This resource was created by Esri Canada Education and Research. To browse our full collection of higher-education learning resources, please visit https://hed.esri.ca/resourcefinder/.Lidar data have become an important source for detailed 3D information for cities as well as forestry, agriculture, archaeology, and many other applications. Topographic lidar surveys, which are conducted by airplane, helicopter or drone, produce data sets that contain millions or billions of points. This can create challenges for storing, visualizing and analyzing the data. In this tutorial you will learn how to create a LAS Dataset and explore the tools available in ArcGIS Pro for visualizing lidar data.To download the tutorial and data folder, click the Open button to the top right. This will download a ZIP file containing the tutorial documents and data files.Software & Solutions Used: ArcGIS Pro Advanced 3.x. Last tested with ArcGIS Pro version 3.3. Time to Complete: 30 - 60 minsFile Size: 337 MBDate Created: August 2020Last Updated: March 2024

This resource was created by Esri Canada Education and Research. To browse our full collection of higher-education learning resources, please visit https://hed.esri.ca/resourcefinder/.

In their native form as a point cloud, lidar data can present challenges for visualization and interpretation. Converting the data into raster data can help with quality assurance, identifying patterns in the data, and comparing the lidar data to data from other sources, such as satellite imagery. In this tutorial, you will explore ways to convert lidar data in LAS Datasets to raster products.

Deprecation notice: This tool is deprecated because this functionality is now available with out-of-the-box tools in ArcGIS Pro. The tool author will no longer be making further enhancements or fixing major bugs.Use Add GTFS to a Network Dataset to incorporate transit data into a network dataset so you can perform schedule-aware analyses using the Network Analyst tools in ArcMap.After creating your network dataset, you can use the ArcGIS Network Analyst tools, like Service Area and OD Cost Matrix, to perform transit/pedestrian accessibility analyses, make decisions about where to locate new facilities, find populations underserved by transit or particular types of facilities, or visualize the areas reachable from your business at different times of day. You can also publish services in ArcGIS Server that use your network dataset.The Add GTFS to a Network Dataset tool suite consists of a toolbox to pre-process the GTFS data to prepare it for use in the network dataset and a custom GTFS transit evaluator you must install that helps the network dataset read the GTFS schedules. A user's guide is included to help you set up your network dataset and run analyses.Instructions:Download the tool. It will be a zip file.Unzip the file and put it in a permanent location on your machine where you won't lose it. Do not save the unzipped tool folder on a network drive, the Desktop, or any other special reserved Windows folders (like C:\Program Files) because this could cause problems later.The unzipped file contains an installer, AddGTFStoaNetworkDataset_Installer.exe. Double-click this to run it. The installation should proceed quickly, and it should say "Completed" when finished.Read the User's Guide for instructions on creating and using your network dataset.System requirements:ArcMap 10.1 or higher with a Desktop Standard (ArcEditor) license. (You can still use it if you have a Desktop Basic license, but you will have to find an alternate method for one of the pre-processing tools.) ArcMap 10.6 or higher is recommended because you will be able to construct your network dataset much more easily using a template rather than having to do it manually step by step. This tool does not work in ArcGIS Pro. See the User's Guide for more information.Network Analyst extensionThe necessary permissions to install something on your computer.Data requirements:Street data for the area covered by your transit system, preferably data including pedestrian attributes. If you need help preparing high-quality street data for your network, please review this tutorial.A valid GTFS dataset. If your GTFS dataset has blank values for arrival_time and departure_time in stop_times.txt, you will not be able to run this tool. You can download and use the Interpolate Blank Stop Times tool to estimate blank arrival_time and departure_time values for your dataset if you still want to use it.Help forum

