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: Data Visualization - Construction permits, part 1/2

url: https://geosaurus.maps.arcgis.com/home/item.html?id=467bc6806c9e40dc8222744e0937b80c

path: ./samples/04_gis_analysts_data_scientists/analyze_patterns_in_construction_permits_part1.ipynb

thumbnail: ./static/thumbnails/analyze_patterns_in_construction_permits_part1.jpg

snippet: Observe spatial and temporal growth trends in construction permits

description: In this notebook, you'll explore Montgomery County permit data. 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.

licenseInfo: ""

tags: ["Data Science", "GIS", "Construction", "Permits"]

- title: Data Summarization - Construction permits, part 2/2

url: https://geosaurus.maps.arcgis.com/home/item.html?id=0d980b0273b14908bcd5b159757e93e1

path: ./samples/04_gis_analysts_data_scientists/analyze_patterns_in_construction_permits_part2.ipynb

thumbnail: ./static/thumbnails/analyze_patterns_in_construction_permits_part2.png

snippet: Run spatial analysis tools to predict permit spikes

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 - and, by extension, future growth - are likely to occur.

licenseInfo: ""

tags: ["Data Science", "GIS", "Construction", "Permits"]

- title: Analyzing United States tornadoes

url: https://geosaurus.maps.arcgis.com/home/item.html?id=ab5d87bffc684f9088c84d2120782e28

path: ./samples/04_gis_analysts_data_scientists/analyze_us_tornadoes.ipynb

thumbnail: ./static/thumbnails/analyze_us_tornadoes.jpg

snippet: Analyze tornadoes in the USA

description: In this notebook, we demonstrate how to use aggregation analysis to summarize the number of data points within each polygon

licenseInfo: ""

tags: ["Data Science", "GIS", "Tornadoes", "USA"]

- title: Analyzing the factors of growth and spatial distribution of Airbnb properties across New York City

url: https://geosaurus.maps.arcgis.com/home/item.html?id=acc8b4e5e0d5422d8af19166c1fc21d5

path: ./samples/04_gis_analysts_data_scientists/analyzing_growth_factors_of_airbnb_properties_in_new_york_city.ipynb

thumbnail: ./static/thumbnails/analyzing_growth_factors_of_airbnb_properties_in_new_york_city.png

snippet: Analyze growth factors of Arbnb properties in New York

description: A study is carried out in this sample notebook to understand the factors that are fuelling widespread growth in the number of Airbnb listings

licenseInfo: ""

tags: ["Data Science", "GIS", "airbnb"]

- title: Analyzing violent crime

url: https://geosaurus.maps.arcgis.com/home/item.html?id=f19cbf3595de4bf898e7228a46b79ffd

path: ./samples/04_gis_analysts_data_scientists/analyzing_violent_crime.ipynb

thumbnail: ./static/thumbnails/analyzing_violent_crime.png

snippet: Analyze violent crime in Chicago

description: Through this sample, we will demonstrate the utility of a number of spatial analysis methods including hot spot analysis, feature overlay, data enrichment and spatial selection using ArGIS API for Python.

licenseInfo: ""

tags: ["Data Science", "GIS", "Violent Crime"]

- title: Automate Building Footprint Extraction using Deep learning

url: https://geosaurus.maps.arcgis.com/home/item.html?id=342919a470044ddaac8a299820a51204

path: ./samples/04_gis_analysts_data_scientists/automate_building_footprint_extraction_using_instance_segmentation.ipynb

thumbnail: ./static/thumbnails/automate_building_footprint_extraction_using_instance_segmentation.png

snippet: Use deep learning to automate building footprints

description: This sample shows how ArcGIS API for Python can be used to train a deep learning model to extract building footprints using satellite images. The trained model can be deployed on ArcGIS Pro or ArcGIS Enterprise to extract building footprints.

licenseInfo: ""

tags: ["Data Science", "GIS", "Building", "Footprint", "Instance", "Segmentation", "Deep Learning"]

- title: Automate Road Surface Investigation Using Deep Learning

url: https://geosaurus.maps.arcgis.com/home/item.html?id=72b6c19f5944424f830b44346f0fef89

path: ./samples/04_gis_analysts_data_scientists/automate_road_surface_investigation_using_deep_learning.ipynb

thumbnail: ./static/thumbnails/automate_road_surface_investigation_using_deep_learning.gif

snippet: Use deep learning to detect adn classify road cracks

description: In this notebook, we use a great labeled dataset of asphalt distress images in order to train our model to detect as well as to classify type of road cracks.

licenseInfo: ""

tags: ["Data Science", "GIS", "Road", "Cracks", "Deep Learning"]

- title: Reconstructing 3D buildings from Aerial LiDAR with Deep Learning

url: https://geosaurus.maps.arcgis.com/home/item.html?id=3b3f9bacf88c4661911cb8c0e1a95757

path: ./samples/04_gis_analysts_data_scientists/building_reconstruction_using_mask_rcnn.ipynb

thumbnail: ./static/thumbnails/building_reconstruction_using_mask_rcnn.png

snippet: Reconstruct 3D building models from aerial LiDAR

description: In this notebook, we demonstrate how to detect instances of roof segments of various types using instance segmentation to make the process more efficient

licenseInfo: ""

tags: ["Data Science", "GIS", "Building", "Reconstruction", "MaskRCNN", "Deep Learning"]

- title: Calculate Impervious Surfaces from Spectral Imagery

url: https://geosaurus.maps.arcgis.com/home/item.html?id=bfbc2594a96f48539b1a1b30996fa76a

path: ./samples/04_gis_analysts_data_scientists/calculate_impervious_surfaces_from_spectral_imagery.ipynb

thumbnail: ./static/thumbnails/calculate_impervious_surfaces_from_spectral_imagery.png

snippet: Detect impervious surfaces

description: In this notebook, we’ll use a high resolution land cover map obtained from Chesapeake Conservancy to determine which parts of the ground are pervious and impervious.

