Merge pull request #1048 from mohi9282/v190_changes

Changes to metadata

Author: Atma Mani

items_metadata.yaml

@@ -1,12 +1,12 @@
 samples:
-# - title: Analyzing New York City taxi data using big data tools
-#   url: https://geosaurus.maps.arcgis.com/home/item.html?id=27017ef3b3864e74ae1b7587719a3391
-#   path: ./samples/04_gis_analysts_data_scientists/analyze_new_york_city_taxi_data.ipynb
-#   thumbnail: ./static/thumbnails/analyze_new_york_city_taxi_data.png
-#   snippet: Use big data tools to analye NYC taxi data
-#   description: This sample demonstrates the steps involved in performing an aggregation analysis on New York city taxi point data using ArcGIS API for Python.
-#   licenseInfo: ""
-#   tags: ["Data Science", "GIS", "Taxi"]
+- title: Analyzing New York City taxi data using big data tools
+  url: https://www.arcgis.com/home/item.html?id=27017ef3b3864e74ae1b7587719a3391
+  path: ./samples/04_gis_analysts_data_scientists/analyze_new_york_city_taxi_data.ipynb
+  thumbnail: ./static/thumbnails/analyze_new_york_city_taxi_data.png
+  snippet: Use big data tools to analye NYC taxi data
+  description: This sample demonstrates the steps involved in performing an aggregation analysis on New York city taxi point data using ArcGIS API for Python.
+  licenseInfo: ""
+  tags: ["Data Science", "GIS", "Taxi"]
 - title: Data Visualization - Construction permits, part 1/2
   url: https://www.arcgis.com/home/item.html?id=467bc6806c9e40dc8222744e0937b80c
   path: ./samples/04_gis_analysts_data_scientists/analyze_patterns_in_construction_permits_part1.ipynb
@@ -162,14 +162,14 @@ samples:
 #   description: This notebook provides you with tools and methods that you can try yourself in performing data modeling, analyzing, and predicting the spread of COVID-19 with the ArcGIS API for Python, and other libraries such as pandas and numpy
 #   licenseInfo: ""
 #   tags: ["Data Science", "GIS", "Predictive", "Covid19", "Part 3"]
-# - title: Creating hurricane tracks using Geoanalytics
-#   url: https://www.arcgis.com/home/item.html?id=c6106b0ead3f49059b326646eda85f9a
-#   path: ./samples/04_gis_analysts_data_scientists/creating_hurricane_tracks_using_geoanalytics.ipynb
-#   thumbnail: ./static/thumbnails/creating_hurricane_tracks_using_geoanalytics.png
-#   snippet: Use GeoAnalytics to create hurricane tracks
-#   description: The sample code below uses big data analytics (GeoAnalytics) to reconstruct hurricane tracks using data registered on a big data file share in the GIS
-#   licenseInfo: ""
-#   tags: ["Data Science", "GIS", "Hurricane", "Tracks", "GeoAnalytics"]
+- title: Creating hurricane tracks using Geoanalytics
+  url: https://www.arcgis.com/home/item.html?id=c6106b0ead3f49059b326646eda85f9a
+  path: ./samples/04_gis_analysts_data_scientists/creating_hurricane_tracks_using_geoanalytics.ipynb
+  thumbnail: ./static/thumbnails/creating_hurricane_tracks_using_geoanalytics.png
+  snippet: Use GeoAnalytics to create hurricane tracks
+  description: The sample code below uses big data analytics (GeoAnalytics) to reconstruct hurricane tracks using data registered on a big data file share in the GIS
+  licenseInfo: ""
+  tags: ["Data Science", "GIS", "Hurricane", "Tracks", "GeoAnalytics"]
 # - title: Creating Raster Information Product using Raster Analytics
 #   url: https://www.arcgis.com/home/item.html?id=f0423a7df2064096a78e150a6fbf5ae4
 #   path: ./samples/04_gis_analysts_data_scientists/creating_raster_information_product_using_raster_analytics.ipynb
@@ -178,14 +178,14 @@ samples:
 #   description: This sample show the capabilities of imagery layers and raster analytics.
 #   licenseInfo: ""
 #   tags: ["Data Science", "GIS", "Raster Analytics", "Product"]
-# - title: Crime analysis and clustering using geoanalytics and pyspark.ml
-#   url: https://www.arcgis.com/home/item.html?id=1410a28d3a8d4d2aa353efcf9b606b69
-#   path: ./samples/04_gis_analysts_data_scientists/crime_analysis_and_clustering_using_geoanalytics_and_pyspark.ipynb
-#   thumbnail: ./static/thumbnails/crime_analysis_and_clustering_using_geoanalytics_and_pyspark.png
-#   snippet: Analyze crime in Chicago
-#   description: Through this sample, we will demonstrate the utility of a number of geoanalytics tools including find_hot_spots, aggregate_points and calculate_density to visually understand geographical patterns.
-#   licenseInfo: ""
-#   tags: ["Data Science", "GIS", "Crime", "Clustering", "GeoAnalytics", "PySpark"]
