{ "total": 31, "start": 1, "num": 31, "nextStart": -1, "items": [ { "id": "1fc3f446a7a04eff8bf4951b808ffb4a", "item": "Check_WebMaps_For_Broken_URLs.ipynb", "itemType": "file", "owner": "esri_notebook", "uploaded": 1556165986000, "modified": 1556165986000, "guid": null, "name": null, "title": "Content Management: Check for broken URLs", "type": "Notebook", "typeKeywords": [ "Notebook", "Python" ], "description": "This notebook will search through WebMap items, and will attempt to connect to each layer URL, basemap URL, etc. If the connection fails for any URL, the owner of the WebMap and other specified users will be notified. This notebook can be used to automatically identify broken WebMaps, alerting the necessary users so they can take the appropriate action.", "tags": [ "Content Management", "Administration", "Web Maps" ], "snippet": "Finds unreacheable URLs in WebMaps and notifies the owner.", "thumbnail": "thumbnail/admin_default.png", "documentation": null, "extent": [ ], "categories": [ "Administration", "Content Management" ], "lastModified": -1, "spatialReference": null, "accessInformation": null, "licenseInfo": null, "culture": "english (united states)", "properties": { "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard", "notebookRuntimeVersion": "10.7.1" }, "url": null, "proxyFilter": null, "access": "public", "size": 504476, "appCategories": [ ], "industries": [ ], "languages": [ ], "largeThumbnail": null, "banner": null, "screenshots": [ ], "listed": false, "numComments": 0, "numRatings": 0, "avgRating": 0, "numViews": 125, "groupCategories": [ "/Categories/Content Management", "/Categories/Administration" ], "scoreCompleteness": 78, "groupDesignations": null }, { "id": "14769f78b8d147a8b480b8887da80f8c", "item": "Validate_Federated_Servers.ipynb", "itemType": "file", "owner": "esri_notebook", "uploaded": 1556165988000, "modified": 1556165988000, "guid": null, "name": null, "title": "Administration: Validate all federated servers", "type": "Notebook", "typeKeywords": [ "Notebook", "Python" ], "description": "Enterprise installations most often contain federated servers to perform different roles within the infrastructure of the organization. Regularly validating the federated servers ensures proper functioning of technical components. The following notebook demonstrates how to validate servers within the Enterprise and send notifications if any servers fail to validate.", "tags": [ "Federation", "Administration" ], "snippet": "Validate all federated servers, report any warnings and errors.", "thumbnail": "thumbnail/admin_default.png", "documentation": null, "extent": [ ], "categories": [ "Administration" ], "lastModified": -1, "spatialReference": null, "accessInformation": null, "licenseInfo": null, "culture": "english (united states)", "properties": { "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard", "notebookRuntimeVersion": "10.7.1" }, "url": null, "proxyFilter": null, "access": "public", "size": 350290, "appCategories": [ ], "industries": [ ], "languages": [ ], "largeThumbnail": null, "banner": null, "screenshots": [ ], "listed": false, "numComments": 0, "numRatings": 0, "avgRating": 0, "numViews": 99, "groupCategories": [ "/Categories/Administration" ], "scoreCompleteness": 70, "groupDesignations": null }, { "id": "1df411cd59e344db9ca9d9bacd0b1b2a", "item": "Setup_Samples_Data.ipynb", "itemType": "file", "owner": "esri_notebook", "uploaded": 1556165990000, "modified": 1556165990000, "guid": null, "name": null, "title": "Administration: Prepare portal items for notebooks", "type": "Notebook", "typeKeywords": [ "Notebook", "Python" ], "description": "Other samples references published services: this notebook wil automatically publish some feature services used by other samples. Run this once before using other samples.", "tags": [ "Content Management", "Administration" ], "snippet": "Prepares the data needed by other sample notebooks. Run this before any other other samples.", "thumbnail": "thumbnail/admin_default.png", "documentation": null, "extent": [ ], "categories": [ "Administration", "Content Management", "Data Science and Management" ], "lastModified": -1, "spatialReference": null, "accessInformation": null, "licenseInfo": null, "culture": "english (united states)", "properties": { "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard", "notebookRuntimeVersion": "10.7.1" }, "url": null, "proxyFilter": null, "access": "public", "size": 335862, "appCategories": [ ], "industries": [ ], "languages": [ ], "largeThumbnail": null, "banner": null, "screenshots": [ ], "listed": false, "numComments": 0, "numRatings": 0, "avgRating": 0, "numViews": 121, "groupCategories": [ "/Categories/Data Science and Management", "/Categories/Content Management", "/Categories/Administration" ], "scoreCompleteness": 68, "groupDesignations": null }, { "id": "7d64dccdb6fb427da0df93410b7d02f8", "item": "Identify_Items_That_Use_Insecure_URLs.ipynb", "itemType": "file", "owner": "esri_notebook", "uploaded": 1556165991000, "modified": 1556165991000, "guid": null, "name": null, "title": "Content Management: Identify insecure items", "type": "Notebook", "typeKeywords": [ "Notebook", "Python" ], "description": "This notebook will search through all WebMap/WebScene/App Items in a portal/organization, identifying the 'insecure' ones if one or more service URLs use http://. These items will be displayed in this notebook, persisted in .csv files, and can have the potentially_insecure tag added to them.", "tags": [ "Content Management", "Administration", "Web Maps", "Web Scenes", "Apps" ], "snippet": "Finds WebMaps, WebScenes, and Apps with http:// URLs instead of https://.", "thumbnail": "thumbnail/admin_default.png", "documentation": null, "extent": [ ], "categories": [ "Administration", "Content Management" ], "lastModified": -1, "spatialReference": null, "accessInformation": null, "licenseInfo": null, "culture": "english (united