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

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.

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 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.

Explore the content in this pathway to see the role of GIS in agriculture education. Understand the opportunities that GIS opens for students in the career cluster for agriculture, food, and natural resources.

Building a resource locator in ArcGIS Online (video).View this short demonstration on how to build a simple resource locator in ArcGIS Online. In this demonstration the presenter publishes an existing Web Map to the Local Perspective configurable application template. The resulting application includes the ability to locate and navigate to different health resources that would be critical in managing a surge of displaced people related to a significant event impacting public health._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

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.

Locations of public health programs in Los Angeles CountyThis dataset is maintained through the County of Los Angeles Location Management System. The Location Management System is used by the County of Los Angeles GIS Program to maintain a single, comprehensive geographic database of locations countywide. For more information on the Location Management System, visithttp://egis3.lacounty.gov/lms/.

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.

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.

The zoo in your community is so popular and successful that it has decided to expand. After careful research, zookeepers have decided to add an exotic animal to the zoo population. They are holding a contest for visitors to guess what the new animal will be. You will use skills you have learned in classification and analysis to find what part of the world the new animal is from and then identify it.

To help you get started, the zoo has provided a list of possible animals. A list of clues will help you choose the correct answers. You will combine information you have in multiple layers of maps to find your answer.

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

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

Locations of substance abuse programs in Los Angeles CountyThis dataset is maintained through the County of Los Angeles Location Management System. The Location Management System is used by the County of Los Angeles GIS Program to maintain a single, comprehensive geographic database of locations countywide. For more information on the Location Management System, visithttp://egis3.lacounty.gov/lms/.

February 2026

AbstractThe California Floristic Province (CFP) is an area of high biodiversity and endemism corresponding roughly to the portion of western North America having a Mediterranean-type climate. High levels of diversity and endemism in the CFP are attributed to the unique geo-climatic setting of the region. In recent years, much has been learned about the origins of plant diversity in western North America. This work, however, has been hindered by a focus on political rather than biotic regions, such that much more is known about diversity and endemism in the state of California than the natural biotic region represented by the CFP. Here we present a preliminary list of native land plants (vascular plants and bryophytes) found in the CFP, as well as an analysis of diversity and endemism patterns at the level of both species and minimum rank taxa (MRT; species and infraspecific taxa). A total of 6,927 MRT are native to the CFP, including 6,143 vascular plants and 784 bryophytes. Of these, 2,612 vascular plants are endemic to the CFP (42%) compared to 37 endemic bryophytes (5%). Finally, 2,506 native CFP vascular plant MRT (41% of the CFP flora) and 454 CFP bryophyte MRT (58% of the CFP flora) are found outside California in the Oregon and Baja California parts of the CFP. This high degree of sharing across political boundaries among both vascular plants and bryophytes highlights the cohesiveness of the CFP, and the need to focus more research effort on biotic regions. Usage notesKMZ file defining border of the California Floristic ProvinceThis data file describes the border of the California Floristic Province in western North America. It is openable in Google Earth or other GIS applications.CFP_KMZ.kmzOccurrence data for native land plants of the California Floristic ProvinceThis data file contains information on the occurrence of native land plants (bryophytes and vascular plants) within major sub-regions of the California Floristic Province, including Baja California (Mexico), Oregon, California, and Nevada.CFP_Online.xlsxGIS file for CFP (DBF)This file is part of a set of files describing the border of the California Floristic Province in western North America. When included in a directory with the other "CFP_GIS" files, the GIS layer is openable in most standard GIS programs.CFP_GIS.dbfGIS file for CFP (PRJ)This file is part of a set of files describing the border of the California Floristic Province in western North America. When included in a directory with the other "CFP_GIS" files, the GIS layer is openable in most standard GIS programs.CFP_GIS.prjGIS file for CFP (SBN)This file is part of a set of files describing the border of the California Floristic Province in western North America. When included in a directory with the other "CFP_GIS" files, the GIS layer is openable in most standard GIS programs.CFP_GIS.sbnGIS file for CFP (SHP)This file is part of a set of files describing the border of the California Floristic Province in western North America. When included in a directory with the other "CFP_GIS" files, the GIS layer is openable in most standard GIS programs.CFP_GIS.shpGIS file for CFP (SHX)This file is part of a set of files describing the border of the California Floristic Province in western North America. When included in a directory with the other "CFP_GIS" files, the GIS layer is openable in most standard GIS programs.CFP_GIS.shxGIS file for CFP (SBX)This file is part of a set of files describing the border of the California Floristic Province in western North America. When included in a directory with the other "CFP_GIS" files, the GIS layer is openable in most standard GIS programs.CFP_GIS.sbx

Location of special curriculum schools and programs in Los Angeles CountyThis dataset is maintained through the County of Los Angeles Location Management System. The Location Management System is used by the County of Los Angeles GIS Program to maintain a single, comprehensive geographic database of locations countywide. For more information on the Location Management System, visit http://egis3.lacounty.gov/lms/.

This datalayer shows the location of Licensed Child Care Programs in Massachusetts. These programs include preschools, which serve infants through pre-kindergarten aged children, and before and after school programs that serve children through elementary school. This data set is provided by the Department of Early Education and Care (EEC) and was downloaded from the Massachusetts Education-to-Career Research and Data Hub, which pulls the data from EEC's Licensing Education Analytic Database (LEAD).

Location of offices providing recreation programs in Los Angeles CountyThis dataset is maintained through the County of Los Angeles Location Management System. The Location Management System is used by the County of Los Angeles GIS Program to maintain a single, comprehensive geographic database of locations countywide. For more information on the Location Management System, visit http://egis3.lacounty.gov/lms/.

This is a polygon GIS data layer showing the location and extent of various sidescan, multibeam and swath bathymetry surveys conducted by the USGS, Coastal and Marine Geology Program. Outlines of individual mosaic areas were combined to create one comprehensive layer that could be used to illustrate areas surveyed by USGS/CMGP seafloor mapping programs.

BackgroundNeural tube defects (NTDs) are congenital birth defects that occur in the central nervous system, and they have the highest incidence among all birth defects. Shanxi Province in China has the world’s highest rate of NTDs. Since the 1990s, China’s government has worked on many birth defect prevention programs to reduce the occurrence of NTDs, such as pregnancy planning, health education, genetic counseling, antenatal ultrasonography and serological screening. However, the rate of NTDs in Shanxi Province is still higher than the world’s average morbidity rate after intervention. In addition, Shanxi Province has abundant coal reserves, and is the largest coal production province in China. The objectives of this study are to determine the temporal and spatial variation of the NTD rate in rural areas of Shanxi Province, China, and identify geographical environmental factors that were associated with NTDs in the risk area.MethodsIn this study, Heshun County and Yuanping County in Shanxi Province, which have high incidence of NTDs, were selected as the study areas. Two paired sample T test was used to analyze the changes in the risk of NTDs from the time dimension. Ripley’s k function and spatial filtering were combined with geographic information system (GIS) software to study the changes in the risk of NTDs from the spatial dimension. In addition, geographical detectors were used to identify the risk geographical environmental factors of NTDs in the study areas, especially the areas close to the coal sites and main roads.ResultsIn both Heshun County and Yuanping County, the incidence of NTDs was significantly (P