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 dataset contains locations and attributes of University and College, created as part of the DC Geographic Information System (DC GIS) for the Office of the Chief Technology Officer (OCTO) and participating D.C. government agencies. Information provided by OCTO, EMA, and other sources identified as University Areas and DC GIS staff geo-processed the data. This layer does not represent university areas contained in the campus plans from the DC Office of Zoning.

Esri UK is providing a digital mapping platform and expertise in biodiversity mapping for the National Education Nature Park. We are providing the Department of Education with ArcGIS Online - an extensible web-based mapping platform to provide staff and students with geospatial tools that will allow them to view, capture, store, analyse and monitor environmental and biodiversity data. We are also providing Professional Services to be delivered using an agile methodology, along with training to key stakeholders.To deploy geospatial tools to all schools, we are using the existing ArcGIS for Schools program.

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.

Colleges and Universities This feature layer, utilizing data from the National Center for Education Statistics (NCES), displays colleges and universities in the U.S. and its territories. NCES uses the Integrated Postsecondary Education Data System (IPEDS) as the "primary source for information on U.S. colleges, universities, and technical and vocational institutions." According to NCES, this layer "contains directory information for every institution in the 2023-24 IPEDS universe. Includes name, address, city, state, zip code and various URL links to the institution"s home page, admissions, financial aid offices and the net price calculator. Identifies institutions as currently active, and institutions that participate in Title IV federal financial aid programs for which IPEDS is mandatory." University of the District of ColumbiaData currency: 2023Data source: IPEDS Complete Data FilesData modification: Removed fields with coded values and replaced with descriptionsFor more information: Integrated Postsecondary Education Data SystemSupport documentation: Data DictionaryFor feedback, please contact: ArcGIScomNationalMaps@esri.com U.S. Department of Education (ED) Per ED, The mission of the Department of Education (ED) is to promote student achievement and preparation for global competitiveness by fostering educational excellence and ensuring equal access for students of all ages.

Students in geographic information systems and science (GIS) require significant experience outside of spatial analysis, cartography, and other traditional geographic topics. Computer science knowledge, skills, and practices exist as essential components of GIS practice, but coursework in this area is not universally offered in geography or GIS degrees. To support those interested in developing such courses, this paper describes the design and implementation of a server-focused course in WebGIS at University Texas A&M University. We provide an in-depth discussion of the equipment and resources required to build and operate an on-premise CyberGIS server infrastructure suitable for supporting such classes, providing comparisons with an equivalent solution built on Amazon Web Services (AWS). We consider the comparative costs of these systems, including benefits and drawbacks of each. In comparing these deployment options, we outline the technical expertise, monetary investments, operational expenses, and organizational strategies necessary to run server-based CyberGIS courses. Finally, we reflect on assignments and feedback from students and consider their experiences in a course of this nature. This article provides a resource for GIS instructors, academic departments, or other academic units to consider during infrastructure investment, curriculum redesign, the addition of courses in degree plans, or for the development of CyberGIS components.

Abstract: Community Engaged Learning (CEL) is a pedagogical approach that involves students, community partners, and instructors working together to analyze and address community-identified concerns through experiential learning. Implementing community-engagement in geography courses and, specifically, in GIS courses is not new. However, while students enrolled in CEL GIS courses critically reflect on social and spatial inequalities, GIS tools themselves are mostly applied in uncritical ways. Yet, CEL GIS courses can specifically help students understand GIS as a socially constructed technology which can not only empower but also disempower the community. This contribution presents the experiences from a community-engaged introductory GIS course, taught at a Predominantly White Institution (PWI) in Virginia (USA) in Spring ’24. It shows how the course helped students gain a conceptual understanding of what is GIS, how to use it, and valuable software skills, while also reflecting about their own privileges, how GIS can (dis)empower the community, and their own role as a GIS analyst. Ultimately, the paper shows how the course supported positive changes in the community, equity in education, reciprocity in university/community relationships, and student civic-mindedness.

