Course Syllabus

CP255 Urban Informatics and Visualization

 Scheduling: Mon/Wed, 12:30am-2:00pm
 Location: 214 Wurster Hall computer lab

Instructor: Paul Waddell,    Office Hours:

GSI: Arezoo Besharati,

Office Hours:

 This is a hands-on course that trains students to analyze urban data, develop indicators, and create visualizations and maps using the Python programming language, open source tools, and public data.  The course will first introduce the fundamentals of programming in Python before moving on to a survey of data analysis/visualization tools and technologies.  Classroom sessions will include lectures and workshops.  A series of exercises will reinforce the skills and topics being presented, and a final project will provide an opportunity for students to develop a more complete project from harvesting data from Open Data portals to synthesizing and analyzing those data to explore a question or problem, to communicating their results in a web map and blog, as well as a final presentation.


This course is designed to provide future city planners with a toolkit of technical skills for quantitative problem solving.  It requires some tolerance for experimentation, self-directed trial and error, and an interest in learning to write code.  If you are willing to roll up your sleeves and embrace some uncertainty, you'll learn the fundamentals of urban data analysis and visualization, and might discover an entirely new lens through which to study, plan, and design neighborhoods, cities, and regions.

Topics to be covered include:

•    Fundamentals of programming with Python and Jupyter notebooks
•    Cleaning, manipulating, and analyzing urban data
•    Visualizing data in Python with charts, graphs, and tables
•    Accessing public data from the web and APIs (including Twitter, Google, Census data, and the Open Data portals of cities)
•    Developing spatial indicators and mapping urban data with open source GIS tools


Prior coursework (such as CP 204c) and experience using a GIS is required. Students are not required to have prior programming experience, although it will be beneficial. Python is an accessible language and the course will emphasize learning by doing.  Prior or concurrent course work in statistics and data analysis (e.g. CP 204D) is encouraged as this course will not provide the theoretical foundations of statistical analysis.

This course is open to students from across campus, but priority enrollment will be given to students in the Master of City Planning program.


Students will develop skills gradually through exercises paced over the semester.  These will typically involve writing some code and documenting it, using IPython Notebooks that can be shared and interactively run inside a web browser, and a writeup discussing the assignment and its results. Exercises will generally be due Sunday night at 11:59pm.

Each exercise except the first one (5 points) will be worth 20 points and the final project/presentation will be worth 100.  Exercises will be tasks that demonstrate a degree of mastery of skills, and will be used as a means of ensuring that students are keeping up with the material and not falling behind. All assignments will be marked down 10% for each day late, so please submit on time.  Assignments will represent 50% of the final course grade.

Final projects will require harnessing the skills practiced in the exercises and developing a more independent work plan to accomplish an analysis of data.  Final projects will represent 45% of the total grade in the course.

Please make sure you arrive on time to class (we will start at 12:40 - please be in class by then). Due to the nature of the computer lab, it can be very distracting to have students trickling in after class has begun. Make sure you have a keycard with access to room 214 and please do not knock on the door to be let in after class has begun. If you are taking this class and are not a Master of City Planning student, you must sign up for a computer account and access to the 214 lab. This may require paying a fee. You can sign up in 477 Wurster Hall.

Attendance policy: students may not miss more than three class sessions without adversely impacting the participation component of their grade, which represents 5% of the total grade in the course.


There will be a reading or set of short readings required for most class sessions. These are essential for coming prepared as the lectures will proceed under the assumption that everyone has already read the assigned reading materials. Most class sessions will begin with a quick discussion of the reading material, and you will have the opportunity to ask us questions about anything you didn't understand.

For the most part, the lecture notes, tutorials and examples provided on this website will provide a good foundation for the skills students will gain in thew course.  Some of the key software packages we will be using have free documentation available, and you should make extensive use of these to help you climb the learning curve.

  1. Downey, Allen.  2013. Think Python: How to Think Like a Computer Scientist.  Green Tea Press.  . 
  2. IPython Documentation. 2017. This online documentation explains what IPython is and how to use it.
  3. McKinney, Wes. 2012. Python for Data Analysis.  O'reilly. Available under Files > Reading Materials. You may also want to access the online Documentation available in pdf.

This course requires a lot of experimentation and trial-and-error. Google is your best friend! Google your questions, Google any error messages, and if you can't find an answer, email Paul and Arezoo. When you email us, tell us what you Googled and what you've discovered, and include screenshots and error messages. The key to success in this course has been repetition: keeping up on the readings, writing lots of code over and over, and stretching to figure out "what next".

The schedule below includes the topics for each class session and the exercises' due dates. Click a class session for information such as required readings, instructions, IPython notebooks, necessary data sets, etc.


The topics this semester are grouped into four phases:

Part 1: Introduction to Programming, Data Acquisition, and Basic Geospatial Data Visualization 

Part 2: Intermediate Data Acquisition, Data Wrangling, and Web Geospatial Data Visualization 

Part 3: More Advanced Topics in Data Acquisition, Spatial Analysis, and Web Geospatial Data Visualization 

Part 4: Workshops and Final Project Delivery 


Course Summary:

Date Details