This ArcGIS model inserts a file name into a feature class attribute table. The tool allows an user to identify features by a field that reference the name of the original file. It is useful when an user have to merge multiple feature classes and needs to identify which layer the features come from.

This permit conversion tool converts ePermit .xls files to quarter-quarter or lat/long locations. Also included is a public lands survey geodatabase necessary to run the POU tool. This Model Builder toolset is available for ArcGIS 10.1-5. The March 2018 update provided here tests for field types and processes the fields accordingly.

The Habitat Suitability Analysis was built using ArcGIS Pro's ModelBuilder tool. This program does not have an option to save the model's inputs as a relative file path. As a result, the model may not run because it's searching for each layer's original file path. If this happens, we have included a file titled Habitat_Suitability_Analysis_Script that outlines the processes we used to build the model. This submission contains three folders and three supplemental files. The folder titled "Data" includes all of the raw data and data input in the Habitat Suitability Analysis. The folder titled "Scripts" describes the steps to build the Habitat Suitability Analysis model in ArcGIS Pro. The Results folder contains the Habitat Suitability Analysis model and the data that was input into the model. The supplemental files are a file titled "Dryad_Folder_Contents" which describes the contents of every folder in this submission, and a file titled "Habitat_Suitability_Analysis_README" which contain...

The Gravity model toolbox, a programmed ArcGIS tool to map and prioritize the potential corridors of urban green space.

Portions of the world's interior, such as central Asia are extremely secluded from the ocean and are more than 2,000 km from the nearest coast. Distance to coast can be used in asset management and modeling project costs. Phenomenon Mapped: Distance to coastUnits: KilometersCell Size: 655.9259912 metersSource Type: DiscretePixel Type: Signed integerSpatial Reference: World Equidistant CylindricalMosaic Projection: Web Mercator Auxiliary SphereExtent: GlobalSource: EsriPublication Date: 2015ArcGIS Server URL: https://oceans2.arcgis.com/arcgis/The Distance to Coast layer was calculated by Esri using the Euclidean Distance Tool in ArcMap and the Esri Country Boundaries layer.What can you do with this layer?Visualization: This layer can be used for visualization online in web maps and in ArcGIS Desktop.Analysis: This layer can be used as an input to geoprocessing tools and model builder.Raster Functions: Unit Conversion – kilometers to miles, Unit Conversion - kilometers to nautical miles, Cartographic Renderer, and Classified Renderer.This layer is part of the Living Atlas of the World that provides access to thousands of beautiful and authoritative layers, web maps, and apps.

Includes settings for a CPI (Crop Production Index) Generation toolbox and instructions on how to alter it to be usable for your county.This documentation assumes the user has a basic understanding of ArcGIS, its tools, and its data structure, Model Builder. Arcade and Python Scripting used here will be covered in the documentation.

This dataset is a compilation of existing and new publicly-available geoscience data that were used to conduct a geothermal play fairway analysis (PFA) in north-western Argentina in the Jujuy and Salta provinces. The 'Model_Input_Datasets' folder includes the original ArcGIS shapefiles and rasters that were used to build the geological favorability models for heat, permeability and fluid. Detailed metadata for each dataset (e.g. provenance; use constraints etc.) can be viewed for each file in ArcCatalog. The 'Area_One' shapefile represents the study area boundary that was used to define the processing extent for the PFA models. The favorability models were built using the ModelBuilder tool in ESRI ArcGIS (this model was run using ESRI ArcMap version 10.7.1). The 'Python_Scripts' folder includes python scripts for building each of the four favorability models (heat, permeability, fluid and overall geothermal favorability). The 'ProcessingNotes_for_PFA_model_development' file (.xlsx or .pdf format) includes a description of the various steps used to weight individual data attribute fields, data layers themselves, and overall model development. This file complements the python scripts.

This dataset accompanies a paper submitted to Geothermics by Lindsey et al., 2021, 'Geothermal play fairway analysis in north-western Argentina'.