licenseInfo: ""

tags: ["Data Science", "GIS", "Impervious Surfaces", "Deep Learning"]

- title: Raster Analytics - Calculate wildfire landslide risk

url: https://geosaurus.maps.arcgis.com/home/item.html?id=0d5367ba88754937becabeae4d8c520b

path: ./samples/04_gis_analysts_data_scientists/calculate_post_fire_landslide_risk.ipynb

thumbnail: ./static/thumbnails/calculate_post_fire_landslide_risk.jpg

snippet: Assess landslide vulnerability using raster analytics

description: In this notebook, we will provide local emergency management teams a summary of post-wildfire landslide risk, so officials can target mitigation efforts to the most vulnerable watershed basins.

licenseInfo: ""

tags: ["Data Science", "GIS", "Landslide", "Post Fire"]

- title: Using weighted overlay analysis to identify areas that are natural and accessible

url: https://geosaurus.maps.arcgis.com/home/item.html?id=d83bc28a865b4ac281ceeca02c5288f7

path: ./samples/04_gis_analysts_data_scientists/calculating_cost_surfaces_using_weighted_overlay_analysis.ipynb

thumbnail: ./static/thumbnails/calculating_cost_surfaces_using_weighted_overlay_analysis.jpg

snippet: Identify accessible and natural areas

description: his sample identifies areas in the State of Washington that are more "natural" and easy to get to

licenseInfo: ""

tags: ["Data Science", "GIS", "Weighted", "Overlay"]

- title: Calculating Origin Destinations nXn Matrix given set of origins and destinations

url: https://geosaurus.maps.arcgis.com/home/item.html?id=94c7f47fd15745f785618d4e7caa591e

path: ./samples/04_gis_analysts_data_scientists/calculating_nXn_od_cost_matrix.ipynb

thumbnail: ./static/thumbnails/calculating_nXn_od_cost_matrix.jpg

snippet: Create origin-destination pairs

description: In this sample notebook , we will use this tool to get OD matrix if given a set of origin and destination points, either as a csv with latitude and longitude or csv file with list of addresses. In later part of this sample, we will format the table to get n by n matrix.

licenseInfo: ""

tags: ["Data Science", "GIS", "Cost Matrix", "Network Analysis"]

- title: California wildfires 2017 - Thomas Fire analysis

url: https://geosaurus.maps.arcgis.com/home/item.html?id=decad31cff114ae0b5881778cfdb6d84

path: ./samples/04_gis_analysts_data_scientists/california_wildfires_2017_thomas_fire_analysis.ipynb

thumbnail: ./static/thumbnails/california_wildfires_2017_thomas_fire_analysis.png

snippet: Analyze the December 2017 thomas fire

description: The Thomas Fire was a massive wildfire that started in early December 2017 in Ventura and Santa Barbara counties and grew into California's largest fire ever.

licenseInfo: ""

tags: ["Data Science", "GIS", "Thomas Fire", "California"]

- title: Chennai Floods 2015–A Geographic Analysis

url: https://geosaurus.maps.arcgis.com/home/item.html?id=44c4fc1e56654768840a03971feb1e77

path: ./samples/04_gis_analysts_data_scientists/chennai_floods_analysis.ipynb

thumbnail: ./static/thumbnails/chennai_floods_analysis.jpg

snippet: Analyze the rainfall in Chennai, India

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.

licenseInfo: ""

tags: ["Data Science", "GIS", "Floods", "Chennai"]

- title: Constructing drive time based service areas

url: https://geosaurus.maps.arcgis.com/home/item.html?id=500bd10ce9d241c1b0769d7e954e0d67

path: ./samples/04_gis_analysts_data_scientists/constructing_drive_time_based_service_areas.ipynb

thumbnail: ./static/thumbnails/constructing_drive_time_based_service_areas.jpg

snippet: Use the `network` module to construct drive time based service areas

description: This sample shows how the `network` module of the ArcGIS API for Python can be used to construct service areas. In this sample, we generate service areas for two of the fire stations in central Tokyo, Japan.

licenseInfo: ""

tags: ["Data Science", "GIS", "Drive Time"]

- title: Counting features in satellite images using scikit-image

url: https://geosaurus.maps.arcgis.com/home/item.html?id=4398498b6ff048c09c4b0ed90f84a1e6

path: ./samples/04_gis_analysts_data_scientists/counting_features_in_satellite_images_using_scikit_image.ipynb

thumbnail: ./static/thumbnails/counting_features_in_satellite_images_using_scikit_image.jpg

snippet: Use scikit-image to count features

description: The example below uses scikit-image library to detect circular features in farms using center pivot irrigation in Saudi Arabia.

licenseInfo: ""

tags: ["Data Science", "GIS", "Scikit Image", "Features"]

- title: Time Series Analysis of the 2019 Novel Coronavirus Pandemic

url: https://geosaurus.maps.arcgis.com/home/item.html?id=1584e1ce508f460c9d40360adfb3e022

path: ./samples/04_gis_analysts_data_scientists/covid19_part2_timeseries_analysis.ipynb

thumbnail: ./static/thumbnails/covid19_part2_timeseries_analysis.png

snippet: This notebook is to perform analysis and time series charting of 2019 novel coronavirus disease (COVID-19) globally

description: This notebook is to perform analysis and time series charting of 2019 novel coronavirus disease (COVID-19) globally

licenseInfo: ""

tags: ["Data Science", "GIS", "Covid19", "Time series", "Part 2"]

- title: Predictive Analysis of the 2019 Novel Coronavirus Pandemic

url: https://geosaurus.maps.arcgis.com/home/item.html?id=36e563f54cd446378d140cd0ceb8125c

path: ./samples/04_gis_analysts_data_scientists/covid19_part3_predictive_analysis.ipynb

thumbnail: ./static/thumbnails/covid19_part3_predictive_analysis.jpg

snippet: Use tools to analyze COVID-19

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://geosaurus.maps.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://geosaurus.maps.arcgis.com/home/item.html?id=f0423a7df2064096a78e150a6fbf5ae4

path: ./samples/04_gis_analysts_data_scientists/creating_raster_information_product_using_raster_analytics.ipynb

thumbnail: ./static/thumbnails/creating_raster_information_product_using_raster_analytics.gif

snippet: This sample show the capabilities of imagery layers and raster analytics.

