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

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 is GIS course announcement flier.

Ranking by percentage of the population without a high school diploma (2014-2018).Source: Agency for Toxic Substances and Disease Registry (ATSDR). 2018.

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.

By awarding the European Diploma, the Council of Europe recognises that the area is of particular European interest for natural-heritage and that the area is properly protected. The Diploma can be awarded to national parks, nature reserves or natural areas, sites or features. The award is for a five-year period. Annual reports are required for each area, and the renewal of the award at 5 years is only made after independent assessment of the site. The Diploma can be withdrawn at any time if the area comes under threat or suffers serious damage.Click here for the full metadata

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

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:

1. explain how ANNs work including weights, bias, activation, and optimization.
2. describe and explain different loss and assessment metrics and determine appropriate use cases.
3. use the tensor data model to represent data as input for deep learning.
4. explain how CNNs work including convolutional operations/layers, kernel size, stride, padding, max pooling, activation, and batch normalization.
5. use PyTorch, Python, and R to prepare data, produce and assess scene classification models, and infer to new data.
6. explain common semantic segmentation architectures and how these methods allow for pixel-level classification and how they are different from traditional CNNs.
7. use PyTorch, Python, and R (or ArcGIS Pro) to prepare data, produce and assess semantic segmentation models, and infer to new data.
8. explain how object and instance segmentation are different from traditional CNNs and semantic segmentation and how they can be used to generate bounding boxes and feature masks for each instance of a class.
9. use ArcGIS Pro to perform object detection (to obtain bounding boxes) and instance segmentation (to obtain pixel-level instance masks).

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.

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.

A Moodle Backup FIle (.mbz) of a course (SB33102 version Semester 1, 2018/19) is a compressed archive of a Moodle course that can be used to restore a course within Moodle. The file preserves course contents, structure and settings, but does not include student work or grades.

This web map provides estimates for the percentage of no high school diploma among adults aged ≥ 25 years from the American Community Survey 5-year data for the United States—50 states and the District of Columbia at county, place, census tract, and ZCTA-levels. Data were downloaded from data.census.gov using Census API and processed by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation in conjunction with the CDC Foundation. Year: 2017-2021 ACS table(s): S0601 Data downloaded from: Census Bureau’s API for American Community Survey Date of API call: September 12, 2023 For questions or feedback send an email to places@cdc.gov.

The dataset contains locations and attributes of Golf Courses, created as part of the DC Geographic Information System (DC GIS) for the D.C. Office of the Chief Technology Officer (OCTO) and participating D.C. government agencies.

Daten zu den Aufgaben aus der VU Angewandte GIS -Grundlagen.

http://openscienceasap.org/education/courses/vu-angewandte-gis-grundlagen/ https://github.com/skasberger/vu-angewandte-gis-grundlagen

This repository contains two Microsoft Excel documents:A quiz with eight questions, assigned to students in a graduate-level GIS programming course as part of Homework Assignment 2. The quiz assesses students' understanding of basic Python programming principles (such as loops and conditional statements).An Excel document with three worksheets, each corresponding to one homework assignment from the same graduate GIS programming course. The document includes self-reported background information (e.g., students' prior programming experience), details about the use of various resources (e.g., websites) for completing assignments, the perceived helpfulness of these resources, and scores for the homework assignments and quizzes.

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.

Instructions for students to use aerial photos, Google Earth and QGIS to explore their fieldwork area prior to their field trip. This material was designed for first-year undergraduate Earth Sciences students, in preparation to a fieldwork in the French Alps. The fieldwork and this guide focuses on understanding the geology and geomorphology.The accompanying dataset.zip contains required gis-data, which are a DEM (SRTM) and Satellite images (Landsat). This dataset is without a topographic map (SCAN25 from IGN) due to licence constraint. For academic use, request your own licence from IGN (ign.fr) directly.

The Hills of Governor's Island Dataset for GRASS GIS
This geospatial dataset contains raster and vector data for the Hills region of Governor's Island, New York City, USA. The top level directory governors_island_hills_for_grass is a GRASS GIS location for NAD_1983_StatePlane_New_York_Long_Island_FIPS_3104_Feet in US Surveyor's Feet with EPSG code 2263. Inside the location there is the PERMANENT mapset, a license file, data record, readme file, workspace, color table, category rules, and scripts for data processing. This dataset was created for the course GIS for Designers.

Instructions
Install GRASS GIS, unzip this archive, and move the location into your GRASS GIS database
directory. If you are new to GRASS GIS read the first time users guide.

Data Sources

• https://orthos.dhses.ny.gov/
• https://data.cityofnewyork.us/

Maps

• Orthophotographs from 2012, 2014, 2016, 2018, and 2020
• Digital elevation model from 2017
• Digital surface models from 2014 and 2017
• Landcover from 2014

License
This dataset is licensed under the ODC Public Domain Dedication and License 1.0 (PDDL) by Brendan Harmon.

Dropout rates for Alaska public school districts. The dropout rate is defined by state regulation 4 AAC 06.895(i)(3) as a fraction of students grades 7-12 who have dropped out during the current school year out of the total students in grades 7-12 enrolled as of October 1st of the school year for which the data is reported.A student is considered to be a dropout when they have discontinued schooling for a reason other than graduation, transfer to another diploma-track program, emigration, or death unless the student is enrolled and in attendance at the same school or at another diploma-track program prior to the end of the school year (June 30).Students who depart a diploma track program in pursuit of GED certification, credit recovery, or non-diploma track vocational training are considered to have dropped out.This data set includes historic data from 1991 to present.GIS layers for individual years can be accessed using the Build Your Own Map application.Source: Alaska Department of Education & Early Development

This data has been visualized in a Geographic Information Systems (GIS) format and is provided as a service in the DCRA Information Portal by the Alaska Department of Commerce, Community, and Economic Development Division of Community and Regional Affairs (SOA DCCED DCRA), Research and Analysis section. SOA DCCED DCRA Research and Analysis is not the authoritative source for this data. For more information and for questions about this data, see: Alaska Department of Education & Early Development Data Center