Thinking Spatially Using GIS

Thinking Spatially Using GIS is a 1:1 set of instructional materials for students that use ArcGIS Online to teach basic geography concepts found in upper elementary school and above.
Each module has both a teacher and student file.

Ferdinand Magellan was the first European explorer to reach the Pacific Ocean by crossing the Atlantic Ocean when his expedition sailed through an opening, or strait, near the tip of South America in 1520. He named the ocean Mar Pacifico, which means peaceful sea. The strait, which connected the Atlantic and Pacific oceans, was later named for him.

At that point in his journey, Magellan and his fleet had been at sea for more than a year. He had lost two of his five ships. Now he would cross the Pacific Ocean with three ships, looking for the coast of Asia and the Spice Islands. However, he had no idea the Pacific Ocean would be so big!

The Thinking Spatially Using GIS home is at: http://esriurl.com/TSG

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

Thinking Spatially Using GIS

Thinking Spatially Using GIS is a 1:1 set of instructional materials for students that use ArcGIS Online to teach basic geography concepts found in upper elementary school and above.
Each module has both a teacher and student file.

Animals are a big part of our life. Animals fascinate us, whether they live with us as pets or roam wild places on our planet. One exciting way to connect with animals from beyond our back yard is to visit the zoo. Zoological parks, or zoos, are a great way to bring people closer to animals. It is a chance for people to more deeply appreciate and understand how animals live and what they are like.

Zoos have been around for a long time. Queen Hatshepsut of Egypt had one about 3,500 years ago, and the Chinese emperor Wen Wang created a large zoo named the Garden of Intelligence about 3,000 years ago. Many leaders used zoos to show power and wealth. Zoos became popular starting about 500 years ago in the 1500s, when European explorers brought animals from the New World (the Americas) back to Europe.

The Thinking Spatially Using GIS home is at: http://esriurl.com/TSG

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

The 2018 Indiana BRFSS Child Optional Modules report contains prevalence estimates for child health risk behaviors and outcomes collected via an optional module.Topics include childhood asthma prevalence.

In Module 2 Lesson 1, we will take a deeper dive into Geographic Information Systems (GIS) technology. We'll explore different types of GIS data, the importance of data attributes and queries, data symbolization, and ways to access GIS technology. Let's just start with a quick refresher on what exactly GIS is. Click the box below for an amazing overview of GIS provided by Esri, the world leader in geospatial technology. Be sure to explore additonal tabs and live buttons. This site is packed full of information, from the history of GIS, to its applications and career opportunities.

117 original plate boundaries from Esri Data and Maps (2007) edited to better match 10 years of earthquakes, land forms and bathymetry from Mapping Our World's WSI_Earth image from module 2. Esri Canada's education layer of plate boundaries and the Smithsonian's ascii file from the download section of the 'This Dynamic Planet' site plate boundaries were used to compare the resulting final plate boundaries for significant differences.

In this course, you will explore the concepts, principles, and practices of acquiring, storing, analyzing, displaying, and using geospatial data. Additionally, you will investigate the science behind geographic information systems and the techniques and methods GIS scientists and professionals use to answer questions with a spatial component. In the lab section, you will become proficient with the ArcGIS Pro software package. This course will prepare you to take more advanced geospatial science courses. You will be asked to work through a series of modules that present information relating to a specific topic. You will also complete a series of lab exercises, assignments, and less guided challenges. Please see the sequencing document for our suggestions as to the order in which to work through the material. To aid in working through the lecture modules, we have provided PDF versions of the lectures with the slide notes included. This course makes use of the ArcGIS Pro software package from the Environmental Systems Research Institute (ESRI), and directions for installing the software have also been provided. If you are not a West Virginia University student, you can still complete the labs, but you will need to obtain access to the software on your own.