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://geosaurus.maps.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://geosaurus.maps.arcgis.com/home/item.html?id=62b874f4e705448a95abac0240f3053d

path: ./samples/04_gis_analysts_data_scientists/designate_bike_routes_for_commuting_professionals.ipynb

thumbnail: ./static/thumbnails/designate_bike_routes_for_commuting_professionals.png

snippet: Analyze biking streets in Seattle, WA

description: This sample uses ArcGIS API for Python to analyze and select streets for making bike routes for people commuting to and from work in the City of Seattle, Washington.

licenseInfo: ""

tags: ["Data Science", "GIS", "Bike", "Routes", "Commute"]

- title: Detecting and Categorizing Brick Kilns from Satellite Imagery

url: https://geosaurus.maps.arcgis.com/home/item.html?id=59a916aaa9114354a89e07a3941f9c98

path: ./samples/04_gis_analysts_data_scientists/detecting_and_categorizing_brick_kilns_from_satellite_imagery.ipynb

thumbnail: ./static/thumbnails/detecting_and_categorizing_brick_kilns_from_satellite_imagery.png

snippet: Categorize Brick Kilns using the Python API

description: In this sample, we will use Deep Learning on ArcGIS Platform to detect the location and design category of all brick kilns around Delhi NCR area in India to find the brick kilns which are not following the directions from CPCB.

licenseInfo: ""

tags: ["Data Science", "GIS", "Brick Kilns", "Categorization", "Deep Learning"]

- title: Detecting settlements using supervised classification and deep learning

url: https://geosaurus.maps.arcgis.com/home/item.html?id=4a4828703f1a494e89ec973086cad715

path: ./samples/04_gis_analysts_data_scientists/detecting_settlements_using_supervised_classification_and_deep_learning.ipynb

thumbnail: ./static/thumbnails/detecting_settlements_using_supervised_classification_and_deep_learning.png

snippet: Use deep learning to detect settlements

description: In this notebook we have attempted to use the supervised classification approach to generate the required volumes of data which after cleaning was used to come through the requirement of larger training data for Deep Learning model.

licenseInfo: ""

tags: ["Data Science", "GIS", "Settlement", "Deep Learning", "Supervised Classification"]

- title: Detecting Swimming Pools using Satellite Imagery and Deep Learning

url: https://geosaurus.maps.arcgis.com/home/item.html?id=122fb4b5a2094f3398b1d96381022f64

path: ./samples/04_gis_analysts_data_scientists/detecting_swimming_pools_using_satellite_image_and_deep_learning.ipynb

thumbnail: ./static/thumbnails/detecting_swimming_pools_using_satellite_image_and_deep_learning.jpg

snippet: Detect swimming pools using remote sensing imagery

description: This notebook demonstrates an end-to-end deep learning workflow in using ArcGIS API for Python. The workflow consists of three major steps. (1) extracting training data, (2) train a deep learning object detection model, (3) deploy the model for inference and create maps.

licenseInfo: ""

tags: ["Data Science", "GIS", "Swimming", "Deep Learning", "Pools"]

- title: Detecting Super Blooms Using Satellite Image Classification

url: https://geosaurus.maps.arcgis.com/home/item.html?id=50d6c2001e864d44ab5278e7b439bf41

path: ./samples/04_gis_analysts_data_scientists/detect_super_blooms_using_satellite_image_classification.ipynb

thumbnail: ./static/thumbnails/detect_super_blooms_using_satellite_image_classification.jpg

snippet: Determine the occurance of super blooms in the study area for a given year

description: This sample is to study three poppy fields where people often go for watching super blooms, compare the sites with historic scenes, capture the differences in vegetation conditions, and calculate the vegetation density of blooms.

licenseInfo: ""

tags: ["Data Science", "GIS", "Super Blooms", "Classification"]

- title: Drive time analysis for opioid epidemic

url: https://geosaurus.maps.arcgis.com/home/item.html?id=c109ab8bb83e42819ffab65d71abb34c

path: ./samples/04_gis_analysts_data_scientists/drive_time_analysis_for_opioid_epidemic.ipynb

thumbnail: ./static/thumbnails/drive_time_analysis_for_opioid_epidemic.jpg

snippet: Perform drive time analysis for the opioid epidemic

description: This notebook performs drive time analysis for clinics of opioid epidemic treatment and/or prevention centers in the county of Oakland, Michigan.

licenseInfo: ""

tags: ["Data Science", "GIS", "Opioid Epidemic", "Drive Time"]

- title: Extracting Building Footprints From Drone Data

url: https://geosaurus.maps.arcgis.com/home/item.html?id=802ccd7dc3a748eeafb20b05d2a8f67c

path: ./samples/04_gis_analysts_data_scientists/extracting_building_footprints_from_drone_data.ipynb

thumbnail: ./static/thumbnails/extracting_building_footprints_from_drone_data.png

snippet: Extract Building footprints from drone data

description: This sample shows how ArcGIS API for Python can be used to train a deep learning model to extract building footprints from drone data.