The Residential Schools Locations Dataset in Geodatabase format (IRS_Locations.gbd) contains a feature layer "IRS_Locations" that contains the locations (latitude and longitude) of Residential Schools and student hostels operated by the federal government in Canada. All the residential schools and hostels that are listed in the Residential Schools Settlement Agreement are included in this dataset, as well as several Industrial schools and residential schools that were not part of the IRRSA. This version of the dataset doesn’t include the five schools under the Newfoundland and Labrador Residential Schools Settlement Agreement. The original school location data was created by the Truth and Reconciliation Commission, and was provided to the researcher (Rosa Orlandini) by the National Centre for Truth and Reconciliation in April 2017. The dataset was created by Rosa Orlandini, and builds upon and enhances the previous work of the Truth and Reconcilation Commission, Morgan Hite (creator of the Atlas of Indian Residential Schools in Canada that was produced for the Tk'emlups First Nation and Justice for Day Scholar's Initiative, and Stephanie Pyne (project lead for the Residential Schools Interactive Map). Each individual school location in this dataset is attributed either to RSIM, Morgan Hite, NCTR or Rosa Orlandini. Many schools/hostels had several locations throughout the history of the institution. If the school/hostel moved from its’ original location to another property, then the school is considered to have two unique locations in this dataset,the original location and the new location. For example, Lejac Indian Residential School had two locations while it was operating, Stuart Lake and Fraser Lake. If a new school building was constructed on the same property as the original school building, it isn't considered to be a new location, as is the case of Girouard Indian Residential School.When the precise location is known, the coordinates of the main building are provided, and when the precise location of the building isn’t known, an approximate location is provided. For each residential school institution location, the following information is provided: official names, alternative name, dates of operation, religious affiliation, latitude and longitude coordinates, community location, Indigenous community name, contributor (of the location coordinates), school/institution photo (when available), location point precision, type of school (hostel or residential school) and list of references used to determine the location of the main buildings or sites. Access Instructions: there are 47 files in this data package. Please download the entire data package by selecting all the 47 files and click on download. Two files will be downloaded, IRS_Locations.gbd.zip and IRS_LocFields.csv. Uncompress the IRS_Locations.gbd.zip. Use QGIS, ArcGIS Pro, and ArcMap to open the feature layer IRS_Locations that is contained within the IRS_Locations.gbd data package. The feature layer is in WGS 1984 coordinate system. There is also detailed file level metadata included in this feature layer file. The IRS_locations.csv provides the full description of the fields and codes used in this dataset.

The Inventory from Model (White)Model Reservoirs (Red)Mary and Co's reservoirs (Blue)NHD water (Orange)CDL water (Yellow)The Inventory from Model map (1) represents all the water found within the three areas identified the 2023 project; (2) is where the model indicates a reservoir based on the elevation difference based on the Yeager et al. (2017) reservoirs; (3) is based on the Yeager et al. (2017) reservoir boundaries which were plugged into the fifth step of the model (the water identification steps were skipped). Then the model was used to evaluate and classify them into elevation classes. NHD Water map (4) is every waterbody polygon from the National Hydrography Dataset that falls within the three study area areas. The CDL Water map (5) is every open water and aquaculture polygon vectorized from the Cropland Data Layer raster within the three study areas.This Data is a zipped, ArcGIS Pro (V3.1.2) project. The project geodatabase contains the model. In this project there are five maps representing different datasets.NOTE (2024-05-13): ISO xml metadata files added to the root of the zipped folder, describing specific items in the project, saved in ArcGIS Pro V.3.2.2.

ArcGIS and QGIS map packages, with ESRI shapefiles for the DSM2 Model Grid. These are not finalized products. Locations in these shapefiles are approximate.

Monitoring Stations - shapefile with approximate locations of monitoring stations.

DSM2 Grid 2025-05-28 Historical

FC_2023.01

DSM2 v8.2.0, calibrated version:

• dsm2_8_2_grid_map_calibrated.mpkx - ArcGIS Pro map package containing all layers and symbology for the calibrated grid map.
• dsm2_8_2_grid_map_calibrated.mpk - ArcGIS Desktop map package containing all layers and symbology for the calibrated grid map.
• dsm2_8_2_0_calibrated_grid_map_qgis.zip - QGIS map package containing all layers and symbology for the calibrated grid map.
• dsm2_8_2_0_calibrated_gridmap_shapefiles.zip - A zip file containing all the shapefiles used in the above map packages:
• dsm2_8_2_0_calibrated_channels_centerlines - channel centerlines, follwing the path of CSDP centerlines
• dsm2_8_2_0_calibrated_network_channels - channels represented by straight line segments which are connected the upstream and downstream nodes
• dsm2_8_2_0_calibrated_nodes - DSM2 nodes
• dsm2_8_2_0_calibrated_dcd_only_nodes - Nodes that are only used by DCD
• dsm2_8_2_0_calibrated_and_dcd_nodes - Nodes that are shared by DSM2 and DCD
• dsm2_8_2_0_calibrated_and_smcd_nodes - Nodes that are shared by DSM2 and SMCD
• dsm2_8_2_0_calibrated_gates_actual_loc - The approximate actual locations of each gate in DSM2
• dsm2_8_2_0_calibrated_gates_grid_loc - The locations of each gate in the DSM2 model grid
• dsm2_8_2_0_calibrated_reservoirs - The approximate locations of the reservoirs in DSM2
• dsm2_8_2_0_calibrated_reservoir_connections - Lines showing connections from reservoirs to nodes in DSM2