This resource contains data inputs and a Jupyter Notebook that is used to introduce Hydrologic Analysis using Terrain Analysis Using Digital Elevation Models (TauDEM) and Python. TauDEM is a free and open-source set of Digital Elevation Model (DEM) tools developed at Utah State University for the extraction and analysis of hydrologic information from topography. This resource is part of a HydroLearn Physical Hydrology learning module available at https://edx.hydrolearn.org/courses/course-v1:Utah_State_University+CEE6400+2019_Fall/about

In this activity, the student learns how to (1) derive hydrologically useful information from Digital Elevation Models (DEMs); (2) describe the sequence of steps involved in mapping stream networks, catchments, and watersheds; and (3) compute an approximate water balance for a watershed-based on publicly available data.

Please note that this exercise is designed for the Logan River watershed, which drains to USGS streamflow gauge 10109000 located just east of Logan, Utah. However, this Jupyter Notebook and the analysis can readily be applied to other locations of interest. If running the terrain analysis for other study sites, you need to prepare a DEM TIF file, an outlet shapefile for the area of interest, and the average annual streamflow and precipitation data. - There are several sources to obtain DEM data. In the U.S., the DEM data (with different spatial resolutions) can be obtained from the National Elevation Dataset available from the national map (http://viewer.nationalmap.gov/viewer/). Another DEM data source is the Shuttle Radar Topography Mission (https://www2.jpl.nasa.gov/srtm/), an international research effort that obtained digital elevation models on a near-global scale (search for Digital Elevation at https://www.usgs.gov/centers/eros/science/usgs-eros-archive-products-overview?qt-science_center_objects=0#qt-science_center_objects). - If not already available, you can generate the outlet shapefile by applying basic terrain analysis steps in geospatial information system models such as ArcGIS or QGIS. - You also need to obtain average annual streamflow and precipitation data for the watershed of interest to assess the annual water balance and calculate the runoff ratio in this exercise. In the U.S., the streamflow data can be obtained from the USGS NWIS website (https://waterdata.usgs.gov/nwis) and the precipitation from PRISM (https://prism.oregonstate.edu/normals/). Note that using other datasets may require preprocessing steps to make data ready to use for this exercise.

In this course, you will explore a variety of open-source technologies for working with geosptial data, performing spatial analysis, and undertaking general data science. The first component of the class focuses on the use of QGIS and associated technologies (GDAL, PROJ, GRASS, SAGA, and Orfeo Toolbox). The second component of the class introduces Python and associated open-source libraries and modules (NumPy, Pandas, Matplotlib, Seaborn, GeoPandas, Rasterio, WhiteboxTools, and Scikit-Learn) used by geospatial scientists and data scientists. We also provide an introduction to Structured Query Language (SQL) for performing table and spatial queries. This course is designed for individuals that have a background in GIS, such as working in the ArcGIS environment, but no prior experience using open-source software and/or coding. You will be asked to work through a series of lecture modules and videos broken into several topic areas, as outlined below. Fourteen assignments and the required data have been provided as hands-on opportunites to work with data and the discussed technologies and methods. If you have any questions or suggestions, feel free to contact us. We hope to continue to update and improve this course. This course was produced by West Virginia View (http://www.wvview.org/) with support from AmericaView (https://americaview.org/). This material is based upon work supported by the U.S. Geological Survey under Grant/Cooperative Agreement No. G18AP00077. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the opinions or policies of the U.S. Geological Survey. Mention of trade names or commercial products does not constitute their endorsement by the U.S. Geological Survey. After completing this course you will be able to: apply QGIS to visualize, query, and analyze vector and raster spatial data. use available resources to further expand your knowledge of open-source technologies. describe and use a variety of open data formats. code in Python at an intermediate-level. read, summarize, visualize, and analyze data using open Python libraries. create spatial predictive models using Python and associated libraries. use SQL to perform table and spatial queries at an intermediate-level.