+- title: Crime analysis and clustering using geoanalytics and pyspark.ml
+  url: https://www.arcgis.com/home/item.html?id=1410a28d3a8d4d2aa353efcf9b606b69
+  path: ./samples/04_gis_analysts_data_scientists/crime_analysis_and_clustering_using_geoanalytics_and_pyspark.ipynb
+  thumbnail: ./static/thumbnails/crime_analysis_and_clustering_using_geoanalytics_and_pyspark.png
+  snippet: Analyze crime in Chicago
+  description: Through this sample, we will demonstrate the utility of a number of geoanalytics tools including find_hot_spots, aggregate_points and calculate_density to visually understand geographical patterns.
+  licenseInfo: ""
+  tags: ["Data Science", "GIS", "Crime", "Clustering", "GeoAnalytics", "PySpark"]
 - title: Designate Bike Routes for Commuting Professionals
   url: https://www.arcgis.com/home/item.html?id=62b874f4e705448a95abac0240f3053d
   path: ./samples/04_gis_analysts_data_scientists/designate_bike_routes_for_commuting_professionals.ipynb
@@ -404,14 +404,14 @@ samples:
 #   description: In this notebook you will analyze the aggregated tracks to answer important questions about hurricane severity and how they correlate over time.
 #   licenseInfo: ""
 #   tags: ["Data Science", "GIS", "Hurricane", "Tracks", "GeoAnalytics", "Part 3"]
-# - title: Predict Floods with Unit Hydrographs
-#   url: https://www.arcgis.com/home/item.html?id=2bbf431943304ddeba48d00d14f8c34f
-#   path: ./samples/04_gis_analysts_data_scientists/predict-floods-with-unit-hydrographs.ipynb
-#   thumbnail: ./static/thumbnails/predict-floods-with-unit-hydrographs.png
-#   snippet: Estimate stream runoff during a predicted rainstorm in Vermont.
-#   description: Estimate stream runoff during a predicted rainstorm in Vermont.
-#   licenseInfo: ""
-#   tags: ["Data Science", "GIS", "Raster", "Floods", "Prediction", "Hydrograph"]
+- title: Predict Floods with Unit Hydrographs
+  url: https://www.arcgis.com/home/item.html?id=2bbf431943304ddeba48d00d14f8c34f
+  path: ./samples/04_gis_analysts_data_scientists/predict-floods-with-unit-hydrographs.ipynb
+  thumbnail: ./static/thumbnails/predict-floods-with-unit-hydrographs.png
+  snippet: Estimate stream runoff during a predicted rainstorm in Vermont.
+  description: Estimate stream runoff during a predicted rainstorm in Vermont.
+  licenseInfo: ""
+  tags: ["Data Science", "GIS", "Raster", "Floods", "Prediction", "Hydrograph"]
 # - title: Predicting El Niño–Southern Oscillation through correlation and time series analysis/deep learning
 #   url: https://www.arcgis.com/home/item.html?id=69df9348e964433d86a5c0fb8aaa48de
 #   path: ./samples/04_gis_analysts_data_scientists/predicting_enso.ipynb
@@ -453,14 +453,14 @@ samples:
   description: Weighted Linear Combination (WLC) method based on combined GIS and Remote Sensing techniques is used in the sample to create a potential hazard map for avalanches.
   licenseInfo: ""
   tags: ["Data Science", "GIS", "Avalanche", "Mapping"]
-# - title: Spatial and temporal distribution of service calls using big data tools
-#   url: https://www.arcgis.com/home/item.html?id=7b6991aa6f4d4ce0be6e43badb04d117
-#   path: ./samples/04_gis_analysts_data_scientists/spatial_and_temporal_trends_of_service_calls.ipynb
-#   thumbnail: ./static/thumbnails/spatial_and_temporal_trends_of_service_calls.png
-#   snippet: Use big data tools for spatial and temporal distribution
-#   description: This sample demonstrates ability of ArcGIS API for Python to perform big data analysis on your infrastructure.
-#   licenseInfo: ""
-#   tags: ["Data Science", "GIS", "Service Calls", "GeoAnalytics", "Trends", "Spatial", "Temporal"]
+- title: Spatial and temporal distribution of service calls using big data tools
+  url: https://www.arcgis.com/home/item.html?id=7b6991aa6f4d4ce0be6e43badb04d117
+  path: ./samples/04_gis_analysts_data_scientists/spatial_and_temporal_trends_of_service_calls.ipynb
+  thumbnail: ./static/thumbnails/spatial_and_temporal_trends_of_service_calls.png
+  snippet: Use big data tools for spatial and temporal distribution
+  description: This sample demonstrates ability of ArcGIS API for Python to perform big data analysis on your infrastructure.
+  licenseInfo: ""
+  tags: ["Data Science", "GIS", "Service Calls", "GeoAnalytics", "Trends", "Spatial", "Temporal"]
 - title: Temperature forecast using time series data
   url: https://www.arcgis.com/home/item.html?id=cf173caaba3f495f9592a9f180361ee4