DSM2 v8.2.1, historical version:

• DSM2 v8.2.1, historical version grid map release notes (PDF), updated 7/12/2022
• DSM2 v8.2.1, historical version grid map, single zoom level (PDF)
• DSM2 v8.2.1, historical version grid map, multiple zoom levels (PDF) - PDF grid map designed to be printed on 3 foot wide plotter paper.
• DSM2 v8.2.1, historical version map package for ArcGIS Desktop: A map package for ArcGIS Desktop containing the grid map layers with symbology.
• DSM2 v8.2.1, historical version grid map shapefiles (zip): A zip file containing the shapefiles used in the grid map.

Change Log

7/12/2022: The document "DSM2 v8.2.1, historical version grid map release notes (PDF)" was corrected by removing section 4.4, which incorrectly stated that the grid included channels 710-714, representing the Toe Drain, and that the Yolo Flyway restoration area was included.

This downloadable zip file contains an ESRI File Geodatabase (FGDB) that is compatible with most versions of ArcGIS Pro, ArcMap, and AutoCAD Map 3D or Civil 3D. To view the geodatabase’s contents, please download the zip file to a local directory and extract its contents. This zipped geodatabase will require approximately 1.57 GB of disc space (1.73 GB extracted). Due to its size, the zip file may take some time to download. The geodatabase in the download includes the following layers:2 foot contours, Spot Elevations, Breaklines 2015 LiDAR derived 2ft topographic contours for Tallahassee and Leon County, Florida. Topographic contours re-projected from NAD83 State Plane to Web Mercator. Source data vertical datum NAVD88.TLCGIS regularly uses digital orthophotos and planimetric/hydrographic/topographic data to support regulatory functions, land management and acquisition, planning, engineering and habitat restoration projects. This dataset is part of a regularly scheduled update of LiDAR and digital orthophotography products. The dataset was created from source imagery acquired by a Trimble TAC80 natural color digital camera and LAS data acquired by a Optech ALTM HA500 (Pegasus) LIDAR sensor from January 18, 2015 to February 5, 2015.

Our Co-design team is from the University of Texas, working on a Department of Energy-funded project focused on the Beaumont-Port Arthur area. As part of this project, we will be developing climate-resilient design solutions for areas of the region. More on www.caee.utexas.edu. We captured aerial photos in the Port Arthur Coastal Neighborhood Community and the Golf Course on Pleasure Island, Texas, in June 2024. Aerial photos taken were through DroneDeploy autonomous flight, and models were processed through the DroneDeploy engine as well. All aerial photos are in .JPG format and contained in zipped files for each area. The processed data package includes 3D models, geospatial data, mappings, and point clouds. Please be aware that DTM, Elevation toolbox, Point cloud, and Orthomosaic use EPSG: 6588. And 3D Model uses EPSG: 3857. For using these data: - The Adobe Suite gives you great software to open .Tif files. - You can use LASUtility (Windows), ESRI ArcGIS Pro (Windows), or Blaze3D (Windows, Linux) to open a LAS file and view the data it contains. - Open an .OBJ file with a large number of free and commercial applications. Some examples include Microsoft 3D Builder, Apple Preview, Blender, and Autodesk. - You may use ArcGIS, Merkaartor, Blender (with the Google Earth Importer plug-in), Global Mapper, and Marble to open .KML files. - The .tfw world file is a text file used to georeference the GeoTIFF raster images, like the orthomosaic and the DSM. You need suitable software like ArcView to open a .TFW file. This dataset provides researchers with sufficient geometric data and the status quo of the land surface at the locations mentioned above. This dataset could streamline researchers' decision-making processes and enhance the design as well.

