provided via ArcGIS Server services hosted by the Kentucky Office of Geographic Information.Some tools are provided to help explore the map data:- Query tool: use this tool to search on the KGS database of lithologic descriptions. Most descriptions are derived from the 707 1:24,000 geological quadrangle maps. Once a search is completed, every unit that contains the search parameters is highlighted on the map service.- ID tools: users can identify and get detailed info on geologic units and other map features using either the point, area, or buffer identification tools.A few notes on this service:- the legend is dynamic for the viewed extent. It is provided via a database call using the current map extent.- the oil and gas and water wells are ArcGIS Server services that update dynamically from the KGS database.- the geologic map and faults are dynamic ArcGIS Server map services.- the user can link to other geologic data for the viewed extent using the links provided in the "Geologic Info" tab.- you can query the entire KGS lithologic description database and highlight the relevant geologic units based on the query.

Feature layer generated via the Create Drive Times tool. Inputs include SPS school access points & a walking distance of 0.25 mi. to each school access point.

Feature layer generated from running the Create Buffers analysis tool.

The portion of Lake Huron within one (1) mile from shore and delineated by the following landmarks: St. Martin's Bay zone - from Rabbit Back Point north and east to Brulee Point. Regulations: The waters described above, shall be the Sault Tribe Tribal zone only during the salmon seasons. At all other times, these waters shall be part of the Northern Lake Huron Inter-Tribal Fishing Zone. Bay Mills fishers shall not fish in the portion of this zone described above. Fishing for salmon by the Tribal commercial fishers is limited to the Sault Tribe Tribal Zone described above. Salmon fishing shall be permitted from August 1 through October 15 in the St. Martin's Bay zone. Nets may be fished at the surface at any time during the specified salmon seasons in the areas described above. Outside the specified salmon season, commercial fishing for salmon is prohibited except for incidental harvest. The Tribes shall prohibit the retention of more than two hundred (200) pounds round weight per vessel per day of salmon caught as incidental catch in gill nets in waters and seasons not open to salmon fishing, and shall prohibit any retention of salmon caught in trap nets. Maps for general reference only: refer to text of Consent Decree 2000 for exact locations and provisions.Created a new polyline shapefile in ArcGIS 8.1. Copied selected features (as outlined in the Consent Decree 2000 documentation) of the US Department of Commerce (Bureau of the Census, Geography Division) county census (1995) layer into new shapefile. A one mile buffer was then generated from the polyline shapefile using the buffer wizard tool in Arc Map. Created a new polygon shapefile in ArcGIS 8.1. Copied selected features (as outlined in the Consent Decree 2000 documentation) of the US Department of Commerce (Bureau of the Census, Geography Division) county census (1995) layer into new shapefile. The new polygon feature was then commbined with the one mile buffer created earlier. The desired features were then selected and exported as a new shapefile. Created a new polygon shapefile in ArcGIS 8.1. The new pollygon layer was created using the snapping tool in ArcMap. Snapping to an adjacent layer and heading in a clockwise direction extending the polygon boundaries beyond the US Department of Commerce (Bureau of the Census, Geography Division) county census (1995) layer to encorporate the target area outlined in the Consent Decree 2000 documentation. The new polygon feature was then clipped to the exported polgon created earlier. A point was located on the USGS Mackinac county 1:24,000 DRG as outlined in the Consent Decree 2000 documentation. A one mile buffer was then generated from the point shapefile using the buffer wizard tool in Arc Map. The new buffer was then commbined with the copied polygon generated from the US Department of Commerce (Bureau of the Census, Geography Division) county census (1995) layer using the union tool from the geoprocessing wizard in Arc Map. The desired features were then selected and exported as a new shapefile. The new polygon feature was commbined with the clipped polygon using the union tool from the geoprocessing wizard inArcMap. The desired features were then selected and exported as a new shapefile. Created a new polygon shapefile in ArcGIS 8.1. Snapped to the exported polygon layer created above to smooth out intersection. The desired features were then selected, merged as new layer in ArcMap, exported as a new shapefile, and reprojected from Michigan georef to Decimal Degrees to create the final St. Martin's Bay Zone layer.The boundaries represented on consent decree maps are approximations based on the text contained in the 2000 Consent Decree. For legal descriptions of geographic extent or details pertaining to regulations for these representations refer to the original 2000 Consent Decree Document.

