This layer provides information regarding Attractions , Recreation center, Park in City of Calgary

Parcel and Service Area data for Cumberland County, North Carolina

This layer provides information regarding Attractions , Recreation center, Park in City of Calgary

This data was created by Esri to represent fictitious oil and gas pit exloration locations in Louisiana.

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

With community spirit at its core, Calgary is a young, energetic and diverse city full of shareable experiences. We encourage you to share your memories with us, and with others by tagging @tourismcalgary and #CaptureCalgary where you can on social media. Let’s learn more about Calgary recreation facilities.

Promo piece created for Tourism Calgary to promote RVC in Calgary in 2017

This resource was created by Esri Canada Education and Research. To download the Spatial Interpolation Labs, click the Open button to the top right. This will automatically download a ZIP file containing all files and data required.Level of Difficulty: Intermediate / AdvancedTime to Complete: 60-90 minFile Size: 2.7 GBDate Created: March, 2020Last Updated: March, 2020By completing this workshop, you will become comfortable with the following skills: Cloning Anaconda (conda) environmentsIntroduction to DL and its benefits Create training samples using ArcGIS ProTrain a deep-learning model using ArcGIS API for pythonExecute your Object Detection model in ArcGIS ProConduct spatial analysis on the detected objects to derive insights

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 downloadable ZIP folder supports the Light Pole Detection from LiDAR Using Deep Learning tutorial. It contains:Raw .las files — LiDAR data sourced from the City of Vancouver Open Data Portal.BC_DEM.tif — A digital elevation model used for calculating relative point heights.DetectLightPole.dlpk — A trained PointCNN deep learning model for detecting light poles from point cloud data.Click Open to download the tutorial package.

The PS FY13 GA DNR Elevation Data Task Order involves collecting and delivering topographic elevation point data derived from multiple return light detection and ranging (LiDAR) measurements in 4 counties in Georgia. The Statement of Work (SOW) was developed by the National Oceanic and Atmospheric Administration's (NOAA) Office for Coastal Management (referred to as the Center) in partnership with the Georgia Department of Natural Resources (GADNR) Environmental Protection Division (EPD). The counties included are Barrow, Clarke, Madison and Oglethorpe. The data collected for these 4 counties will exhibit Hydro Flattened DEMs for inclusion into the National Elevation Dataset (NED). The purpose of the data is for use in coastal management decision making, including applications such as floodplain mapping and water rights management.LiDAR was collected at 1.0 points per square meter (1.0m GSD) for Barrow, Clarke, Madison and Oglethorpe Counties.This area was flown during snow free and leaf-off conditions.

This map shows the proximity of public art installations from CTrain stations in Calgary, Alberta. The purpose of this map is to understand the accessibility of these art installations via Calgary's light rail transit system. It can also be used in navigating to locations of public art depending on the closest CTrain station. Train stations are divided up based on route and locations of public art are shown in white.The layer for public art locations was retrieved from The City of Calgary's open data portal, Open Calgary (https://data.calgary.ca/Recreation-and-Culture/Public-Art-Locations/643n-987f). It was converted from a .csv file with values for latitude and longitude into a .shp file. It was then clipped to the extent of the city boundary.The layer for CTrain stations was retrieved from The City of Calgary's open data portal, Open Calgary (https://data.calgary.ca/Transportation-Transit/Transit-LRT-Stations-Map/c6ee-wk9g).The layer for the city boundary was retrieved from Stats Canada as a boundary file for population centres (https://www12.statcan.gc.ca/census-recensement/2011/geo/bound-limit/bound-limit-2016-eng.cfm). The City of Calgary was exported from this as a separate .shp file.All data were projected in WGS 1984 Web Mercator Auxiliary Sphere.