   path: ./samples/04_gis_analysts_data_scientists/temperature_forecast_using_time_series_data.ipynb
@@ -640,15 +640,15 @@ samples:
 #   licenseInfo: ''
 #   tags: ['Data Science', 'GIS', "Maps", "Web Scenes", "Publish"]
 - title: Building a change detection app using Jupyter Dashboard
-  url: https://geosaurus.maps.arcgis.com/home/item.html?id=e3a0e48329cf4213a15574dd4b6b7694
+  url: https://www.arcgis.com/home/item.html?id=e3a0e48329cf4213a15574dd4b6b7694
   path: ./samples/02_power_users_developers/building_a_change_detection_app_using_jupyter_dashboard.ipynb
   thumbnail: ./static/thumbnails/jupyter_dashboard_change.png
   snippet: Create an interactive jupyter dashboard
   description: This sample illustrates an interactive Jupyter dashboard web app which can be used to detect the changes in vegetation between the two dates.
   licenseInfo: ''
   tags: ['Jupyter', 'Dashboard', "Vegetation", "raster"]
 - title: Identifying country names from incomplete house addresses
-  url: https://www.arcgis.com/home/item.html?id=55ec14f803774022862bcbda96653a0e
+  url: https://www.arcgis.com/home/item.html?id=d52e28b3cd854c7fa92157f5cc46ca2c
   path: ./samples/04_gis_analysts_data_scientists/identifying-country-names-from-incomplete-house-addresses.ipynb
   thumbnail: ./static/thumbnails/identifying_country_names_from_incomplete_house_addresses.jpg
   snippet: Build a classifier to predict the country for incomplete house addresses.
@@ -745,7 +745,7 @@ samples:
 #   runtime: advanced_gpu
 #   tags: ["Data Science", "GIS", "Coastline Extraction", "Imagery"]
 - title: Translating Story Map from one language to another using Deep Learning
-  url: https://www.arcgis.com/home/item.html?id=15a731751bad43c7abf7258b7aa90bac
+  url: https://www.arcgis.com/home/item.html?id=747174e3770940369ee9b14499ae5014
   path: ./samples/04_gis_analysts_data_scientists/translating_story_map_from_one_language_to_another.ipynb
   thumbnail: ./static/thumbnails/default.png
   snippet: In this notebook, we will pick a story map written in English language, and create another story map with the text translated to Spanish language using the arcgis.learn.text's TextTranslator class.
@@ -789,6 +789,33 @@ samples:
   licenseInfo: ""
   runtime: advanced_gpu
   tags: ["Data Science", "GIS", "Stream Extraction", "Deep Learning"]
+# - title: Supervised learning of tabular data using AutoML
+#   url: https://www.arcgis.com/home/item.html?id=06486550d1e148e298a9d572cfedcf5e
+#   path: ./samples/04_gis_analysts_data_scientists/tabular_data_supervised_learning_using_automl.ipynb
+#   thumbnail: ./static/thumbnails/default.png
+#   snippet: arcgis.learn users will now be able to use AutoML for supervised learning classification or regression problems involving tabular data.
+#   description: arcgis.learn users will now be able to use AutoML for supervised learning classification or regression problems involving tabular data.
+#   licenseInfo: ""
+#   runtime: advanced
+#   tags: ["Data Science", "GIS", "Supervised Learning", "Tabular Data"]
+# - title: Model explainability for ML Models
+#   url: https://www.arcgis.com/home/item.html?id=3eaade48a6204a08861b9cfb2497be83
+#   path: ./samples/04_gis_analysts_data_scientists/model_explainability_using_shap_for_tabular_data.ipynb
+#   thumbnail: ./static/thumbnails/default.png
+#   snippet: arcgis.learn has now added explainability feature to all of its models that work with tabular data. This includes all the MLModels and the fully connected networks.
+#   description: arcgis.learn has now added explainability feature to all of its models that work with tabular data. This includes all the MLModels and the fully connected networks.
+#   licenseInfo: ""
+#   runtime: advanced
+#   tags: ["Data Science", "GIS", "Supervised Learning", "Tabular Data"]
+# - title: Determining site suitability for oil palm plantation
+#   url: https://www.arcgis.com/home/item.html?id=47d07342d1204449bb661d6cb12d0368
+#   path: ./samples/04_gis_analysts_data_scientists/determining_site_suitability_for_oil_palm_plantation.ipynb
+#   thumbnail: ./static/thumbnails/default.png
+#   snippet: In this notebook, we determine the site suitability for oil palm development.
+#   description: In this notebook, we determine the site suitability for oil palm development.
+#   licenseInfo: ""
+#   runtime: advanced
+#   tags: ["Data Science", "GIS", "Site Suitability", "Raster Analysis"]
 guides: []
 labs:
 - title: Create Data