This web-based application was created by BCDC to support the Long Term Management Strategy for the Placement of Dredged Material in the San Francisco Bay Region (LTMS) program and the National Marine Fisheries Service’s 2011 LTMS Programmatic Essential Fish Habitat (EFH) consultation. The web application can assist project planners in identifying potential impacts of dredging projects in San Francisco Bay to eelgrass based on the LTMS EFH consultation. Once inside the application, click on the “about” button to learn more about assessing impacts and make sure to refer to the EFH consultation linked above for more specific information. Layers in this application include: 1) the maximum extent of eelgrass beds that have been surveyed in San Francisco Bay shown in green; 2) a 45-meter growth buffer for potential bed expansion shown in blue; 3) Polygons demonstrating where dredging occurs within San Francisco Bay; and 4) a 250-meter turbidity buffer around dredging footprints. The eelgrass survey data used in this web application represents the best available data on comprehensive eelgrass extent throughout San Francisco Bay as of 2021. The original eelgrass survey data were developed by Merkel & Associates, Inc. (Merkel) using a combination of acoustic and aerial surveys and site-specific ground truthing. This web application may be used to determine potential direct and indirect impacts to eelgrass habitat from dredging projects as described in the LTMS EFH consultation. These data do not replace the need for site-specific eelgrass surveys as directed by the regulatory and resource agencies.Data from the 2003, 2009, and 2014 baywide eelgrass surveys and associated Merkel reports, which include information on mapping methodology, are available for download on the San Francisco Estuary Institute’s (SFEI) website. Data from a Richardson Bay survey conducted by Merkel in 2019 is also included in this application. For further information on methods used here please enter the application by clicking “View Application” on the right, then click the “…” next to each layer, and then select “Show item details" in the drop-down menu for each individual layer.

Information contained includes project ID, flight start date, flight end date, index confirmation, camera type, project type, production status, and whether the flight has been geo-referenced or not.

This polygon feature class was created to model the flight coverage of geo-rectified aerial photography for the State Water Project for each flight group. The coordinate system utilized for this feature class is NAD1983_CaTM. The purpose for the creation of this dataset is to help classify the various flight groups according to the corresponding project ID and supply relevant spatial data information. To create the photo centers, archived aerial photography was first scanned and converted to .tiff file format that would be later geo-referenced to NAIP 2016. The photo centers at the ends of each fight line would be marked using the polygon drawing tool by creating an "X" from the vertex of each corner fiducial. The flight line would then be created using these photo center for end points with a line feature class. The photo centers were then created by constructing points using the editor tool. To develop the coverage extent, the photo scale was inputted into an excel flight plan distance calculator to formulate coverage distance. Next, the buffer tool under the geo-processing tab was utilized to input the coverage distance found previously to create the flight coverage area.

This data set represents a series of 502 mixed-species bird flock compositions, and derived taxonomic, functional, and phylogenetic diversity indices, that were gathered along a gradient of forest fragment sizes (range = 10-173 ha) in the Colombian Western Andes. We sampled mixed-species flocks using transect surveys along 14 transects in 8 fragments and a continuous forest reference site in the same landscape and at the same elevation (~1900-2200 m.a.s.l.). We also used buffer analysis to quantify the proportion of forest cover and forest edge within 1 km of each transect, and calculated local vegetation density and complexity, as well as distance from edge, for each 100-meter transect segment (n = 70 segments). Flock composition data observed on a transect were used to calculate overall species richness and flock size as well as two indices of functional and phylogenetic diversity; we calculated the stadardized effect size (SES) of each measure to account for the correlation between these measures and species richness. We also provide the raw counts of each species for each flock composition. These data were used for the analyses in Jones and Robinson (2020).

