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

ArcGIS Pro, verileri analiz etmek, haritalar oluşturmak ve görselleştirmek, modellemek ve otomatikleştirmek için çeşitli araçlar sunar. ArcGIS Pro içerisinde Notebook uygulaması, bu araçlardan biridir.Notebook, Python programlama dilini kullanarak CBS iş akışlarını gerçekleştirmek için interaktif bir ortamdır. Notebook, kod hücreleri ve metin hücreleri olarak adlandırılan iki tür hücreden oluşur. Kod hücreleri, Python kodunu çalıştırmak ve sonuçları görüntülemek için kullanılır. Metin hücreleri ise, kodun açıklamasını, belgelerini veya yorumlarını yazmak için kullanılır.Notebook kullanımının avantajları şunlardır:

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.

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.

This layer shows Technology Access by Household. Data is from US Census American Community Survey (ACS) 5-year estimates.This layer represents the underlying data for several data visualizations on the Tempe Equity Map.Data visualized as a percent of total households in given census tract.Layer includes:Key demographicsTotal Households % With a Desktop or Laptop Computer% With only a Desktop or Laptop% With a Smartphone% With only a Smartphone% With a Tablet% With only a tablet% With other type of computing device% With other type of computing device only% No computerCurrent Vintage: 2017-2021ACS Table(s): S2801 (Not all lines of this ACS table are available in this feature layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of Census update: Dec 8, 2022Data Preparation: Data table downloaded and joined with Census Tract boundaries that are within or adjacent to the City of Tempe boundaryNational Figures: data.census.gov

A survey among English farm holdings that used a PC or laptop in October of 2019 saw ** percent of responding farms use their device to run mapping software to help with the management of environmental schemes (for example Landapp or ArcGIS). The most commonly stated use for PCs and laptops in relation to business operations was to use the device to use services from the Department for Environment, Food and Rural Affairs and related services such as CTS, Basic Payments Scheme, surveys, and others.

THE GEOINQUIRIES™ COLLECTION FOR AMERICAN LITERATURE

http://www.esri.com/geoinquiries

The Esri GeoInquiry™ collection for American Literature contains 15 free, standards-based activities that correspond and extend map-based concepts found in course texts frequently used in high school literature. The activities use a common inquiry-based instructional model, require only 15 minutes to deliver, and are device/laptop agnostic. Each activity includes an ArcGIS Online map but requires no login or installation. The activities harmonize with the Common Core ELA national curriculum standards.

All American Literature GeoInquiries™ can be found at: http://esriurl.com/litGeoInquiries

All GeoInquiries™ can be found at: http://www.esri.com/geoinquiries

Explore time-discrete statistical climate downscaling using regression tools and a Jupyter notebook with Python to automate temperature predictions and build a time-series mosaic. This has been created for the Learn ArcGIS lesson Downscale climate data with machine learning.This is an archived copy of the tutorial data and will no longer be updated. For an up-to-date version, available only in English, please see Regression Analysis: Building a Regression Model Using ArcGIS Pro, Regression Analysis: Performing Random Forest Regression Using ArcGIS Pro, and Downscaling a Prediction Model Using ArcGIS Notebooks and ArcGIS Pro.

Author: Titus, Maxwell (mtitus@esri.com)Last Updated: 3/4/2025Intended Environment: ArcGIS ProPurpose: This Notebook was designed to automate updates for Hosted Feature Services hosted in ArcGIS Online (or ArcGIS Portal) from ArcGIS Pro and a spatial join of two live datasets.Description: This Notebook was designed to automate updates for Hosted Feature Services hosted in ArcGIS Online (or ArcGIS Portal) from ArcGIS Pro. An associated ArcGIS Dashboard would then reflect these updates. Specifically, this Notebook would:First, pull two datasets - National Weather Updates and Public Schools - from the Living Atlas and add them to an ArcGIS Pro map.Then, the Notebook would perform a spatial join on two layers to give Public Schools features information on whether they fell within an ongoing weather event or alert. Next, the Notebook would truncate the Hosted Feature Service in ArcGIS Online - that is, delete all the data - and then append the new data to the Hosted Feature ServiceAssociated Resources: This Notebook was used as part of the demo for FedGIS 2025. Below are the associated resources:Living Atlas Layer: NWS National Weather Events and AlertsLiving Atlas Layer: U.S. Public SchoolsArcGIS Demo Dashboard: Demo Impacted Schools Weather DashboardUpdatable Hosted Feature Service: HIFLD Public Schools with Event DataNotebook Requirements: This Notebook has the following requirements:This notebook requires ArcPy and is meant for use in ArcGIS Pro. However, it could be adjusted to work with Notebooks in ArcGIS Online or ArcGIS Portal with the advanced runtime.If running from ArcGIS Pro, connect ArcGIS Pro to the ArcGIS Online or ArcGIS Portal environment.Lastly, the user should have editable access to the hosted feature service to update.

