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run update script for AGO sample notebooks and labs
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‎items_metadata.yaml

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samples:
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- title: Analyzing New York City taxi data using big data tools
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url: https://www.arcgis.com/home/item.html?id=27017ef3b3864e74ae1b7587719a3391
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path: ./samples/04_gis_analysts_data_scientists/analyze_new_york_city_taxi_data.ipynb
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thumbnail: ./static/thumbnails/analyze_new_york_city_taxi_data.png
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snippet: Use big data tools to analye NYC taxi data
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description: This sample demonstrates the steps involved in performing an aggregation analysis on New York city taxi point data using ArcGIS API for Python.
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licenseInfo: ""
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tags: ["Data Science", "GIS", "Taxi"]
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# - title: Analyzing New York City taxi data using big data tools
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# url: https://www.arcgis.com/home/item.html?id=27017ef3b3864e74ae1b7587719a3391
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# path: ./samples/04_gis_analysts_data_scientists/analyze_new_york_city_taxi_data.ipynb
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# thumbnail: ./static/thumbnails/analyze_new_york_city_taxi_data.png
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# snippet: Use big data tools to analye NYC taxi data
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# description: This sample demonstrates the steps involved in performing an aggregation analysis on New York city taxi point data using ArcGIS API for Python.
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# licenseInfo: ""
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# tags: ["Data Science", "GIS", "Taxi"]
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- title: Data Visualization - Construction permits, part 1/2
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url: https://www.arcgis.com/home/item.html?id=467bc6806c9e40dc8222744e0937b80c
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path: ./samples/04_gis_analysts_data_scientists/analyze_patterns_in_construction_permits_part1.ipynb
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# tags: ["Data Science", "GIS", "Thomas Fire", "California"]
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- title: Chennai Floods 2015 - A Geographic Analysis
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url: https://www.arcgis.com/home/item.html?id=44c4fc1e56654768840a03971feb1e77
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path: ./samples/04_gis_analysts_data_scientists/chennai_floods_analysis.ipynb
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path: ./samples/04_gis_analysts_data_scientists/chennai_floods_analysis_rn.ipynb
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thumbnail: ./static/thumbnails/chennai_floods_analysis.jpg
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snippet: Analyze the rainfall in Chennai, India
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description: This sample showcases not just the analysis and visualization capabilities of your GIS, but also the ability to store illustrative text, graphics and live code in a Jupyter notebook.
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# description: Use the ArcGIS API for Python to answer if wildfires are increasing over time.
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# licenseInfo: ""
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# tags: ["Data Science", "GIS", "Wildfire"]
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- title: How much green is Delhi as on 15 Oct 2017?
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- title: How much green is Delhi as on 21 Oct 20122?
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url: https://www.arcgis.com/home/item.html?id=8094be16f34e46e48880883a1ae6a4f1
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path: ./samples/04_gis_analysts_data_scientists/how-much-green-is-Delhi-as-on-15-oct-2017.ipynb
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path: ./samples/04_gis_analysts_data_scientists/how-much-green-is-Delhi-as-on-21-oct-2022.ipynb
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thumbnail: ./static/thumbnails/how-much-green-is-Delhi-as-on-15-oct-2017.jpg
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snippet: Use Landsat 8 imagery to detect green cover of New Delhi, India
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description: This sample shows the capabilities of spectral indices such as Normalized Difference Vegetation index (NDVI) for the calculation of green cover in Delhi, India on 15 October 2017 using Landsat 8 imagery.
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tags: ['Data Science', 'GIS', "Geometry"]
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- title: Using Geoprocessing Tools
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url: https://www.arcgis.com/home/item.html?id=5a5839d87b4645e685bcd46d79995358
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path: ./samples/02_power_users_developers/using_geoprocessing_tools.ipynb
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path: ./samples/02_power_users_developers/using_geoprocessing_tools_rn.ipynb
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thumbnail: ./static/thumbnails/using_geoprocessing_tools.png
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snippet: using geoprocessing tools
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description: The analysis below uses a geoprocessing tool to deduce the path that the debris of a crashed airplane would take if it went down at different places in the ocean.
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licenseInfo: ""
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runtime: advanced_gpu
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tags: ["Data Science", "GIS", "Hyperspectral", "Deep Learning"]
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- title: Streams Extraction using MultiTaskRoadExtractor
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- title: Streams Extraction using Deep Learning
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url: https://www.arcgis.com/home/item.html?id=356899f6baad407b9db49bb526073ee1
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path: ./samples/04_gis_analysts_data_scientists/streams_extraction_using_multi_task_road_extractor.ipynb
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path: ./samples/04_gis_analysts_data_scientists/streams_extraction_using_deeplearning.ipynb
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thumbnail: ./static/thumbnails/default.png
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snippet: The aim of this notebook is to make use of arcgis.learn MultiTaskRoadExtractor model to extract streams.
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description: The aim of this notebook is to make use of arcgis.learn MultiTaskRoadExtractor model to extract streams.