The Global Solar Atlas provide relevant information of solar power potential for energy generation. It is a project administered by the World Bank Group as part of the Energy Sector Assistance Program (ESMAP). The Global Solar Atlas was implemented by Solargis. The goal of the atlas is to expose solar resource and photovoltaic power potential data.Output variables as processing templates:PV electricity output: Total electrical energy produced per capacity installed (kWh/kWp) per yearMonthly PV electricity output (12 layers): Average monthly electrical energy produced per capacity installed (x1,000 kWh/kWp) per day.Direct normal irradiation: Amount of solar energy per unit area (kWh/m2) coming from a direct (i.e. perpendicular) pathDiffuse horizontal irradiation: Amount of solar energy per unit area (kWh/m2) received from scattered sources (e.g. clouds)Global horizontal irradiation: Amount of solar radiation received (kWh/m2) at a theoretical plane horizontal to the groundGlobal tilted irradiation at optimum angle: Largest amount of solar radiation that can be received (kWh/m2) at the ground at the optimum angle (i.e. OPTA)Optimum tilt of PV modules: Optimal angle (segrees) of a plane that receives the highest solar radiation.Air temperature: Annual average of air temperature (°C) at 2m from the groundElevation: Elevation (m) above mean sea level.What can you do with this layer?This layer can be used to primarily to estimate the total energy yield of a PV system and its inter-annual variation or compare energy yield between sites. The layer can also be used to determine the optimal angle of PV panels and quantify the gap between received radiation at a horizontal plane against the radiation received in a plane tilted at the optimal angle. This layer can also be used to quantify the difference between direct and diffuse irradiation for a given location. Additionally, the layer provides information on the mean air temperature and elevation used in the model.Associated web mapsPV electricity outputHorizontal and tilted irradiationsDirect and diffuse irradiationsCell Size: 30 arc-secondsSource Type: ContinousPixel Type: IntegerProjection: GCS WGS84Extent: GlobalSource: Global Solar AtlasArcGIS Server URL: https://earthobs3.arcgis.com/arcgis

The 2018 Indiana BRFSS Adult Optional Modules report contains prevalence estimates for several health risk behaviors and outcomes collected via optional modules.Topics include cancer survivorship, prediabetes, and e-cigarette use.

Mapping Our World Using GIS is a 1:1 set of instructional materials for teaching basic concepts found in middle school world geography. Each module consists of multiple files.

The Mapping Our World collection is at: http://esriurl.com/MOW.

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

        This computer activity will show you how to start the ArcGIS Online program. You will be guided

through the basics of using ArcGIS Online map viewer to explore maps. After you do this activity, you will be prepared to complete other GIS activities.

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

The Made in Alaska program is a state guaranteed program administered by the Department of Community, Commerce and Economic Development. The Made in Alaska program's mission is to promote products made, manufactured, or handcrafted in the state. Alaska's businesses manufacture high quality products for markets in Alaska domestically and internationally. Products range from small gift items to large industrial modules. This dataset contains all active permit holders in the Made in Alaska program.

National Forest Estate Bridges are managed by Forestry Civil Engineering in one of the Forestry Commission's Forester GIS modules. This data set comprises location and category of construction.

Note: This image service has been consolidated into a unified service that now includes Alaska (AK), the Continental United States (CONUS), and Hawaii (HI). For access to the updated and combined image service, please visit the new item page: https://usfs.maps.arcgis.com/home/item.html?id=75ca2433ca2e4828a5a0302d92efe64a.National burn probability (BP) and conditional fire intensity level (FIL) data were generated for Hawaii using a geospatial Fire Simulation (FSim) system developed by the US Forest Service Missoula Fire Sciences Laboratory to estimate probabilistic components of wildfire risk (Finney et al. [2011]). The FSim system includes modules for weather generation, wildfire occurrence, fire growth, and fire suppression. FSim is designed to simulate the occurrence and growth of wildfires under tens of thousands of hypothetical contemporary fire seasons in order to estimate the probability of a given area (i.e., pixel) burning under current landscape conditions and fire management practices. The data presented here represent modeled BP and FIL for the conterminous US at a 270-meter grid spatial resolution. The six FILs correspond to flame-length classes as follows: FIL1 = < 2 feet (ft); FIL2 = 2 < 4 ft.; FIL3 = 4 < 6 ft.; FIL4 = 6 < 8 ft.; FIL5 = 8 < 12 ft.; FIL6 = 12+ ft. Because they indicate conditional probabilities (i.e., representing the likelihood of burning at a certain intensity level, given that a fire occurs), the FIL*_20160830 data must be used in conjunction with the BP_20160830 data for risk assessment.