states)", "properties": { "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard", "notebookRuntimeVersion": "10.7.1" }, "url": null, "proxyFilter": null, "access": "public", "size": 410331, "appCategories": [ ], "industries": [ ], "languages": [ ], "largeThumbnail": null, "banner": null, "screenshots": [ ], "listed": false, "numComments": 0, "numRatings": 0, "avgRating": 0, "numViews": 124, "groupCategories": [ "/Categories/Content Management", "/Categories/Administration" ], "scoreCompleteness": 80, "groupDesignations": null }, { "id": "f173671de6254c019f5fb37d706ae866", "item": "Notifications.ipynb", "itemType": "file", "owner": "esri_notebook", "uploaded": 1556165993000, "modified": 1556165993000, "guid": null, "name": null, "title": "Administration: Deploy automatic notifications", "type": "Notebook", "typeKeywords": [ "Notebook", "Python" ], "description": "This notebook will walk you through the technical details of how to configure and control external notification services for your notebooks.", "tags": [ "Notifications", "Content Management", "Administration" ], "snippet": "Programatically send out notifications from Python notebooks.", "thumbnail": "thumbnail/admin_default.png", "documentation": null, "extent": [ ], "categories": [ "Administration", "Content Management" ], "lastModified": -1, "spatialReference": null, "accessInformation": null, "licenseInfo": null, "culture": "english (united states)", "properties": { "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard", "notebookRuntimeVersion": "10.7.1" }, "url": null, "proxyFilter": null, "access": "public", "size": 1337333, "appCategories": [ ], "industries": [ ], "languages": [ ], "largeThumbnail": null, "banner": null, "screenshots": [ ], "listed": false, "numComments": 0, "numRatings": 0, "avgRating": 0, "numViews": 131, "groupCategories": [ "/Categories/Content Management", "/Categories/Administration" ], "scoreCompleteness": 71, "groupDesignations": null }, { "id": "d13c3ae432724bb0ae1562a146521f5f", "item": "Validate_User_Profiles.ipynb", "itemType": "file", "owner": "esri_notebook", "uploaded": 1556165994000, "modified": 1556165994000, "guid": null, "name": null, "title": "Administration: Validate user profiles", "type": "Notebook", "typeKeywords": [ "Notebook", "Python" ], "description": "This notebook will check attribute values for users in your GIS to monitor profiles.", "tags": [ "User Management", "Administration" ], "snippet": "Check users in your GIS for attributes above the minimum required ones.", "thumbnail": "thumbnail/admin_default.png", "documentation": null, "extent": [ ], "categories": [ "Administration" ], "lastModified": -1, "spatialReference": null, "accessInformation": null, "licenseInfo": null, "culture": "english (united states)", "properties": { "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard", "notebookRuntimeVersion": "10.7.1" }, "url": null, "proxyFilter": null, "access": "public", "size": 311507, "appCategories": [ ], "industries": [ ], "languages": [ ], "largeThumbnail": null, "banner": null, "screenshots": [ ], "listed": false, "numComments": 0, "numRatings": 0, "avgRating": 0, "numViews": 108, "groupCategories": [ "/Categories/Administration" ], "scoreCompleteness": 66, "groupDesignations": null }, { "id": "6def46ecd7dd411fb5934865153de274", "item": "Manage_Inactive_Users.ipynb", "itemType": "file", "owner": "esri_notebook", "uploaded": 1556165996000, "modified": 1556165996000, "guid": null, "name": null, "title": "Administration: Manage inactive users", "type": "Notebook", "typeKeywords": [ "Notebook", "Python" ], "description": "This notebook will search through all Users in a portal/organization, and will disable users that haven't logged in for a certain amount of days. This notebook will also email users a warning that they haven't logged in for a different amount of days.", "tags": [ "User Management", "Administration" ], "snippet": "Manage users in your GIS that haven't logged in in a while.", "thumbnail": "thumbnail/admin_default.png", "documentation": null, "extent": [ ], "categories": [ "Administration" ], "lastModified": -1, "spatialReference": null, "accessInformation": null, "licenseInfo": null, "culture": "english (united states)", "properties": { "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard", "notebookRuntimeVersion": "10.7.1" }, "url": null, "proxyFilter": null, "access": "public", "size": 373915, "appCategories": [ ], "industries": [ ], "languages": [ ], "largeThumbnail": null, "banner": null, "screenshots": [ ], "listed": false, "numComments": 0, "numRatings": 0, "avgRating": 0, "numViews": 117, "groupCategories": [ "/Categories/Administration" ], "scoreCompleteness": 71, "groupDesignations": null }, { "id": "ae0c2323dd08453fa0da4dd24034c9bb", "item": "Notify_of_License_Expiration.ipynb", "itemType": "file", "owner": "esri_notebook", "uploaded": 1556165997000, "modified": 1556165997000, "guid": null, "name": null, "title": "Administration: Set up license expiration notifications", "type": "Notebook", "typeKeywords": [ "Notebook", "Python" ], "description": "This notebook will search through all portal and server licenses in a portal/organization, and and send an email notification with an item id of a file to review for which, if any, licenses will expire within the next 30 days.", "tags": [ "Licensing", "Administration" ], "snippet": "Review portal and server licenses in your GIS.", "thumbnail": "thumbnail/admin_default.png", "documentation": null, "extent": [ ], "categories": [ "Administration" ], "lastModified": -1, "spatialReference": null, "accessInformation": null, "licenseInfo": null, "culture": "english (united states)", "properties": { "notebookRuntimeName": "ArcGIS Notebook 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Store", "DataStore", "Administration" ], "snippet": "Review and validate registered datastores in your GIS.", "thumbnail": "thumbnail/admin_default.png", "documentation": null, "extent": [ ], "categories": [ "Administration" ], "lastModified": -1, "spatialReference": null, "accessInformation": null, "licenseInfo": null, "culture": "english (united states)", "properties": { "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard", "notebookRuntimeVersion": "10.7.1" }, "url": null, "proxyFilter": null, "access": "public", "size": 337356, "appCategories": [ ], "industries": [ ], "languages": [ ], "largeThumbnail": null, "banner": null, "screenshots": [ ], "listed": false, "numComments": 0, "numRatings": 0, "avgRating": 0, "numViews": 183, "groupCategories": [ "/Categories/Administration" ], "scoreCompleteness": 75, "groupDesignations": null }, { "id": "e30856e2023b4190987a4f8b0f9ad890", "item": "Service_Report_by_Server.ipynb", "itemType": "file", "owner": "esri_notebook", "uploaded": 1556166000000, "modified": 1556166000000, "guid": null, "name": null, "title": "Content Management: Create service report by folder", "type": "Notebook", "typeKeywords": [ "Notebook", "Python" ], "description": "This notebook will search through all servers in the Enterprise and print out a list of services on those servers functioning to store services.", "tags": [ "Content Management", "Servers", "Services", "Administration" ], "snippet": "Print and then write out report files for services in each folder of servers in the GIS.", "thumbnail": "thumbnail/content_default.png", "documentation": null, "extent": [ ], "categories": [ "Content Management" ], "lastModified": -1, "spatialReference": null, "accessInformation": null, "licenseInfo": null, "culture": "english (united states)", "properties": { "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard", "notebookRuntimeVersion": "10.7.1" }, "url": null, "proxyFilter": null, "access": "public", "size": 392587, "appCategories": [ ], "industries": [ ], "languages": [ ], "largeThumbnail": null, "banner": null, "screenshots": [ ], "listed": false, "numComments": 0, "numRatings": 0, "avgRating": 0, "numViews": 127, "groupCategories": [ "/Categories/Content Management" ], "scoreCompleteness": 76, "groupDesignations": null }, { "id": "a94d9b4b5fa44ab69846e7a33e62c311", "item": "Validate_Item_Metadata.ipynb", "itemType": "file", "owner": "esri_notebook", "uploaded": 1556166002000, "modified": 1556166002000, "guid": null, "name": null, "title": "Content Management: Validate item metadata", "type": "Notebook", "typeKeywords": [ "Notebook", "Python" ], "description": "This notebook will search each item each user owns and report on the metadata properties status according to the organizational metadata requirements.", "tags": [ "Content Management", "Items", "Metadata", "Administration", "User Management" ], "snippet": "Print and then write out a report file for metadata property values for items in the GIS.", "thumbnail": "thumbnail/content_default.png", "documentation": null, "extent": [ ], "categories": [ "Content Management" ], "lastModified": -1, "spatialReference": null, "accessInformation": null, "licenseInfo": null, "culture": "english (united states)", "properties": { "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard", "notebookRuntimeVersion": "10.7.1" }, "url": null, "proxyFilter": null, "access": "public", "size": 330005, "appCategories": [ ], "industries": [ ], "languages": [ ], "largeThumbnail": null, "banner": null, "screenshots": [ ], "listed": false, "numComments": 0, "numRatings": 0, "avgRating": 0, "numViews": 115, "groupCategories": [ "/Categories/Content Management" ], "scoreCompleteness": 76, "groupDesignations": null }, { "id": "33f7e31abcb843979875501a28547bc2", "item": "Analyze_Urban_Heat_Using_Kriging.ipynb", "itemType": "file", "owner": "esri_notebook", "uploaded": 1556166003000, "modified": 1556166003000, "guid": null, "name": null, "title": "EBK Regression: Identify urban heat islands", "type": "Notebook", "typeKeywords": [ "Notebook", "Python" ], "description": "The urban heat island effect is the tendency for city centers to have significantly higher temperatures than surrounding rural areas. You will interpolate temperature measurements to identify areas with both high temperatures and a high density of residents over the age of 65, who are at highest risk for heat-related illnesses.", "tags": [ "EBK", "Kriging", "spatial interpolation", "cross validation", "model evaluation", "prediction" ], "snippet": "Interpolate temperature measurements to identify city areas with high temperatures and high density of residents.", "thumbnail": "thumbnail/urban_heat.png", "documentation": null, "extent": [ ], "categories": [ "Data Science and Analysis" ], "lastModified": -1, "spatialReference": null, "accessInformation": null, "licenseInfo": " - Uses arcpy", "culture": "english (united states)", "properties": { "notebookRuntimeName": "ArcGIS Notebook Python 3 Advanced", "notebookRuntimeVersion": "10.7.1" }, "url": null, "proxyFilter": null, "access": "public", "size": 3073033, "appCategories": [ ], "industries": [ ], "languages": [ ], "largeThumbnail": null, "banner": null, "screenshots": [ ], "listed": false, "numComments": 0, "numRatings": 0, "avgRating": 0, "numViews": 127, "groupCategories": [ "/Categories/Data Science and Analysis" ], "scoreCompleteness": 96, "groupDesignations": null }, { "id": "ec8f6ab785b34a2792d69a4abd05dc4b", "item": "Finding_Zones_For_a_New_Restaurant_Using_Clustering.ipynb", "itemType": "file", "owner": "esri_notebook", "uploaded": 1556166010000, "modified": 1556166010000, "guid": null, "name": null, "title": "Site Selection: Restaurant clusters", "type": "Notebook", "typeKeywords": [ "Notebook", "Python" ], "description": "This notebook will use the spatially-enabled dataframe to explore a dataset of restaurants, showing charts, maps, and performing clustering analysis to determine the potential best zones for a new restaurant.", "tags": [ "Clustering", "Ranking", "Scoring", "Planning" ], "snippet": "Perform data exploration and analysis to find zones for a new restaurant in Pittsburgh, PA.", "thumbnail": "thumbnail/restaurant_clusters.jpg", "documentation": null, "extent": [ ], "categories": [ "Data