Retirement Notice: This item is in mature support as of April 2024 and will be retired in December 2026. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.Sea Surface Temperature is a key climate and weather measurement used for weather prediction, ocean forecasts, tropical cyclone forecasts, and in coastal applications such as fisheries, pollution monitoring and tourism. El Niño and La Niña are two examples of climate events which are forecast through the use of sea surface temperature maps. The Naval Oceanographic Office sea surface temperature dataset is calculated from satellite-based microwave and infrared imagery. These data are optimally interpolated to provide a daily, global map of the midday (12:00 pm) sea surface temperature. Learn more about the source data. Phenomenon Mapped: Sea Surface TemperatureUnits: Degrees CelsiusTime Interval: Daily Time Extent: 2008/04/01 12:00:00 UTC to presentCell Size: 11 kmSource Type: ContinuousPixel Type: Floating PointData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: Global OceansSource: Naval Oceanographic OfficeArcGIS Server URL: https://earthobs2.arcgis.com/arcgis Time: This is a time-enabled layer. It shows the average sea surface temperature during the map's time extent, or if time animation is disabled, a time range can be set using the layer's multidimensional settings. The map shows the average of all days in the time extent. Minimum temporal resolution is one day; maximum is one month. What can you do with this layer? Visualization: This layer can be used for visualization online in web maps and in ArcGIS Desktop. Analysis: This layer can be used as an input to geoprocessing tools and model builder. Units are in degrees Celsius, and there is a processing template to convert pixels to Fahrenheit. Do not use this layer for analysis while the Cartographic Renderer processing template is applied.

Marine life have certain thresholds for temperature that they can live in. For instance, deep-water corals have been recorded in temperatures of -1⁰C. Seafloor temperatures generally decrease with increasing depth. Phenomenon Mapped: Seafloor temperatureUnits: Degrees CelsiusCell Size: 30 arc seconds, approximately 1 kmSource Type: DiscretePixel Type: Signed integerSpatial Reference: GCS_WGS_1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: Global oceansSource: Marine Conservation Institute (MCI)Citation: Boyer TP, Levitus S, Garcia HE, Locamini RA, Stephens C, et al. (2005) Objective analyses of annual, seasonal, and monthly temperature and salinity for the World Ocean on a 0.25° grid. International Journal of Climatology 25: 931–945.Publication Date: 2005ArcGIS Server URL: https://oceans2.arcgis.com/arcgis/The Marine Conservation Institute used this dataset as an input to a predictive habitat model documented in the publication Global Habitat Suitability for Framework-Forming Cold-Water Corals.What can you do with this layer?Visualization: This layer can be used for visualization online in web maps and in ArcGIS Desktop.Analysis: This layer can be used as an input to geoprocessing tools and model builder.Raster Functions: Unit Conversion – Celsius to Fahrenheit, Unit Conversion – Celsius to Kelvin, and Cartographic Renderer - see this blog for more information.This layer is part of the Living Atlas of the World that provides access to thousands of beautiful and authoritative layers, web maps, and apps.

There is a lack of understanding on the different types of dynamics of building stocks, in real life and in models. Moreover, there is now a particular interest in the embodied impacts of construction materials, since with the increasing efficiency of buildings operation, embodied impacts gain more importance in the overall building life cycle. This critical review wants to advance the understanding of the type of dynamics, methods, and tools used. The well-known IPAT equation is adapted for building stocks and three dynamics are defined: spatial, evolutionary temporal, and spatial-cohort dynamic. A framework is defined that can help researchers choose a method, tool, and dynamics of input parameters depending on their research goal, case study, and data. Moreover, generally valid conclusions are drawn, including MFA is useful to model spatially dynamic material flows; GIS is needed to include spatial dynamics. Retrofit, compared to construction and demolition, is understudied and usually analyzed through top-down methods. Material intensity and emission intensity are rarely modeled in a dynamic way. Overall, scholars seem to perform increasingly data intensive and complex studies tailored to a specific case study. However, there are big differences in the quality depending on the dynamic of input parameters.