This zip file contains files used for the manuscript "The Aeolian Environment of the Landing Site for the ExoMars Rosalind Franklin Rover in Oxia Planum, Mars".1.The ArcGIS Pro files used to analyze the distribution, orientation, and morphologies of periodic bedrock ridges and transverse aeolian ridges.2.Excel datasets describing the dust devil work presented.Note: These .lyrx files are not backwards compatible with Arc 10.6. The files contained in this zip file are: 1. The HiRISE images used2. The 1-sigma ellipses3. The study area grid4. The ripple and PBRs analyzed5. Excel file for the density calculations of dust devils6. Excel file for the statistics associated with the dust devil tracks

Geostatistics analyzes and predicts the values associated with spatial or spatial-temporal phenomena. It incorporates the spatial (and in some cases temporal) coordinates of the data within the analyses. It is a practical means of describing spatial patterns and interpolating values for locations where samples were not taken (and measures the uncertainty of those values, which is critical to informed decision making). This archive contains results of geostatistical analysis of COVID-19 case counts for all available US counties. Test results were obtained with ArcGIS Pro (ESRI). Sources are state health departments, which are scraped and aggregated by the Johns Hopkins Coronavirus Resource Center and then pre-processed by MappingSupport.com.

This update of the Zenodo dataset (version 6) consists of three compressed archives containing geostatistical analyses of SARS-CoV-2 testing data. This dataset utilizes many of the geostatistical techniques used in previous versions of this Zenodo archive, but has been significantly expanded to include analyses of up-to-date U.S. COVID-19 case data (from March 24th to September 8^th, 2020):

Archive #1: “1.Geostat. Space-Time analysis of SARS-CoV-2 in the US (Mar24-Sept6).zip” – results of a geostatistical analysis of COVID-19 cases incorporating spatially-weighted hotspots that are conserved over one-week timespans. Results are reported starting from when U.S. COVID-19 case data first became available (March 24^th, 2020) for 25 consecutive 1-week intervals (March 24th through to September 6th, 2020). Hotspots, where found, are reported in each individual state, rather than the entire continental United States.

Archive #2: "2.Geostat. Spatial analysis of SARS-CoV-2 in the US (Mar24-Sept8).zip" – the results from geostatistical spatial analyses only of corrected COVID-19 case data for the continental United States, spanning the period from March 24^th through September 8th, 2020. The geostatistical techniques utilized in this archive includes ‘Hot Spot’ analysis and ‘Cluster and Outlier’ analysis.

Archive #3: "3.Kriging and Densification of SARS-CoV-2 in LA and MA.zip" – this dataset provides preliminary kriging and densification analysis of COVID-19 case data for certain dates within the U.S. states of Louisiana and Massachusetts.

These archives consist of map files (as both static images and as animations) and data files (including text files which contain the underlying data of said map files [where applicable]) which were generated when performing the following Geostatistical analyses: Hot Spot analysis (Getis-Ord Gi*) [‘Archive #1’: consecutive weeklong Space-Time Hot Spot analysis; ‘Archive #2’: daily Hot Spot Analysis], Cluster and Outlier analysis (Anselin Local Moran's I) [‘Archive #2’], Spatial Autocorrelation (Global Moran's I) [‘Archive #2’], and point-to-point comparisons with Kriging and Densification analysis [‘Archive #3’].

The Word document provided ("Description-of-Archive.Updated-Geostatistical-Analysis-of-SARS-CoV-2 (version 6).docx") details the contents of each file and folder within these three archives and gives general interpretations of these results.

Shapefile of the Salt Lake or Bidhannagar's administrative boundary only for the residential area. The shapefile was created with QGIS and Google Earth. Verified on ArcGIS Pro (Enterprise). Kolkata's (KMDA) administrative boundary does not contain elite Bidhannagar. It is under a different local administration unit. This shapefile is not covering Sector V zonal distribution.

