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.

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.

Get an introduction to the basic components of a GIS. Learn fundamental concepts that underlie the use of a GIS with hands-on experience with maps and geographic data.

This dataset is about books. It has 1 row and is filtered where the book is Learning GIS using open source software : an applied guide for geo-spatial analysis. It features 7 columns including author, publication date, language, and book publisher.

The Civil Engineering Students Society organized an 'ArcGIS Online Training for Beginners.' Geographical Information System (GIS) technology provides the tools for creating, managing, analyzing, and visualizing data associated with developing and managing infrastructure.

It also allowed civil engineers to manage and share data, turning it into easily understood reports and visualizations that could be analyzed and communicated to others. Additionally, it helped civil engineers in spatial analysis, data management, urban development, town planning, and site analysis.

It is equally important for beginner geospatial students.

Dataset for the textbook Computational Methods and GIS Applications in Social Science (3rd Edition), 2023 Fahui Wang, Lingbo Liu Main Book Citation: Wang, F., & Liu, L. (2023). Computational Methods and GIS Applications in Social Science (3rd ed.). CRC Press. https://doi.org/10.1201/9781003292302 KNIME Lab Manual Citation: Liu, L., & Wang, F. (2023). Computational Methods and GIS Applications in Social Science - Lab Manual. CRC Press. https://doi.org/10.1201/9781003304357 KNIME Hub Dataset and Workflow for Computational Methods and GIS Applications in Social Science-Lab Manual Update Log If Python package not found in Package Management, use ArcGIS Pro's Python Command Prompt to install them, e.g., conda install -c conda-forge python-igraph leidenalg NetworkCommDetPro in CMGIS-V3-Tools was updated on July 10,2024 Add spatial adjacency table into Florida on June 29,2024 The dataset and tool for ABM Crime Simulation were updated on August 3, 2023, The toolkits in CMGIS-V3-Tools was updated on August 3rd,2023. Report Issues on GitHub https://github.com/UrbanGISer/Computational-Methods-and-GIS-Applications-in-Social-Science Following the website of Fahui Wang : http://faculty.lsu.edu/fahui Contents Chapter 1. Getting Started with ArcGIS: Data Management and Basic Spatial Analysis Tools Case Study 1: Mapping and Analyzing Population Density Pattern in Baton Rouge, Louisiana Chapter 2. Measuring Distance and Travel Time and Analyzing Distance Decay Behavior Case Study 2A: Estimating Drive Time and Transit Time in Baton Rouge, Louisiana Case Study 2B: Analyzing Distance Decay Behavior for Hospitalization in Florida Chapter 3. Spatial Smoothing and Spatial Interpolation Case Study 3A: Mapping Place Names in Guangxi, China Case Study 3B: Area-Based Interpolations of Population in Baton Rouge, Louisiana Case Study 3C: Detecting Spatiotemporal Crime Hotspots in Baton Rouge, Louisiana Chapter 4. Delineating Functional Regions and Applications in Health Geography Case Study 4A: Defining Service Areas of Acute Hospitals in Baton Rouge, Louisiana Case Study 4B: Automated Delineation of Hospital Service Areas in Florida Chapter 5. GIS-Based Measures of Spatial Accessibility and Application in Examining Healthcare Disparity Case Study 5: Measuring Accessibility of Primary Care Physicians in Baton Rouge Chapter 6. Function Fittings by Regressions and Application in Analyzing Urban Density Patterns Case Study 6: Analyzing Population Density Patterns in Chicago Urban Area >Chapter 7. Principal Components, Factor and Cluster Analyses and Application in Social Area Analysis Case Study 7: Social Area Analysis in Beijing Chapter 8. Spatial Statistics and Applications in Cultural and Crime Geography Case Study 8A: Spatial Distribution and Clusters of Place Names in Yunnan, China Case Study 8B: Detecting Colocation Between Crime Incidents and Facilities Case Study 8C: Spatial Cluster and Regression Analyses of Homicide Patterns in Chicago Chapter 9. Regionalization Methods and Application in Analysis of Cancer Data Case Study 9: Constructing Geographical Areas for Mapping Cancer Rates in Louisiana Chapter 10. System of Linear Equations and Application of Garin-Lowry in Simulating Urban Population and Employment Patterns Case Study 10: Simulating Population and Service Employment Distributions in a Hypothetical City Chapter 11. Linear and Quadratic Programming and Applications in Examining Wasteful Commuting and Allocating Healthcare Providers Case Study 11A: Measuring Wasteful Commuting in Columbus, Ohio Case Study 11B: Location-Allocation Analysis of Hospitals in Rural China Chapter 12. Monte Carlo Method and Applications in Urban Population and Traffic Simulations Case Study 12A. Examining Zonal Effect on Urban Population Density Functions in Chicago by Monte Carlo Simulation Case Study 12B: Monte Carlo-Based Traffic Simulation in Baton Rouge, Louisiana Chapter 13. Agent-Based Model and Application in Crime Simulation Case Study 13: Agent-Based Crime Simulation in Baton Rouge, Louisiana Chapter 14. Spatiotemporal Big Data Analytics and Application in Urban Studies Case Study 14A: Exploring Taxi Trajectory in ArcGIS Case Study 14B: Identifying High Traffic Corridors and Destinations in Shanghai Dataset File Structure 1 BatonRouge Census.gdb BR.gdb 2A BatonRouge BR_Road.gdb Hosp_Address.csv TransitNetworkTemplate.xml BR_GTFS Google API Pro.tbx 2B Florida FL_HSA.gdb R_ArcGIS_Tools.tbx (RegressionR) 3A China_GX GX.gdb 3B BatonRouge BR.gdb 3C BatonRouge BRcrime R_ArcGIS_Tools.tbx (STKDE) 4A BatonRouge BRRoad.gdb 4B Florida FL_HSA.gdb HSA Delineation Pro.tbx Huff Model Pro.tbx FLplgnAdjAppend.csv 5 BRMSA BRMSA.gdb Accessibility Pro.tbx 6 Chicago ChiUrArea.gdb R_ArcGIS_Tools.tbx (RegressionR) 7 Beijing BJSA.gdb bjattr.csv R_ArcGIS_Tools.tbx (PCAandFA, BasicClustering) 8A Yunnan YN.gdb R_ArcGIS_Tools.tbx (SaTScanR) 8B Jiangsu JS.gdb 8C Chicago ChiCity.gdb cityattr.csv ...

