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.

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

Learn state-of-the-art skills to build compelling, useful, and fun Web GIS apps easily, with no programming experience required.Building on the foundation of the previous three editions, Getting to Know Web GIS, fourth edition,features the latest advances in Esri’s entire Web GIS platform, from the cloud server side to the client side.Discover and apply what’s new in ArcGIS Online, ArcGIS Enterprise, Map Viewer, Esri StoryMaps, Web AppBuilder, ArcGIS Survey123, and more.Learn about recent Web GIS products such as ArcGIS Experience Builder, ArcGIS Indoors, and ArcGIS QuickCapture. Understand updates in mobile GIS such as ArcGIS Collector and AuGeo, and then build your own web apps.Further your knowledge and skills with detailed sections and chapters on ArcGIS Dashboards, ArcGIS Analytics for the Internet of Things, online spatial analysis, image services, 3D web scenes, ArcGIS API for JavaScript, and best practices in Web GIS.Each chapter is written for immediate productivity with a good balance of principles and hands-on exercises and includes:A conceptual discussion section to give you the big picture and principles,A detailed tutorial section with step-by-step instructions,A Q/A section to answer common questions,An assignment section to reinforce your comprehension, andA list of resources with more information.Ideal for classroom lab work and on-the-job training for GIS students, instructors, GIS analysts, managers, web developers, and other professionals, Getting to Know Web GIS, fourth edition, uses a holistic approach to systematically teach the breadth of the Esri Geospatial Cloud.AUDIENCEProfessional and scholarly. College/higher education. General/trade.AUTHOR BIOPinde Fu leads the ArcGIS Platform Engineering team at Esri Professional Services and teaches at universities including Harvard University Extension School. His specialties include web and mobile GIS technologies and applications in various industries. Several of his projects have won specialachievement awards. Fu is the lead author of Web GIS: Principles and Applications (Esri Press, 2010).Pub Date: Print: 7/21/2020 Digital: 6/16/2020 Format: Trade paperISBN: Print: 9781589485921 Digital: 9781589485938 Trim: 7.5 x 9 in.Price: Print: $94.99 USD Digital: $94.99 USD Pages: 490TABLE OF CONTENTSPrefaceForeword1 Get started with Web GIS2 Hosted feature layers and storytelling with GIS3 Web AppBuilder for ArcGIS and ArcGIS Experience Builder4 Mobile GIS5 Tile layers and on-premises Web GIS6 Spatial temporal data and real-time GIS7 3D web scenes8 Spatial analysis and geoprocessing9 Image service and online raster analysis10 Web GIS programming with ArcGIS API for JavaScriptPinde Fu | Interview with Esri Press | 2020-07-10 | 15:56 | Link.

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.

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...

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.

Participants in this course will learn about remote sensing of wildfires from instructors at the University of Alaska Fairbanks, located in one of the world’s most active wildfire zones. Students will learn about wildfire behavior, and get hands-on experience with tools and resources used by professionals to create geospatial maps that support firefighters on the ground. Upon completion, students will be able to: Access web resources that provide near real-time updates on active wildfires, Navigate databases of remote sensing imagery and data, Analyze geospatial data to detect fire hot spots, map burn areas, and assess severity, Process image and GIS data in open source tools like QGIS and Google Earth Engine.

Prior experience of GIS is variable, but a number of PGCE students and in-service teachers reported negative prior experiences with geospatial technology. Common complaints include a course focussed on data students found irrelevant, with learning exercises in the form of list-like instructions. The complexity of desktop GIS software is also often mentioned as off-putting.

This Innovative Technology Experiences for Students and Teachers (ITEST) project has developed, implemented, and evaluated a series of innovative Socio-Environmental Science Investigations (SESI) using a geospatial curriculum approach. It is targeted for economically disadvantaged 9th grade high school students in Allentown, PA, and involves hands-on geospatial technology to help develop STEM-related skills. SESI focuses on societal issues related to environmental science. These issues are multi-disciplinary, involve decision-making that is based on the analysis of merged scientific and sociological data, and have direct implications for the social agency and equity milieu faced by these and other school students. This project employed a design partnership between Lehigh University natural science, social science, and education professors, high school science and social studies teachers, and STEM professionals in the local community to develop geospatial investigations with Web-based Geographic Information Systems (GIS). These were designed to provide students with geospatial skills, career awareness, and motivation to pursue appropriate education pathways for STEM-related occupations, in addition to building a more geographically and scientifically literate citizenry. The learning activities provide opportunities for students to collaborate, seek evidence, problem-solve, master technology, develop geospatial thinking and reasoning skills, and practice communication skills that are essential for the STEM workplace and beyond. Despite the accelerating growth in geospatial industries and congruence across STEM, few school-based programs integrate geospatial technology within their curricula, and even fewer are designed to promote interest and aspiration in the STEM-related occupations that will maintain American prominence in science and technology. The SESI project is based on a transformative curriculum approach for geospatial learning using Web GIS to develop STEM-related skills and promote STEM-related career interest in students who are traditionally underrepresented in STEM-related fields. This project attends to a significant challenge in STEM education: the recognized deficiency in quality locally-based and relevant high school curriculum for under-represented students that focuses on local social issues related to the environment. Environmental issues have great societal relevance, and because many environmental problems have a disproportionate impact on underrepresented and disadvantaged groups, they provide a compelling subject of study for students from these groups in developing STEM-related skills. Once piloted in the relatively challenging environment of an urban school with many unengaged learners, the results will be readily transferable to any school district to enhance geospatial reasoning skills nationally.