Water Meter points within Fuquay-Varina. Most meter devices are owned and maintained by the Town, which provides water utility services. However, on some commercial sites, for example, the meter box and meter yoke are actually privately owned and maintained while the meter device is owned and maintained by the Town. This water meter dataset is constantly under development and improvement as there is increasing demand to integrate GIS meter information with other solutions. Please note that some meter points are not field-validated and some are not associated with a valid METERID for water service, and may essentially be duplicated legacy locations from old GIS data. Please note that ALL public utility data layers can be downloaded in a single .mpkx (ArcGIS Pro map package file), updated every Friday evening. This .mpkx file can be opened directly with ArcGIS Pro version 3+. Alternatively, you can extract the file geodatabase within it by renaming the file ending .mpkx to .zip and treating it like a zip archive file, for use in any version of ArcGIS Pro or ArcMap software. You can also use QGIS, a powerful, free, and open-source GIS software.The Town of Fuquay-Varina creates, maintains, and serves out a variety of utility information to the public, including its Potable Water System, Sanitary Sewer System, and Stormwater Collection System features. This is the same utility data displayed in our public web map. This utility data includes some features designated as 'private' that are not owned or maintained by the Town, but may be helpful for modeling and other informational purposes. Please pay particular attention to the terms of use and disclaimer associated with these data. Some data includes the use of Subtypes and Domains that may not translate well to Shapefile or GeoJSON downloads available through our Open Data site. Please beware the dangers of cartographic misrepresentation if you are unfamiliar with filtering and symbolizing data based on attributes. Water System Layers:Water LinesWater ValvesWater ManholesFire HydrantsFire Department ConnectionsWater MetersRPZ (Backflow Preventers)Water TankWater Booster StationsHarnett County Water District AreaSewer System Layers:Gravity Sewer LinesForced Sewer LinesSewer ManholesSewer ValvesSewer CleanoutsSewer Pump StationsWastewater Treatment PlantsStormwater System Layers:Stormwater Lines (Pipes)Stormwater Points (Inlets/Outlets/Manholes)Stormwater Control Measure Points (SCM's, such as Wet Ponds / Retention Basins)

Statewide Ecopia 3 foot Land Cover (2021-2022)This raster land cover data is based off of high-resolution statewide imagery from 2021-2022. It was used by Ecopia to extract and digitize the entire state into 7 different land cover classes. Download Notes:This service can be entered into ArcGIS Pro where "Download Rasters" can be used to download approximately 20 square miles at a time. (Rt. click layer in TOC > Data > Download Rasters)Alternatively, the entire statewide 3ft dataset is available as a zipped download from here (includes colormap file): Ecopia_Statewide_3ft_Raster_TilesClasses available at bottom of this pages.Data SpecificationImagery Used for Extraction: Pixel resolution: 15 cm (6")Camera sensor: Hexagon Pushbroom (Content Mapper)Date of capture: 06/25/2021 - 08/14/2022Date of Vector Extraction: June 2023Extraction Methodology:Ecopia uses proprietary extraction and modeling software to process raw images into high-resolution land cover classifications.Quality Measurements:Measure Name - Threshold across Impervious Polygons:False Negatives <= 5% All PolygonsFalse Positives <= 5% All PolygonsValid Interpretation >= 95% All PolygonsMinimum Area 100% All PolygonsValid Geometry 100% All PolygonsMeasure Name - Threshold across Natural Polygons:False Negatives <=5% All PolygonsFalse Positives <=5% All PolygonsValid Interpretation >=90% All PolygonsMinimum Area 100% All PolygonsValid Geometry 100% All PolygonsLand Cover Classes:UnclassifiedImperviousImpervious, covered by treesShrub/low vegetationTree/forest/high vegetationOpen waterRailroadVegetation (Canopy Mapping)Tree canopy will be captured as a unique polygon layer. It can therefore overlap impervious layers.High vegetation is distinguished from low vegetation based on crown, texture, and derived height models. Leveraging stereo imagery produces results using 3D elevation models used to aid the distinction of vegetation categories. Distinguishing low from high vegetation is based on a 5m threshold, but this is not always feasible, especially in areas where heavy canopy prevents a visualization of the ground. In these circumstances, high vegetation will be given the priority over low vegetation. For more information visit: www.ecopiatech.comClasses:0: No data - Null, clear1: Unclassified2: Impervious3: Impervious, Covered by Tree Canopy6: Shrub/Low Vegetation7: Tree/Forest/High Vegetation8: Open Water12: Railroad

Project package used in manuscript entitled: Use of Weighted Voronoi diagram for forest thinning proposal and skidding trail layout for teak plantation in Thailand. Project package can be open in ArcGIS Pro and contains raster datasets and shapefiles. Zip file contains unpacked package into the folder with spatial data and project file (APRX).