Science and Analysis" ], "lastModified": -1, "spatialReference": null, "accessInformation": null, "licenseInfo": " - Uses arcpy", "culture": "english (united states)", "properties": { "notebookRuntimeName": "ArcGIS Notebook Python 3 Advanced", "notebookRuntimeVersion": "10.7.1" }, "url": null, "proxyFilter": null, "access": "public", "size": 8097069, "appCategories": [ ], "industries": [ ], "languages": [ ], "largeThumbnail": null, "banner": null, "screenshots": [ ], "listed": false, "numComments": 0, "numRatings": 0, "avgRating": 0, "numViews": 112, "groupCategories": [ "/Categories/Data Science and Analysis" ], "scoreCompleteness": 95, "groupDesignations": null }, { "id": "f7be838467724aa2ae23a5441942bbdc", "item": "Climate_Downscaling.ipynb", "itemType": "file", "owner": "esri_notebook", "uploaded": 1556166017000, "modified": 1556166017000, "guid": null, "name": null, "title": "Deep Learning: Downscale climate models", "type": "Notebook", "typeKeywords": [ "Notebook", "Python" ], "description": "This notebook will demonstrate spatial data wrangling, spatial and non-spatial exploratory data analysis, and integrating machine learning functionality from libraries such as scikit-learn and arcPy for spatial modelling", "tags": [ "Machine Learning", "Predictive Modeling", "Climate Science", "Climate", "Global Climate Models", "EBK" ], "snippet": "Harness ArcGIS platform's internal machine learning libraries and strong extensibility to external machine learning libraries to downscale climate models", "thumbnail": "thumbnail/climate_downscaling.png", "documentation": null, "extent": [ ], "categories": [ "Data Science and Analysis" ], "lastModified": -1, "spatialReference": null, "accessInformation": null, "licenseInfo": " - Uses arcpy", "culture": "english (united states)", "properties": { "notebookRuntimeName": "ArcGIS Notebook Python 3 Advanced", "notebookRuntimeVersion": "10.7.1" }, "url": null, "proxyFilter": null, "access": "public", "size": 880175, "appCategories": [ ], "industries": [ ], "languages": [ ], "largeThumbnail": null, "banner": null, "screenshots": [ ], "listed": false, "numComments": 0, "numRatings": 0, "avgRating": 0, "numViews": 112, "groupCategories": [ "/Categories/Data Science and Analysis" ], "scoreCompleteness": 95, "groupDesignations": null }, { "id": "cf171fbc391241d78908dbaef1024076", "item": "Asthma_Prediction.ipynb", "itemType": "file", "owner": "esri_notebook", "uploaded": 1556166019000, "modified": 1556166019000, "guid": null, "name": null, "title": "Forest-based Classification: Predict asthma rates", "type": "Notebook", "typeKeywords": [ "Notebook", "Python" ], "description": "Determine what census block groups in Connecticut have the highest children's hospitalization rates for asthma related issues.", "tags": [ "Machine Learning", "Predictive Modeling", "Public Health", "Data Science" ], "snippet": "Determine what census block groups in Connecticut have the highest children's hospitalization rates for asthma related issues.", "thumbnail": "thumbnail/asthma_prediction.png", "documentation": null, "extent": [ ], "categories": [ "Data Science and Analysis" ], "lastModified": -1, "spatialReference": null, "accessInformation": null, "licenseInfo": " - Uses arcpy, Uses Notebook Server Advanced", "culture": "english (united states)", "properties": { "notebookRuntimeName": "ArcGIS Notebook Python 3 Advanced", "notebookRuntimeVersion": "10.7.1" }, "url": null, "proxyFilter": null, "access": "public", "size": 5369891, "appCategories": [ ], "industries": [ ], "languages": [ ], "largeThumbnail": null, "banner": null, "screenshots": [ ], "listed": false, "numComments": 0, "numRatings": 0, "avgRating": 0, "numViews": 130, "groupCategories": [ "/Categories/Data Science and Analysis" ], "scoreCompleteness": 93, "groupDesignations": null }, { "id": "bf23d91c87e04c00be81c969d8065cb0", "item": "Boston_Crime_Analysis.ipynb", "itemType": "file", "owner": "esri_notebook", "uploaded": 1556166023000, "modified": 1556166023000, "guid": null, "name": null, "title": "Crime Analysis: Boston Police", "type": "Notebook", "typeKeywords": [ "Notebook", "Python" ], "description": "This notebook is to create a default technology platform to support the publication of the City's public information, in the form of data, and to make this information easy to find, access, and use by a broad audience", "tags": [ ], "snippet": "Explore crime incidents in Boston.", "thumbnail": "thumbnail/boston_crime.jpg", "documentation": null, "extent": [ ], "categories": [ "Data Science and Analysis" ], "lastModified": -1, "spatialReference": null, "accessInformation": null, "licenseInfo": " - Uses arcpy", "culture": "english (united states)", "properties": { "notebookRuntimeName": "ArcGIS Notebook Python 3 Advanced", "notebookRuntimeVersion": "10.7.1" }, "url": null, "proxyFilter": null, "access": "public", "size": 3709120, "appCategories": [ ], "industries": [ ], "languages": [ ], "largeThumbnail": null, "banner": null, "screenshots": [ ], "listed": false, "numComments": 0, "numRatings": 0, "avgRating": 0, "numViews": 105, "groupCategories": [ "/Categories/Data Science and Analysis" ], "scoreCompleteness": 70, "groupDesignations": null }, { "id": "4e904b8532d94f25a06b8f85537e16c5", "item": "forecasting_enso_via_correlation_time_series_deep_learning.ipynb", "itemType": "file", "owner": "esri_notebook", "uploaded": 1556166028000, "modified": 1556166028000, "guid": null, "name": null, "title": "Deep Learning: Predict El Niño\u2013southern oscillation", "type": "Notebook", "typeKeywords": [ "Notebook", "Python" ], "description": "ENSO can have tremendous potential impact such as droughts, floods, and tropical storms. Accurate characterization of ENSO is critical for understanding the trends. In climate science, ENSO is characterized through Southern Oscillation Index (SOI), a standardized index based on the observed sea level pressure differences between Tahiti and Darwin, Australia. Predicting SOI is the first step of ENSO