Ocean depth plays an important role in the distribution and abundance of living organisms and has important implications for shipping and offshore development projects such as wind power and oil extraction.Phenomenon Mapped: Seafloor depth, bathymetryUnits: Meters below sea levelCell Size: 30 arc seconds, approximately 1 kmSource Type: DiscretePixel Type: Signed integerSpatial Reference: GCS_WGS_1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: Global oceansSource: Marine Conservation Institute (MCI)Citation: Becker JJ, Sandwell DT, Smith WHF, Braud J, Binder B, et al. (2009) Global bathymetry and elevation data at 30 arc seconds resolution: SRTM30_PLUS. Marine Geodesy 32: 355–371.Publication Date: 2009ArcGIS Server URL: https://oceans2.arcgis.com/arcgis/The Marine Conservation Institute used this dataset as an input to a predictive habitat model documented in the publication Global Habitat Suitability for Framework-Forming Cold-Water Corals.The source data is available from the Scripps Institution of Oceanography Satellite Geodesy Webpage.What can you do with this layer?Visualization: This layer can be used for visualization online in web maps and in ArcGIS Desktop.Analysis: This layer can be used as an input to geoprocessing tools and model builder.Raster Functions: Unit Conversion – meters to feet, Cartographic Renderer, Aspect, Slope, and Hillshade - see this blog for more information.This layer is part of the Living Atlas of the World that provides access to thousands of beautiful and authoritative layers, web maps, and apps.

This deep learning model is used to extract building footprints from high-resolution (10–40 cm) imagery. Building footprint layers are useful in preparing base maps and analysis workflows for urban planning and development, insurance, taxation, change detection, infrastructure planning, and a variety of other applications.Digitizing building footprints from imagery is a time-consuming task and is commonly done by digitizing features manually. Deep learning models have a high capacity to learn these complex workflow semantics and can produce superior results. Use this deep learning model to automate this process and reduce the time and effort required for acquiring building footprints.Using the modelFollow the guide to use the model. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Fine-tuning the modelThis model can be fine-tuned using the Train Deep Learning Model tool. Follow the guide to fine-tune this model.Input8-bit, 3-band high-resolution (10–40 cm) imagery.OutputFeature class containing building footprints.Applicable geographiesThe model is expected to work in Africa and gives the best results in Uganda and Tanzania.Model architectureThe model uses the MaskRCNN model architecture implemented using ArcGIS API for Python.Accuracy metricsThe model has an average precision score of 0.786.Sample resultsHere are a few results from the model. To view more, see this story.

The purpose of the Tool is to use CPI, CER (Crop Equivalency Rating) or NCCPI (National Commodity Crop Productivity Index) to assess tax values accurately and fairly, regarding tillable acres of land. The steps in this document will focus solely on CPI values, but can be modified to use CER or NCCPI data. This project was created by counties for counties to use as a no cost solution for assessing tax values to tillable acres.This documentation assumes the user has a basic understanding of ArcGIS, its tools, and its data structure, Model Builder, and basic Arcade and Python Scripting.

