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13 results for “ArcGIS”
ArcGIS Map Packages and GIS Data for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al. (2019)
<p><strong>ArcGIS Map Packages and GIS Data for Gillreath-Brown, Nagaoka, and Wolverton (2019)</strong></p> <p>**When using the GIS data included in these map packages, please cite all of the following:</p> <blockquote> <p>Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, 2019. PLoSONE 14(8):e0220457. <a href="http://doi.org/10.1371/journal.pone.0220457">http://doi.org/10.1371/journal.pone.0220457</a></p> <p>Gillreath-Brown, Andrew, Lisa Nagaoka, and Steve Wolverton. ArcGIS Map Packages for: A Geospatial Method for Estimating Soil Moisture Variability in Prehistoric Agricultural Landscapes, Gillreath-Brown et al., 2019. Version 1. Zenodo. <a href="https://doi.org/10.5281/zenodo.2572018">https://doi.org/10.5281/zenodo.2572018</a></p> </blockquote> <p><strong>OVERVIEW OF CONTENTS</strong></p> <p>This repository contains map packages for Gillreath-Brown, Nagaoka, and Wolverton (2019), as well as the raw digital elevation model (DEM) and soils data, of which the analyses was based on. The map packages contain all GIS data associated with the analyses described and presented in the publication. The map packages were created in ArcGIS 10.2.2; however, the packages will work in recent versions of ArcGIS. (Note: I was able to open the packages in ArcGIS 10.6.1, when tested on February 17, 2019). The primary files contained in this repository are:</p> <ul> <li>Raw DEM and Soils data <ul> <li>Digital Elevation Model Data (Map services and data available from U.S. Geological Survey, National Geospatial Program, and can be downloaded from the <a href="https://viewer.nationalmap.gov/basic/">National Elevation Dataset</a>) <ul> <li><strong>DEM_Individual_Tiles</strong>: Individual DEM tiles prior to being merged (1/3 arc second) from USGS National Elevation Dataset.</li> <li><strong>DEMs_Merged</strong>: DEMs were combined into one layer. Individual watersheds (i.e., Goodman, Coffey, and Crow Canyon) were clipped from this combined DEM. </li> </ul> </li> <li> Soils Data (Map services and data available from <a href="https://data.nal.usda.gov/dataset/natural-resources-conservation-service-web-soil-survey">Natural Resources Conservation Service Web Soil Survey</a>, U.S. Department of Agriculture) <ul> <li><strong>Animas-Dolores_Area_Soils</strong>: Small portion of the soil mapunits cover the northeastern corner of the Coffey Watershed (CW).</li> <li><strong>Cortez_Area_Soils</strong>: Soils for Montezuma County, encompasses all of Goodman (GW) and Crow Canyon (CCW) watersheds, and a large portion of the Coffey watershed (CW).</li> </ul> </li> </ul> </li> <li>ArcGIS Map Packages <ul> <li><strong>Goodman_Watershed_Full_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the full Goodman Watershed (GW).</li> <li><strong>Goodman_Watershed_Mesa-Only_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the mesa-only Goodman Watershed.</li> <li><strong>Crow_Canyon_Watershed_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the Crow Canyon Watershed (CCW).</li> <li><strong>Coffey_Watershed_SMPM_Analysis</strong>: Map Package contains the necessary files to rerun the SMPM analysis on the Coffey Watershed (CW).</li> </ul> </li> </ul> <p>For additional information on contents of the map packages, please see see "Map Packages Descriptions" or open a map package in ArcGIS and go to "properties" or "map document properties."</p> <p><strong>LICENSES</strong></p> <p>Code: <a href="http://opensource.org/licenses/MIT">MIT</a> year: 2019 <br> Copyright holders: Andrew Gillreath-Brown, Lisa Nagaoka, and Steve Wolverton</p> <p><strong>CONTACT</strong></p> <p><strong>Andrew Gillreath-Brown, PhD Candidate, RPA</strong><br> <a href="https://anthro.wsu.edu/">Department of Anthropology</a>, Washington State University<br> <a href="mailto:andrew.brown1234@gmail.com">andrew.brown1234@gmail.com</a> – Email<br> <a href="https://andrewgillreathbrown.wordpress.com/">andrewgillreathbrown.wordpress.com</a> – Web</p>
Example data map compressed with ISO29500-2 with 3 entry point for ArcGIS, MiraMon and OWS context file.