forecasting which this notebook attempts to do using time series analysis using a LSTM model.", "tags": [ "LSTM", "ENSO", "weather", "climate", "prediction", "deep learning", "keras", "time series", "El Niño", "SOI" ], "snippet": "This example uses correlation analysis and time series analysis to predict El Niño\u2013Southern Oscillation (ENSO) based on climate variables and indices.", "thumbnail": "thumbnail/forecasting-enso.png", "documentation": null, "extent": [ ], "categories": [ "Data Science and Analysis" ], "lastModified": -1, "spatialReference": null, "accessInformation": null, "licenseInfo": null, "culture": "english (united states)", "properties": { "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard", "notebookRuntimeVersion": "10.7.1" }, "url": null, "proxyFilter": null, "access": "public", "size": 1947910, "appCategories": [ ], "industries": [ ], "languages": [ ], "largeThumbnail": null, "banner": null, "screenshots": [ ], "listed": false, "numComments": 0, "numRatings": 0, "avgRating": 0, "numViews": 118, "groupCategories": [ "/Categories/Data Science and Analysis" ], "scoreCompleteness": 81, "groupDesignations": null }, { "id": "30348417574343bc9b324a10f43de8e0", "item": "calculate_post_fire_landslide_risk.ipynb", "itemType": "file", "owner": "esri_notebook", "uploaded": 1556166030000, "modified": 1556166030000, "guid": null, "name": null, "title": "Raster Analytics: Calculate wildfire landslide risk", "type": "Notebook", "typeKeywords": [ "Notebook", "Python" ], "description": "In this notebook sample, you'll use the available image services to assess landslide risk per watershed within the burn area. You'll create a landslide risk map and then summarize the landslide risk based on watershed subbasin. You'll use raster function chains to derive a burn severity map, topographic slope map, and a landcover index map. These individual processing chains will be combined into one processing chain for distributed processing in your Raster Analytics system and then be summarized by watershed subbasin. The landslide risk map you create will help to aid emergency management and rescue workers.", "tags": [ "Raster", "ArcGIS Image Server", "Raster Analysis", "Weighted Overlay Analysis" ], "snippet": "Use raster function chains to create a landslide risk map,and summarized risk by watershed subbasin in Sonoma and Napa counties, CA.", "thumbnail": "thumbnail/calculate-landslide-risk-for-communities-affected-by-wildfires.png", "documentation": null, "extent": [ ], "categories": [ "Data Science and Analysis" ], "lastModified": -1, "spatialReference": null, "accessInformation": null, "licenseInfo": null, "culture": "english (united states)", "properties": { "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard", "notebookRuntimeVersion": "10.7.1" }, "url": null, "proxyFilter": null, "access": "public", "size": 5258196, "appCategories": [ ], "industries": [ ], "languages": [ ], "largeThumbnail": null, "banner": null, "screenshots": [ ], "listed": false, "numComments": 0, "numRatings": 0, "avgRating": 0, "numViews": 117, "groupCategories": [ "/Categories/Data Science and Analysis" ], "scoreCompleteness": 81, "groupDesignations": null }, { "id": "d909d8b6d6294fb4bffae94f4e8532a0", "item": "no_dumping_drains_to_ocean.ipynb", "itemType": "file", "owner": "esri_notebook", "uploaded": 1556166031000, "modified": 1556166031000, "guid": null, "name": null, "title": "Network Analysis: Track river pollutants", "type": "Notebook", "typeKeywords": [ "Notebook", "Python" ], "description": "This notebook shows how to find the watershed area that drains to a storm drain and the route that pollutants will take if they are dumped or washed into the drain.", "tags": [ "hydrology", "trace downstream", "watershed", "pollution", "water", "conservation" ], "snippet": "Use hydrology tools to create watershed boundaries and trace downstream path to analyze how pollutants reach larger water bodies", "thumbnail": "thumbnail/no_dumping.jpeg", "documentation": null, "extent": [ ], "categories": [ "Data Science and Analysis" ], "lastModified": -1, "spatialReference": null, "accessInformation": null, "licenseInfo": " - Needs AGO helper services configured", "culture": "english (united states)", "properties": { "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard", "notebookRuntimeVersion": "10.7.1" }, "url": null, "proxyFilter": null, "access": "public", "size": 810634, "appCategories": [ ], "industries": [ ], "languages": [ ], "largeThumbnail": null, "banner": null, "screenshots": [ ], "listed": false, "numComments": 0, "numRatings": 0, "avgRating": 0, "numViews": 118, "groupCategories": [ "/Categories/Data Science and Analysis" ], "scoreCompleteness": 95, "groupDesignations": null }, { "id": "991881fa47fc47fba5975435ab5c2a64", "item": "part1_prepare_hurricane_data.ipynb", "itemType": "file", "owner": "esri_notebook", "uploaded": 1556166035000, "modified": 1556166035000, "guid": null, "name": null, "title": "Data Preparation: Hurricane analysis, part 1/3", "type": "Notebook", "typeKeywords": [ "Notebook", "Python" ], "description": "This notebook downloads data from NCEI portal, does extenstive pre-processing in the form of clearing headers, merges redundant columns and aggregate the observations into hurricane tracks.", "tags": [ "Data Exploration", "Data Cleaning", "Climate Science", "Climate", "GeoAnalytics", "Aggregation" ], "snippet": "Download and clean hurricane data for the past 169 years from the NCEI portal.", "thumbnail": "thumbnail/hurricane_part1.png", "documentation": null, "extent": [ ], "categories": [ "Data Science and Analysis" ], "lastModified": -1, "spatialReference": null, "accessInformation": null, "licenseInfo": " - Must have access to public NCEI websites to download data", "culture": "english (united states)", "properties": { "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard", "notebookRuntimeVersion": "10.7.1" }, "url": null, "proxyFilter": null, "access": "public", "size": 