This resource was created by Esri Canada Education and Research. To browse our full collection of higher-education learning resources, please visit https://hed.esri.ca/resourcefinder/.This tutorial introduces you to using Python code in a Jupyter Notebook, an open source web application that enables you to create and share documents that contain rich text, equations and multimedia, alongside executable code and visualization of analysis outputs. The tutorial begins by stepping through the basics of setting up and being productive with Python notebooks. You will be introduced to ArcGIS Notebooks, which are Python Notebooks that are well-integrated within the ArcGIS platform. Finally, you will be guided through a series of ArcGIS Notebooks that illustrate how to create compelling notebooks for data science that integrate your own Python scripts using the ArcGIS API for Python and ArcPy in combination with thousands of open source Python libraries to enhance your analysis and visualization.To download the dataset Labs, click the Open button to the top right. This will automatically download a ZIP file containing all files and data required.You can also clone the tutorial documents and datasets for this GitHub repo: https://github.com/highered-esricanada/arcgis-notebooks-tutorial.git.Software & Solutions Used: Required: This tutorial was last tested on August 27th, 2024, using ArcGIS Pro 3.3. If you're using a different version of ArcGIS Pro, you may encounter different functionality and results.Recommended: ArcGIS Online subscription account with permissions to use advanced Notebooks and GeoEnrichmentOptional: Notebook Server for ArcGIS Enterprise 11.3+Time to Complete: 2 h (excludes processing time)File Size: 196 MBDate Created: January 2022Last Updated: August 27, 2024

Author: Titus, Maxwell (mtitus@esri.com)Last Updated: 10/22/2025 Intended Environment: ArcGIS Notebooks on ArcGIS Online, ArcGIS Portal, or ArcGIS Pro. Purpose: This Notebook demonstrates a way to send out emails with ArcGIS Online (AGOL) or ArcGIS Portal based on whether new data entries have been detected. This does not require admin privileges to run this script. Requirements: There should be a:Hosted Feature Table or Layer to Monitor (e.g., a Survey123 Dataset)An ArcGIS Online or ArcGIS Portal with users who have their email associated with their accounts. This is more so an AGOL requirement as the method used for ArcGIS Portal is custom and can be adapted (though it is more difficult to use).

ArcGIS Notebooks, etkili mekansal veri analizi gerçekleştirebilmeniz için size çok yönlü bir web tabanlı arayüz sağlar. ArcGIS Notebooks ile analizler yapabilir, iş akışlarınızı otomatikleştirebilir ve sonuçlarınızı anında bir harita üzerinde görselleştirebilirsiniz. Aynı zamanda, çalıştığınız not defterlerinde yazdığınız kodu anında görselleştirebilir, böylece haritaları ve veri bilimi araçlarını bir araya getirerek verimli ve modern bir ortamda çalışabilirsiniz. Python kodunu tek bir yerde yazabilir, kaydedebilir ve çalıştırabilirsiniz. ArcGIS Notebooks’ta çalışırken Esri’nin Python kaynaklarının (ArcGIS API for Python ve ArcPy) yanı sıra popüler (analitik, istatistiksel ve makine öğrenimi) açık kaynak kütüphanelerini de kullanabilirsiniz.

ArcGIS Survey123 utilizes CSV data in several workflows, including external choice lists, the search() appearance, and pulldata() calculations. When you need to periodically update the CSV content used in a survey, a useful method is to upload the CSV files to your ArcGIS organization and link the CSV items to your survey. Once linked, any updates to the CSV items will automatically pull through to your survey without the need to republish the survey. To learn more about linking items to a survey, see Linked content.This notebook demonstrates how to automate updating a CSV item in your ArcGIS organization.Note: It is recommended to run this notebook on your computer in Jupyter Notebook or ArcGIS Pro, as that will provide the best experience when reading locally stored CSV files. If you intend to schedule this notebook in ArcGIS Online or ArcGIS Notebook Server, additional configuration may be required to read CSV files from online file storage, such as Microsoft OneDrive or Google Drive.