The Air, Water, and Aquatic Environments (AWAE) research program is one of eight Science Program areas within the Rocky Mountain Research Station (RMRS). Our science develops core knowledge, methods, and technologies that enable effective watershed management in forests and grasslands, sustain biodiversity, and maintain healthy watershed conditions. We conduct basic and applied research on the effects of natural processes and human activities on watershed resources, including interactions between aquatic and terrestrial ecosystems. The knowledge we develop supports management, conservation, and restoration of terrestrial, riparian and aquatic ecosystems and provides for sustainable clean air and water quality in the Interior West. With capabilities in atmospheric sciences, soils, forest engineering, biogeochemistry, hydrology, plant physiology, aquatic ecology and limnology, conservation biology and fisheries, our scientists focus on two key research problems: Core watershed research quantifies the dynamics of hydrologic, geomorphic and biogeochemical processes in forests and rangelands at multiple scales and defines the biological processes and patterns that affect the distribution, resilience, and persistence of native aquatic, riparian and terrestrial species. Integrated, interdisciplinary research explores the effects of climate variability and climate change on forest, grassland and aquatic ecosystems. Resources in this dataset:Resource Title: Projects, Tools, and Data. File Name: Web Page, url: https://www.fs.fed.us/rm/boise/AWAE/projects.html Projects include Air Temperature Monitoring and Modeling, Biogeochemistry Lab in Colorado, Rangewide Bull Trout eDNA Project, Climate Shield Cold-Water Refuge Streams for Native Trout, Cutthroat trout-rainbow trout hybridization - data downloads and maps, Fire and Aquatic Ecosystems science, Fish and Cattle Grazing reports, Geomophic Road Analysis and Inventory Package (GRAIP) tool for erosion and sediment delivery to streams, GRAIP_Lite - Geomophic Road Analysis and Inventory Package (GRAIP) tool for erosion and sediment delivery to streams, IF3: Integrating Forests, Fish, and Fire, National forest climate change maps: Your guide to the future, National forest contributions to streamflow, The National Stream Internet network, people, data, GIS, analysis, techniques, NorWeST Stream Temperature Regional Database and Model, River Bathymetry Toolkit (RBT), Sediment Transport Data for Idaho, Nevada, Wyoming, Colorado, SnowEx, Stream Temperature Modeling and Monitoring, Spatial Statistical Modeling on Stream netowrks - tools and GIS downloads, Understanding Sculpin DNA - environmental DNA and morphological species differences, Understanding the diversity of Cottusin western North America, Valley Bottom Confinement GIS tools, Water Erosion Prediction Project (WEPP), Great Lakes WEPP Watershed Online GIS Interface, Western Division AFS - 2008 Bull Trout Symposium - Bull Trout and Climate Change, Western US Stream Flow Metric Dataset

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.

This course is intended to get you thinking about your future. Learn about where GIS professionals work, what they do, and how their educational choices prepare them for different types of jobs.GoalsDiscover the types of projects that GIS professionals work on.Identify qualities and skills that can help you get a GIS-related job.Choose educational options that match your goals and support your future career plans.

Access ArcGIS Online and sign in. (Your credentials work across the platform.)This is the home screen (for now). This account is being rolled into the existing Brookhaven Campus GIS degree program, but your credentials & files will stay the same.Click on Content to find your Survey data.

IntroductionGeographic Information Systems (GIS) and spatial analysis are emerging tools for global health, but it is unclear to what extent they have been applied to HIV research in Africa. To help inform researchers and program implementers, this scoping review documents the range and depth of published HIV-related GIS and spatial analysis research studies conducted in Africa.MethodsA systematic literature search for articles related to GIS and spatial analysis was conducted through PubMed, EMBASE, and Web of Science databases. Using pre-specified inclusion criteria, articles were screened and key data were abstracted. Grounded, inductive analysis was conducted to organize studies into meaningful thematic areas.Results and discussionThe search returned 773 unique articles, of which 65 were included in the final review. 15 different countries were represented. Over half of the included studies were published after 2014. Articles were categorized into the following non-mutually exclusive themes: (a) HIV geography, (b) HIV risk factors, and (c) HIV service implementation. Studies demonstrated a broad range of GIS and spatial analysis applications including characterizing geographic distribution of HIV, evaluating risk factors for HIV, and assessing and improving access to HIV care services.ConclusionsGIS and spatial analysis have been widely applied to HIV-related research in Africa. The current literature reveals a diversity of themes and methodologies and a relatively young, but rapidly growing, evidence base.

