Geographic Information Systems and Data Communication Professional Certificate

Snapshot:

Summer Start Date: May 31, 2023
Instructor: Dennis Baldwin

Fall Start Date: August 23, 2023
Instructor: Brian Voigt

Do I enroll in Introduction to GIS or Advanced GIS?

A Geographic Information System (GIS) is a computer-based tool for managing, documenting, analyzing, and presenting data and information. GIS allows us to investigate the spatial (location, size, and shape), temporal and descriptive qualities (e.g. population, % coverage) of data across multiple disciplines and is increasingly used to tell complicated “stories” using printed and web-based materials.

This course uses the most current version of ESRI’s ArcGIS Pro software.

This course will introduce the student to the basic principles and techniques of GIS through a mixture of readings, videos, slide-based presentations, and real-world examples. Participants will gain practical skills in the application of GIS to data analysis and problem-solving through the completion of lab assignments and participation in the course Discussion Forum.

By the end of the course, the student will be able to:

• Develop a practical understanding of GPS and ArcGIS concepts (e.g., data & file types, projections & coordinate systems, and spatial relationships) & geoprocessing techniques
• Perform spatial analysis using desktop and web-based ArcGIS software
• Practice effective communication using the (technical) language of ArcGIS, presentation-quality maps, and web applications
• Understand best practices for creating, storing, and managing data in a variety of geospatial and tabular formats
• Gain experience identifying and accessing publicly available data

Snapshot:

Instructor: Brian Voigt
Fall Start Date: October 25, 2023 (Registration coming soon)

Do I enroll in Introduction to GIS or Advanced GIS?

Geographic Information Systems (GIS) are increasingly featured as an integrating technology for analyzing economic, social, and environmental data. This course will expose the learner to advanced techniques for collecting, curating, analyzing, and understanding spatial data and communicating technical findings to a broad range of audiences. Building on the content of the Introductory Course, Advanced GIS will prepare the student to independently conduct spatial analyses.

This course uses the most current version of ESRI’s ArcGIS Pro software.

Learning materials will include a mixture of readings, videos, slide-based presentations, and critical evaluation of real-world examples. Participants will gain practical skills in the application of ArcGIS to data analysis and problem-solving through the completion of lab assignments and participation in the course Discussion Forum.

By the end of the course, the student will be able to:

• Understand & identify ethical questions surrounding data creation, analysis, and representation
• Integrate public data resources from multiple repositories into a cohesive analytic framework
• Apply advanced vector (e.g. network analyses, attribute conflation, queries) and raster (e.g. hydrology, interpolation, raster calculator) geoprocessing tools to analyze spatial data
• Create automated geoprocessing workflows using Model Builder and the Python programming language
• Apply critical thinking skills to assess the suitability of available data (e.g. scale, resolution, accuracy, and currency) and/or geoprocessing approaches
• Acquire the necessary skills to develop and execute a project utilizing ArcGIS as a management, analytical, and/or visualization tool

Snapshot:

Instructor: Bill Shander

Summer Start Date: June 19, 2023

Decisions in our work lives are driven more and more by data. Analyzing this data can reveal patterns and relationships that can be used to better inform decision-makers. One way to present this analysis is through data visualization, which is the graphical representation of information.

Visualizations such as charts, graphs, or maps can help make data easier to understand, leading to better decisions. Data-driven decisions could mean the difference between the success or failure of any organization. There are many tools for visualizing data, but without a solid grasp on the fundamentals of graphic design and perceptual theory, visualizations may not convey the intended message.

This course will introduce students to the fundamentals of data visualization and communication. Students will learn the ways humans use their cognitive and perceptual abilities to comprehend information, best practices for creating compelling and effective data visualizations, and the many nuanced factors influencing the successful application of these practices.

By the end of the course, the student will be able to:

• Apply a framework for communication to get the right people to the right content at the right time
• Recognize, explain, and identify solutions for some of the challenges we face working with data
• Produce an entire conceptual data story in a form that will allow you to move on to execution of individual components, in detail, later
• Describe human visual perception and apply it to specific design and visualization decisions
• Effectively use visual variables such as size, value, texture, color, orientation, and shape to communicate information
• Recognize the purpose for different data visuals and when and how to apply them
• Create impactful maps and visualizations that enable understanding

Snapshot:

Instructor: Amanda Armstrong
Spring 2024 Start Date: TBD

Significant advances in access to geospatial datasets and cloud-based computing resources have ushered in a new era of user-friendly big data analysis, and satellite remote sensing has become a critical component of many environmental research and monitoring programs. However, effective use of satellite imagery requires a foundational understanding of sensor, image, and surface characteristics as well as methods for translating analysis-ready data to decision-ready analysis.

This course will introduce students to fundamentals of remote sensing theory and image processing techniques using the Google Earth Engine platform, which “combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities.” By integrating hands-on practical training with evidence-based experiential learning, students will establish a basic understanding of how to use remote sensing as a tool for environmental problem-solving. This course uses Javascript for creating geospatial workflows in the Google Earth Engine Code Editor; any coding background is helpful but not required.

