Fall Quarter 2018 Gould 442
Tue-Thu 9:30-10:50 4 Credits
Instructor: Marina Alberti E-mail: email@example.com
Department of Urban Design and Planning
This course is designed to provide graduate students in the applied social and natural sciences
the theoretical and practical skills for conducting research in complex urban settings. The objective
is to develop critical and analytical skills for designing and conducting empirical and applied
research in urban science. The emphasis is on integration and synthesis of theories, concepts,
and data across multiple disciplines. Research design is framed as an emergent process. Students
will be exposed to the issues involved in research decisions and to diverse problem-solving
strategies, methods, and technical tools. The course examines the logic and limits of scientific
inquiry, conceptualization and measurement of social and ecological phenomena in urbanizing
systems, and principles of research design and practice.
The course is structured in two components: a theoretical/methodological component and an
applied research component. The theoretical component consists of lectures on research design
principles and approaches. Lectures cover statistical principles of research design, hypothesis
testing and statistical inference, sampling strategies, and analytical approaches to randomized
experimental, quasi-experimental, longitudinal, and cross-comparative studies. Major theoretical
issues include: threats to internal validity, sampling and external validity, reliability of measures,
causality, interpretation of statistical analysis, and ethics in research. The applied research
component focuses on emerging problems across Metropolitan Areas. Students will apply their
skills on selected pilot projects in collaboration with public, non-profit, and private partner
organizations. The class features interactions with diverse urban scientists and experts of big data
on research applications, challenges, and lessons learned through their experience.
Themes of inquiry include: Urban change and evolution, predicting and imagining the future city,
urban ecology and climate change, social networks, transportation and virtual mobility, shared
economies and innovation, urban analytics, urban sensors, and big data.
Prerequisites: Introduction to statistical methods, including the basic idea of random
sampling, basic probability laws, regression analysis, and statistical tests.