This service is available to all ArcGIS Online users with organizational accounts. For more information on this service, including the terms of use, visit us online at http://goto.arcgisonline.com/earthobs2/REMSS_SeaSurfaceTempSea Surface Temperature is a key climate and weather measurement used for weather prediction, ocean forecasts, tropical cyclone forecasts, and in coastal applications such as fisheries, pollution monitoring and tourism. El Niño and La Niña are two examples of climate events which are forecast through the use of sea surface temperature maps. The Naval Oceanographic Office sea surface temperature dataset is calculated from satellite-based microwave and infrared imagery. These data are optimally interpolated to provide a daily, global map of the midday (12:00 pm) sea surface temperature. Learn more about the source data. Phenomenon Mapped: Sea Surface TemperatureUnits: Degrees CelsiusTime Interval: DailyTime Extent: 2008/04/01 12:00:00 UTC to presentCell Size: 11 kmSource Type: ContinuousPixel Type: Floating PointData Projection: GCS WGS84Mosaic Projection: Web Mercator Auxiliary SphereExtent: Global OceansSource: Naval Oceanographic OfficeUpdate Cycle: SporadicArcGIS Server URL: http://earthobs2.arcgis.com/arcgisTime: This is a time-enabled layer. It shows the average sea surface temperature during the map's time extent, or if time animation is disabled, a time range can be set using the layer's multidimensional settings. The map shows the average of all days in the time extent. Minimum temporal resolution is one day; maximum is one month.What can you do with this layer?Visualization: This layer can be used for visualization online in web maps and in ArcGIS Desktop.Analysis: This layer can be used as an input to geoprocessing tools and model builder. Units are in degrees Celsius, and there is a processing template to convert pixels to Fahrenheit. See this Esri blog post for more information on how to use this layer in your analysis. Do not use this layer for analysis while the Cartographic Renderer processing template is applied.This layer is part of the Living Atlas of the World that provides an easy way to explore the earth observation layers and many other beautiful and authoritative maps on hundreds of topics.

Building polygons were created in February 2013 by Geoscience Australia by manually digitising the outline of each building off the 2011 orthophotography. Digitisation was done from scratch off the 2011 orthophotography within Quantum GIS. Using the ArcMap 'zonal statistics' tool the minimum, mean and maximum heights were found for each building polygon from the 2011 digital elevation model and the 2011 digital surface model (DSM). This information was then joined to the building polygon attribute table. To find the building height from ground to roof, the difference between the Mean DSM and mean DEM was calculated and added as a field to the attribute table. To find the maximum height of each building the difference between the Maximum DSM and Mean DEM was calculated. Polygon area, perimeter, and x and y coordinates of each building were also attached as attributes. Accuracy is high as the layer was based on the 2011 orthophotography. Error may have been introduced through the digitisation process. Building lean in the orthophotography may also contribute to polygons which are slightly inaccurately placed. Height attribute accuracy is inaccurate for building polygons which have tree cover above them, as the tree elevation would influence the digital surface model. Particularly the Max_height field may include tree heights rather than building heights in some cases. Attribute accuracy could be improved by using the raw 2011 lidar data (.las files) which are classified at 'buildings' to attach heights. This method was tested and was extremely time consuming - only the height_max field was significantly improved. Disclaimer

Portions of the world's oceans are extremely remote including areas in the South Pacific that are more the 2,500 km from the nearest land. Distance from shore can be used in asset management, modeling project costs, and as an index of human influence. Phenomenon Mapped: Distance from shoreUnits: KilometersCell Size: 655.9259912 metersSource Type: DiscretePixel Type: Signed integerSpatial Reference: World Equidistant CylindricalMosaic Projection: Web Mercator Auxiliary SphereExtent: Global oceansSource: EsriPublication Date: 2015ArcGIS Server URL: https://oceans2.arcgis.com/arcgis/The Distance from Shore layer was calculated by Esri using the Euclidean Distance Tool in ArcMap and the Esri Country Boundaries layer.What can you do with this layer?Visualization: This layer can be used for visualization online in web maps and in ArcGIS Desktop.Analysis: This layer can be used as an input to geoprocessing tools and model builder.Raster Functions: Unit Conversion – kilometers to miles, Unit Conversion - kilometers to nautical miles, Cartographic Renderer, and Classified Renderer see this blog for more information.This layer is part of the Living Atlas of the World that provides access to thousands of beautiful and authoritative layers, web maps, and apps.