Methods Study System and Sites

We conducted all fieldwork in subtropical humid forests located within the municipality of El Cairo, Valle del Cauca department in Colombia. The study region is part of the Serrania de los Paraguas in the Western Andes mountain range, a center of avian threatened species diversity and endemism within Colombia. The study landscape in this municipality consists of a patchwork of forest fragments embedded in a matrix of cattle pasture, regenerating scrub, and coffee farms. Within this landscape, we selected eight fragments representing a gradient in patch sizes (range 10 to 170 ha). Sites are in the same altitudinal belt (1900-2200 m.a.s.l.) and matrix type (cattle pasture) to control for effects of altitude and matrix type on flock size and composition. Within-patch disturbance is common in fragmented Andean forests in Colombia, particularly illegal selective logging, which in our landscape typically occurred as removal of select old-growth trees for lumber by landowners; logging histories varied considerably from historical to ongoing, and extensive to limited, both within and between patches. We established 500-meter transects through forest interior (n = 14 total transects) which were opportunistically placed on existing trails, at variable distances from the edge of the fragments. We further divided each transect into 100-meter segments to account for heterogeneity in vegetation structure within transects. We accounted for edge effects by measuring the distance to forest edge of each transect segment.

We stratified forest fragments into large (≥ 100 ha), medium (~30-50 ha), and small (≤ 20 ha) size categories and selected a minimum of two replicates of each; these represent the range of fragment sizes available in our study landscape. We also included a non-fragmented reference site (Reserva Natural Comunitária Cerro El Inglés, ~750 ha) connected to over 10,000 ha of continuous forest to the north and west along the spine of the Serranía de los Paraguas. We only selected fragments with primary or late-successional secondary forest; vegetation structure and canopy height varied substantially between patches based on intensities of selective logging and land-use histories (see above). Fragments were all separated by ≥ 100 meters to minimize among-patch movement of birds, and all transects in different fragments were at least 250 meters apart.

Transect Surveys for Mixed-species Flocks

We performed transect surveys for mixed-species flocks, adapted from Goodale et al. (2014), in forest fragments from June-August 2017 (boreal migrants absent) and January-March 2018 (boreal migrants present). Both sampling periods corresponded to a dry season in the Western Andes, which has a bimodal two-dry, two-wet seasonality pattern. For each transect, we spent two and a half sequential field days performing continuous transect surveys; we conducted surveys in small fragments, large fragments, and continuous forest sites in random order to avoid a temporal bias in sampling. Surveys were distributed across the morning (7:30-11:30) and evening (15:00-17:30) hours. Transects were walked slowly and continuously by 2-3 observers, including local birdwatchers familiar with all species (Harrison Jones present for all surveys); flocking birds were identified by both sight and sound. When we encountered a flock, we noted the time of day and transect segment in which it was observed and spent up to a maximum of 45 minutes characterizing it with 10x binoculars. At least 5 minutes were spent with each flock, following it if possible. Because detection of species in flocks was imperfect, we only included a flock observation in the analysis if we felt that at least 80% of the individuals were observed (e.g. after spending several minutes of continuous observation at the end of the survey period without observing a new species or individual); incomplete flock observations were not included in analyses. We feel that our survey methodology accurately described flock composition because birds moved and called frequently in flocks, leading to high detectability. We noted the start and end time of each survey, and the presence of incomplete flocks to calculate flock encounter rate. We also supplemented the transect surveys with data from flocks opportunistically observed on a transect while performing other fieldwork. Some flocks in the data set represent flock compositions recorded near but not on a transect; these compositions have no associated transect segment.