‎labs/create_data.ipynb

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"anaconda-cloud": {},
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"esriNotebookRuntime": {
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"notebookRuntimeName": "ArcGIS Notebook Python 3 Standard",
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"notebookRuntimeVersion": "5.0"
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"notebookRuntimeVersion": "9.0"
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},
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"kernelspec": {
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"display_name": "Python 3",

‎labs/display_web_map.ipynb

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"anaconda-cloud": {},
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"esriNotebookRuntime": {
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"notebookRuntimeName": "ArcGIS Notebook Python 3 Standard",
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"notebookRuntimeVersion": "5.0"
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"notebookRuntimeVersion": "9.0"
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},
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"kernelspec": {
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"display_name": "Python 3",

‎labs/display_web_scene.ipynb

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"anaconda-cloud": {},
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"esriNotebookRuntime": {
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"notebookRuntimeName": "ArcGIS Notebook Python 3 Standard",
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"notebookRuntimeVersion": "9.0"
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},
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"kernelspec": {
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"display_name": "Python 3",

‎labs/download_data.ipynb

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"anaconda-cloud": {},
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"esriNotebookRuntime": {
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"notebookRuntimeVersion": "9.0"
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"kernelspec": {
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‎labs/find_places.ipynb

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"metadata": {
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"esriNotebookRuntime": {
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"notebookRuntimeVersion": "9.0"
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"kernelspec": {
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"display_name": "Python 3",

‎labs/import_data.ipynb

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"anaconda-cloud": {},
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"esriNotebookRuntime": {
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"notebookRuntimeVersion": "5.0"
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"notebookRuntimeVersion": "9.0"
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},
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"kernelspec": {
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"display_name": "Python 3",

‎labs/load_item_by_id.ipynb

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"metadata": {
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"esriNotebookRuntime": {
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"notebookRuntimeVersion": "9.0"
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"kernelspec": {
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‎labs/load_spatial_data_frame.ipynb

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"metadata": {
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"esriNotebookRuntime": {
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"notebookRuntimeVersion": "9.0"
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‎labs/search_and_geocode.ipynb

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"metadata": {
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‎labs/share_your_content.ipynb

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"metadata": {
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‎samples/02_power_users_developers/building_a_change_detection_app_using_jupyter_dashboard.ipynb

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‎samples/02_power_users_developers/jupyter_dashboard_for_raster_analytics.ipynb

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‎samples/02_power_users_developers/openstreetmap_exploration.ipynb

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"metadata": {
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"extensions": {
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‎samples/02_power_users_developers/population_exploration_dashboard.ipynb

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"extensions": {
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‎samples/02_power_users_developers/tour_the_world_with_landsat_imagery_and_raster_functions.ipynb

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‎samples/02_power_users_developers/using_geometry_functions.ipynb

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‎samples/02_power_users_developers/using_geoprocessing_tools_rn.ipynb

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‎samples/04_gis_analysts_data_scientists/address-standardization-and-correction-with-sequencetosequence.ipynb

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‎samples/04_gis_analysts_data_scientists/analyze_new_york_city_taxi_data.ipynb

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‎samples/04_gis_analysts_data_scientists/analyze_patterns_in_construction_permits_part1.ipynb

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‎samples/04_gis_analysts_data_scientists/analyze_patterns_in_construction_permits_part2.ipynb

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‎samples/04_gis_analysts_data_scientists/analyze_us_tornadoes.ipynb

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‎samples/04_gis_analysts_data_scientists/analyzing_growth_factors_of_airbnb_properties_in_new_york_city.ipynb

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‎samples/04_gis_analysts_data_scientists/analyzing_violent_crime.ipynb

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‎samples/04_gis_analysts_data_scientists/automate_building_footprint_extraction_using_instance_segmentation.ipynb

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‎samples/04_gis_analysts_data_scientists/automate_road_surface_investigation_using_deep_learning.ipynb

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‎samples/04_gis_analysts_data_scientists/building_reconstruction_using_mask_rcnn.ipynb

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‎samples/04_gis_analysts_data_scientists/change_detection_of_buildings_from_satellite_imagery.ipynb

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‎samples/04_gis_analysts_data_scientists/chennai_floods_analysis_rn.ipynb

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‎samples/04_gis_analysts_data_scientists/creating_hurricane_tracks_using_geoanalytics.ipynb

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