Teaching

Quantitative Methods in Community Research (Fall 2018)

Data are everywhere. Data are no longer just collected by means of traditional surveys and questionnaires, but through simple acts like hailing a taxi, biking, and looking at your phone.  Communities are now relying more and more on traditional and new forms of data to address social problems and policy issues such as crime, displacement, and poverty.  This course is an introduction to the use of statistical methods and tools to uncover, understand and conceptualize patterns in data.  The empirical and theoretical emphasis will be on the community; that is, the class will give you the methodological skills to use data to better describe communities and examine community-level phenomena.  You will work with both nonspatial and spatial data from traditional (e.g. U.S. Census) and nontraditional sources (e.g. open data portals).  Specific topics covered include data acquisition, management, and presentation (graphs, tables, maps), descriptive analysis (opportunity mapping, spatial clustering), citizen science and participatory mapping, and measuring place-based inequalities. Lectures will present abstract statistical concepts alongside data analysis examples motivated through real-world problems.  Labs will provide hands-on practice of the methods covered in lecture using software programs (R, ArcGIS Online).

Class website: https://crd150.github.io/

Spatial Methods in Community Research (Spring 2018; Winter 2019)

Many community socioeconomic and demographic processes such as poverty, crime, healthy food access, school quality, migration, and segregation have important spatial components.  Spatial data are becoming more ubiquitous and the tools for managing, processing, examining, and modelling these data are becoming more accessible.  This course introduces students to the important theoretical roles that space and place have in community research.  Here, the community is broadly defined as a geographic unit with recognizable boundaries that possesses a resident sense of place.  The course will also have a large analytical component, exposing students to the acquisition, management, examination, and modelling of spatial data for understanding communities.  The course will focus on applications in the social sciences and public health, including demography, epidemiology, sociology, criminology, human geography, public policy, education, and others.  The course assumes you have taken an introductory class in statistics and have familiarity with univariate statistics such as linear regression.  Experience with a statistical package (like Stata, SAS or R) is useful but not required.

Community Economic Development (Winter 2018; Winter 2019)

Community economic development (CED) is the process by which members of a low-income community, working with one another through community-based organizations and with other supporters, private and public, improve their economic well-being, increase their control over their economic lives, and build community power and decision-making.

This course introduces students to the theory and practice of CED.  The first section of the course sets the context for CED, including its historical basis, core principles, stakeholders, strategies and projects.  We will go through the what, where, why, and how of CED.  The second section of the course provides a deeper introduction to specific strategies in business, workforce, locality, and off-the-market development.  Although theory will be presented throughout the quarter, the focus will be on application, including an introduction to the data, tools, and methods used in CED assessment, implementation, and evaluation.