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 introduces you to using Python code in a Jupyter Notebook, an open source web application that enables you to create and share documents that contain rich text, equations and multimedia, alongside executable code and visualization of analysis outputs. The tutorial begins by stepping through the basics of setting up and being productive with Python notebooks. You will be introduced to ArcGIS Notebooks, which are Python Notebooks that are well-integrated within the ArcGIS platform. Finally, you will be guided through a series of ArcGIS Notebooks that illustrate how to create compelling notebooks for data science that integrate your own Python scripts using the ArcGIS API for Python and ArcPy in combination with thousands of open source Python libraries to enhance your analysis and visualization.To download the dataset Labs, 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/arcgis-notebooks-tutorial.git.Software & Solutions Used: Required: This tutorial was last tested on August 27th, 2024, using ArcGIS Pro 3.3. If you're using a different version of ArcGIS Pro, you may encounter different functionality and results.Recommended: ArcGIS Online subscription account with permissions to use advanced Notebooks and GeoEnrichmentOptional: Notebook Server for ArcGIS Enterprise 11.3+Time to Complete: 2 h (excludes processing time)File Size: 196 MBDate Created: January 2022Last Updated: August 27, 2024

For information about the Education Data Playground and it's associated feature services, please read the Getting Started document.The Map My Campus service allows users to map out their campus using symbology found in the Esri Campus Basemap. Data is hosted publically and can be edited by anyone using the ArcGIS Web or Mobile Applications. If your school is adding data, please send the Esri Canada Education team an email and we will add a spatial bookmark to the map to highlight your school.

The Monthly sea surface temperature (SST) was aggregated using the daily SST layer from the ArcGIS Living Atlas of the world. It is used in the Hotter Ocean Tutorial developed by the Education and Research Team at Esri Canada. The source data for the daily SST comes from the NOAA Coral Reef Watch (CRW) program. SST is a critical variable in weather and climate prediction, ocean forecasting, and a wide range of coastal applications such as fisheries management, pollution tracking, and tourism planning. It plays a vital role in predicting tropical cyclone development and understanding oceanic conditions that influence atmospheric patterns. For instance, scientists rely on SST data to forecast large-scale climate phenomena like El Niño and La Niña, which have significant global impacts on weather systems.SST patterns are also instrumental in understanding marine ecosystems. By analyzing these patterns, researchers can detect oceanic features such as eddies, frontal systems, and upwelling zones. Over time, SST data enables scientists to track changes, identify long-term trends, and assess the impacts of climate change on marine environments. This information is crucial for managing fisheries, conserving biodiversity, and preparing for shifts in ocean behavior that affect coastal communities and industries.More information about the SST product from CRW:https://coralreefwatch.noaa.gov/product/5km/index_5km_sst.php

The primary intent of this workshop is to provide practical training in using Statistics Canada geography files with the leading industry standard software: Environmental Systems Research Institute, Inc.(ESRI) ArcGIS 9x. Participants will be introduced to the key features of ArcGIS 9x, as well as to geographic concepts and principles essential to understanding and working with geographic information systems (GIS) software. The workshop will review a range of geography and attribute files available from Statistics Canada, as well as some best practices for accessing this information. A brief overview of complementary data sets available from federal and provincial agencies will be provided. There will also be an opportunity to complete a practical exercise using ArcGIS9x. (Note: Data associated with this presentation is available on the DLI FTP site under folder 1873-221.)

This map is now deprecated.Visit the new official map Explore the Map on our website, or visit the new map on ArcGIS Online.

This table of Education profile information for Dissemination Area was downloaded from the Statistics Canada Website and joined with bndDisseminationArea2016 in DEM. It contains the information gathered during the 2016 Census with respect to the population by education attainment within the dissemination. This data covers the census division in York Region only. Statistics Canada has suppressed the profiles for certain areas due to very low population count. Suppressed areas will appear as NULL values in the attribute table.For more information on the 2016 Census, please go to the Statistics Canada website at :http://www12.statcan.gc.ca/census-recensement/index-eng.cfm

