Data Practicum in Community Analytics
This course uses current topics and debates in urban policy and economic development to demonstrate the usefulness of data-driven approaches to public policy. Students will be introduced to contemporary theories of community revitalization and neighborhood change, then given tools for operationalizing the theories using Census data, GIS and spatial packages in R, machine learning tools, and regression analysis in order to identify important structural components of neighborhood change in large metro areas. The models presented can help city managers and urban policy-makers better target programs and resources appropriate for neighborhoods that are vulnerable to decline, and those that are likely to experience rapid gentrification. Students will replicate results from lectures using Census data from a major metro area of their choosing.