licenseInfo: ""

tags: ["Data Science", "GIS", "Building", "Foorprint", "Deep Learning"]

- title: Extracting Slums from Satellite Imagery

url: https://geosaurus.maps.arcgis.com/home/item.html?id=5b5461f3df814fc1b65539365668904d

path: ./samples/04_gis_analysts_data_scientists/extracting_slums_from_satellite_imagery.ipynb

thumbnail: ./static/thumbnails/extracting_slums_from_satellite_imagery.png

snippet: Extract slum boundaries from satellite imagery

description: This sample shows how we can extract the slum boundaries from satellite imagery using the learn module in ArcGIS API for Python.

licenseInfo: ""

tags: ["Data Science", "GIS", "Deep Learning", "Slums", "Extracting"]

- title: Feature Categorization using Satellite Imagery and Deep Learning

url: https://geosaurus.maps.arcgis.com/home/item.html?id=c5de157941034407bc719c5bde8e1ea7

path: ./samples/04_gis_analysts_data_scientists/feature_categorization_using_satellite_imagery_and_deep_learning.ipynb

thumbnail: ./static/thumbnails/feature_categorization_using_satellite_imagery_and_deep_learning.jpg

snippet: Use deep learning to perform feature categorization

description: This sample notebook demonstrates the use of deep learning capabilities in ArcGIS to perform feature categorization.

licenseInfo: ""

tags: ["Data Science", "GIS", "Feature", "Categorization", "Deep Learning"]

- title: Fighting California forest fires using spatial analysis

url: https://geosaurus.maps.arcgis.com/home/item.html?id=6bd7735c79024e1687d342b66a1b313c

path: ./samples/04_gis_analysts_data_scientists/fighting_california_forest_fires_using_spatial_analysis.ipynb

thumbnail: ./static/thumbnails/fighting_california_forest_fires_using_spatial_analysis.png

snippet: This sample demonstrates the application of spatial analysis tools such as buffer and overlay.

description: This notebook describes a scenario wherein an analyst automates the process of identifying facilities at risk from forest fires and sharing this information with other departments such as the fire department, etc.

licenseInfo: ""

tags: ["Data Science", "GIS", "California", "Forest Fires"]

- title: Finding a New Home

url: https://geosaurus.maps.arcgis.com/home/item.html?id=a4518082dbe14885b45680ee54c74aed

path: ./samples/04_gis_analysts_data_scientists/finding_a_new_home.ipynb

thumbnail: ./static/thumbnails/finding_a_new_home.png

snippet: Study the housing market for the goal of finding a new home

description: In this case study, we will explore the current housing market, estimate average house prices in their area and hunt for a new one.

licenseInfo: ""

tags: ["Data Science", "GIS", "Home"]

- title: Monitoring hydrologic water quality in pasturelands through spatial overlay analysis

url: https://geosaurus.maps.arcgis.com/home/item.html?id=aeb9f2a680eb451b9186dfb68353143d

path: ./samples/04_gis_analysts_data_scientists/finding_grazing_allotments.ipynb

thumbnail: ./static/thumbnails/finding_grazing_allotments.png

snippet: Monitor watersheds using spatial overlay analysis

description: This sample uses ArcGIS API for Python to find out which watershed, or watersheds, each grazing allotment falls in, for water quality monitoring.

licenseInfo: ""

tags: ["Data Science", "GIS", "Grazing", "Allotments"]

- title: Find hospitals closest to an incident

url: https://geosaurus.maps.arcgis.com/home/item.html?id=9056733512624eeda8eb1b32625d518b

path: ./samples/04_gis_analysts_data_scientists/finding_hospitals_closest_to_an_incident.ipynb

thumbnail: ./static/thumbnails/finding_hospitals_closest_to_an_incident.png

snippet: Use the network module to find a hospital closest to an incident

description: In this sample, we see how to find the hospital that is closest to an incident.

licenseInfo: ""

tags: ["Data Science", "GIS", "Hospitals"]

- title: Find hospitals closest to an incident

url: https://geosaurus.maps.arcgis.com/home/item.html?id=4b1fcd1af9ea4f0f9dce8f71a729890b

path: ./samples/04_gis_analysts_data_scientists/finding_routes_for_appliance_delivery_with_VRP_solver.ipynb

thumbnail: ./static/thumbnails/finding_routes_for_appliance_delivery_with_VRP_solver.jpg

snippet: Find hospitals using the network module

description: In this sample, we see how to find the hospital that is closest to an incident using the `network` module.

licenseInfo: ""

tags: ["Data Science", "GIS", "Routes", "VRP Solver"]

- title: Finding suitable spots for placing heart defibrillator equipments in public

url: https://geosaurus.maps.arcgis.com/home/item.html?id=225ff27ffb504c88be4ff069d5d34b60

path: ./samples/04_gis_analysts_data_scientists/finding_suitable_spots_for_AED_devices_using_raster_analytics.ipynb

thumbnail: ./static/thumbnails/finding_suitable_spots_for_AED_devices_using_raster_analytics.png

snippet: Find suitable spots for AED devices

description: In this sample, we will observe how site suitability analyses can be performed using the ArcGIS API for Python.

licenseInfo: ""

tags: ["Data Science", "GIS", "AED Devices", "Raster Analytics"]

- title: Historical Wildfire Analysis

url: https://geosaurus.maps.arcgis.com/home/item.html?id=eb4eaad6661b45d689a6ec5b9852ac8d

path: ./samples/04_gis_analysts_data_scientists/historical_wildfire_analysis.ipynb

thumbnail: ./static/thumbnails/historical_wildfire_analysis.png

snippet: Analyze historical wildfire trends

description: Use the ArcGIS API for Python to answer if wildfires are increasing over time.