Please note that this dataset is not an official City of Toronto land use dataset. It was created for personal and academic use using City of Toronto Land Use Maps (2019) found on the City of Toronto Official Plan website at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/official-plan-maps-copy, along with the City of Toronto parcel fabric (Property Boundaries) found at https://open.toronto.ca/dataset/property-boundaries/ and Statistics Canada Census Dissemination Blocks level boundary files (2016). The property boundaries used were dated November 11, 2021. Further detail about the City of Toronto's Official Plan, consolidation of the information presented in its online form, and considerations for its interpretation can be found at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/ Data Creation Documentation and Procedures Software Used The spatial vector data were created using ArcGIS Pro 2.9.0 in December 2021. PDF File Conversions Using Adobe Acrobat Pro DC software, the following downloaded PDF map images were converted to TIF format. 9028-cp-official-plan-Map-14_LandUse_AODA.pdf 9042-cp-official-plan-Map-22_LandUse_AODA.pdf 9070-cp-official-plan-Map-20_LandUse_AODA.pdf 908a-cp-official-plan-Map-13_LandUse_AODA.pdf 978e-cp-official-plan-Map-17_LandUse_AODA.pdf 97cc-cp-official-plan-Map-15_LandUse_AODA.pdf 97d4-cp-official-plan-Map-23_LandUse_AODA.pdf 97f2-cp-official-plan-Map-19_LandUse_AODA.pdf 97fe-cp-official-plan-Map-18_LandUse_AODA.pdf 9811-cp-official-plan-Map-16_LandUse_AODA.pdf 982d-cp-official-plan-Map-21_LandUse_AODA.pdf Georeferencing and Reprojecting Data Files The original projection of the PDF maps is unknown but were most likely published using MTM Zone 10 EPSG 2019 as per many of the City of Toronto's many datasets. They could also have possibly been published in UTM Zone 17 EPSG 26917 The TIF images were georeferenced in ArcGIS Pro using this projection with very good results. The images were matched against the City of Toronto's Centreline dataset found here The resulting TIF files and their supporting spatial files include: TOLandUseMap13.tfwx TOLandUseMap13.tif TOLandUseMap13.tif.aux.xml TOLandUseMap13.tif.ovr TOLandUseMap14.tfwx TOLandUseMap14.tif TOLandUseMap14.tif.aux.xml TOLandUseMap14.tif.ovr TOLandUseMap15.tfwx TOLandUseMap15.tif TOLandUseMap15.tif.aux.xml TOLandUseMap15.tif.ovr TOLandUseMap16.tfwx TOLandUseMap16.tif TOLandUseMap16.tif.aux.xml TOLandUseMap16.tif.ovr TOLandUseMap17.tfwx TOLandUseMap17.tif TOLandUseMap17.tif.aux.xml TOLandUseMap17.tif.ovr TOLandUseMap18.tfwx TOLandUseMap18.tif TOLandUseMap18.tif.aux.xml TOLandUseMap18.tif.ovr TOLandUseMap19.tif TOLandUseMap19.tif.aux.xml TOLandUseMap19.tif.ovr TOLandUseMap20.tfwx TOLandUseMap20.tif TOLandUseMap20.tif.aux.xml TOLandUseMap20.tif.ovr TOLandUseMap21.tfwx TOLandUseMap21.tif TOLandUseMap21.tif.aux.xml TOLandUseMap21.tif.ovr TOLandUseMap22.tfwx TOLandUseMap22.tif TOLandUseMap22.tif.aux.xml TOLandUseMap22.tif.ovr TOLandUseMap23.tfwx TOLandUseMap23.tif TOLandUseMap23.tif.aux.xml TOLandUseMap23.tif.ov Ground control points were saved for all georeferenced images. The files are the following: map13.txt map14.txt map15.txt map16.txt map17.txt map18.txt map19.txt map21.txt map22.txt map23.txt The City of Toronto's Property Boundaries shapefile, "property_bnds_gcc_wgs84.zip" were unzipped and also reprojected to EPSG 26917 (UTM Zone 17) into a new shapefile, "Property_Boundaries_UTM.shp" Mosaicing Images Once georeferenced, all images were then mosaiced into one image file, "LandUseMosaic20211220v01", within the project-generated Geodatabase, "Landuse.gdb" and exported TIF, "LandUseMosaic20211220.tif" Reclassifying Images Because the original images were of low quality and the conversion to TIF made the image colours even more inconsistent, a method was required to reclassify the images so that different land use classes could be identified. Using Deep learning Objects, the images were re-classified into useful consistent colours. Deep Learning Objects and Training The resulting mosaic was then prepared for reclassification using the Label Objects for Deep Learning tool in ArcGIS Pro. A training sample, "LandUseTrainingSamples20211220", was created in the geodatabase for all land use types as follows: Neighbourhoods Insitutional Natural Areas Core Employment Areas Mixed Use Areas Apartment Neighbourhoods Parks Roads Utility Corridors Other Open Spaces General Employment Areas Regeneration Areas Lettering (not a land use type, but an image colour (black), used to label streets). By identifying the letters, it then made the reclassification and vectorization results easier to clean up of unnecessary clutter caused by the labels of streets. Reclassification Once the training samples were created and saved, the raster was then reclassified using the Image Classification Wizard tool in ArcGIS Pro, using the Support...