<p>A simple map consisting of a 1:1 000 000 country boundaries vector file, produced by the FAO (United Nations – FAOStat; geodata.grid.unep.ch/options.php?selectedID=2135) on top of a 5’ digital elevation model raster file (produced by the NOAA and NGDC; geodata.grid.unep.ch/options.php?selectedID=1414). Data has been obtained from the UNEP EDE Data Portal (UNEP 2013). Vector file is a Shapefile (a de facto standard) (ESRI 1998), while the raster file consists of either a raw signed 16-bit data or a long known TIFF file (Adobe 1992, Perkins 1995). Metadata and symbolization files are included. OPC specifies how to explicitly relate different parts using .rels files. These files are XML files with the same name as that of its respective source part, adding “.rels” and placed in a “rels” folder. Each of these files lists the target parts related to its source and the semantics of this relation.</p> <p>OPC can define entry points to the data by listing them in a “.rels” part in the root “rels” folder. In this file, three map files: for the ESRI software a world.mxd map, for the MiraMon software a world.mmm map, and a world.xml map in the form of an atom file following the new Web Service common standard (OGC OWS) context document. A geospatial application reading the package will determine which entry part it better supports to start recovering the data.</p>
Text-fig. 1. Geographic position of the beaver-bearing sites discussed in this paper. Red triangles – records of Castor, blue dots – records of Trogontherium. Bilz II – Bilzingsleben II, Ehr – Weimar-Ehringsdorf, Mosb 2 – Mosbach 2, Taub – Weimar-Taubach, Teg – Tegelen. (This map was created using ArcGIS® software by Esri. ArcGIS® and ArcMap™ are the intellectual property of Esri and are used herein under license. Copyright © Esri). in Mortality Profiles Of Castor And Trogontherium (Mammalia: Rodentia, Castoridae), With Notes On The Site Formation Of The Mid-Pleistocene Hominin Locality Bilzingsleben Ii (Thuringia, Central Germany)
Text-fig. 1. Geographic position of the beaver-bearing sites discussed in this paper. Red triangles – records of Castor, blue dots – records of Trogontherium. Bilz II – Bilzingsleben II, Ehr – Weimar-Ehringsdorf, Mosb 2 – Mosbach 2, Taub – Weimar-Taubach, Teg – Tegelen. (This map was created using ArcGIS® software by Esri. ArcGIS® and ArcMap™ are the intellectual property of Esri and are used herein under license. Copyright © Esri).
Information System for Cycling Navigation for Aveiro, Portugal - Database and ArcGIS Graph
<p>This data was collected within the scope of a master's thesis in mechanical engineering from University of Aveiro, Portugal. The goal was to develop a information system for cycling navigation for an urban area of the portuguese city of Aveiro. Therefore, data collection was achieved through an instrumented alluminium bicycle equipped with a 1) GNSS Data Logger, 2) wireless heart rate recorder device and 3) video camera. 120 km were covered to collect 8h of video and about 100000 second by second data points, which were analyzed and organized through an 449 link-map built in ArcGIS. Seven different bikeability indicators were built: travel time, energy expenditure, effort distribution, infrastructure performance, safety, comfort and emissions hotspots of two pollutants (CO2 and NOx).</p> <p>The spreadsheet dataset is divided into three sections:</p> <ol> <li>Final Attributes - all the collected and treated data of each link, both used in the indicators and in another set for system' network analysis;</li> <li>Attributes Statistics - reveals some statistics of the weighted final atrributes achieved, such as mean, standard error, standard deviation, range, confidence level, among others;</li> <li>Link Type Statistics - reveals the distribution of the different link-types along the map and the respective average speeds recorded.</li> </ol> <p>In order to associate the FID numbers with the respective links, the developed ArcGIS graph was also made available. </p>
BSc Project 2022 Ivo van Middelkoop data Excel and ArcGIS
<p>This online repository consists if the data used for the BSc thesis/project of Ivo van Middelkoop. It consists of a ArcGIS Project file (ArcGIS Pro BSc Project 2022 Ivo van Middelkoop.aprx) and an Excel worksheet file (Excel data BSc Project 2022 Ivo van Middelkoop.xlsx). The ArcGIS Project file was used to create shapefiles through a sea-level fluctuation model to make maps about paleo coastline reconstructions. The Excel worksheet file was used to analyse the output data coming from the ArcGIS Project file. The topic of this BSc project: How did the sea-level rise following the Late Pleistocene impact the connectivity over time between Sumatra and Borneo? </p> <p>This repository is openly accessible to everyone. The copyright is owned by Ivo van Middelkoop and Dr. Kenneth F. Rijsdijk</p>