This is GIS course announcement flier.

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 Environmental Learning Centers dataset.

Open-Source GIS plays a pivotal role in advancing GIS education, fostering research collaboration, and supporting global sustainability by enabling the sharing of data, models, and knowledge. However, the integration of big data, deep learning methods, and artificial intelligence deep learning in geospatial research presents significant challenges for GIS education. These include increasing software learning costs, higher computational power demand, and the management of fragmented information in the Web 2.0 context. Addressing these challenges while integrating emerging GIS innovations and restructuring GIS knowledge systems is crucial for the evolution of GIS Education 3.0. This study introduces a Visual Programming-based Geospatial Cyberinfrastructure (V-GCI) framework, integrated with the replicable and reproducible (R&R) framework, to enhance GIS function compatibility, learning scalability, and web GIS application interoperability. Through a case study on spatial accessibility using the generalized two-step floating catchment area method (G2SFCA), this paper demonstrates how V-GCI can reshape the GIS knowledge tree and its potential to enhance replicability and reproducibility within open-source GIS Education 3.0.

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.

Learn the basics of GIS. Work with ArcGIS Online to interact with GIS maps, explore real world problems, and tell a story. Find out how workers use GIS and what it takes to become a GIS professional.

Locations Environmental Learning Centers operated by Seattle Parks.Refresh Cycle: WeeklyFeature Class: DPR.EnvEdCtr

Enroll in this plan to understand ArcGIS Online capabilities, publish content to an ArcGIS Online organizational site, create web maps and apps, and review common ArcGIS Online administrative tasks.

Goals Access web maps, apps, and other GIS resources that have been shared to an ArcGIS Online organizational site. Publish GIS data as services to an ArcGIS Online organizational site. Create, configure, and share web maps and apps. Manage ArcGIS Online user roles and privileges.

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.

Geographic Information Systems (GIS) technology allows users to make maps and analyze data. Savvy educators have been using GIS since the early 1990s, but online GIS makes it easy for educators to get started quickly, even just learning on their own, online. Here is a sequenced set of resources and activities with which to begin; they start fast and easy, scaffold ideas and skills, and generally take more time and require more background as one progresses, so items should be experienced in order.

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.

Download UrbanTreeCanopy_2019.zip. The following information was produced from the 2019 Urban Tree Canopy Assessment for Jefferson County, KY sponsored by Trees Louisville. It is based on 2019 LOJIC Base Map data. It includes shapefiles and rasters. The study was performed by the University of Vermont Spatial Analysis Lab.

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.

This submission contains an ESRI map package (.mpk) with an embedded geodatabase for GIS resources used or derived in the Nevada Machine Learning project, meant to accompany the final report. The package includes layer descriptions, layer grouping, and symbology. Layer groups include: new/revised datasets (paleo-geothermal features, geochemistry, geophysics, heat flow, slip and dilation, potential structures, geothermal power plants, positive and negative test sites), machine learning model input grids, machine learning models (Artificial Neural Network (ANN), Extreme Learning Machine (ELM), Bayesian Neural Network (BNN), Principal Component Analysis (PCA/PCAk), Non-negative Matrix Factorization (NMF/NMFk) - supervised and unsupervised), original NV Play Fairway data and models, and NV cultural/reference data. See layer descriptions for additional metadata. Smaller GIS resource packages (by category) can be found in the related datasets section of this submission. A submission linking the full codebase for generating machine learning output models is available through the "Related Datasets" link on this page, and contains results beyond the top picks present in this compilation.