THE GEOINQUIRIES™ COLLECTION FOR U.S. History

http://www.esri.com/geoinquiries

The GeoInquiry™ collection for World History contains 15 free, standards-based activities that correspond and extend spatial concepts found in course textbooks frequently used in introductory world history classes. The activities use a common inquiry-based instructional model, require only 15 minutes to deliver, and are device/laptop agnostic. Each activity includes an ArcGIS Online map but requires no login or installation. The activities harmonize with the C3 Framework for social studies curriculum standards.

All World History GeoInquiries™ can be found at: http://esriurl.com/worldHistoryGeoInquiries

All GeoInquiries™ can be found at: http://www.esri.com/geoinquiries

THE GEOINQUIRIES™ COLLECTION FOR U.S. History

http://www.esri.com/geoinquiries

The GeoInquiry™ collection for World History contains 15 free, standards-based activities that correspond and extend spatial concepts found in course textbooks frequently used in introductory world history classes. The activities use a common inquiry-based instructional model, require only 15 minutes to deliver, and are device/laptop agnostic. Each activity includes an ArcGIS Online map but requires no login or installation. The activities harmonize with the C3 Framework for social studies curriculum standards.

All World History GeoInquiries™ can be found at: http://esriurl.com/worldHistoryGeoInquiries

All GeoInquiries™ can be found at: http://www.esri.com/geoinquiries

THE GEOINQUIRIES™ COLLECTION FOR U.S. History

http://www.esri.com/geoinquiries

The GeoInquiry™ collection for World History contains 15 free, standards-based activities that correspond and extend spatial concepts found in course textbooks frequently used in introductory world history classes. The activities use a common inquiry-based instructional model, require only 15 minutes to deliver, and are device/laptop agnostic. Each activity includes an ArcGIS Online map but requires no login or installation. The activities harmonize with the C3 Framework for social studies curriculum standards.

All World History GeoInquiries™ can be found at: http://esriurl.com/worldHistoryGeoInquiries

All GeoInquiries™ can be found at: http://www.esri.com/geoinquiries

THE GEOINQUIRIES™ COLLECTION FOR U.S. History

http://www.esri.com/geoinquiries

The GeoInquiry™ collection for World History contains 15 free, standards-based activities that correspond and extend spatial concepts found in course textbooks frequently used in introductory world history classes. The activities use a common inquiry-based instructional model, require only 15 minutes to deliver, and are device/laptop agnostic. Each activity includes an ArcGIS Online map but requires no login or installation. The activities harmonize with the C3 Framework for social studies curriculum standards.

All World History GeoInquiries™ can be found at: http://esriurl.com/worldHistoryGeoInquiries

All GeoInquiries™ can be found at: http://www.esri.com/geoinquiries

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.

THE GEOINQUIRIES™ COLLECTION FOR AMERICAN LITERATURE

http://www.esri.com/geoinquiries

The Esri GeoInquiry™ collection for American Literature contains 15 free, standards-based activities that correspond and extend map-based concepts found in course texts frequently used in high school literature. The activities use a common inquiry-based instructional model, require only 15 minutes to deliver, and are device/laptop agnostic. Each activity includes an ArcGIS Online map but requires no login or installation. The activities harmonize with the Common Core ELA national curriculum standards.

All American Literature GeoInquiries™ can be found at: http://esriurl.com/litGeoInquiries

All GeoInquiries™ can be found at: http://www.esri.com/geoinquiries

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

THE GEOINQUIRIES™ COLLECTION FOR U.S. History

http://www.esri.com/geoinquiries

The GeoInquiry™ collection for World History contains 15 free, standards-based activities that correspond and extend spatial concepts found in course textbooks frequently used in introductory world history classes. The activities use a common inquiry-based instructional model, require only 15 minutes to deliver, and are device/laptop agnostic. Each activity includes an ArcGIS Online map but requires no login or installation. The activities harmonize with the C3 Framework for social studies curriculum standards.