licenseInfo: ""

tags: ["Data Science", "GIS", "Wildfire"]

- title: How much green is Delhi as on 15 Oct 2017?

url: https://geosaurus.maps.arcgis.com/home/item.html?id=8094be16f34e46e48880883a1ae6a4f1

path: ./samples/04_gis_analysts_data_scientists/how-much-green-is-Delhi-as-on-15-oct-2017.ipynb

thumbnail: ./static/thumbnails/how-much-green-is-Delhi-as-on-15-oct-2017.jpg

snippet: Use Landsat 8 imagery to detect green cover of New Delhi, India

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.

licenseInfo: ""

tags: '["Data Science", "GIS"]'

- title: Identifying suitable sites for new ALS clinics using location allocation analysis

url: https://geosaurus.maps.arcgis.com/home/item.html?id=948ed526d07b481a9f401f643f02e97b

path: ./samples/04_gis_analysts_data_scientists/identifying-suitable-sites-for-als-clinics-using-location-allocation-analysis.ipynb

thumbnail: ./static/thumbnails/identifying-suitable-sites-for-als-clinics-using-location-allocation-analysis.png

snippet: Use network analysis to identify potential sites for new ALS clinics

description: 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.

licenseInfo: ""

tags: ["Data Science", "GIS"]

- title: Increase Image Resolution using SuperResolution

url: https://geosaurus.maps.arcgis.com/home/item.html?id=f02fe8a68e5e444d8a15aaf0cd18cb65

path: ./samples/04_gis_analysts_data_scientists/increase-image-resolution-using-superresolution.ipynb

thumbnail: ./static/thumbnails/increase-image-resolution-using-superresolution.gif

snippet: Increase image resolution using the ArcGIS API for Python

description: This sample notebook demonstrates how the SuperResolution model in arcgis.learn module can be used to increase image resolution.

licenseInfo: ""

tags: ["Data Science", "GIS", "Super Resolution", "Deep learning"]

- title: Information extraction from Madison city crime incident reports using Deep Learning

url: https://geosaurus.maps.arcgis.com/home/item.html?id=c1e38321865b40d4b01ec5de17b27442

path: ./samples/04_gis_analysts_data_scientists/information-extraction-from-madison-city-crime-incident-reports-using-deep-learning.ipynb

thumbnail: ./static/thumbnails/information-extraction-from-madison-city-crime-incident-reports-using-deep-learning.png

snippet: Extract info from crime reports

description: In this notebook we will extract information from crime incident reports obtained from Madison police department [1]using arcgis.learn.EntityRecognizer().

licenseInfo: ""

tags: ["Data Science", "GIS", "Information", "Extraction", "deep Learning", "Madison", "Crime"]

- title: Land cover classification using sparse training data

url: https://geosaurus.maps.arcgis.com/home/item.html?id=b82a104720a349fe96d47f0f12ed86a8

path: ./samples/04_gis_analysts_data_scientists/land_cover_classification_using_sparse_training_data.ipynb

thumbnail: ./static/thumbnails/land_cover_classification_using_sparse_training_data.png

snippet: Perform land cover classification

description: This notebook showcases an approach to performing land cover classification using sparse training data and multispectral imagery.

licenseInfo: ""

tags: ["Data Science", "GIS", "Sparse", "Land cover", "Classification", "Deep Learning"]

- title: Land Cover Classification using Satellite Imagery and Deep Learning

url: https://geosaurus.maps.arcgis.com/home/item.html?id=8a3f6601f67049f1a311e88c7ba02125

path: ./samples/04_gis_analysts_data_scientists/land_cover_classification_using_unet.ipynb

thumbnail: ./static/thumbnails/land_cover_classification_using_unet.png

snippet: Use the ArcGIS API for Python for land cover classification

description: This notebook showcases an end-to-end to land cover classification workflow using ArcGIS API for Python.

licenseInfo: ""

tags: ["Data Science", "GIS", "Land Cover", "Classification", "Deep Learning"]

- title: Locating a new retirement community

url: https://geosaurus.maps.arcgis.com/home/item.html?id=95236a13179b40c39c9fc01ab96719e3

path: ./samples/04_gis_analysts_data_scientists/locating_a_new_retirement_community.ipynb

thumbnail: ./static/thumbnails/locating_a_new_retirement_community.png

snippet: Locate new retirement communites

description: This sample demonstrates the utility of ArcGIS API for Python to identify some great locations for a new retirement community, which will satisfy these needs of senior citizens.

licenseInfo: ""

tags: ["Data Science", "GIS", "Retirement", "Community"]

- title: Data Preparation - Hurricane analysis, part 1/3

url: https://geosaurus.maps.arcgis.com/home/item.html?id=a93787d32d71458ba733c432f231be19

path: ./samples/04_gis_analysts_data_scientists/part1_prepare_hurricane_data.ipynb

thumbnail: ./static/thumbnails/part1_prepare_hurricane_data.jpg

snippet: Analyze meteorological data of hurricanes

description: In this notebook, we use meteorological data of hurricanes recorded in the past 169 years to analyze their location, intensity and investigate if there are any statistically significant trends.

licenseInfo: ""

tags: ["Data Science", "GIS", "Hurricane", "GeoAnalytics", "Part 1"]

- title: Exploratory Statistics - Hurricane analysis, part 2/3

url: https://geosaurus.maps.arcgis.com/home/item.html?id=55bc3720bb4f4d44a2326d771a3eab9b

path: ./samples/04_gis_analysts_data_scientists/part2_explore_hurricane_tracks.ipynb

thumbnail: ./static/thumbnails/part2_explore_hurricane_tracks.png

snippet: Analyze aggregate tracks of hurricanes

description: In this notebook you will analyze the aggregated tracks to investigate the communities that are most affected by hurricanes, as well as as answer important questions about the prevalance of hurricanes, their seasonality, their density, and places where they make landfall.

