GEOG 580 - Geovisual Analytics
This is a sample syllabus.
This sample syllabus is a representative example of the information and materials included in this course. Information about course assignments, materials, and dates listed here is subject to change at any time. Definitive course details and materials will be available in the official course syllabus, in Canvas, when the course begins.
Overview
GEOG 580 is a required course for the Master of Science in Spatial Data Science.
This seminar course focuses on the science of analytical reasoning mediated through human-centered interactive geographic visualization and computational methods. The course engages students with the theoretical and computational frontiers of the emerging science of geovisual analytics, a core subfield in spatial data science. This is complemented by hands-on experiences with the design, implementation, and application of geovisual analytics tools to solve complex problems. Students will read, discuss, and synthesize research articles and design solutions to geovisual analytics problems through a series of lab exercises. Research reviewed in this course will reflect the state-of-the-art in the design and evaluation of geovisual analytics applications and showcase the diversity of relevant application domain contexts. Lab exercises will provide students with experience designing and building geovisual analytics applications in a variety of contemporary technological approaches. Students will apply concepts presented in the readings to critique the effectiveness of current geovisual analytics platforms and will leverage their experiences in development of a semester project.
Objectives
Students who excel in this course are able to:
- Characterize and critique the state-of-art in geovisual analytics science through the survey and synthesis of seminal and current literature;
- Recommend and design geovisual analytics tools and techniques for context-specific applications;
- Compare, critique, and design methodologies for evaluating geovisual analytics tools that address real-world problems.
Required Materials
Typically, there are no required materials for this course. If this changes, students will find a definitive list in the course syllabus, in Canvas, when the course begins.
Prerequisites
None.
Expectations
We have worked hard to make this the most effective and convenient educational experience possible. How much and how well you learn is dependent on your attitude, diligence, and willingness to ask for clarifications or help when you need them. We are here to help you succeed. Please keep up with the class schedule and take advantage of opportunities to communicate with us and with your fellow students. You can expect to spend an average of 12 – 15 hours per week on class work.
Major Assignments
Discussion Forums (30% of final grade)
Individual participation via online discussions. You are expected to contribute to discussions in a timely manner with substantive posts and responses to others' posts. You are also encouraged to post questions and answer the questions posed by others in the discussions.
Labs (35% of final grade)
Labs will give you experience working with geovisual analytics applications and challenge your analytical skills as you evaluate each application.
Literature Review (15% of final grade)
This is a written deliverable where you will define a narrowly focused topic or interest and engage the relevant literature to arrive at new insights.
Final Project (20% of final grade)
Here, you will develop a project that (a) identifies a problem that could be solved by the use of geovisual analytics, (b) prototypes a geovisual analytics application to solve that problem, and (c) presents a proposal for how to systematically evaluate your design solution. The deliverables are:
- Project Proposal
- System Evaluation Proposal
- Demo Video
- Final Report
Course Schedule
| Week | Topic | Assignment |
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| 1 | Introduction to (Geo)Visual Analytics & Overview of the Research Landscape |
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| 2 | Introduction to (Geo)Visual Analytics & Overview of the Research Landscape, cont'd. |
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| 3 | Characterizing Insight & Supporting the Sensemaking Process |
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| 4 | Literature Review |
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| 5 | Decision-Making & Dealing with Uncertainty |
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| 6 | User-Centered Design & System Evaluation |
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| 7 | User-Centered Design & System Evaluation, cont'd. |
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| 8 | Social Media Applications |
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| 9 | Artificial Intelligence, Big Data Perspectives, and the Future of Geovisual Analytics |
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| 10 | Final Project |
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