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.

This dataset holds all materials for the Inform E-learning GIS course

This seminar is an applied study of deep learning methods for extracting information from geospatial data, such as aerial imagery, multispectral imagery, digital terrain data, and other digital cartographic representations. We first provide an introduction and conceptualization of artificial neural networks (ANNs). Next, we explore appropriate loss and assessment metrics for different use cases followed by the tensor data model, which is central to applying deep learning methods. Convolutional neural networks (CNNs) are then conceptualized with scene classification use cases. Lastly, we explore semantic segmentation, object detection, and instance segmentation. The primary focus of this course is semantic segmenation for pixel-level classification. The associated GitHub repo provides a series of applied examples. We hope to continue to add examples as methods and technologies further develop. These examples make use of a vareity of datasets (e.g., SAT-6, topoDL, Inria, LandCover.ai, vfillDL, and wvlcDL). Please see the repo for links to the data and associated papers. All examples have associated videos that walk through the process, which are also linked to the repo. A variety of deep learning architectures are explored including UNet, UNet++, DeepLabv3+, and Mask R-CNN. Currenlty, two examples use ArcGIS Pro and require no coding. The remaining five examples require coding and make use of PyTorch, Python, and R within the RStudio IDE. It is assumed that you have prior knowledge of coding in the Python and R enviroinments. If you do not have experience coding, please take a look at our Open-Source GIScience and Open-Source Spatial Analytics (R) courses, which explore coding in Python and R, respectively. After completing this seminar you will be able to: explain how ANNs work including weights, bias, activation, and optimization. describe and explain different loss and assessment metrics and determine appropriate use cases. use the tensor data model to represent data as input for deep learning. explain how CNNs work including convolutional operations/layers, kernel size, stride, padding, max pooling, activation, and batch normalization. use PyTorch, Python, and R to prepare data, produce and assess scene classification models, and infer to new data. explain common semantic segmentation architectures and how these methods allow for pixel-level classification and how they are different from traditional CNNs. use PyTorch, Python, and R (or ArcGIS Pro) to prepare data, produce and assess semantic segmentation models, and infer to new data.

Through the Department of the Interior-Bureau of Indian Affairs Enterprise License Agreement (DOI-BIA ELA) program, BIA employees and employees of federally-recognized Tribes may access a variety of geographic information systems (GIS) online courses and instructor-led training events throughout the year at no cost to them. These online GIS courses and instructor-led training events are hosted by the Branch of Geospatial Support (BOGS) or offered by BOGS in partnership with other organizations and federal agencies. Online courses are self-paced and available year-round, while instructor-led training events have limited capacity and require registration and attendance on specific dates. This dataset does not any training where the course was not completed by the participant or where training was cancelled or otherwise not able to be completed. Point locations depict BIA Office locations or Tribal Office Headquarters. For completed trainings where a participant location was not provided a point locations may not be available. For more information on the Branch of Geospatial Support Geospatial training program, please visit:https://www.bia.gov/service/geospatial-training.

Through the Department of the Interior-Bureau of Indian Affairs Enterprise License Agreement (DOI-BIA ELA) program, BIA employees and employees of federally-recognized Tribes may access a variety of geographic information systems (GIS) online courses and instructor-led training events throughout the year at no cost to them. These online GIS courses and instructor-led training events are hosted by the Branch of Geospatial Support (BOGS) or offered by BOGS in partnership with other organizations and federal agencies. Online courses are self-paced and available year-round, while instructor-led training events have limited capacity and require registration and attendance on specific dates. This dataset does not any training where the course was not completed by the participant or where training was cancelled or otherwise not able to be completed. Point locations depict BIA Office locations or Tribal Office Headquarters. For completed trainings where a participant _location was not provided a point locations may not be available. For more information on the Branch of Geospatial Support Geospatial training program, please visit:https://www.bia.gov/service/geospatial-training.

Seattle Parks and Recreation ARCGIS park feature map layer web services are hosted on Seattle Public Utilities' ARCGIS server. This web services URL provides a live read only data connection to the Seattle Parks and Recreations Golf Courses dataset.

GIS maps are windows into a database. Learn how to access the data connected to map features to answer questions about the real world.GoalsExplore patterns with GIS maps.Create GIS maps.Display map labels.Use a table to select features on a map.

Climate data and geographic data from Madagascar for learning multi-criteria analysis in GIS courses.

• MultiCriteriaAnalysis_ENG_v2022_v3.pdf - summary of Multi-Criteria Analysis methods for suitability of mango tree fruitculture.
• Country borders: https://www.naturalearthdata.com/downloads/50m-cultural-vectors/50m-admin-0-countries-2/
• Rivers: https://www.naturalearthdata.com/downloads/10m-physical-vectors/
• Roads: https://www.naturalearthdata.com/downloads/50m-cultural-vectors/
• GLOBCOVER: http://due.esrin.esa.int/page_globcover.php
• GlobCover Legend and More XLSX - sheets with support material from videos in lecture channel
• Temperature and precipiatation - climate variables: clipped from WorldClim database rasters (see http://www.worldclim.org/)
• DEM from EarthExplorer web portal (see https://www.cirgeo.unipd.it/didattica/GIS/01x_access_geodata.html#USGS:_EarthExplorer
  

This vector dataset provides polygons that represent significant golf course facility locations in Suffolk County. These courses can be publicly (State, County, Town, Village) or privately owned. This dataset can be linked with the GolfCoursePoint feature class by the FACILITYID field. In some cases, there may be multiple Golf Course Points for a single Golf Course Polygon. These data are organized for consumption in desktop and web applications.