labs/create_data.ipynb

@@ -236,7 +236,7 @@
   "anaconda-cloud": {},
   "esriNotebookRuntime": {
    "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard",
-   "notebookRuntimeVersion": "4.0"
+   "notebookRuntimeVersion": "5.0"
   },
   "kernelspec": {
    "display_name": "Python 3",

labs/display_web_map.ipynb

@@ -87,7 +87,7 @@
   "anaconda-cloud": {},
   "esriNotebookRuntime": {
    "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard",
-   "notebookRuntimeVersion": "4.0"
+   "notebookRuntimeVersion": "5.0"
   },
   "kernelspec": {
    "display_name": "Python 3",

labs/display_web_scene.ipynb

@@ -192,7 +192,7 @@
   "anaconda-cloud": {},
   "esriNotebookRuntime": {
    "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard",
-   "notebookRuntimeVersion": "4.0"
+   "notebookRuntimeVersion": "5.0"
   },
   "kernelspec": {
    "display_name": "Python 3",

labs/download_data.ipynb

@@ -144,7 +144,7 @@
   "anaconda-cloud": {},
   "esriNotebookRuntime": {
    "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard",
-   "notebookRuntimeVersion": "4.0"
+   "notebookRuntimeVersion": "5.0"
   },
   "kernelspec": {
    "display_name": "Python 3",

labs/find_places.ipynb

@@ -253,7 +253,7 @@
  "metadata": {
   "esriNotebookRuntime": {
    "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard",
-   "notebookRuntimeVersion": "4.0"
+   "notebookRuntimeVersion": "5.0"
   },
   "kernelspec": {
    "display_name": "Python 3",

labs/import_data.ipynb

@@ -205,7 +205,7 @@
   "anaconda-cloud": {},
   "esriNotebookRuntime": {
    "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard",
-   "notebookRuntimeVersion": "4.0"
+   "notebookRuntimeVersion": "5.0"
   },
   "kernelspec": {
    "display_name": "Python 3",

labs/load_item_by_id.ipynb

@@ -134,7 +134,7 @@
  "metadata": {
   "esriNotebookRuntime": {
    "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard",
-   "notebookRuntimeVersion": "4.0"
+   "notebookRuntimeVersion": "5.0"
   },
   "kernelspec": {
    "display_name": "Python 3",

labs/load_spatial_data_frame.ipynb

@@ -349,7 +349,7 @@
  "metadata": {
   "esriNotebookRuntime": {
    "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard",
-   "notebookRuntimeVersion": "4.0"
+   "notebookRuntimeVersion": "5.0"
   },
   "kernelspec": {
    "display_name": "Python 3",

labs/search_and_geocode.ipynb

@@ -193,7 +193,7 @@
  "metadata": {
   "esriNotebookRuntime": {
    "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard",
-   "notebookRuntimeVersion": "4.0"
+   "notebookRuntimeVersion": "5.0"
   },
   "kernelspec": {
    "display_name": "Python 3",