1 / 8
Notebook
2 / 8
Notebook
Testimonial
3 / 8
Notebook
Parks and Airbnb in San Diego Map (Assignment 2 example)
4 / 8
Notebook
Testimonial
5 / 8
Notebook
Damage of Hurricane Harvey Map (Assignment 3 example)
6 / 8
Notebook
Testimonial
7 / 8
Notebook
Walking Time to Fremont and Warm Springs BART Station Map (Assignment 2 example)
8 / 8
Notebook
Testimonial

Offerings

Course Description

This course (1) provides a basic intro to census and economic data collection, processing, and analysis; (2) explores visualization and story mapping techniques in planning; (3) teaches methods of urban analytics; and (4) provides a socio-economic-political context for the urban analytics movement, focusing on data ethics and governance.

Synopsis

CP 101 introduces students to the systematic analysis of urban data in its institutional context. Recognizing that defining this context relies on critical thinking with regard to economic, social, and environmental outcomes, this course explores how stakeholders conceptualize “smart” and inclusive urbanity. Accordingly, this course teaches students systematic approaches to collecting, analyzing, modeling, and interpreting quantitative and qualitative data used to inform robust research, and, ultimately, urban planning practice and policymaking. Beyond instruction in urban data science and analytics, students will be introduced to theory and critical discourses on topics such as big data, open data and e-governance. Instructors will expect students to engage with technical and theoretical - with particular focus placed on ethical - considerations associated with these subjects in lecture and laboratory sections. The course will introduce students to programming in Excel and Python, using open source software, accessing open and scraped data, and other tools and techniques for urban analysis.

The course will be structured following three (3) modules:

Module 1: Introduction to Urban Data

During this module students will be introduced to fundamental data applications and ethical dilemmas in urban planning. They will be instructed on sourcing data, analyzing data via statistical testing, and presenting data through written reports and visualizations. In Module 1, students will gain skills in working with Census and economic data, statistical testing, and static data visualization. The deliverable for this module will be a descriptive profile of a Bay Area neighborhood.

Module 2: Mapping the City

In the course’s second module, students will learn different tools to make maps. We will gain an understanding of the basic elements of maps, how to map with online programs and geographic information systems software (Carto), and how to construct story map websites. Students will produce a story map as the product for this module.

Module 3: Data Science for Planners: Big Data and Analytics

In the course’s final module, students will use knowledge acquired in earlier modules to explore urban data science techniques. Classes will cover topics such as big data, open data, volunteered geographic information, smart cities, and civic hacking; and students will gain skills in real-time and crowd-sourced data collection and use. As the final project for the class, students will use novel sources of data to answer a research question of their choice.


------------

This class was created by Karen Chapple, Professor of City and Regional Planning at the University of California, Berkeley