licenseInfo: ""

tags: ["Data Science", "GIS", "Hurricane", "Tracks", "GeoAnalytics", "Part 2"]

- title: Correlation - Hurricane analysis, part 3/3

url: https://geosaurus.maps.arcgis.com/home/item.html?id=5a34fa2a151b40659894c0b4f0d30704

path: ./samples/04_gis_analysts_data_scientists/part3_analyze_hurricane_tracks.ipynb

thumbnail: ./static/thumbnails/part3_analyze_hurricane_tracks.png

snippet: Analyze hurricane severity and trends over time

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://geosaurus.maps.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://geosaurus.maps.arcgis.com/home/item.html?id=69df9348e964433d86a5c0fb8aaa48de

path: ./samples/04_gis_analysts_data_scientists/predicting_enso.ipynb

thumbnail: ./static/thumbnails/predicting_enso.png

snippet: Predict ENSO from climate variables and indices

description: This example uses correlation analysis and time series analysis to predict El Niño–Southern Oscillation (ENSO) based on climate variables and indices.

licenseInfo: ""

tags: ["Data Science", "GIS", "Predict", "ENSO"]

- title: Analyzing and predicting Service Request Types in DC

url: https://geosaurus.maps.arcgis.com/home/item.html?id=08219e66481d4c809ce1bf46350f1995

path: ./samples/04_gis_analysts_data_scientists/predict_service_request_types.ipynb

thumbnail: ./static/thumbnails/predict_service_request_types.png

snippet: Predict service request types in DF

description: This notebook constructs models that predicts service types

licenseInfo: ""

tags: ["Data Science", "GIS", "Service Request", "Predict"]

- title: Safe Streets to Schools

url: https://geosaurus.maps.arcgis.com/home/item.html?id=6e0aa56305284ed7b430432506b97a07

path: ./samples/04_gis_analysts_data_scientists/safe_streets_to_schools.ipynb

thumbnail: ./static/thumbnails/safe_streets_to_schools.png

snippet: Improve pedestrian and bicycle safety

description: The sample uses ArcGIS API for Python to help city officials in improving pedestrian and bicycle safety near schools in the city.

licenseInfo: ""

tags: ["Data Science", "GIS", "Schools", "Danger Zones"]

- title: Shipwrecks detection using bathymetric data

url: https://geosaurus.maps.arcgis.com/home/item.html?id=e7a53954d99144be868bd30e8cde2523

path: ./samples/04_gis_analysts_data_scientists/shipwrecks_detection_using_bathymetric_data.ipynb

thumbnail: ./static/thumbnails/shipwrecks_detection_using_bathymetric_data.png

snippet: Use bathymetric data to detect shipwrecks

description: In this notebook, we will use bathymetry data provided by NOAA to detect shipwrecks from the Shell Bank Basin area located near New York City in United States.

licenseInfo: ""

tags: ["Data Science", "GIS", "Shipwrecks", "Bathymetric", "Deep Learning"]

- title: Snow Avalanche Hazard Mapping for Lake Tahoe

url: https://geosaurus.maps.arcgis.com/home/item.html?id=bd6e2767cb294b88b09c5cb38441131e

path: ./samples/04_gis_analysts_data_scientists/snow_avalanche_hazard_mapping_for_lake_tahoe.ipynb

thumbnail: ./static/thumbnails/snow_avalanche_hazard_mapping_for_lake_tahoe.png

snippet: Map avalanches in Lake Tahoe, California

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://geosaurus.maps.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://geosaurus.maps.arcgis.com/home/item.html?id=cf173caaba3f495f9592a9f180361ee4

path: ./samples/04_gis_analysts_data_scientists/temperature_forecast_using_time_series_data.ipynb

thumbnail: ./static/thumbnails/temperature_forecast_using_time_series_data.png

snippet: Use time series data to forecast temperature

description: This sample showcases two autoregressive methods. one using a deep learning and another using a machine learning framework to predict temperature of England.

licenseInfo: ""

tags: ["Data Science", "GIS", "Time Series", "Temperature", "Forecast"]

- title: Plant species identification using a TensorFlow-Lite model within mobile devices

url: https://geosaurus.maps.arcgis.com/home/item.html?id=fc21cc2f4a014a8e88f72d846b5afff1

path: ./samples/04_gis_analysts_data_scientists/train_a_tensorflow-lite_model_for_identifying_plant_species.ipynb

thumbnail: ./static/thumbnails/train_a_tensorflow-lite_model_for_identifying_plant_species.png

snippet: Identify plant species using a tensorflow model

description: This notebook intends to showcase this capability to train a deep learning model that can be used in mobile applications for a real time inferencing using TensorFlow Lite framework.

licenseInfo: ""

tags: ["Data Science", "GIS", "TansorFlow Lite", "Plant Species", "Deep Learning"]

- title: Vehicle detection and tracking using deep learning

url: https://geosaurus.maps.arcgis.com/home/item.html?id=871057ea4c864343900f36f3bf64b675

path: ./samples/04_gis_analysts_data_scientists/vehicle_detection_and_tracking.ipynb

thumbnail: ./static/thumbnails/vehicle_detection_and_tracking.gif

snippet: Use deep learning to track vehicles

description: In this notebook, we'll demonstrate how we can use deep learning to detect vehicles and then track them in a video.

licenseInfo: ""

tags: ["Data Science", "GIS", "Vehicle", "Detection", "Deep Learning", "Tracking"]