Want to keep the data in your Hosted Feature Service current? Not interested in writing a lot of code?Leverage this Python Script from the command line, Windows Scheduled Task, or from within your own code to automate the replacement of data in an existing Hosted Feature Service. It can also be leveraged by your Notebook environment and automatically managed by the MNCD Tool!See the Sampler Notebook that features the OverwriteFS tool run from Online to update a Feature Service. It leverages MNCD to cache the OverwriteFS script for import to the Notebook. A great way to jump start your Feature Service update workflow! RequirementsPython v3.xArcGIS Python APIStored Connection Profile, defined by Python API 'GIS' module. Also accepts 'pro', to specify using the active ArcGIS Pro connection. Will require ArcGIS Pro and Arcpy!Pre-Existing Hosted Feature ServiceCapabilitiesOverwrite a Feature Service, refreshing the Service Item and DataBackup and reapply Service, Layer, and Item properties - New at v2.0.0Manage Service to Service or Service to Data relationships - New at v2.0.0Repair Lost Service File Item to Service Relationships, re-enabling Service Overwrite - New at v2.0.0'Swap Layer' capability for Views, allowing two Services to support a View, acting as Active and Idle role during Updates - New at v2.0.0Data Conversion capability, able to invoke following a download and before Service update - New at v2.0.0Includes 'Rss2Json' Conversion routine, able to read a RSS or GeoRSS source and generate GeoJson for Service Update - New at v2.0.0Renamed 'Rss2Json' to 'Xml2GeoJSON' for its enhanced capabilities, 'Rss2Json' remains for compatability - Revised at v2.1.0Added 'Json2GeoJSON' Conversion routine, able to read and manipulate Json or GeoJSON data for Service Updates - New at v2.1.0Can update other File item types like PDF, Word, Excel, and so on - New at v2.1.0Supports ArcGIS Python API v2.0 - New at v2.1.2RevisionsSep 29, 2021: Long awaited update to v2.0.0!Sep 30, 2021: v2.0.1, Patch to correct Outcome Status when download or Coversion resulted in no change. Also updated documentation.Oct 7, 2021: v2.0.2, workflow Patch correcting Extent update of Views when Overwriting Service, discovered following recent ArcGIS Online update. Enhancements to 'datetimeUtil' Support script.Nov 30, 2021: v2.1.0, added new 'Json2GeoJSON' Converter, enhanced 'Xml2GeoJSON' Converter, retired 'Rss2Json' Converter, added new Option Switches 'IgnoreAge' and 'UpdateTarget' for source age control and QA/QC workflows, revised Optimization logic and CRC comparison on downloads.Dec 1, 2021: v2.1.1, Only a patch to Conversion routines: Corrected handling of null Z-values in Geometries (discovered immediately following release 2.1.0), improve error trapping while processing rows, and added deprecation message to retired 'Rss2Json' conversion routine.Feb 22, 2022: v2.1.2, Patch to detect and re-apply case-insensitive field indexes. Update to allow Swapping Layers to Service without an associated file item. Added cache refresh following updates. Patch to support Python API 2.0 service 'table' property. Patches to 'Json2GeoJSON' and 'Xml2GeoJSON' converter routines.Sep 5, 2024: v2.1.4, Patch service manager refresh failure issue. Added trace report to Convert execution on exception. Set 'ignore-DataItemCheck' property to True when 'GetTarget' action initiated. Hardened Async job status check. Update 'overwriteFeatureService' to support GeoPackage type and file item type when item.name includes a period, updated retry loop to try one final overwrite after del, fixed error stop issue on failed overwrite attempts. Removed restriction on uploading files larger than 2GB. Restores missing 'itemInfo' file on service File items. Corrected false swap success when view has no layers. Lifted restriction of Overwrite/Swap Layers for OGC. Added 'serviceDescription' to service detail backup. Added 'thumbnail' to item backup/restore logic. Added 'byLayerOrder' parameter to 'swapFeatureViewLayers'. Added 'SwapByOrder' action switch. Patch added to overwriteFeatureService 'status' check. Patch for June 2024 update made to 'managers.overwrite' API script that blocks uploads > 25MB, API v2.3.0.3. Patch 'overwriteFeatureService' to correctly identify overwrite file if service has multiple Service2Data relationships.Includes documentation updates!