Author: Titus, Maxwell (mtitus@esri.com)Last Updated: 3/4/2025 Intended Environment: ArcGIS Notebooks on ArcGIS Online, ArcGIS Portal, or ArcGIS Pro. Purpose: This Notebook can batch share content from one group to another within ArcGIS Online Organization or an ArcGIS Portal. This does not require admin privileges to do this script and does not impact the original group having content moved. Requirements: Whoever runs the Notebook must have:They have access to both groups; where they share content from (i.e., the origin group) and (i.e., the target group).Can share content with the targeted group.

We implemented automated workflows using Jupyter notebooks for each state. The GIS processing, crucial for merging, extracting, and projecting GeoTIFF data, was performed using ArcPy—a Python package for geographic data analysis, conversion, and management within ArcGIS (Toms, 2015). After generating state-scale LES (large extent spatial) datasets in GeoTIFF format, we utilized the xarray and rioxarray Python packages to convert GeoTIFF to NetCDF. Xarray is a Python package to work with multi-dimensional arrays and rioxarray is rasterio xarray extension. Rasterio is a Python library to read and write GeoTIFF and other raster formats. Xarray facilitated data manipulation and metadata addition in the NetCDF file, while rioxarray was used to save GeoTIFF as NetCDF. These procedures resulted in the creation of three HydroShare resources (HS 3, HS 4 and HS 5) for sharing state-scale LES datasets. Notably, due to licensing constraints with ArcGIS Pro, a commercial GIS software, the Jupyter notebook development was undertaken on a Windows OS.

An ArcGIS Notebooks app used to update contingent and domain values on the Requests feature layer.

Installation, functional details, and examples for using the MNCD Python tool in your Notebooks. Use the MNCD tool to cache other Python 'Code Sample' and 'Notebook' item content in your online account home folder, enabling your Notebooks to reuse logic from existing items. Or, offload a complex Notebook to an IDE managed Python Code Sample item type, reducing the size and complexity of your Notebook.Now supports Enterprise Portal, ArcGIS Pro Notebooks, and local Python.See the MNCD tool and Installer for more details!RevisionsOct 20, 2020: Version 1.1.0 ReleaseAug 20, 2021: Version 1.1.1 ReleaseMay 23, 2023: Version 1.2.0 ReleaseJun 23, 2023: Version 1.3.0 ReleaseJun 26, 2023: Version 1.3.1 Release

This notebook inputs annual precipitation data for the world and focuses on the study of precipitation near the Himalayas, generates a line graph of its monthly precipitation variation, and compares the monsoon season with the annual precipitation.