Calculation of Landscape-level Variables

    We obtained landscape-level variables for analyses using geographic information software (GIS) analysis in ArcGIS (ArcMap 10.3.1; Esri; Redlands, CA). To quantify landscape composition and configuration, we buffered each transect (n = 14) by 1 km; buffers extended from the entire length of the transect. We then calculated measures of landscape composition and configuration using a recent land-cover/use categorization made by the Corporación Autónoma Regional del Valle del Cauca, converted to a 25-m cell-size raster. To quantify landscape composition, we calculated percentages of the forest-cover type within each buffer using the ‘isectpolyrst’ tool in Geospatial Modelling Environment (version 0.7.4.0). We measured landscape configuration for each transect as edge density, or length of all forest edges (in meters) divided by total buffer area (in hectares). The distance to edge was calculated in meters for each 100-meter transect segment (n = 70) as the shortest straight-line distance between the center point of the segment and the nearest edge of the fragment. 

Vegetation Measurements and Principal Component Analysis

    We measured vegetation structure in each 100-m transect segment used for flock sampling. Vegetation measurements were made from June-August 2017; based on our observations of vegetation, we assumed variation between the two sampling periods was minimal. We used the sampling methodology of James and Shugart (1970), following the modifications made by Stratford and Stouffer (2013), and further modified to be used with belt transects. Broadly, the methodology comprises two components for every 100-meter transect segment: (1) the quantification of canopy cover, ground cover, canopy height, and foliage height diversity of vegetation using point sampling every 10 meters and (2) the quantification of shrub, vine, fern, palm, and tree fern and tree density using 3 meter-wide belt sampling.

For the point sampling, we measured eight variables at ten-meter intervals, for 10 points per 100-meter segment. As a measure of foliage height diversity along the transect, we noted the presence or absence of live vegetation at five heights: <0.5 m, >0.5–3 m, >3–10 m, >10–20 m, and >20 m. Above 3 meters, we used a rangefinder to determine heights while sighting through a tube with crosshairs. Canopy and ground cover were calculated to the nearest 1/8th of the field of view by sighting through a vertical canopy densiometer (GRS Densiometer, Geographic Resource Solutions, Arcata, CA). For each segment, we averaged values for canopy cover, and ground cover, and calculated the proportion of points at which vegetation was present for each height category. For the belt transect sampling, we surveyed vegetation along the same transects and calculated densities for each 100-m transect interval. We counted all shrubs, vines, ferns, tree ferns, and palms encountered on 1.5 meters to either side. Secondly, we counted all trees (woody vegetation > 2 m in height) within 1.5 meters of the transect and measured their diameter at breast height (DBH). Trees were later categorized into six DBH size classes for analysis: 1-7 cm, 8-15 cm, 16-23 cm, 24-30 cm, 31-50 cm, and > 50 cm. We additionally recorded the largest tree’s DBH.

    To quantify foliage height diversity, we calculated the Shannon Diversity Index of the proportion of points with vegetation present in each of the five height bands for each segment (n = 70 segments). To reduce redundancy and minimize correlation between variables, we (separately) ordinated our tree DBH and understory plant density data using principal component analysis (PCA: McGarigal et al. 2000) for each 100-meter transect segment. We column (Z score) standardized data prior to ordination to account for differences in the units of measurement and used the covariance matrix to run the PCA. The principal components were interpreted using the significance of the principal component loadings. The PCA was run in R (version 3.5.1) using the princomp function in the stats package. The Shannon Index was calculated using the diversity function of the vegan package