- title: Visualize monthly changes in Hirakund reservoir using video

url: https://geosaurus.maps.arcgis.com/home/item.html?id=8fdbf15d65674f69a947cbe449eb3647

path: ./samples/04_gis_analysts_data_scientists/visualize_monthly_changes_in_hirakund_reservoir_using_video.ipynb

thumbnail: ./static/thumbnails/visualize_monthly_changes_in_hirakund_reservoir_using_video.gif

snippet: Visualize monthly changes in the Hirakund reservoir

description: This notebook creates a movie to visualize monthly changes in Hirakund reservoir, Odisha.

licenseInfo: ""

tags: ["Data Science", "GIS", "Changes", "Video"]

- title: Which areas are good cougar habitat?

url: https://geosaurus.maps.arcgis.com/home/item.html?id=74f55ed014014f90a1ff81d469abbf22

path: ./samples/04_gis_analysts_data_scientists/which_areas_are_good_cougar_habitat.ipynb

thumbnail: ./static/thumbnails/which_areas_are_good_cougar_habitat.png

snippet: Use spatial analysis tools to identify cougar habitat areas

description: Through this notebook, we will demonstrate the utility of a number of spatial analysis tools including create_buffer, extract_data, dissolve_boundaries, and derive_new_locations.

licenseInfo: ""

tags: ["Data Science", "GIS", "Cougar Habitat"]

- title: Which college district has the fewest low-income families?

url: https://geosaurus.maps.arcgis.com/home/item.html?id=abae8a0ca9554ae2b46e7ada352e502a

path: ./samples/04_gis_analysts_data_scientists/which_college_district_has_the_fewest_low_income_families.ipynb

thumbnail: ./static/thumbnails/which_college_district_has_the_fewest_low_income_families.png

snippet: Use the `summarize_within` tool to get the number of low-income families

description: This case study uses ArcGIS API for Python to find districts that have the fewest low income families in order to empower these students.

licenseInfo: ""

tags: ["Data Science", "GIS", "low income families"]

- title: Pawnee Fire Analysis

url: https://geosaurus.maps.arcgis.com/home/item.html?id=927ff9cf8d9241308af317317224bd81

path: ./samples/04_gis_analysts_data_scientists/wildfire_analysis_using_sentinel-2_imagery.ipynb

thumbnail: ./static/thumbnails/wildfire_analysis_using_sentinel-2_imagery.jpg

snippet: Use remote sensing to analyze the pawnee fire

description: In this notebook example, we used Sentinel-2 data in order to perform remote sensing.

licenseInfo: ""

tags: ["Data Science", "GIS", "Wildfire", "Sentinel-2"]

- title:

url: https://geosaurus.maps.arcgis.com/home/item.html?id=fca4cf6436a04325bbd62b7330830a80

path: ./samples/04_gis_analysts_data_scientists/wildfire_analysis_using_sentinel-2_imagery.ipynb

thumbnail: ./static/thumbnails/wildfire_analysis_using_sentinel-2_imagery.jpg

snippet: Use remote sensing to analyze the pawnee fire

description: In this notebook example, we used Sentinel-2 data in order to perform remote sensing.

licenseInfo: ""

tags: ["Data Science", "GIS", "Wildfire", "Sentinel-2"]

- title: Interactive raster analytics using Jupyter Dashboards

url: https://geosaurus.maps.arcgis.com/home/item.html?id=f4e7815c90064f4bb058a1f7c9fdc745

path: ./samples/02_power_users_developers/jupyter_dashboard_for_raster_analytics.ipynb

thumbnail: ./static/thumbnails/jupyter_dashboard_for_raster_analytics.png

snippet: Jupyter dashboard for raster analytics

description: This sample illustrates one such app which can be used to detect the changes in vegetation between the two dates.

licenseInfo: ''

tags: ['Data Science', 'GIS', "Dashboard"]

- title: Exploring OpenStreetMap using Pandas and the Python API

url: https://geosaurus.maps.arcgis.com/home/item.html?id=1fcf2158a8844b3fa2fec87b9b9bbc51

path: ./samples/02_power_users_developers/openstreetmap_exploration.ipynb

thumbnail: ./static/thumbnails/openstreetmap_exploration.png

snippet: OpenStreetMap exploration

description: Develop map-driven tools to explore OSM with the full capabilities of the ArcGIS platform

licenseInfo: ''

tags: ['Data Science', 'GIS', "Open Street Map"]

- title: A dashboard to explore world population

url: https://geosaurus.maps.arcgis.com/home/item.html?id=a2d94e983a354b608ee60e29011ed02f

path: ./samples/02_power_users_developers/population_exploration_dashboard.ipynb

thumbnail: ./static/thumbnails/population_exploration_dashboard.png

snippet: Population exploration dashboard

description: This sample illustrates one such app which can be used to detect the changes in vegetation between the two dates. Increases in vegetation are shown in green, and decreases are shown in magenta.

licenseInfo: ''

tags: ['Data Science', 'GIS', "Population", "Dashboard"]

- title: Tour the World with Landsat Imagery and Raster Functions

url: https://geosaurus.maps.arcgis.com/home/item.html?id=6e08b2b2be7948258440cfca8821d7b8

path: ./samples/02_power_users_developers/tour_the_world_with_landsat_imagery_and_raster_functions.ipynb

thumbnail: ./static/thumbnails/tour_the_world_with_landsat_imagery_and_raster_functions.png

snippet: tour the world with landsat imagery and raster functions

description: This notebook provides links to interesting locations using different band combinations of Landsat 8 imagery.

licenseInfo: ''

tags: ['Data Science', 'GIS', "Raster", "Functions", "Landsat"]