Dealing with large Notebooks can be daunting. Wouldn’t it be nice to import existing logic from other Modules and Code Libraries just like you can with standalone installs of Python? Well, now you can! The Manage Notebook Code Dependencies (MNCD) tool allows you to manage a cache of Python ‘Code Sample’ and ‘Notebook’ item content in your user home folder. Once cached, the content from these items can be imported into your Notebook using the Python import statement, just like your standalone scripts do. Once you import the Python modules from these items, they become ‘Code Dependencies’ to your Notebook and local scripts.This Python logic contains Functions that manage caching Python 'Code Sample' and 'Notebook' item types from ArcGIS Online or Enterprise Portal, unpacking and storing their contents in your Notebook home directory. This makes them accessible to the Notebook Kernel and Python.Once the Python objects are stored, their locations are then added to Python's import path, allowing Python to import the locally cached Modules, Classes, and Functions right to your logic.This enables you to build or leverage existing libraries of reusable code or just offload the bulk of your Notebook logic as a modular Python Script, greatly simplifying your Notebook. Share your IDE developed Python code as a 'Python Code Sample' item, call the manageDependents function to load the item contents, and then import what you need. Later, when updating your Code Sample, the manageDependents function will automatically update the cached Module next time you run the Notebook or call the function.During the MNCD import process, the logic will check for and apply updates to the MNCD logic automatically, just like it does for any managed item.RevisionsOct 20, 2020: Version 1.1 provides the abilty to import logic from other Pyton Notebooks. By default, this will extract any Function or Class Code Block it finds, focusing on modular use. Disable this option when caching a Notebook item to allow full import of available code.May 23, 2023: Version 1.2 includes a new 'getDependentCode' function to handle loading code into the local namespace or scope. It also includes improvements that allow you to provide a gis connection or it can discover an active connection when managing items.Jun 23, 2023: Version 1.3 includes updates that allow support for ArcGIS Enterprise Portal, ArcGIS Pro, and local Python use. Added 'setCachePath' function to support custom cache storage locations.To get started, launch the Installer Notebook and install this Python logic in your user home, accessible to the Notebook Kernel. A ReadMe document has been provided in the install folder. Be sure to review the examples available in the Installer Notebook.Review the MNCD documentation before you installTo get started, use the Installer Notebook to deploy the MNCD tool to ArcGIS Online or Enterprise PortalUsage:Run Installer to add the MNCD logic to your account home folder. Or you can download and store MNCD locally for use in ArcGIS Pro and Python.Import the MNCD module in your Notebook or Python logic using: import mncdWhen ready, call mncd.manageDependents function and provide the Online Item Id(s) you wish MNCD to cache and manageNow use standard Python import command to import the Functions and Classes from the cached Module(s), just like you would from a standalone script.If a cached item is no longer needed, call mncd.removeDependents function and incude the Item Id(s) you wish to remove from the cacheIf the Managed Notebook Code Dependencies logic is no longer required, run the Installer once again to remove

GIS_data_and_jupyter_python_notebook.zip: Data for Modeling SDS via Random Forest Models. Contains a ArcGIS Pro project with example data collected at Marston Farm (Boone, IA) and cropped Planet scope 4-band imagery of the area for 2016, 2017 and 2018.Preview for jupyter notebook: preview of a jupyter (Python 3) notebook that demonstrates the use of Random forest classifier using the GIS data.

This project aims to identify areas in Los Angeles that are at high risk of crime in the future and to propose optimal locations for new police stations in those areas. By applying machine learning to post-COVID-19 crime data and various socioeconomic indicators, we predict crime risk at the ZIP Code level. Using a location-allocation model, we then determine suitable locations for new police stations to improve coverage of high-risk zones. The results of our analysis can support the efficient allocation of public safety resources in response to growing demand and budget constraints, helping city officials optimize law enforcement services. The content of the archive- Jupyter Notebook- Data (GeoJSON, CSV)- Summary report PDF FileThe platform on which the notebook should be run.This notebook is designed to run on Datahub.Project materials - Project Material we created on AGOL 1 Los Angeles Crime Hotspothttps://ucsdonline.maps.arcgis.com/home/item.html?id=4bddbae65c164f2d9b0285e09cb2820e 2 Choropleth Map of Predicted Crime Levels by ZIP Codehttps://ucsdonline.maps.arcgis.com/home/item.html?id=e47abb448f0a411ab77c6ac754ba0c34 3. Optimizing LA Police Station: A Location Allocation Analysishttps://ucsdonline.maps.arcgis.com/home/item.html?id=2409da85c3fe410e9578a0eaaed8471e - ArcGIS StoryMaphttps://ucsdonline.maps.arcgis.com/home/item.html?id=cfbd4fc27a3b400296e4e31555951d27 Software dependencies - pandas: Used for loading, formatting, and performing matrix operations on tabular data.- geopandas: Used for loading and processing spatial data, including spatial joins and coordinate transformations.- shapely.geometry.Point: Used to create spatial point objects from latitude and longitude coordinates.- arcgis.gis, arcgis.features, arcgis.geometry, arcgis.geoenrichment: Used to retrieve and manipulate geographic data from ArcGIS Online and to extract population statistics using the GeoEnrichment module.- numpy: Used for feature matrix formatting and numerical computations prior to model training.- IPython.display (display, Markdown, Image): Used to format and display Markdown text, data tables, and images within Jupyter Notebooks.- scikit-learn: Used for building and evaluating machine learning models. Specifically, it was used for data preprocessing (StandardScaler), splitting data (train_test_split), model selection and tuning (GridSearchCV, cross_val_score), training various regressors (e.g.,LinearRegression, RandomForestRegressor, KNeighborsRegressor), and assessing performance using metrics such as R², RMSE, and MAE.Other Components we used - ArcGIS Online: Used to create and host interactive web maps for spatial visualization and public presentation purposes.- Flourish: Used to create interactive graphs and charts for visualizing trends and supporting the analysis.