The Southeast Texas Urban Integrated field lab’s Co-design team captured aerial photos in the Port Arthur Coastal Neighborhood Community and the Golf Course on Pleasure Island, Texas, in June 2024. Aerial photos taken were through autonomous flight, and models were processed through the DroneDeploy engine. All aerial photos are in .JPG format and contained in zipped files for each area. The processed data package includes 3D models, geospatial data, mappings, and point clouds. Please be aware that DTM, Elevation toolbox, Point Cloud, and Orthomosaic use EPSG: 6588. And 3D Model uses EPSG: 3857. For using these data: The Adobe Suite gives you great software to open .Tif files. You can use LASUtility (Windows), ESRI ArcGIS Pro (Windows), or Blaze3D (Windows, Linux) to open a LAS file and view the data it contains.

Antibiotics in the Global River System Arising from Human ConsumptionData repositoryLast updated: April 2025prepared byHeloisa Ehalt Macedo (heloisa.ehaltmacedo@mail.mcgill.ca) and Bernhard Lehner (bernhard.lehner@mcgill.ca)1. Overview and backgroundThis repository contains the Python code, input, and output data for the research article: Ehalt Macedo, H., Lehner, B., Nicell, J., Khan, U., Klein, E. (2025). Antibiotics in the global river system arising from human consumption. PNAS Nexus. Further information and description of the model can be found in the same publication.The data repository includes 3 datasets:1. Python code: python project repository including the structure necessary for the model to run.2. Input data:a. A table containing information on all river segments associated with geometric attributes from RiverATLAS (Linke et al., 2019) and HydroROUT (Lehner and Grill, 2013), and attributes used in the HydroFATE model (Ehalt Macedo et al., 2024) based on underlying data such as HydroLAKES (Messager et al., 2016), and HydroWASTE (Ehalt Macedo et al., 2022).b. A table of parameters for the model run. The literature sources of the parameters for all substances and the scenarios are described in the research paper.c. A table of country-level consumption per capita of all contaminants being analyzed, as provided by Klein et al. (2018).3. Output data:a. A table including the unique river reach identifier associated with the river network, the resulting concentration and risk quotient for each contaminant, and totals using HydroFATE as described in the research paper.2. Repository contentThe data repository has the following structure:HydroFATE_v1_1.zip/: repository containing:|---------Main_script/:|---------------------Input/: empty folder to add input data from “Input_data.gdb.zip”|---------------------Output/: empty folder where results will be saved after model run|---------------------HydroFATE_v1_1.py : python code with HydroFATE model version 1.1|---------------------config.py: config file with model parameters|---------LICENSE: license file for python code|---------README.md: readme file for code description and compilation instructions|---------Technical_documentation_Antibiotics: technical documentation for the code and datasetsInput_data.gdb.zip/: file geodatabase in ESRI® geodatabase format containing 3 feature classes (zipped):|---------streams: table including global river network attributes.|---------parameters: table including parameters and configuration settings.|---------consumption: table including country-level consumption per capita of all substances.Output.gdb.zip/: file geodatabase in ESRI® geodatabase format containing 1 feature class (zipped):|---------Total_results: table with model predictions for every river reach of the global river network.3. Data format and projectionA license for the software ArcGIS Pro is required to run the provided scripts. These datasets are available electronically in compressed zip file format. To use the data files, the zip files must first be decompressed. All data layers are provided in geographic (latitude/longitude) projection, referenced to datum WGS84. In ESRI® software this projection is defined by the geographic coordinate system GCS_WGS_1984 and datum D_WGS_1984 (EPSG: 4326). Full descriptions of dataset attributes can be found in the Technical documentation in this repository.4. License and citations4.1 License AgreementThis documentation and datasets are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (CC-BY-4.0 License). For all regulations regarding license grants, copyright, redistribution restrictions, required attributions, disclaimer of warranty, indemnification, liability, waiver of damages, and a precise definition of licensed materials, please refer to the License Agreement (https://creativecommons.org/licenses/by/4.0/legalcode). For a human-readable summary of the license, please see https://creativecommons.org/licenses/by/4.0/.4.2 Citations and Acknowledgements.Citations and acknowledgements of this dataset should be made as follows:Ehalt Macedo, H., Lehner, B., Nicell, J., Khan, U., Klein, E. (2025). Antibiotics in the global river system arising from human consumption. PNAS Nexus.We kindly ask users to cite this study in any published material produced using it. Onlineclass links to this repository (https://doi.org/10.6084/m9.figshare.25829464) should also be provided.5. ReferencesEhalt Macedo, H., Lehner, B., Nicell, J. & Grill, G. HydroFATE (v1): a high-resolution contaminant fate model for the global river system. Geosci. Model Dev. 17, 2877-2899, doi:10.5194/gmd-17-2877-2024 (2024).Ehalt Macedo, H., Lehner, B., Nicell, J., Grill, G., Li, J., Limtong, A., and Shakya, R.: Distribution and characteristics of wastewater treatment plants within the global river network, Earth Syst. Sci. Data, 14, 559-577, doi: 10.5194/essd-14-559-2022, 2022.Klein, E. Y., Boeckel, T. P. V., Martinez, E. M., Pant, S., Gandra, S., Levin, S. A., Goossens, H., and Laxminarayan, R.: Global increase and geographic convergence in antibiotic consumption between 2000 and 2015, Proceedings of the National Academy of Sciences, 115, E3463-E3470, doi: 10.1073/pnas.1717295115, 2018.Lehner, B. and Grill, G.: Global river hydrography and network routing: baseline data and new approaches to study the world's large river systems, Hydrol Process, 27, 2171-2186, doi: 10.1002/hyp.9740, 2013.Linke, S., Lehner, B., Ouellet Dallaire, C., Ariwi, J., Grill, G., Anand, M., Beames, P., Burchard-Levine, V., Maxwell, S., Moidu, H., Tan, F., and Thieme, M.: Global hydro-environmental sub-basin and river reach characteristics at high spatial resolution, Scientific Data, 6, 283, doi: 10.1038/s41597-019-0300-6, 2019.Messager, M. L., Lehner, B., Grill, G., Nedeva, I., and Schmitt, O.: Estimating the volume and age of water stored in global lakes using a geo-statistical approach, Nature Communications, 7, 13603, doi: 10.1038/ncomms13603, 2016.