Current data from 2023-24 school year. Dataset to be updated annually.Data sources:Public Schools (includes charter and Adult): CDE - https://www.cde.ca.gov/schooldirectory/report?rid=dl1&tp=txtPublic Schools enrollment and enhanced location: CDE - https://lacounty.maps.arcgis.com/home/item.html?id=61a4260e68b14a5ab91daf27d4415e7dPrivate Schools type and location: CDE - https://www.cde.ca.gov/schooldirectory/, query for private schoolsPrivate Schools enrollment and contact: CDE - https://www.cde.ca.gov/ds/si/ps/documents/privateschooldata2324.xlsxColleges and Universities: HIFLD - https://hifld-geoplatform.hub.arcgis.com/datasets/geoplatform::colleges-and-universities/aboutPublic schools use location from the CDE AGOL Layer where available. This source assigns X, Y coordinates using a quality controlled geocoding and validation process to optimize positional accuracy, often geocoding to parcel.Field Descriptions:Category1: Always "Education"Category2: School Level Category3: School Type Organization: School District for primary and secondary schools; data maintainer otherwise Source: Source of data (see source links above) Source ID: CDS Code for primary and secondary schools; IPEDS ID for colleges and universities Source Date: Date listed in source Enrollment: School EnrollmentLabel Class: School classification for symbology (matches either Category2 or Category3)Last Update: Date last updated by LA County Enterprise GIS

The data release for the geologic terranes of the Hailey 1 x 2 degrees quadrangle and the western part of the Idaho Falls 1 x 2 degrees quadrangle, south-central Idaho is a Geologic Map Schema (GeMS)-compliant version that updates the GIS files for the geologic map published in U.S. Geological Survey (USGS) Bulletin 2064-A (Worl and Johnson, 1995). The updated digital data present the attribute tables and geospatial features (lines and polygons) in the format that meets GeMS requirements. This data release presents the geologic map as shown on the plate and captured in geospatial data for the published map. Minor errors, such as mistakes in line decoration or differences between the digital data and the map image, are corrected in this version. The database represents the geology for the 6.1 million-acre, geologically complex Hailey quadrangle and the western part of the Idaho Falls quadrangle, at a publication scale of 1:250,000. The map covers primarily Blaine, Camas, Custer and Elmore Counties, but also includes minor parts of Ada, Butte, Gooding, Lincoln, and Minidoka Counties. These GIS data supersede those in the interpretive report: Worl, R.G. and Johnson, K.M., 1995, Geology and mineral deposits of the Hailey 1 degree x 2 degrees quadrangle and the western part of the Idaho Falls 1 degree x 2 degrees quadrangle, south-central Idaho - an overview: U.S. Geological Survey, Bulletin 2064-A, scale 1:250,000, https://pubs.usgs.gov/bul/b2064-a/.

ArcGIS for Schools Programme, Esri Ireland

I’d love to begin by saying that I have not “arrived” as I believe I am still on a journey of self-discovery. I have heard people say that they find my journey quite interesting and I hope my story inspires someone out there.I had my first encounter with Geographic Information System (GIS) in the third year of my undergraduate study in Geography at the University of Ibadan, Oyo State Nigeria. I was opportune to be introduced to the essentials of GIS by one of the prominent Environmental and Urban Geographers in person of Dr O.J Taiwo. Even though the whole syllabus and teaching sounded abstract to me due to the little exposure to a practical hands-on approach to GIS software, I developed a keen interest in the theoretical learning and I ended up scoring 70% in my final course exam.

A fifteen degree grid in latitude and longitude covering the entire world

Today’s data-driven world requires earth and environmental scientists to have skills at the intersection of domain and data science. These skills are imperative to harness information contained in a growing volume of complex data to solve the world’s most pressing environmental challenges. Despite the importance of these skills, Earth and Environmental Data Science (EDS) training is not equally accessible, contributing to a lack of diversity in the field. This creates a critical need for EDS training opportunities designed specifically for underrepresented groups. In response, we developed the Earth Data Science Corps (EDSC) which couples a paid internship for undergraduate students with faculty training to build capacity to teach and learn EDS using Python at smaller Minority Serving Institutions. EDSC faculty participants are further empowered to teach these skills at their home institutions which scales the program beyond the training lead by our team. Using a Rasch modeling approach, we found that participating in the EDSC program had a significant impact on undergraduate learners’ comfort and confidence with technical and nontechnical data science skills, as well as their science identity and sense of belonging in science, two critical aspects of recruiting and retaining members of underrepresented groups in STEM. Supplementary materials for this article are available online.