Add up-to-date Canadian weather conditions to your map! This service contains a layer of weather stations across Canada with current (up to the hour) weather conditions. The popups display weather icons which link you to Environment Canada's forecast website. The conditions are updated using a Python script that leverages weathergc to retrieve current weather conditions from Environment Canada and ArcREST to apply those updates to this service. Revisions: November 6, 2025Added 638 additional weather stations that can be found in this ECCC resourceUpdated the Url (Description) field to the correct URLs --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Ajoutez les conditions météorologiques canadiennes à jour à votre carte ! Ce service contient une couche de stations météorologiques à travers le Canada avec les conditions météorologiques actuelles (jusqu'à l'heure). Les fenêtres contextuelles affichent des icônes météo qui vous relient au site Web de prévisions d'Environnement Canada. Les conditions sont mises à jour à l'aide d'ArcGIS Notebooks dans ArcGIS Online.

Los ArcGIS Notebooks de ESRI integran los Jupyter Notebooks en el sistema ArcGIS para brindar una experiencia intuitiva y optimizada para el análisis espacial. Permiten combinar algoritmos de análisis espacial con bibliotecas Python de código abierto para crear modelos de ciencia de datos espaciales avanzados al alcance de todos los usuarios.** Ultima actualización: Agosto 2023 **Desde el año 2019 ESRI comenzó a integrar las capacidades de los Jupyter Notebooks en el sistema ArcGIS a través de los ArcGIS Notebooks, pudiendo trabajar con ellos desde ArcGIS Enterprise, ArcGIS Pro, ArcGIS Online y ArcGIS for Developers.

Minnesota Geospatial Commons Metadata ValidatorWelcome to the Minnesota Geospatial Commons Metadata Validator! This notebook helps you check the metadata for your ArcGIS Online item or group items.InstructionsRun All Cells: Click on "Run All Cells" or Click "Run" for each cell.Input Your ArcGIS ID: When prompted, enter your ArcGIS Item ID or Group ID.What This Notebook DoesParses the metadata for the provided ArcGIS Online item or items in the provided group.Prints out any missing required attributes.Additional RequirementsPlease ensure your item also meets the following criteria:Publicly Accessible: The item must be accessible to the public.ISO Topic Category: The item should have an ISO Topic Category.Relevant Tags: The item should include relevant tags.

This Jupyter Notebook created by Laurence lin and Young-Don Choi to simulate the Paine Run subwatershed (12.7 km2) of Shenandoah National Park. This notebook shows how to create RHESssys input using grass GIS from GIS data, simulate RHESsys Model and visualize the output of RHESsys model.

Hydrologic models are growing in complexity: spatial representations, model coupling, process representations, software structure, etc. New and emerging datasets are growing, supporting even more detailed modeling use cases. This complexity is leading to the reproducibility crisis in hydrologic modeling and analysis. We argue that moving hydrologic modeling to the cloud can help to address this reproducibility crisis. - We create two notebooks: 1. The first notebook demonstrates the process of collecting and manipulating GIS and Time-series data using GRASS GIS, Python and R to create RHESsys Model input. 2. The second notebook demonstrates the process of model compilation, parallel simulation, and visualization.

