Spielman et al (2020) Study Reproduction

The reproduction of Evaluating social vulnerability indicators: criteria and their application to the Social Vulnerability Index by Spielman et al returns results which support the original study. Our results mirrored the original by illustrating the scale dependency of Social Vulnerability Indexes (SVI). When an SVI groups variables, the resulting weighted groups differ depending on scale and extent of a study area. Each extent change, the principle component analysis could select different characteristics of a county to weigh most. Lassen County, California appears as one of the five most or least at-risk counties in the state. This could be attributed to the principle component analysis weighing the county’s voluntary population or the large incarcerated population in the local state and federal penitentiaries.

I contributed to the reproduction study by expanding upon the information conveyed in the analysis. I visualize the variance between the weighted factors of the principle component analysis and compare loadings of the initial reproduction and the variance weighted reproduction. The additions included using Geopandas to calculate weighted factor variance and edits and visualization of Pandas dataframes.

​​Please follow this link to the entire reproduction study.

Please follow this link to the reproduction study’s Github repository.