Report 3: Replication Study of Spielman et al. (2020)
This reanalysis pertains to a paper by Spielman et al. (2020) which explores the consistency of the social vulnerability index (SoVI) over geographic space. For this assignment, the GEOG0323 class analyzed a particular set of static changes made to the workflow by Professor Joseph Holler, namely whether reweighting the principal components in principal component analysis (PCA) by percentage of variance explained would have an impact on the results and visualizations in the original study. I aimed to discuss whether this affected internal consistency and construct validity based on how certain figures and tables changed.
From this reanalysis, I have learned about SoVI (including from a paper by Cutter et al. [2003]), including its history and how it has been used by government bodies in recent years. I also learned more about PCA as a statistical method and how it could be implemented in Python code. This was one of my first experiences with Python, so I am grateful to be able to have a workflow from which to see how to use important packages for spatial analysis work.
Here is a link to the full repository for the reproduction study.
Cutter, S. L., Boruff, B. J., & Shirley, W. L. (2003). Social vulnerability to environmental hazards. Social Science Quarterly, 84(2), 242–261. https://doi.org/10.1111/1540-6237.8402002
Spielman, S. E., Tuccillo, J., Folch, D. C., Schweikert, A., Davies, R., Wood, N., & Tate, E. (2020). Evaluating social vulnerability indicators: criteria and their application to the Social Vulnerability Index. Natural Hazards, 100(1), 417–436. https://doi.org/10.1007/s11069-019-03820-z