This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

An ArcGIS Dashboard used by GIS Managers to show the alignment of GIS activities with organizational goals, GIS program goals and ArcGIS capabilities.

Wake County college and university locations

This file provides the raw data of an online survey intended at gathering information regarding remote sensing (RS) and Geographical Information Systems (GIS) for conservation in academic education. The aim was to unfold best practices as well as gaps in teaching methods of remote sensing/GIS, and to help inform how these may be adapted and improved. A total of 73 people answered the survey, which was distributed through closed mailing lists of universities and conservation groups.

snapshot-tab-pane Geographic Information System Analytics Market Size 2024-2028The geographic information system analytics market size is forecast to increase by USD 12 billion at a CAGR of 12.41% between 2023 and 2028.The GIS Analytics Market analysis is experiencing significant growth, driven by the increasing need for efficient land management and emerging methods in data collection and generation. The defense industry's reliance on geospatial technology for situational awareness and real-time location monitoring is a major factor fueling market expansion. Additionally, the oil and gas industry's adoption of GIS for resource exploration and management is a key trend. Building Information Modeling (BIM) and smart city initiatives are also contributing to market growth, as they require multiple layered maps for effective planning and implementation. The Internet of Things (IoT) and Software as a Service (SaaS) are transforming GIS analytics by enabling real-time data processing and analysis.Augmented reality is another emerging trend, as it enhances the user experience and provides valuable insights through visual overlays. Overall, heavy investments are required for setting up GIS stations and accessing data sources, making this a promising market for technology innovators and investors alike.What will be the Size of the GIS Analytics Market during the forecast period? Request Free SampleThe geographic information system analytics market encompasses various industries, including government sectors, agriculture, and infrastructure development. Smart city projects, building information modeling, and infrastructure development are key areas driving market growth. Spatial data plays a crucial role in sectors such as transportation, mining, and oil and gas. Cloud technology is transforming GIS analytics by enabling real-time data access and analysis. Startups are disrupting traditional GIS markets with innovative location-based services and smart city planning solutions. Infrastructure development in sectors like construction and green buildings relies on modern GIS solutions for efficient planning and management. Smart utilities and telematics navigation are also leveraging GIS analytics for improved operational efficiency.GIS technology is essential for zoning and land use management, enabling data-driven decision-making. Smart public works and urban planning projects utilize mapping and geospatial technology for effective implementation. Surveying is another sector that benefits from advanced GIS solutions. Overall, the GIS analytics market is evolving, with a focus on providing actionable insights to businesses and organizations.How is this Geographic Information System Analytics Industry segmented?The geographic information system analytics industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in "USD billion" for the period 2024-2028, as well as historical data from 2018-2022 for the following segments.End-user Retail and Real EstateGovernmentUtilitiesTelecomManufacturing and AutomotiveAgricultureConstructionMiningTransportationHealthcareDefense and IntelligenceEnergyEducation and ResearchBFSIComponents SoftwareServicesDeployment Modes On-PremisesCloud-BasedApplications Urban and Regional PlanningDisaster ManagementEnvironmental Monitoring Asset ManagementSurveying and MappingLocation-Based ServicesGeospatial Business IntelligenceNatural Resource ManagementGeography North America USCanadaEurope FranceGermanyUKAPAC ChinaIndiaSouth KoreaMiddle East and Africa UAESouth America BrazilRest of WorldBy End-user InsightsThe retail and real estate segment is estimated to witness significant growth during the forecast period.The GIS analytics market analysis is witnessing significant growth due to the increasing demand for advanced technologies in various industries. In the retail sector, for instance, retailers are utilizing GIS analytics to gain a competitive edge by analyzing customer demographics and buying patterns through real-time location monitoring and multiple layered maps. The retail industry's success relies heavily on these insights for effective marketing strategies. Moreover, the defense industries are integrating GIS analytics into their operations for infrastructure development, permitting, and public safety. Building Information Modeling (BIM) and 4D GIS software are increasingly being adopted for construction project workflows, while urban planning and designing require geospatial data for smart city planning and site selection.The oil and gas industry is leveraging satellite imaging and IoT devices for land acquisition and mining operations. In the public sector, government agencies are using GIS analytics for zoning, land use management, and smart public w