3608842, "appCategories": [ ], "industries": [ ], "languages": [ ], "largeThumbnail": null, "banner": null, "screenshots": [ ], "listed": false, "numComments": 0, "numRatings": 0, "avgRating": 0, "numViews": 96, "groupCategories": [ "/Categories/Data Science and Analysis" ], "scoreCompleteness": 95, "groupDesignations": null }, { "id": "f2451793dc3840fa8bbe40f01739044e", "item": "part2_explore_hurricane_tracks.ipynb", "itemType": "file", "owner": "esri_notebook", "uploaded": 1556166040000, "modified": 1556166040000, "guid": null, "name": null, "title": "Exploratory Statistics: Hurricane analysis, part 2/3", "type": "Notebook", "typeKeywords": [ "Notebook", "Python" ], "description": "This notebook will demonstrates exploratory analysis on hurricane tracks. You will map hurricanes into basins, explore the number of hurricanes in each basin and sub basin. You find which hurricane names are most popular. Through overlay analysis, you will find the what percent of hurricanes make landfall, the distance they travel inland after landfall. You will explore the landfall locations further, do a density analysis and through geo-enrichment, you will understand the demographics of the population that lives in these high risk places.", "tags": [ "Density Analysis", "Climate Science", "Climate", "GeoAnalytics", "Aggregation" ], "snippet": "Perform exploratory data analysis on hurricane tracks.", "thumbnail": "thumbnail/hurricane_part2.jpg", "documentation": null, "extent": [ ], "categories": [ "Data Science and Analysis" ], "lastModified": -1, "spatialReference": null, "accessInformation": null, "licenseInfo": null, "culture": "english (united states)", "properties": { "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard", "notebookRuntimeVersion": "10.7.1" }, "url": null, "proxyFilter": null, "access": "public", "size": 2782181, "appCategories": [ ], "industries": [ ], "languages": [ ], "largeThumbnail": null, "banner": null, "screenshots": [ ], "listed": false, "numComments": 0, "numRatings": 0, "avgRating": 0, "numViews": 118, "groupCategories": [ "/Categories/Data Science and Analysis" ], "scoreCompleteness": 76, "groupDesignations": null }, { "id": "42822770113843639848c77920c7d522", "item": "part3_analyze_hurricane_tracks.ipynb", "itemType": "file", "owner": "esri_notebook", "uploaded": 1556166042000, "modified": 1556166042000, "guid": null, "name": null, "title": "Correlation: Hurricane analysis, part 3/3", "type": "Notebook", "typeKeywords": [ "Notebook", "Python" ], "description": "This notebook analyzes various hurricane metrics to answer the question: 'Does hurricane severity increase over time?'. You will identify and fill for missing data, perform correlation analysis for various metrics over time.", "tags": [ "Climate Science", "Climate", "GeoAnalytics", "Aggregation" ], "snippet": "Aalyze the aggregated hurricane tracks to answer important questions about hurricane severity and how they correlate over time.", "thumbnail": "thumbnail/hurricane_part3.png", "documentation": null, "extent": [ ], "categories": [ "Data Science and Analysis" ], "lastModified": -1, "spatialReference": null, "accessInformation": null, "licenseInfo": null, "culture": "english (united states)", "properties": { "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard", "notebookRuntimeVersion": "10.7.1" }, "url": null, "proxyFilter": null, "access": "public", "size": 4241662, "appCategories": [ ], "industries": [ ], "languages": [ ], "largeThumbnail": null, "banner": null, "screenshots": [ ], "listed": false, "numComments": 0, "numRatings": 0, "avgRating": 0, "numViews": 119, "groupCategories": [ "/Categories/Data Science and Analysis" ], "scoreCompleteness": 78, "groupDesignations": null }, { "id": "8577f4612f0f41dbab47355d144797c3", "item": "identifying_suitable_sites_for_new_ALS_clinics_using_location_allocation_analysis.ipynb", "itemType": "file", "owner": "esri_notebook", "uploaded": 1556166044000, "modified": 1556166044000, "guid": null, "name": null, "title": "Site Selection: Health clinics", "type": "Notebook", "typeKeywords": [ "Notebook", "Python" ], "description": "Location is everything for the chronically ill. For patients with amyotrophic lateral sclerosis (ALS), visits to clinics are exhausting full-day engagements involving sessions with highly trained specialists from several disciplines. Patients with long drives to their nearest clinic may also face the additional hardship of having to plan for travel days to and from the clinic as well as for food and lodging. This notebook demonstrates how ArcGIS can perform network analysis to identify potential sites for new ALS clinics in California to improve access for patients who do not live near a clinic.", "tags": [ "Location Allocation Analysis", "Network Analysis", "Network" ], "snippet": "Use location allocation tools to identify a suitable location for siting a new clinic for ALS patients.", "thumbnail": "thumbnail/als-location-allocation.jpeg", "documentation": null, "extent": [ ], "categories": [ "Data Science and Analysis" ], "lastModified": -1, "spatialReference": null, "accessInformation": null, "licenseInfo": null, "culture": "english (united states)", "properties": { "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard", "notebookRuntimeVersion": "10.7.1" }, "url": null, "proxyFilter": null, "access": "public", "size": 1684567, "appCategories": [ ], "industries": [ ], "languages": [ ], "largeThumbnail": null, "banner": null, "screenshots": [ ], "listed": false, "numComments": 0, "numRatings": 0, "avgRating": 0, "numViews": 120, "groupCategories": [ "/Categories/Data Science and Analysis" ], "scoreCompleteness": 81, "groupDesignations": null }, { "id": "4dd7029b82b8481a8ed3689ab33497b0", "item": "Finding_Hospitals_Closest_to_an_Incident.ipynb", "itemType": "file", "owner": "esri_notebook", "uploaded": 1556166046000, "modified": 1556166046000, "guid": null, "name": null, "title": "Routing: Find the nearest hospital", "type": "Notebook", "typeKeywords": [ "Notebook", "Python" ], "description": "Given the location of an emergency incident where people have been harmed, this notebook creates routes to all nearby hospitals so personnel can identify the route to get them to a hospital in shortest time.", "tags": [ "Network Analysis", "Data Exploration and Analysis" ], "snippet": "Solve network routes for the nearest hospitals to a specified incident.", "thumbnail": "thumbnail/hospitals_route.png", "documentation": null, "extent": [ ], "categories": [ "Data Science and Analysis" ], "lastModified": -1, "spatialReference": null, "accessInformation": null, "licenseInfo": null, "culture": "english (united states)", "properties": { "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard", "notebookRuntimeVersion": "10.7.1" }, "url": null, "proxyFilter": null, "access": "public", "size": 1762313, "appCategories": [ ], "industries": [ ], "languages": [ ], "largeThumbnail": null, "banner": null, "screenshots": [ ], "listed": false, "numComments": 0, "numRatings": 0, "avgRating": 0, "numViews": 122, "groupCategories": [ "/Categories/Data Science and Analysis" ], "scoreCompleteness": 70, "groupDesignations": null }, { "id": "ff3b54c53ae14f869312288176be4caa", "item": "detecting_swimming_pools_using_satellite_image_and_deep_learning.ipynb", "itemType": "file", "owner": "esri_notebook", "uploaded": 1556166048000, "modified": 1556166048000, "guid": null, "name": null, "title": "Deep Learning: Detect swimming pools", "type": "Notebook", "typeKeywords": [ "Notebook", "Python" ], "description": "This notebook demonstrates the workflow of applying deep learning to detecting swimming pools using ArcGIS API for Python, including how to export training data, train a model and deploy the model for inference.", "tags": [ "Deep Learning", "Data Analysis", "Object Detection", "Raster Analysis" ], "snippet": "Detect swimming pools using deep learning/object detection.", "thumbnail": "thumbnail/swimming_part1.png", "documentation": null, "extent": [ ], "categories": [ "Data Science and Analysis" ], "lastModified": -1, "spatialReference": null, "accessInformation": null, "licenseInfo": null, "culture": "english (united states)", "properties": { "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard", "notebookRuntimeVersion": "10.7.1" }, "url": null, "proxyFilter": null, "access": "public", "size": 12604237, "appCategories": [ ], "industries": [ ], "languages": [ ], "largeThumbnail": null, "banner": null, "screenshots": [ ], "listed": false, "numComments": 0, "numRatings": 0, "avgRating": 0, "numViews": 126, "groupCategories": [ "/Categories/Data Science and Analysis" ], "scoreCompleteness": 73, "groupDesignations": null }, { "id": "b6ef1a796dbc432094b0b3a774d4e07e", "item": "counting_features_in_satellite_images_using_scikit_image.ipynb", "itemType": "file", "owner": "esri_notebook", "uploaded": 1556166050000, "modified": 1556166050000, "guid": null, "name": null, "title": "Raster Analytics: Count features in satellite images", "type": "Notebook", "typeKeywords": [ "Notebook", "Python" ], "description": "Given a Landat image, we count the number of center pivots through Difference of Gaussian method using scikit-image library.", "tags": [ "Raster Analysis", "scikit-image", "image segmentation", "satellite imagery" ], "snippet": "Count the number of center pivots farms using scikit-image.", "thumbnail": "thumbnail/countingfeatures.png", "documentation": null, "extent": [ ], "categories": [ "Data Science and Analysis" ], "lastModified": -1, "spatialReference": null, "accessInformation": null, "licenseInfo": null, "culture": "english (united states)", "properties": { "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard", "notebookRuntimeVersion": "10.7.1" }, "url": null, "proxyFilter": null, "access": "public", "size": 2991600, "appCategories": [ ], "industries": [ ], "languages": [ ], "largeThumbnail": null, "banner": null, "screenshots": [ ], "listed": false, "numComments": 0, "numRatings": 0, "avgRating": 0, "numViews": 114, "groupCategories": [ "/Categories/Data Science and Analysis" ], "scoreCompleteness": 75, "groupDesignations": null }, { "id": "4af324c4bd2c4cfaba20eb6d3fa3ac5c", "item": "Chennai_Floods_Analysis.ipynb", "itemType": "file", "owner": "esri_notebook", "uploaded": 1556166052000, "modified": 1556166052000, "guid": null, "name": null, "title": "Network Analysis: Investigate Chennai floods", "type": "Notebook", "typeKeywords": [ "Notebook", "Python" ], "description": "Plot the locations of rainfall guages and interpolate the data to create a continuous surface representing the amount of rainfall throughout the state. Plot the locations of major lakes and trace downstream the path floods waters would take. We create a buffer around this path to demark at risk areas.", "tags": [ "downstream", "interpolation", "time series", "routing" ], "snippet": "Analyze rainfall and flood data in the Indian city of Chennai. ", "thumbnail": null, "documentation": null, "extent": [ ], "categories": [ "Data Science and Analysis" ], "lastModified": -1, "spatialReference": null, "accessInformation": null, "licenseInfo": null, "culture": "english (united states)", "properties": { "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard", "notebookRuntimeVersion": "10.7.1" }, "url": null, "proxyFilter": null, "access": "public", "size": 3440919, "appCategories": [ ], "industries": [ ], "languages": [ ], "largeThumbnail": null, "banner": null, "screenshots": [ ], "listed": false, "numComments": 0, "numRatings": 0, "avgRating": 0, "numViews": 130, "groupCategories": [ "/Categories/Data Science and Analysis" ], "scoreCompleteness": 63, "groupDesignations": null }, { "id": "4a554d6049d8432e853bc8684234796a", "item": "Analyze_Patterns_in_Construction_Permits_Part2.ipynb", "itemType": "file", "owner": "esri_notebook", "uploaded": 1556166054000, "modified": 1556166054000, "guid": null, "name": null, "title": "Data Summarization: Construction permits, part 2/2", "type": "Notebook", "typeKeywords": [ "Notebook", "Python" ], "description": "In this lesson, we'll move beyond exploration and run spatial analysis tools to answer specific questions that can't be answered by the data itself. In particular, we want to know why permits spiked in Germantown in 2011 and predict where future permit spikes\u2014and, by extension, future growth\u2014are likely to occur.", "tags": [ "aggregation", "construction", "enrichment" ], "snippet": "Analyze past permit spikes and predict where future permit spikes will happen.", "thumbnail": null, "documentation": null, "extent": [ ], "categories": [ "Data Science and Analysis" ], "lastModified": -1, "spatialReference": null, "accessInformation": null, "licenseInfo": null, "culture": "english (united states)", "properties": { "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard", "notebookRuntimeVersion": "10.7.1" }, "url": null, "proxyFilter": null, "access": "public", "size": 1774687, "appCategories": [ ], "industries": [ ], "languages": [ ], "largeThumbnail": null, "banner": null, "screenshots": [ ], "listed": false, "numComments": 0, "numRatings": 0, "avgRating": 0, "numViews": 117, "groupCategories": [ "/Categories/Data Science and Analysis" ], "scoreCompleteness": 63, "groupDesignations": null }, { "id": "59bdee784308481196b9214974f0c5b3", "item": "Analyze_Patterns_in_Construction_Permits_Part1.ipynb", "itemType": "file", "owner": "esri_notebook", "uploaded": 1556166055000, "modified": 1556166055000, "guid": null, "name": null, "title": "Data Visualization: Construction permits, part 1/2", "type": "Notebook", "typeKeywords": [ "Notebook", "Python" ], "description": "First, you'll add the permit data from ArcGIS Living Atlas of the World. You'll explore the data and become familiar with exactly what kind of information it contains. Then, you'll analyze the data to detect patterns and find out why growth is occurring. Once you've gathered your findings from your exploration and analysis, you'll share your work online.", "tags": [ "data cleaning", "temporal trends" ], "snippet": "Detect growth via construction permits.", "thumbnail": null, "documentation": null, "extent": [ ], "categories": [ "Data Science and Analysis" ], "lastModified": -1, "spatialReference": null, "accessInformation": null, "licenseInfo": null, "culture": "english (united states)", "properties": { "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard", "notebookRuntimeVersion": "10.7.1" }, "url": null, "proxyFilter": null, "access": "public", "size": 5735024, "appCategories": [ ], "industries": [ ], "languages": [ ], "largeThumbnail": null, "banner": null, "screenshots": [ ], "listed": false, "numComments": 0, "numRatings": 0, "avgRating": 0, "numViews": 100, "groupCategories": [ "/Categories/Data Science and Analysis" ], "scoreCompleteness": 46, "groupDesignations": null }, { "id": "24a3730996d0483e8264f4483e3a7f3b", "item": "which_college_district_has_the_fewest_low_income_families.ipynb", "itemType": "file", "owner": "esri_notebook", "uploaded": 1556166057000, "modified": 1556166057000, "guid": null, "name": null, "title": "Which college district has the fewest low-income families", "type": "Notebook", "typeKeywords": [ "Notebook", "Python" ], "description": "Use aggregation analysis to find out how many low-income families are within each community college district.", "tags": [ "Aggregation analysis", "Data Exploration and Analysis" ], "snippet": "Summarize number of low-income families by college district.", "thumbnail": "thumbnail/college_district.png", "documentation": null, "extent": [ ], "categories": [ "Data Science and Analysis" ], "lastModified": -1, "spatialReference": null, "accessInformation": null, "licenseInfo": null, "culture": "english (united states)", "properties": { "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard", "notebookRuntimeVersion": "10.7.1" }, "url": null, "proxyFilter": null, "access": "public", "size": 1957942, "appCategories": [ ], "industries": [ ], "languages": [ ], "largeThumbnail": null, "banner": null, "screenshots": [ ], "listed": false, "numComments": 0, "numRatings": 0, "avgRating": 0, "numViews": 115, "groupCategories": [ "/Categories/Data Science and Analysis" ], "scoreCompleteness": 65, "groupDesignations": null }, { "id": "8a86dc4b4c764de2ba2ce828b27714b8", "item": "Wildfire_analysis_using_Sentinel-2_imagery.ipynb", "itemType": "file", "owner": "esri_notebook", "uploaded": 1556166059000, "modified": 1556166059000, "guid": null, "name": null, "title": "Pawnee Fire Analysis", "type": "Notebook", "typeKeywords": [ "Notebook", "Python" ], "description": "In this notebook sample, you will use the sentinel-2 data to perform remote sensing. You will perform visual assessment of the burnt area, compute NBR on the pre fire and post fire scenes, compute the NBR difference to identify places that have been affected by the fire, normalize the values and classify the severity of the burnt areas all using raster functions. You will also perform human impact assessment due to the fire.", "tags": [ "Raster", "ArcGIS Image Server", "Raster Analysis", "GeoAnalytics", "Raster Functions" ], "snippet": "Perform wildfire analysis using Sentinel-2 Imagery Layer", "thumbnail": "thumbnail/wildfire_analysis_using_sentinel-2.png", "documentation": null, "extent": [ ], "categories": [ "Data Science and Analysis" ], "lastModified": -1, "spatialReference": null, "accessInformation": null, "licenseInfo": null, "culture": "english (united states)", "properties": { "notebookRuntimeName": "ArcGIS Notebook Python 3 Standard", "notebookRuntimeVersion": "10.7.1" }, "url": null, "proxyFilter": null, "access": "public", "size": 3325059, "appCategories": [ ], "industries": [ ], "languages": [ ], "largeThumbnail": null, "banner": null, "screenshots": [ ], "listed": false, "numComments": 0, "numRatings": 0, "avgRating": 0, "numViews": 120, "groupCategories": [ "/Categories/Data Science and Analysis" ], "scoreCompleteness": 76, "groupDesignations": null } ] }