Planning by Numbers

This course will use planning and policy data and applications to introduce you to a variety of useful techniques of inferential statistics and unstructured data learning techniques. Each of the techniques will be introduced and developed through the use of commonly available planning and urban policy data in order to address a planning problem or question. Applications and examples may include: (i) identifying determinants of transit ridership; (ii) identifying factors contributing to traffic crashes; (iii) identifying the characteristics that explain travel behavior and mode choice; and (iv) comparing socio-economic characteristics across neighborhoods. Class sessions will involve a mixture of lecture and in-class statistical modeling. Students will make extensive use of R, a free, open source statistical programming language. This course is especially appropriate for students whose future professional and academic work will involve the design and testing of planning and policy analysis models using quantitative data.