This is a simple map service showing latitude/longitude lines that can be used as an overlay along with other layers for reference.Spatial reference: GCS_WGS_1984.This map layer is used in NOAA's Data in the Classroom module(s).Data in the Classroom is an online curriculum to foster data literacy. With NOAA’s Data in the Classroom, students use historical and real-time NOAA data to explore today’s most pressing environmental issues. Each of the modules addresses research questions, includes stepped levels of engagement and builds students’ abilities to understand, interpret, and think critically about data. The modules available include:Investigating El NiñoInvestigating Sea LevelInvestigating Coral BleachingMonitoring Estuarine Water QualityUnderstanding Ocean & Coastal AcidificationVisit Data in the Classroom for more information.All Data in the Classroom modules follow guiding principles found in the Next Generation Science Standards (NGSS)* and Common Core State Standards.*NGSS Lead States. 2013. Next Generation Science Standards: For States, By States. Washington, DC: The National Academies Press. Next Generation Science Standards is a registered trademark of Achieve. Neither Achieve nor the lead states and partners that developed the Next Generation Science Standards was involved in the production of, and does not endorse, this product.

Second of four zipfiles providing all data and Python code necessary to replicate any of the 13 development potential indexes (DPIs) described within Oakleaf et al. (2019), “Mapping global development potential for renewable energy, fossil fuels, mining and agriculture sectors”. A README.pdf guides users on setting up environment necessary to use data and run Python code.

To run Python code with accompanying spatial data, 64 GBs of disk space is required. Additionally ArcPY, a python module associated with ESRI’s ArcGIS Desktop, and an accompanying Spatial Analyst extension license are required to run Python code. All code was created by J.R. Oakleaf during 2018 and is licensed under Creative Commons Attribution-NonCommercial 4.0 International License http://creativecommons.org/licenses/by-nc/4.0/.

Purpose Of The ItemA feature layer is used to store emergency incident locations and related information. It plays a crucial role in the geographic coordinates, natures, and details of incidents like natural disasters, accidents, and other emergencies. AudienceThis web map's primary audience includes emergency managers, first responders, and local government officials responsible for disaster response and recovery in DuPage County, Illinois. Data SourceThe data stored in this feature layer includes points, lines, and areas for field observations and emergency response teams.

Thinking Spatially Using GIS

Thinking Spatially Using GIS is a 1:1 set of instructional materials for students that use ArcGIS Online to teach basic geography concepts found in upper elementary school and above.
Each module has both a teacher and student file.

Meteorologists study storms that produce tornadoes. They use tools such as forecasting (predicting the weather), research (observing and trying to understand the weather), and storm chasing (following and watching storms close-up). Some tornadoes and tornado outbreaks may stick even in your memory. Some students grow up to study the science of tornadoes because of a personal experience or out of curiosity about an outbreak. Perhaps you will decide to be a meteorologist yourself one day!

In this GIS activity, you will see how tornadoes are classified into weak, strong, and violent categories. You will also take a close look at some of the memorable tornado outbreaks in history.

Let’s explore Tornado Alley.

The Thinking Spatially Using GIS home is at: http://esriurl.com/TSG

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