Louisiana Dataset for ArcGIS
<p><strong>Louisiana Dataset for ArcGIS</strong><br> This geodatabase contains statewide raster and vector data for Louisiana, USA in NAD 1983 / UTM zone 15N with <a href="https://epsg.io/26915">EPSG code 26915</a>. Unzip the archive and open in ArcGIS.</p> <p><strong>Data Sources</strong></p> <ul> <li>USGS National Elevation Dataset (NED)</li> <li>USGS National Landcover Dataset (NLCD)</li> <li>USGS National Hydrography Dataset (NHD)</li> <li>USGS National Transportation Dataset (NTD)</li> <li>USGS National Boundary Dataset (NBD)</li> </ul> <p><strong>License</strong><br> This dataset is licensed under the <a href="https://opendatacommons.org/licenses/pddl/index.html">ODC Public Domain Dedication and License 1.0 (PDDL)</a> by Brendan Harmon.</p>
New Orleans Dataset for ArcGIS
<p><strong>New Orleans Dataset for ArcGIS</strong><br> This geodatabase contains citywide raster and vector data for New Orleans, Louisiana, USA in the North American Datum of 1983 (NAD 83) / Louisiana South State Plane Feet with <a href="https://epsg.io/3452">EPSG code 3452</a>. Unzip the archive and open in ArcGIS.</p> <p><strong>Data Sources</strong></p> <ul> <li><a href="https://coast.noaa.gov/htdata/lidar2_z/geoid12b/data/6350/">U.S. Army Corps of Engineers 2012 Lidar Survey of New Orleans</a></li> <li><a href="https://datadriven.nola.gov/open-data/">New Orleans Open Data</a></li> </ul> <p><strong>License</strong><br> This dataset is licensed under the <a href="https://opendatacommons.org/licenses/pddl/index.html">ODC Public Domain Dedication and License 1.0 (PDDL)</a> by Brendan Harmon.</p>
Datasets for predicting river bakline using ML and ArcGIS
<p>These datasets are used for predicting the Padma River bankline using different machine learning models, where ArcGIS was integrated. The coordinates of the river bankline were recorded at every 400 meters for different years, using offsets of 400 meters. The coordinates, years, and locations served as input variables for the models.</p>
ArcGIS GDB for CPBS Report 23UCB01 - A Context-sensitive Street Classification Framework for Speed Limit Setting
<p>ArcGIS .gdb geodatabase files and a data dictionary in Excel format.</p>
Governor's Island Dataset for ArcGIS
<p><strong>Governor's Island Dataset for ArcGIS</strong><br> This archive contains an ArcGIS Pro project with a geodatabase of raster and vector data for Governor's Island, New York City, USA. The SRS is NAD83 / New York Long Island (ftUS) with the EPSG code 2263.</p>
The Hills of Governor's Island Dataset for ArcGIS
<p>This archive contains an ArcGIS Pro project with a geodatabase of raster and vector data for the Hills region of Governor's Island, New York City, USA. The SRS is NAD83 / New York Long Island (ftUS) with the EPSG code 2263.</p>
An ArcGIS Pro workflow to extract vegetation indices from aerial imagery of small‐plot turfgrass research
<p>Collection of multispectral imagery from an aerial sensor is a means to obtain plot-level vegetation index (VI) values; however, post-capture image processing and analysis remain a challenge for small-plot researchers. An ArcGIS Pro workflow of two task items was developed with established routines and commands to extract plot-level VI values (Normalized Difference VI, Ratio VI, and Chlorophyll Index-Red Edge) from multispectral aerial imagery of small-plot turfgrass experiments. Users can access and download task item(s) from the ArcGIS Online platform for use in ArcGIS Pro. The workflow standardizes the processing of aerial imagery to ensure repeatability between sampling dates and across site locations. A guided workflow saves time with assigned commands, ultimately allowing users to obtain a table with plot descriptions and index values within a .csv file for statistical analysis. The workflow was used to analyze aerial imagery from a small-plot turfgrass research study evaluating herbicide effects on St. Augustinegrass [<em>Stenotaphrum secundatum</em> (Walt.) Kuntze] grow-in. To compare methods, index values were extracted from the same aerial imagery by TurfScout, LLC and were obtained by handheld sensor. Index values from the three methods were correlated with visual percentage cover to determine the sensitivity (i.e., the ability to detect differences) of the different methodologies.</p>
An ArcGIS Pro workflow to extract vegetation indices from aerial imagery of small‐plot turfgrass research
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.