This resource was created by Esri Canada Education and Research. To browse our full collection of higher-education learning resources, please visit https://hed.esri.ca/resourcefinder/.This Tutorial consists of four tutorials that deal with integrating the statistical programming language R with ArcGIS for Desktop. Several concepts are covered which include configuring ArcGIS with R, writing basic R scripts, writing R scripts that work with ArcGIS data, and constructing R Tools for use within ArcGIS Pro. It is recommended that the tutorials are completed in sequential order. Each of the four tutorials (as well as a version of this document), can viewed directly from your Web browser by following the links below. However, you must obtain a complete copy of the tutorial files by downloading the latest release (or by cloning the tutorial repository on GitHub) if you wish to follow the tutorials interactively using ArcGIS and R software, along with pre-configured sample data.To download the tutorial documents and datasets, click the Open button to the top right. This will automatically download a ZIP file containing all files and data required.You can also clone the tutorial documents and datasets for this GitHub repo: https://github.com/highered-esricanada/r-arcgis-tutorials.gitSoftware & Solutions Used: ArcGIS Pro 3.4 Internet browser (e.g., Mozilla Firefox, Google Chrome, Safari) R Statistical Computing Language – version 4.3.3 R-ArcGIS Bindings – version 1.0.1.311RStudio Desktop – version 2024.09.0+375Time to Complete: 2.5 h (excludes installation time)File Size: 115 MBDate Created: November 2017Last Updated: December 2024

The towns of Connecticut (CT) Parcels and Computer-Assisted Mass Appraisal (CAMA) data for 2022 are part of a zipped file containing two items: CT parcels in geodatabases organized by COGs and associated CAMA files. The parcel information includes 169 out of 169 town organized with geodatabases for each of the 9 Council of Governments. Most of the parcel data sets can be linked to the CAMA data which has attribute information (e.g. value of house, number of bedrooms) about real property. The parcel features for each town are in shape files, feature classes, or within a geodatabase. Most parcels are organized by town and COG and placed within a geodatabases. The CAMA data sets have information about real property within the towns of CT. It may be linked to the parcels using a join process within a GIS package like ArcGIS Pro or QGIS. 154 out of 169 towns have complete CAMA information. Of the remaining 15 towns, four have no information and the remaining have some limited information mixed into the parcel attribute tables. These files were gathered from the CT towns by the COGs and then submitted to CT OPM. Town data is organized by COG. Attribute names, primary key, secondary key, naming conventions, and file formats are not fully consistent but some cleaning and reorganization was conducted to improve quality. This file was created on 03/08/2023 from data collected in 2021-2022.

