CP255 Urban Informatics and Visualization
Instructor: Paul Waddell (waddell at berkeley.edu)
GSI: Geoff Boeing (gboeing at berkeley.edu)
Scheduling: Fall, 2014. Tuesdays and Thursdays, 11:00-12:30
Location: Wurster 214 Computer Lab
Office Hours: T Th, 10-11 in the DCRP office
Office Hours: T Th, 10-11 in 214 lab
COURSE SYNOPSIS AND LEARNING OBJECTIVES
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 will learn the fundamentals of urban spatial 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 IPython notebooks
• Cleaning, manipulating, and analyzing urban data with Python’s pandas library
• Visualizing data in Python with charts, graphs, and tables
• Accessing public data from the web and APIs (including Twitter, Google, and census data)
• Analyzing location accessibility and building simple regression models
• Developing spatial indicators and mapping urban data with open source GIS tools, Mapbox, and TileMill
This course is open to students from across campus, but priority enrollment will be given to students in the Master of City Planning program.
COURSE ASSIGNMENTS AND ASSESSMENT
Exercises and Assignments will account for 90% of the course grade, and class participation for 10%. Exercises will be smaller tasks that demonstrate a degree of mastery of skills on a smaller scale, and will be used mainly as a means of ensuring that students are keeping up with the material and not falling behind. Assignments will be somewhat larger and more involved tasks, that require a higher degree of facility and independent work, putting pieces together to complete larger tasks. Assignments will be given generally on a Thursday, and be due by end of the day on the following Monday. Thursdays will generally involve more of a workshop format, with students working individually on exercises or assignments in class to have opportunities to interact with the instructor and GSI and classmates while gaining skills and confidence.
If a student wishes to undertake a larger project using the skills developed in this course, they may explore doing an independent study with the instructor.
READINGS AND RESOURCES
- Downey, Allen. 2013. Think Python: How to Think Like a Computer Scientist. Green Tea Press. Available as a free PDF download. This text provides a fairly comprehensive overview of Python as a programming language.
- IPython Documentation. 2013. This online documentation explains what IPython is and how to use it.
- McKinney, Wes. 2012. pandas: powerful Python data analysis toolkit. Python for Data Analysis. O'reilly. Available as an e-book from the library, and as a PDF. You may also want to access the online Documentation available in pdf.
- Statsmodels Documentation. 2013. This is online documentation for the statsmodels library we will use for developing statistical models.
- Rossant, Cirylle. 2013. Learning IPython for Interactive Computing and Data Visualization. PACKT Publishing.
- Westra, Eric. 2013. Python Geospatial Development - Second Edition. PACKT Publishing. Available as an e-book from the library.
The syllabus page shows a table-oriented view of the course schedule, and the basics of course grading. You can add any other comments, notes, or thoughts you have about the course structure, course policies or anything else.
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