GEOG 570 - Capstone in Spatial Data Science
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
This course is the culminating experience in the MS in Spatial Data Science (SDS). The course includes a semester project that requires students to demonstrate their ability to apply advanced knowledge, skills, and Spatial Data Science principles to make a substantial contribution to research in Spatial Data Science. Students will collaborate to iteratively develop and refine project topics, methods, and solutions. Students are oriented with a review of research methods, public presentation strategies, and scholarly communication skills. Students will meet weekly milestones throughout the semester, engage in peer review meetings, and receive guidance from their instructor.
Objectives
At the conclusion of this course, students will be able to:
- Develop and investigate a project that applies spatial data science principles to make a substantial contribution to research in Spatial Data Science.
- Demonstrate proficiency in spatial data science to define a problem and implement an analytical solution to address challenges within a domain.
- Demonstrate the technical and analytical competencies required to effectively design, manage, and apply spatial, analytical and visual technologies to create a solution to a challenge in spatial data science scientific research.
- Collaborate with peers and content experts to advance ideas, designs, data analysis, and findings in a spatial data science scientific study.
- Communicate spatial data quality and spatial analysis technical knowledge, including ideas, designs, data analysis, findings, and/or decision justification in a publication-worthy manuscript.
Required Materials
The materials listed here represent those that may be included in this course. Students will find a definitive list in the course syllabus, in Canvas, when the course begins.
In order to take this course, you need to have the required course materials listed below. Some of the materials needed for this course are presented in Canvas. To access the online materials, you need to have an active Penn State Access Account user ID and password. If you have any questions about obtaining or activating your Penn State Access Account, please contact the Help Desk: https://www.it.psu.edu/support/.
Required Textbooks: *Note these are available for free through the PSU library*
Booth, Wayne C., et al. The Craft of Research, Third Edition, University of Chicago Press, 2008. ProQuest Ebook Central, https://ebookcentral.proquest.com/lib/pensu/detail.action?docID=432155.
Schimel, Joshua. Writing Science: How to Write Papers That Get Cited and Proposals That Get Funded, Oxford University Press, Incorporated, 2011. ProQuest Ebook Central, https://ebookcentral.proquest.com/lib/pensu/detail.action?docID=845932.
De Smith, M., et al. 2021. Geospatial Analysis: A Comprehensive Guide (6th edition). https://www.spatialanalysisonline.com/HTML/index.html
Software
The software and data used will be specific to students’ research projects.
Prerequisites
Completion of the Spatial Data Science workshop.
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 classwork.
Major Assignments
- Class discussion participation & peer assessment activities: 10%
- Project description and literature review document: 15%
- Exploratory data analysis & workflow document: 15%
- Project proposal paper: 20%
- Final paper draft: 20%
- Final project scholarly paper: 20%
Course Schedule
| Week | Topic | Assignment |
|---|---|---|
| 1 | Research Question | Define a research question and discuss how the proposed research will advance or address a gap in the field of interest.\ |
| 2 - 4 | Literature Review | Synthesize findings across a minimum of five articles and discuss how findings will inform research. Meet with Writer-in-Residence. Synthesize findings across a minimum of ten articles and discuss how findings will inform research. |
| 5 | Data Exploration | Conduct an exploratory data analysis to identify key data characteristics. Report implications of findings on the proposed methodology. |
| 6 - 7 | Project Proposal | Meet with the instructor to discuss the proposed research question and methodology. Develop a project proposal that includes an Introduction, Background, and Proposed Methodology sections. |
| 8 | Peer-to-Peer Presentation | Present research proposal to 2-3 peers. Post a summary of key advice received from peers to the discussion forum. |
| 9 - 11 | Data Analysis | Conduct data analysis and report preliminary results. Meet with the instructor to discuss initial project results. Conduct remaining data analysis and report primary findings. |
| 12 - 13 | Final Paper Draft | Complete the first draft of your Capstone paper. |
| 14 | Peer-to-Peer Presentation | Present Capstone research project to 2-3 peers. Post critical reflection of peers’ presentations to the discussion forum. |
| 15 | Final Paper Revisions | Submit final Capstone paper, implementing required revisions and honoring formatting guidelines. |