Sewer Cleanout points in Fuquay-Varina. Note: some cleanouts are privately owned and maintaned but mapped for modeling and informational purposes. Cleanout points from developments prior to 2010's or so may have questionable accuracy in this dataset. Please note that ALL public utility data layers can be downloaded in a single .mpkx (ArcGIS Pro map package file), updated every Friday evening. This .mpkx file can be opened directly with ArcGIS Pro version 3+. Alternatively, you can extract the file geodatabase within it by renaming the file ending .mpkx to .zip and treating it like a zip archive file, for use in any version of ArcGIS Pro or ArcMap software. You can also use QGIS, a powerful, free, and open-source GIS software.The Town of Fuquay-Varina creates, maintains, and serves out a variety of utility information to the public, including its Potable Water System, Sanitary Sewer System, and Stormwater Collection System features. This is the same utility data displayed in our public web map. This utility data includes some features designated as 'private' that are not owned or maintained by the Town, but may be helpful for modeling and other informational purposes. Please pay particular attention to the terms of use and disclaimer associated with these data. Some data includes the use of Subtypes and Domains that may not translate well to Shapefile or GeoJSON downloads available through our Open Data site. Please beware the dangers of cartographic misrepresentation if you are unfamiliar with filtering and symbolizing data based on attributes. Water System Layers:Water LinesWater ValvesWater ManholesFire HydrantsFire Department ConnectionsWater MetersWater Meter VaultsRPZ (Backflow Preventers)Water TankWater Booster StationsHarnett County Water District AreaSewer System Layers:Gravity Sewer LinesForced Sewer LinesSewer ManholesSewer ValvesSewer CleanoutsSewer Pump StationsWastewater Treatment PlantsStormwater System Layers:Stormwater Lines (Pipes)Stormwater Points (Inlets/Outlets/Manholes)Stormwater Control Measure Points (SCM's, such as Wet Ponds / Retention Basins)

Stormwater Control Measures (SCM's) in Fuquay-Varina, represented as points. Please note that these SCM's (e.g. Wet Pond Detention Basins) are generally privately owned and maintained systems, but these are essential for mapping and understanding the stormwater drainage network sub-systems at the neighborhood level. These points do not currently contain much attribute information; rather, they are primarily an aid in locating and referencing these features, as there can be many on a single property or subdivision. Not all SCM features have been added to this dataset yet, but we are working on it. Please note that ALL public utility data layers can be downloaded in a single .mpkx (ArcGIS Pro map package file), updated every Friday evening. This .mpkx file can be opened directly with ArcGIS Pro version 3+. Alternatively, you can extract the file geodatabase within it by renaming the file ending .mpkx to .zip and treating it like a zip archive file, for use in any version of ArcGIS Pro or ArcMap software. You can also use QGIS, a powerful, free, and open-source GIS software.The Town of Fuquay-Varina creates, maintains, and serves out a variety of utility information to the public, including its Potable Water System, Sanitary Sewer System, and Stormwater Collection System features. This is the same utility data displayed in our public web map. This utility data includes some features designated as 'private' that are not owned or maintained by the Town, but may be helpful for modeling and other informational purposes. Please pay particular attention to the terms of use and disclaimer associated with these data. Some data includes the use of Subtypes and Domains that may not translate well to Shapefile or GeoJSON downloads available through our Open Data site. Please beware the dangers of cartographic misrepresentation if you are unfamiliar with filtering and symbolizing data based on attributes. Water System Layers:Water LinesWater ValvesWater ManholesFire HydrantsFire Department ConnectionsWater MetersWater Meter VaultsRPZ (Backflow Preventers)Water TankWater Booster StationsHarnett County Water District AreaSewer System Layers:Gravity Sewer LinesForced Sewer LinesSewer ManholesSewer ValvesSewer CleanoutsSewer Pump StationsWastewater Treatment PlantsStormwater System Layers:Stormwater Lines (Pipes)Stormwater Points (Inlets/Outlets/Manholes)Stormwater Control Measure Points (SCM's, such as Wet Ponds / Retention Basins)