By the end of the course, the student will be able to:

• Improve their understanding of the physical processes involved in the acquisition of remote sensing imagery, as well as the unique spectral, spatial, temporal, and radiometric properties of different image sources
• Build a working knowledge of a wide array of geospatial datasets available in the Earth Engine Data Catalog, including optical, thermal, and microwave imagery from the Landsat, MODIS, Sentinel-2, and Sentinel-1 satellites, and derived products such as the Hansen Global Forest Change and JRC Global Surface Water datasets
• Use the Earth Engine Code Editor to develop basic geospatial workflows using the JavaScript API
• Explore both static and interactive data visualization techniques including tables, maps, charts, GIFs, and Earth Engine Apps
• Apply standard multi-spectral and multi-temporal image processing techniques, including basics of land cover classification and change detection
• Develop a portfolio of example scripts, data visualizations, and analyses across a range of environmental application areas

Snapshot:

Instructor: Deidre Zoll
Fall Start Date: TBD

Geospatial computation is the study of general computational methods that are applied to spatial and spatiotemporal data for exploratory, confirmatory, descriptive or predictive analysis. This course introduces foundational concepts in geospatial computation and its applications in spatial data science within the context of geographic information systems.

This course uses the current version of ESRI’s ArcGIS Pro, as well as, Python, Rstudio, and R-Bridge for ArcGIS Pro.

Computational approaches in spatial simulation, exploratory data analysis, predictive analysis and geospatial data visualization will be elaborated. Implementations of data-science centric geospatial approaches in R and ArcGIS will be covered in the context of spatial problem solving in a string of applied computational project assignments. No prior knowledge of R programming is required.

By the end of this course, the student will be able to:

• Identify feasible geocomputational approaches in spatial data science for a wide variety of spatial problems
• Gain knowledge of spatial pattern mining, spatial regression and exploratory data analysis
• Recognize stages of spatial analysis that rely on geospatial computation and form repeatable analysis workflows leveraging ArcGIS and R
• Obtain working knowledge of R language within the context of geospatial computation and its integration to a ArcGIS

Snapshot

• Summer Session Dates: July 5 – August 29, 2023 *Registration Coming Soon
• Summer Session Application Deadline: June 14, 2023
• Summer Session applications will be reviewed on a rolling basis

Study proposals include but are not limited to topics in: Agriculture, Education, Environment, Environmental Justice, Data Science, Engineering, Food Systems, Forestry, Government, Health, Natural Resources, Water Resources, Urban Planning, Transportation, Marketing/Market Research. Technical approaches may focus on student-identified topics in GIS, remote sensing, and data communication.

Learning Objectives

The Independent Study proposal will describe customized educational objectives for the student. In general, the learning objectives for this course are to:

• Develop customized learning objectives in collaboration with your mentor.
• Design and complete a project with a clear study question whose answer(s) are derived through the application of GIS, remote sensing, data visualization, geospatial computation or related analyses.
• Find, process, and apply data to investigate a study question.
• Revise study design, initial results, and findings based on targeted feedback from your mentor.
• Adapt project goals based on data availability, format, or other logistical constraints.
• Present professional-quality study findings that effectively communicate the results of your work.

Prerequisites

You must have GIS experience (e.g., Intro and Advanced GIS courses or equivalent experience) & receive Director and Independent Study mentor approval of the Independent Study Proposal.

The Process

An independent study is a major project intended to allow students to reinforce and polish their GIS and/or remote sensing skills. Participants generate their own project proposals. Each project should attempt to answer a particular question, and these projects should involve the integration of GIS and/or remote sensing concepts, data sets, and approaches. After defining the scope, students should plan on spending 10-12 hours each week working on the project for the 8-week course. They will have meetings once per week at a selected, predetermined time with their Independent Study Mentor (see description below “To Apply” section). The purpose of these meetings is to ensure that adequate progress is being made and that the final product will meet the educational objectives defined by the student. Independent study proposals are due one month before the start of the Independent Study course to allow review by the UVM GIS Certificate Program Director and Independent Study Mentor. Once approved, students will register for the course. Proposals needing further development, revision, or input from an Independent Study Mentor may be deferred to the following Independent Study term. Proposal submissions in advance of the deadline are highly encouraged.

To Apply

To apply for the Independent Study, please submit your proposal with the Independent Study Proposal Application.

What is an Independent Study Mentor?

An Independent Study Mentor is an experienced professional who has agreed to mentor a student in the independent study project. The mentor engages in regularly scheduled progress updates (e.g., weekly) to evaluate student progress, advise on critical decision points, and foster student problem-solving. Ideally, the mentor’s area of expertise is aligned with the student’s project to the extent they might advise on data sets or methodologies, although the mentor is not meant to be a technical troubleshooter. The UVM GIS Certificate Program has a number of mentors available to work with 5-6 students per year based on their availability. Students may identify a new mentor not yet working with the program, such as a professional in their field/region of interest. All mentors will be contacted by the UVM GIS Certificate Program to verify their agreement to serve on a specific student project.