Applied Urban Analytics

Permission of Instructor
Limited to 12 students

The course investigates a number of qualitative and quantitative methods to measure and analyze urban spatial problems relevant to contemporary urban planning and design practice. The course is based in part on literature on spatial analysis and in part on newly emerging topics in urban analytics. The course aims to offer students tools for integrating spatial information and decision making with planning and design solutions. It is structured around three experiments:

 ·       Pedestrian flow and route choice analysis

 ·       Understanding business (or other spatial facility) location and patronage

 ·       Explaining spatial activity patterns in big datasets.

 Each experiment will run for five weeks, during which groups of participants are asked to tackle a real-world urban analytics exercise from beginning to end, starting with a introduction of theory and methods, followed by data collection and analysis and ending with a presentation of findings in class. By exposing class participants to different experimental set-ups that move from conceptualization and experimental design, to data collection, analysis, to the presentation and interpretation of findings, the course aims to prepare students for applied urban analysis projects.

Each experiment is conducted in teams. A positive and constructive attitude for team-work is essential for a successful completion of the course.

There is no mid-term or final exam; each of the experiments counts equally, distributing the workload throughout the semester. Class meetings introduce students to both relevant theory and software applications needed for each experiment.

The course is very hands-on. We use multiple software platforms including Rhino, ArcGIS and Excel, along with some functionality that is new and experimental. If you do not enjoy experimentation and have no interest in quantitative analysis, this is probably not be the right course for you. But if you are willing to explore and embrace some uncertainty, you should experience enough to become a self-sufficient learner in urban analytics and visualization, and might discover a whole new lens through which to study, plan and design built environments.