Winter 2024
Mondays 5pm - 7:50pm
Live-Online/Synchronous
This course will meet in real time via Zoom. Classes meet at regularly scheduled times and you will have the opportunity to interact with your instructor and your classmates. Just like a regular on-campus class, you are expected to attend and participate in discussions.
Camera use
Cameras are expected to be on during instruction. If you have any concerns about using your camera during class, please share those in the survey distributed at the beginning of the quarter, contact me via email, and/or talk to me during office hours. We will work together to determine the best approach to address any concerns you may have.
Chris Giamarino (cgiamarino@g.ucla.edu)
Ariella Ventura (ariellasventura@gmail.com)
Lucy Briggs (lbriggs@g.ucla.edu)
For issues regarding JupyterHub.
Ben Winjum (bwinjum@ucla.edu)
Chris (schedule via email)
Ariella, Tuesdays from 1-3 pm (schedule via email/Zoom)
Lucy, Mondays from 2 - 4 pm (schedule via email/Zoom)
The goal for this course is to expose you to the foundations of spatial data science. Where once there was a dearth of available digital information, we now live in a world of too much data. How can these data be transformed into human expressions and narratives, and how can these be represented spatially? Our understanding of social phenomena through spatial constructs in urban data allows us to address location-based questions on social justice, the environment, transportation, community development and design, amongst many other themes, and how these factors may affect different population groups in different ways.
This course prepares you for challenges in urban data beyond off-the-shelf cartographic approaches. It looks at the various components of data’s journey—acquisition, exploration, modeling, and communication through visualization—and how it enables and advances your research from a data science perspective.
We begin with an introduction to various data science tools, and review the basics of programming with Python. Once a foundation of Python programming and data wrangling is achieved, spatial analysis through Python Libraries, and subsequently, through advanced geoprocessing will be introduced. All lessons will be based on “real” data with analytical methods addressing relevant and contemporary urban problems.
In addition to the programming lab sessions, you will be given weekly or bi-weekly “thinking cap” assignments, where you will be asked to think critically about contemporary urban issues. Be prepared to address various topics from the perspective of your own lived experiences, how it informs the topic, and what kind of research can advance knowledge in a positive way.
While there are no prerequisites for taking this course, people who are approaching programming for the first time will admittedly find the course to be intense and challenging.
At the conclusion of this course, students will be able to critically describe, analyze, and visualize spatial data for planning practices and research.
Specifically, students will learn to:
The following applications must be launched at the start of every class session:
I would be remiss if I do not mention various inspirations that have led to the creation of this course. First and foremost, Yoh Kawano crafted this course and was gracious enough to mentor me as a TA for several years, lend me the material, and show me how to teach GIS and python effectively. Second, the late and great Leo Estrada, who was Yoh’s mentor and confidant over the years, who taught GIS at our department for many decades. Leo and Yoh spoke extensively about modifying this course to a more “modern” approach, and I am delighted and honored to uphold his legacy moving forward.
Third, we have taken the liberty (with permission) to borrow ideas and materials from other courses. We specifically took inspiration from Paul Waddell’s “Urban Informatics and Visualization” course at UC Berkeley, and Geoff Boeing’s “Data, Evidence, and Communication for the Public Good” at the Department of Urban Planning and Spatial Analysis at USC’s Sol Price School of Public Policy.
The course will almost entirely be conducted on Jupyter Notebooks. A JupyterHub, a web-based Jupyter Notebook environment, has been set up specifically for this class, and is available at the following URL. Note that you will need multi-factored authentication to login:
Weekly course materials, including presentations, interactive notebooks (.ipynb), and data will be made available through a course github repository (here) that you will interact with through your JupyterHub account.
All assignments, unless otherwise specified, must be posted on your individual GitHub accounts or on the class GitHub discussion section by midnight of the Sunday prior to our class on Monday. Assignments are posted in each week’s page, so make sure to read the instructions carefully.
| Task | Number of items | Points |
|---|---|---|
| Participation and individual assignments | 7-10 | 100 |
| Group Assignments | 4 | 400 |
| Mid-term | 1 | 200 |
| Final report | 1 | 300 |
All assignments are graded on the following criteria:
| Grade | Description |
|---|---|
| A/A+ | Exceptional/creative/innovative work, demonstrating grasp of material, application of concepts to your own inquiry, going above and beyond course material |
| A- | Good grasp of material and solid application to your own research inquiry |
| B+ | Average understanding of material, largely duplicating course material to fit with your own research inquiry |
| B and below | Submission is incomplete or produces errors |
Hints for creating an exceptional assignment
Late assignments will be marked down one grade for each day it is late. For example, if your assigment warrants an “A” but is a day late, you will receive an “A-.” As long as you submit your assignment before 10th week, you will get at least a “C.”
Note: Weekly content is subject to change, and will be modified as needed based on class discussions, needs, and progress.
| Week | Topic |
|---|---|
| Week 1 | Introduction to spatial data science |
| Week 2 | Data in Urban Studies: The challenge in data acquisition → Group assignment #1: Project Proposal |
| Week 3 | Understanding communities: Census data profiles → Group assignment #2: Census Data Exploration |
| Week 4 | Open Data and APIs |
| Week 5 | Open street maps |
| Week 6 | Mid-terms → Midterms |
| Week 7 | Geocoding, multiple overlays, and functions → Group assignment #3: Data Visualization |
| Week 8 | Spatial statistics |
| Week 9 | Point pattern analysis → Group assignment #4: Spatial Analysis |
| Week 10 | Remote Sensing and Sentiment Analysis |
| Finals Week | Finals |
Given the nature of the course, you will have many, many questions along the way. However, we ask that you adhere to the following guidelines in order to make consultations as productive as possible. Students who do not follow these guidelines will be asked to reschedule.
Before asking a question:
If the above steps haven’t solved your problem, send an email (or attend office hours) and include the following information:
Readings will be posted in the assignment sections for each week. The following are a list of resources to help you with the more technical aspects of the course:
I intend to make this classroom a space that affirms all identities and perspectives, including your race, color, national origin, ethnic origin, ancestry, marital status, religion, age, sex, gender, gender expression, gender identity, transgender status, pregnancy, physical or mental disability, medical condition, genetic information (including family medical history), sexual orientation, political ideology and affiliations, citizenship, or service in the uniformed services. Regardless of background, all students have a right to an equitable education. Because of the multi-faceted and complex nature of our identities, it is imperative that we are committed to affirming one another’s perspectives as a community for all enrolled in this course. I encourage all members to embrace and learn from the diversity in this classroom, school, and university. I want to highlight that discrimination, harassment, or forms of hateful transgressions will not be tolerated in our learning environment. If you have any recommendations about how to make our environment more inclusive please feel free to let me know.
If you are already registered with the Center for Accessible Education (CAE), please request your Letter of Accommodation on the Student Portal. If you are seeking registration with the CAE, please submit your request for accommodations via the CAE website. Please note that the CAE does not send accommodations letters to instructors–you must request that I view the letter in the online Faculty Portal. Once you have requested your accommodations via the Student Portal, please notify me immediately so I can view your letter.
Students with disabilities requiring academic accommodations should submit their request for accommodations as soon as possible, as it may take up to two weeks to review the request. For more information, please visit the CAE website (www.cae.ucla.edu), visit the CAE at A255 Murphy Hall, or contact by phone at (310) 825-1501.