SONG, Carol X., Rosen Center for Advanced Computing, Purdue University, 155 South Grant Street, Young Hall, West Lafayette, IN 47907

Science gateways are becoming an integral component of modern collaborative research. They find widespread adoption by research groups to share data, code and tools both within a project and with the broader community. Sustainability beyond initial funding is a significant challenge for a science gateway to continue to operate, update and support the communities it serves. MyGeoHub.org is a geospatial science gateway powered by HUBzero. MyGeoHub employs a business model of hosting multiple research projects on a single HUBzero instance to manage the gateway operations more efficiently and sustainably while lowering the cost to individual projects. This model allows projects to share the gateway’s common capabilities and the underlying hardware and other connected computing resources, and continued maintenance of their sites even after the original funding has run out allowing time for acquiring new funding. MyGeoHub has hosted a number of projects, ranging from hydrologic modeling and data sharing, plant phenotyping, global and local sustainable development, climate variability impact on crops, and most recently, modeling of industry processes to improve reuse and recycling of materials. The shared need to manage, visualize and process geospatial data across the projects has motivated the Geospatial Data Building Blocks (GABBs) development funded by NSF DIBBs. GABBs provides a “File Explorer” type user interface for managing geospatial data (no coding is needed), a builder for visualizing and exploring geo-referenced data without coding, a Python map library and other toolkits for building geospatial analysis and computational tools without requiring GIS programming expertise. GABBs can be added to an existing or new HUBzero site, as is the case on MyGeoHub. Teams use MyGeoHub to coordinate project activities, share files and information, publish tools and datasets (with DOI) to provide not only easy access but also improved reuse and reproducibility of data and code as the interactive online tools and workflows can be used without downloading or installing software. Tools on MyGeoHub have also been used in courses, training workshops and summer camps. MyGeoHub is supporting more than 8000 users annually.

Salinity is the quantity of dissolved salt in water. Marine life have a limited range of salinity that they can live in. Open ocean seawater typically has a salinity of 32 to 37. Temperature and salinity characteristics help determine origin of water masses.Phenomenon Mapped: Seafloor SalinityUnits: g/kg - practical salinity scaleCell Size: 30 arc seconds, approximately 1 kmSource Type: DiscretePixel Type: Unsigned integerSpatial Reference: GCS_WGS_1984Mosaic Projection: Web Mercator Auxiliary SphereExtent: Global oceansSource: Marine Conservation Institute (MCI)Citation: Boyer TP, Levitus S, Garcia HE, Locamini RA, Stephens C, et al. (2005) Objective analyses of annual, seasonal, and monthly temperature and salinity for the World Ocean on a 0.25° grid. International Journal of Climatology 25: 931–945.Publication Date: 2005ArcGIS Server URL: https://oceans2.arcgis.com/arcgis/The Marine Conservation Institute used this dataset as an input to a predictive habitat model documented in the publication Global Habitat Suitability for Framework-Forming Cold-Water Corals.What can you do with this layer?Visualization: This layer can be used for visualization online in web maps and in ArcGIS Desktop.Analysis: This layer can be used as an input to geoprocessing tools and model builder.Raster Functions: Cartographic Renderer - see this blog for more information.This layer is part of the Living Atlas of the World that provides access to thousands of beautiful and authoritative layers, web maps, and apps.

Springsheds were delineated with geospatial tools in an Esri Model Builder environment using 65 potentiometric surfaces of the Upper Floridan Aquifer from 1976 through 2010. These springsheds will be used to evaluate the impact of groundwater withdrawals on spring flows, springsheds, defined as groundwater contributing areas.

This dataset was created using the spring 2023 ortho imagery of Lake County, Indiana and the Extract Features Using AI Models geoprocessing tool in ArcGIS Pro 3.5. The model used was the ESRI provided Building Footprint Extraction - USA. The polygon regularization method used was Right Angles with a tolerance of 1 meters. After the layer was created, a small amount of QA/QC was done manually by the Lake County Surveyor's Office. This work was mostly done along the major highways within Lake County. In an effort to eliminate 'noise' polygons the decision was made to delete all building footprints that were less than 151 square feet. Because of this many sheds and garages will not be included in this dataset.