- title: Using Geometry Functions

url: https://geosaurus.maps.arcgis.com/home/item.html?id=4d09e890e36b446f8aaa17e366e58b80

path: ./samples/02_power_users_developers/using_geometry_functions.ipynb

thumbnail: ./static/thumbnails/using_geometry_functions.png

snippet: using geometry functions

description: This notebook uses the arcgis.geometry module to compute the length of a path that the user draws on the map.

licenseInfo: ''

tags: ['Data Science', 'GIS', "Geometry"]

- title: Using Geoprocessing Tools

url: https://geosaurus.maps.arcgis.com/home/item.html?id=5a5839d87b4645e685bcd46d79995358

path: ./samples/02_power_users_developers/using_geoprocessing_tools.ipynb

thumbnail: ./static/thumbnails/using_geoprocessing_tools.png

snippet: using geoprocessing tools

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.

licenseInfo: ''

tags: ['Data Science', 'GIS', "Geoprocessing tools"]

- title: Hey GIS, Give me a map of the recent natural disasters

url: https://geosaurus.maps.arcgis.com/home/item.html?id=7eae3c9f586f4d7ab7494b0494c9a97c

path: ./samples/05_content_publishers/hey_gis_give_me_a_map_of_the_recent_natural_disasters.ipynb

thumbnail: ./static/thumbnails/hey_gis_give_me_a_map_of_the_recent_natural_disasters.png

snippet: hey gis give me a map of the recent natural disasters

description: The sample notebook takes advantage of NASA's Earth Observatory Natural Event Tracker (EONET) API to collect a curated and continuously updated set of natural event metadata, and transform them into ArcGIS FeatureCollection(s) and save them into Web Maps in your GIS.

licenseInfo: ''

tags: ['Data Science', 'GIS', "Natural Disasters", "Map"]

- title: HTML Table to Pandas Data Frame to Portal Item

url: https://geosaurus.maps.arcgis.com/home/item.html?id=8bbc583569244b2daabe8079c5644fc2

path: ./samples/05_content_publishers/html_table_to_pandas_data_frame_to_portal_item.ipynb

thumbnail: ./static/thumbnails/html_table_to_pandas_data_frame_to_portal_item.png

snippet: html table to pandas data frame to portal item

description: This sample shows how Pandas can be used to extract data from a table within a web page (in this case, a Wikipedia article) and how it can be then brought into the GIS for further analysis and visualization.

licenseInfo: ''

tags: ['Data Science', 'GIS', "Table", "DataFrame"]

- title: Identify Items That Use Insecure URLs

url: https://geosaurus.maps.arcgis.com/home/item.html?id=beb39a173457489f8a23e8254a8112ef

path: ./samples/05_content_publishers/Identify_Items_That_Use_Insecure_URLs.ipynb

thumbnail: ./static/thumbnails/Identify_Items_That_Use_Insecure_URLs.png

snippet: Identify Items That Use Insecure URLs

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\://.

licenseInfo: ''

tags: ['Data Science', 'GIS', "Insecure URL"]

- title: Overwriting feature layers

url: https://geosaurus.maps.arcgis.com/home/item.html?id=691578df03c04f88862bc61774501699

path: ./samples/05_content_publishers/overwriting_feature_layers.ipynb

thumbnail: ./static/thumbnails/overwriting_feature_layers.png

snippet: overwriting feature layers

description: In this sample, we edit individual features as updated datasets are available

licenseInfo: ''

tags: ['Data Science', 'GIS', "Overwrite", "Feature", "Layers"]

- title: PDF Table to PDF Map

url: https://geosaurus.maps.arcgis.com/home/item.html?id=d4124579b757443fa0577a93ad1d07ab

path: ./samples/05_content_publishers/pdf_table_to_pdf_map.ipynb

thumbnail: ./static/thumbnails/pdf_table_to_pdf_map.png

snippet: pdf table to pdf map

description: This sample shows how Pandas can be used to extract data from a table within a PDF file into the GIS for further analysis and visualization

licenseInfo: ''

tags: ['Data Science', 'GIS', "pdf", "Table", "Map"]

- title: Publishing packages as web layers

url: https://geosaurus.maps.arcgis.com/home/item.html?id=d759771e21344942b5b67cf34439c91c

path: ./samples/05_content_publishers/publishing_packages_as_web_layers.ipynb

thumbnail: ./static/thumbnails/publishing_packages_as_web_layers.png

snippet: publishing packages as web layers

description: In this sample, we will observe how to publish web layers from tile, vector tile and scene layer packages.

licenseInfo: ''

tags: ['Data Science', 'GIS', "Web", "Publish", "Layers"]

- title: Publishing SDs, Shapefiles, and CSVs

url: https://geosaurus.maps.arcgis.com/home/item.html?id=a1db6db172bc49a8932daacc2ed3d3ac

path: ./samples/05_content_publishers/publishing_sd_shapefiles_and_csv.ipynb

thumbnail: ./static/thumbnails/publishing_sd_shapefiles_and_csv.png

snippet: publishing sd shapefiles and csv

description: This sample notebook shows how different types of GIS datasets can be added to the GIS, and published as web layers.

licenseInfo: ''

tags: ['Data Science', 'GIS', "Shapefiles", "Publish", "CSV"]

- title: Publishing web maps and web scenes

url: https://geosaurus.maps.arcgis.com/home/item.html?id=9840ca386ee7480a880d84a497db41de

path: ./samples/05_content_publishers/publishing_web_maps_and_web_scenes.ipynb

thumbnail: ./static/thumbnails/publishing_web_maps_and_web_scenes.png

snippet: publishing web maps and web scenes

description: This sample demonstrates how to create and publish simple examples of web maps and scenes using the Python API.

licenseInfo: ''

tags: ['Data Science', 'GIS', "Maps", "Web Scenes", "Publish"]

guides: []