Geographic Information Systems and Data Communication Certificate

Snapshot:

Spring Start Date: January 13, 2025
Spring Registration Deadline: January 6, 2025
Instructor: Dennis Baldwin

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
• Use 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:

Spring Start Date: March 24, 2025
Spring Registration Deadline: March 17, 2025
Instructor: Dennis Baldwin

Note: You need to have completed Introduction to GIS or received prior instructor approval before enrolling in this course.

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:

• Use best practices for creating, storing and managing data in a variety of geospatial and tabular formats
• Integrate public data resources from multiple repositories (e.g. The National Map, US Census Bureau, Vermont Center for Geographic Information) into a cohesive analytic framework
• Identify ethical questions surrounding data creation, analysis, and representation
• Apply advanced vector (e.g. network analyses, attribute conflation, queries) and raster (e.g. hydrology, interpolation, raster calculator) geoprocessing tools to analyze spatial data
• Automate 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 for addressing real-world issues
• Acquire the necessary skills to develop and execute a project requiring GIS as a management, analytical, and/or visualization tool
• Practice effective communication using the (technical) language of GIS, presentation-quality maps and web applications

Snapshot:

Spring Start Date: January 13, 2025
Spring Registration Deadline: January 6, 2025
Instructor: Simran Parwani

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 for future execution of detailed individual components
• 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 of complex information

Snapshot:

Spring Start Date: January 13, 2025
Spring Registration Deadline: January 6, 2025
Instructor: Eva Kinnebrew

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:

• Demonstrate 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
• Identify what constitutes a remotely sensed data product
• 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
• Use 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

Geospatial Computation is the study of general computational methods 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 (GIS). Computational approaches in spatial simulation, exploratory data analysis, predictive analysis, and geospatial data visualization will be elaborated. Implementation of data-science geospatial approaches in ArcGIS will be covered in the context of spatial problem-solving in a string of applied computational project assignments. Prior experience with ArcGIS is required.

This course uses the most current version of ESRI’s ArcGIS Pro software. Access to software will
be provided by the department.

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

• Apply appropriate geo-computational approaches in spatial data science for a wide variety of spatial problems
• Apply and interpret spatial pattern mining, spatial regression, and exploratory data analysis
• Recognize stages of spatial analysis that rely on geospatial computation
• Develop repeatable analysis workflows leveraging GIS
• Create and present a project and professional quality story map based on your research
  questions and data
• Apply knowledge of GIS approaches to geospatial computation

Snapshot

Spring 2025

• Spring I Session Start Date: January 27, 2025 – Application Deadline: January 13, 2025
• Spring II Session Start Date: April 7, 2025 – Application Deadline: March 24, 2025

Applications will be reviewed by the selected mentor on a rolling basis. Applications are due two weeks before the session’s start date. 

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.

Independent Study Process

Prerequisites

1. You must have successfully completed: 

• Intro to GIS and  
• Advanced GIS  
• Or receive approval of equivalent experience. 

2. You have selected an Independent Study mentor from our list of instructors. We encourage students to contact their identified mentor before submitting the proposal. 

Proposal Process

Prospective students should fill out the GIS Independent Study Proposal.  

Proposal Components 

• Proposed Independent Study Description 
• Educational Learning Objectives 
• Scaffolded Learning 
• Approach / Methods 
• Deliverables and Evaluation 
• Bibliography 
• Mentor Check-ins 

Upon submission, the form is sent to the mentor of choice. The mentor has up to 5 business days to review the proposal and confirm their availability.  

If approved: 

• The mentor will contact you directly to confirm approval.  
• The PACE GIS Team will contact you with registration details. 

If not approved: 

• The PACE GIS Team will contact you. 

Learning Objectives

By the end of this course, you will be able 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. 

What is an Independent Study?

An independent study is a major project intended to allow students to reinforce and polish their GIS and data communication skills. Participants design their own project which attempts to answer a specific question. Projects should involve the integration of GIS, remote sensing concepts and / or data visualization, spatial and tabular data sets and the use of geospatial and statistical methods. 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. 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. 

What are some examples of Independent Study Projects?

We encourage you to browse through our GIS Independent Study Student Showcase. The projects in the Showcase provide an example of the diversity of projects completed by the GST Certificate students.  Check back often as more projects will continue to be added to the Showcase.

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 check-ins (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.