• The first notebook includes:

  1. Create Project Directory and Download Raw GIS Data from HydroShare
  2. Set GRASS Database and GISBASE Environment
  3. Preprocessing GIS Data for RHESsys Model using GRASS GIS and R script
  4. Preprocess Time series data for RHESsys Model
  5. Construct worldfile and flowtable to RHESSys

• The second notebook includes:

  1. Download and compile RHESsys Execution file
  2. Simulate RHESsys model
  3. Plotting RHESsys output

Hydrologic models are growing in complexity: spatial representations, model coupling, process representations, software structure, etc. New and emerging datasets are growing, supporting even more detailed modeling use cases. This complexity is leading to the reproducibility crisis in hydrologic modeling and analysis. We argue that moving hydrologic modeling to the cloud can help to address this reproducibility crisis. - We create two notebooks: 1. The first notebook demonstrates the process of collecting and manipulating GIS and Time-series data using GRASS GIS, Python and R to create RHESsys Model input. 2. The second notebook demonstrates the process of model compilation, simulation, and visualization.

• The first notebook includes:

  1. Create Project Directory and Download Raw GIS Data from HydroShare
  2. Set GRASS Database and GISBASE Environment
  3. Preprocessing GIS Data for RHESsys Model using GRASS GIS and R script
  4. Preprocess Time series data for RHESsys Model
  5. Construct worldfile and flowtable to RHESSys

• The second notebook includes:

  1. Download and compile RHESsys Execution file
  2. Simulate RHESsys model
  3. Plotting RHESsys output

Air Quality Aware Canada App leverages Air Quality Health Index (AQHI) real-time data from (Environment and Climate Change Canada API).AQHI is a scale designed to help quantify the quality of the air in a certain region on a scale from 1 to 10. When the amount of air pollution is very high, the number is reported as 10+. It also includes a category that describes the health risk associated with the index reading (e.g. Low, Moderate, High, or Very High Health Risk).AQHI is calculated from data observed in real time. Python programming and ArcGIS Notebook were used to create (aqhi_stations_observations_realtime) online feature service and map the index from the API. Update frequency: Every 15 minutes. Disclaimer: Air Quality Aware Canada App highlights the latest AQHI observations from the API. The frequency of updates for each observation station may vary but this application will always show the latest update provided by Environment and Climate Change Canada.Contact informationFor inquiries on Air Quality Health Index (AQHI), contact (Environment and Climate Change Canada)For inquiries regarding the service, please leave a comment below

scripts.zip

arcgisTools.atbx: terrainDerivatives: make terrain derivatives from digital terrain model (Band 1 = TPI (50 m radius circle), Band 2 = square root of slope, Band 3 = TPI (annulus), Band 4 = hillshade, Band 5 = multidirectional hillshades, Band 6 = slopeshade). rasterizeFeatures: convert vector polygons to raster masks (1 = feature, 0 = background).

makeChips.R: R function to break terrain derivatives and chips into image chips of a defined size. makeTerrainDerivatives.R: R function to generated 6-band terrain derivatives from digital terrain data (same as ArcGIS Pro tool). merge_logs.R: R script to merge training logs into a single file. predictToExtents.ipynb: Python notebook to use trained model to predict to new data. trainExperiments.ipynb: Python notebook used to train semantic segmentation models using PyTorch and the Segmentation Models package. assessmentExperiments.ipynb: Python code to generate assessment metrics using PyTorch and the torchmetrics library. graphs_results.R: R code to make graphs with ggplot2 to summarize results. makeChipsList.R: R code to generate lists of chips in a directory. makeMasks.R: R function to make raster masks from vector data (same as rasterizeFeatures ArcGIS Pro tool).

terraceDL.zip

dems: LiDAR DTM data partitioned into training, testing, and validation datasets based on HUC8 watershed boundaries. Original DTM data were provided by the Iowa BMP mapping project: https://www.gis.iastate.edu/BMPs. extents: extents of the training, testing, and validation areas as defined by HUC 8 watershed boundaries. vectors: vector features representing agricultural terraces and partitioned into separate training, testing, and validation datasets. Original digitized features were provided by the Iowa BMP Mapping Project: https://www.gis.iastate.edu/BMPs.