Web-based GIS for spatiotemporal crop climate niche mapping Interactive Google Earth Engine Application—Version 2, July 2020 https://cropniche.cartoscience.com https://cartoscience.users.earthengine.app/view/crop-niche Google Earth Engine Code /* ---------------------------------------------------------------------------------------------------------------------- # CropSuit-GEE Authors: Brad G. Peter (bpeter@ua.edu), Joseph P. Messina, and Zihan Lin Organizations: BGP, JPM - University of Alabama; ZL - Michigan State University Last Modified: 06/28/2020 To cite this code use: Peter, B. G.; Messina, J. P.; Lin, Z., 2019, "Web-based GIS for spatiotemporal crop climate niche mapping", https://doi.org/10.7910/DVN/UFC6B5, Harvard Dataverse, V1 ------------------------------------------------------------------------------------------------------------------------- This is a Google Earth Engine crop climate suitability geocommunication and map export tool designed to support agronomic development and deployment of improved crop system technologies. This content is made possible by the support of the American People provided to the Feed the Future Innovation Lab for Sustainable Intensification through the United States Agency for International Development (USAID). The contents are the sole responsibility of the authors and do not necessarily reflect the views of USAID or the United States Government. Program activities are funded by USAID under Cooperative Agreement No. AID-OAA-L-14-00006. ------------------------------------------------------------------------------------------------------------------------- Summarization of input options: There are 14 user options available. The first is a country of interest selection using a 2-digit FIPS code (link available below). This selection is used to produce a rectangular bounding box for export; however, other geometries can be selected with minimal modification to the code. Options 2 and 3 specify the complete temporal range for aggregation (averaged across seasons; single seasons may also be selected). Options 4–7 specify the growing season for calculating total seasonal rainfall and average season temperatures and NDVI (NDVI is for export only and is not used in suitability determination). Options 8–11 specify the climate parameters for the crop of interest (rainfall and temperature max/min). Option 12 enables masking to agriculture, 13 enables exporting of all data layers, and 14 is a text string for naming export files. ------------------------------------------------------------------------------------------------------------------------- ••••••••••••••••••••••••••••••••••••••••••• USER OPTIONS ••••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• */ // CHIRPS data availability: https://developers.google.com/earth-engine/datasets/catalog/UCSB-CHG_CHIRPS_PENTAD // MOD11A2 data availability: https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MOD11A2 var country = 'MI' // [1] https://en.wikipedia.org/wiki/List_of_FIPS_country_codes var startRange = 2001 // [2] var endRange = 2017 // [3] var startSeasonMonth = 11 // [4] var startSeasonDay = 1 // [5] var endSeasonMonth = 4 // [6] var endSeasonDay = 30 // [7] var precipMin = 750 // [8] var precipMax = 1200 // [9] var tempMin = 22 // [10] var tempMax = 32 // [11] var maskToAg = 'TRUE' // [12] 'TRUE' (default) or 'FALSE' var exportLayers = 'TRUE' // [13] 'TRUE' (default) or 'FALSE' var exportNameHeader = 'crop_suit_maize' // [14] text string for naming export file // ••••••••••••••••••••••••••••••••• NO USER INPUT BEYOND THIS POINT •••••••••••••••••••••••••••••••••••••••••••••••••••• // Access precipitation and temperature ImageCollections and a global countries FeatureCollection var region = ee.FeatureCollection('USDOS/LSIB_SIMPLE/2017') .filterMetadata('country_co','equals',country) var precip = ee.ImageCollection('UCSB-CHG/CHIRPS/PENTAD').select('precipitation') var temp = ee.ImageCollection('MODIS/006/MOD11A2').select(['LST_Day_1km','LST_Night_1km']) var ndvi = ee.ImageCollection('MODIS/006/MOD13Q1').select(['NDVI']) // Create layers for masking to agriculture and masking out water bodies var waterMask = ee.Image('UMD/hansen/global_forest_change_2015').select('datamask').eq(1) var agModis = ee.ImageCollection('MODIS/006/MCD12Q1').select('LC_Type1').mode() .remap([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17], [0,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0]) var agGC = ee.Image('ESA/GLOBCOVER_L4_200901_200912_V2_3').select('landcover') .remap([11,14,20,30,40,50,60,70,90,100,110,120,130,140,150,160,170,180,190,200,210,220,230], [1,1,1,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0]) var cropland = ee.Image('USGS/GFSAD1000_V1').neq(0) var agMask = agModis.add(agGC).add(cropland).gt(0).eq(1) // Modify user input options for processing with raw data var years = ee.List.sequence(startRange,endRange) var bounds = region.geometry().bounds() var tMinMod = (tempMin+273.15)/0.02 var tMaxMod = (tempMax+273.15)/0.02 //...

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 asynchronous session, you will use some of the free GIS tools from the Teach With GIS website, created and maintained by the Esri UK education team. All of these tools are free to use and accessible as websites from laptops, tablets and mobile devices. We recommend that you view them on a laptop or tablet if possible, to give you plenty of screen space to see every detail. They do not require any logins or subscriptions. We want you to experience using modern, online GIS tools from the perspective of a student before you begin to create your own tools, maps, and lessons. We have chosen a range of tools that let you experience GIS as a tool to examine physical and human geography, and to compare and contrast over space and time.

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