All World History GeoInquiries™ can be found at: http://esriurl.com/worldHistoryGeoInquiries

All GeoInquiries™ can be found at: http://www.esri.com/geoinquiries

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

Every published digital survey is designated as either ‘Final’, or ‘Provisional’, depending upon its status in a peer review process. Final surveys are peer reviewed with extensive quality control methods to confirm that field attributes reflect the most detailed and specific land-use classification available, following the standard DWR Land Use Legendspecific to the survey year. Data sets are considered ‘final’ following the reconciliation of peer review comments and confirmation by the originating Regional Office. During final review, individual polygons are evaluated using a combination of aerial photointerpretation, satellite image multi-spectral data and time series analysis, comparison with other sources of land use data, and general knowledge of land use patterns at the local level.Provisional data sets have been reviewed for conformance with DWR’s published data record format, and for general agreement with other sources of land use trends. Comments based on peer review findings may not be reconciled, and no significant edits or changes are made to the original survey data.The 2012 Sonoma County land use survey data was developed by the State of California, Department of Water Resources (DWR) through its Division of Integrated Regional Water Management (DIRWM) and Division of Statewide Integrated Water Management (DSIWM). Land use boundaries were digitized and land use data was gathered by staff of DWR’s North Central Region using extensive field visits and aerial photography. Land use polygons in agricultural areas were mapped in greater detail than areas of urban or native vegetation. Quality control procedures were performed jointly by staff at DWR’s DSIWM headquarters, under the leadership of Jean Woods, and North Central Region, under the supervision of Kim Rosmaier. This data was developed to aid DWR’s ongoing efforts to monitor land use for the main purpose of determining current and projected water uses. The associated data are considered DWR enterprise GIS data, which meet all appropriate requirements of the DWR Spatial Data Standards, specifically the DWR Spatial Data Standards version 2.1, dated March 9, 2016. DWR makes no warranties or guarantees - either expressed or implied - as to the completeness, accuracy, or correctness of the data. DWR neither accepts nor assumes liability arising from or for any incorrect, incomplete, or misleading subject data. Comments, problems, improvements, updates, or suggestions should be forwarded to gis@water.ca.gov. This data represents a land use survey of Sonoma County conducted by the California Department of Water Resources, North Central Regional Office staff. The field work for this survey was conducted during July - September 2012 by staff visiting each field and noting what was grown. The county was divided into five survey areas using major road as centerlines and other geographic features for boundaries. The county was surveyed with two teams. The linework was heads up digitized in ArcGIS 10.0 with 2010 National Agriculture Imagery Program (NAIP) one-meter imagery as the base. Field Boundaries were reviewed with ArcGIS 10.2 and NAIP 2012 imagery when it became available. The data was recombined after it was finished. The Virtual Basic Landuse Attributor was used for the survey and to start the post survey process; after converting to ArcGIS 10.2, the domain file geodatabase structure was used to attribute and help finish facilitating the post survey process. Tables were run through a Python script to put the data in the standard landuse format. ArcGIS geoprocessing tools and topology rules were used to locate errors and for quality control and assurance. Horse pastures were designated either S2 or S6. The special condition 'G' was used to denote vineyards that had sprinklers for frost protection rather than representing a cover crop as stated in the February 2009 Standard Land Use Legend used for this survey. Field Boundaries were not drawn to represent legal parcel (ownership) boundaries, or meant to be used as parcel boundaries. Images and land use boundaries were loaded onto laptop computers that were used as the field data collection tools. GPS units connected to the laptops were used to confirm surveyor's location with respect to the fields. Staff took these laptops into the field and virtually all the areas were visited to positively identify the land use. Land use codes were digitized in the field on laptop computers using ESRI ArcMAP software, version 10.0. Before final processing, standard quality control procedures were performed jointly by staff at DWR’s North Central Region, and at DSIWM headquarters under the leadership of Jean Woods. Senior Land and Water Use Supervisor. After quality control procedures were completed, the data was finalized. The positional accuracy of the digital line work, which is based upon the orthorectified NAIP imagery, is approximately 6 meters. The land use attribute accuracy for agricultural fields is high, because almost every delineated field was visited by a surveyor. The accuracy is 95 percent because some errors may have occurred. Possible sources of attribute errors are: a) Human error in the identification of crop types, b) Data entry errors.