In successful geoinformatics education, students’ active role in the learning process, e.g. through applying self-assessment, show an increasing interest but the evidence of benefits and challenges of self-assessment are sporadic. In this article, we examine the usefulness of an online self-assessment tool developed for geoinformatics education. We gathered data in two Finnish universities on five courses (n = 11–73 students/course) between 2019 and 2021. We examined 1) how the students’ self-assessed knowledge and understanding in geoinformatics subject topics changed during a course, 2) how the competencies at the end of a course changed between the years in different courses, and 3) what was the perceived usefulness of the self-assessment approach among the students. The results indicate support for the implementation of self-assessment, both as a formative and summative assessment. However, it is crucial to ensure that the students understand the contents of the self-assessment subject topics. To increase students’ motivation to take a self-assessment, it is crucial that the teacher actively highlights how it supports their studying and learning. As the teachers of the examined courses, we discuss the benefits and challenges of the self-assessment approach and the applied tool for the future development of geoinformatics education.

Seattle Parks and Recreation Golf Course locations. SPR Golf Courses are managed by contractors.Refresh Cycle: WeeklyFeature Class: DPR.GolfCourse

In this course, you are introduced to the Hot Spot Analysis tools and the Cluster and Outlier Analysis tools. You will discover how these analysis tools can help you make smarter decisions. You will also learn the foundational skills and concepts required to begin your analysis and interpret your results.GoalsExplain how statistical cluster analysis can help you make smarter decisions.Describe key concepts related to statistical cluster analysis.Describe the Hot Spot Analysis and Cluster and Outlier Analysis tools.

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.

Scenic Waterway Courses

This course explores the theory, technology, and applications of remote sensing. It is designed for individuals with an interest in GIS and geospatial science who have no prior experience working with remotely sensed data. Lab exercises make use of the web and the ArcGIS Pro software. You will work with and explore a wide variety of data types including aerial imagery, satellite imagery, multispectral imagery, digital terrain data, light detection and ranging (LiDAR), thermal data, and synthetic aperture RaDAR (SAR). Remote sensing is a rapidly changing field influenced by big data, machine learning, deep learning, and cloud computing. In this course you will gain an overview of the subject of remote sensing, with a special emphasis on principles, limitations, and possibilities. In addition, this course emphasizes information literacy, and will develop your skills in finding, evaluating, and using scholarly information.

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 to reinforce the material. Lastly, you will complete paper reviews and a term project. We have also provided additional bonus material and links associated with surface hydrologic analysis with TauDEM, geographic object-based image analysis (GEOBIA), Google Earth Engine (GEE), and the geemap Python library for Google Earth Engine. Please see the sequencing document for our suggested order in which to work through the material. We have also provided PDF versions of the lectures with the notes included.

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

This course introduces you to the powerful capabilities of ArcGIS Pro and how it can be used in your work.Goals Describe capabilities of ArcGIS Pro. Use basic ArcGIS Pro functionality.

Information related to the GIS course launch on Wednesday 18th May 2022

Bucknell's summer 2015 "New Orleans in 12 Movements" course aims to help students view New Orleans' natural environment, built infrastructure, and human experience in an integrated way. The course is co-taught by faculty from 3 departments and includes a week of field work in New Orleans. In this course, students will develop an integrated, holistic understanding of how the city of New Orleans has evolved over time. To support this learning, students have been provided an ArcGIS Online web-based map containing key cultural and historic information about New Orleans selected by their instructors. This interactive tool will enable them to explore New Orleans’ natural environment, built infrastructure and human experience through a variety of lenses. Faculty will use the map to deliver presentations and course materials to students. Students will use their own copy of the map to take notes, complete and deliver course assignments, and add their own materials to the course collection. Link to ArcGIS Online resource guide for class: click hereLink to data dictionary for NOLA class map layers: click hereLink to class website/blog: click here

Site a water tower shared by two towns at the midpoint and determine the costs involved using the Pythagorean theorem. THE GEOINQUIRIES™ COLLECTION FOR MATHEMATICShttp://www.esri.com/geoinquiriesThe GeoInquiry™ collection for Mathematics contains 15 free, standards-based activities that correspond and extend spatial concepts found in course textbooks frequently used in introductory algebra or geometry 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 Common Core math national curriculum standards. Activities include:· Rates & Proportions: A lost beach· D=R x T· Linear rate of change: Steady growth· How much rain? Linear equations· Rates of population change· Distance and midpoint· The coordinate plane· Euclidean vs Non-Euclidean· Area and perimeter at the mall· Measuring crop circles· Area of complex figures· Similar triangles· Perpendicular bisectors· Centers of triangles· Volume of pyramids

Teachers, GeoMentors, and school administrators can learn more at http://www.esri.com/geoinquiries.