This Archaeological Survey of Ireland dataset is published from the database of the National Monuments Service Sites and Monuments Record (SMR). This dataset also can be viewed and interrogated through the online Historic Environment Viewer: https://heritagedata.maps.arcgis.com/apps/webappviewer/index.html?id=0c9eb9575b544081b0d296436d8f60f8 A Sites and Monuments Record (SMR) was issued for all counties in the State between 1984 and 1992. The SMR is a manual containing a numbered list of certain and possible monuments accompanied by 6-inch Ordnance Survey maps (at a reduced scale). The SMR formed the basis for issuing the Record of Monuments and Places (RMP) - the statutory list of recorded monuments established under Section 12 of the National Monuments (Amendment) Act 1994. The RMP was issued for each county between 1995 and 1998 in a similar format to the existing SMR. The RMP differs from the earlier lists in that, as defined in the Act, only monuments with known locations or places where there are believed to be monuments are included. The large Archaeological Survey of Ireland archive and supporting database are managed by the National Monuments Service and the records are continually updated and supplemented as additional monuments are discovered. On the Historic Environment viewer an area around each monument has been shaded, the scale of which varies with the class of monument. This area does not define the extent of the monument, nor does it define a buffer area beyond which ground disturbance should not take place – it merely identifies an area of land within which it is expected that the monument will be located. It is not a constraint area for screening – such must be set by the relevant authority who requires screening for their own purposes. This data has been released for download as Open Data under the DPER Open Data Strategy and is licensed for re-use under the Creative Commons Attribution 4.0 International licence. http://creativecommons.org/licenses/by/4.0 Please note that the centre point of each record is not indicative of the geographic extent of the monument. The existing point centroids were digitised relative to the OSI 6-inch mapping and the move from this older IG-referenced series to the larger-scale ITM mapping will necessitate revisions. The accuracy of the derived ITM co-ordinates is limited to the OS 6-inch scale and errors may ensue should the user apply the co-ordinates to larger scale maps. Records that do not refer to 'monuments' are designated 'Redundant record' and are retained in the archive as they may relate to features that were once considered to be monuments but which on investigation proved otherwise. Redundant records may also refer to duplicate records or errors in the data structure of the Archaeological Survey of Ireland. This dataset is provided for re-use in a number of ways and the technical options are outlined below. For a live and current view of the data, please use the web services or the data extract tool in the Historic Environment Viewer. The National Monuments Service also provide an Open Data snapshot of its national dataset in CSV as a bulk data download. Users should consult the National Monument Service website https://www.archaeology.ie/ for further information and guidance on the National Monument Act(s) and the legal significance of this dataset. Open Data Bulk Data Downloads (version date: 23/08/2023) The Sites and Monuments Record (SMR) is provided as a national download in Comma Separated Value (CSV) format. This format can be easily integrated into a number of software clients for re-use and analysis. The Longitude and Latitude coordinates are also provided to aid its re-use in web mapping systems, however, the ITM easting/northings coordinates should be quoted for official purposes. ERSI Shapefiles of the SMR points and SMRZone polygons are also available The SMRZones represent an area around each monument, the scale of which varies with the class of monument. This area does not define the extent of the monument, nor does it define a buffer area beyond which ground disturbance should not take place – it merely identifies an area of land within which it is expected that the monument will be located. It is not a constraint area for screening – such must be set by the relevant authority who requires screening for their own purposes. GIS Web Service APIs (live views): For users with access to GIS software please note that the Archaeological Survey of Ireland data is also available spatial data web services. By accessing and consuming the web service users are deemed to have accepted the Terms and Conditions. The web services are available at the URL endpoints advertised below: SMR; https://services-eu1.arcgis.com/HyjXgkV6KGMSF3jt/arcgis/rest/services/SMROpenData/FeatureServer SMRZone; https://services-eu1.arcgis.com/HyjXgkV6KGMSF3jt/arcgis/rest/services/SMRZoneOpenData/FeatureServer Historic Environment Viewer - Query Tool The "Query" tool can alternatively be used to selectively filter and download the data represented in the Historic Environment Viewer. The instructions for using this tool in the Historic Environment Viewer are detailed in the associated Help file: https://www.archaeology.ie/sites/default/files/media/pdf/HEV_UserGuide_v01.pdf