This site is hosted on GitHub, and it was designed using Mobirise.

I acknowledge the inherent contradictions of hosting a project centered around jail and prison abolition on GitHub, which openly has a contract with ICE. I encourage you to read more about that here.

As stated above, the original dataset from Reuters contains information on 7,571 jail deaths throughout the U.S. between 2008-2019. Using Google Sheets, I selected and copied the 119 female jail deaths in CA into a new dataset. I then made some formatting changes – some names were all lowercase, some of the entries in various fields were inconsistent (for instance, some entries for gender were labeled "female" while others were labeled "f" or "F").

Before creating the visualizations in Tableau Public (more on that below), there were calculations I did to create two new fields in my data: age and lost years. For age, I created a formula in Google Sheets using the Dates of Birth and Dates of Death fields provided in the original dataset. As mentioned in the Narrative & Data Visualizations, both of these fields were not available for every woman.

I then used data from a National Vital Statistics Report from the National Center for Health Statistics (NCHS) on life expectancy to calculate estimates of the "lost years" for each of these women. Within the NCHS report, I used what they refer to as a "period life table." As the NCHS describes it, "The period life table does not represent the mortality experience of an actual birth cohort. Rather, the period life table presents what would happen to a hypothetical cohort if it experienced throughout its entire life the mortality conditions of a particular period in time... The period life table may thus be characterized as rendering a snapshot of current mortality experience and shows the long-range implications of a set of age-specific death rates that prevailed in a given year."

Within the NCHS period life tables for each race and gender, there is a column labeled "expectation of life at age x." The NCHS explains this as "the average number of years remaining to be lived by those surviving to that age, based on a given set of age-specific rates of dying." For each woman, I used the appropriate period life table based on race, and then found the "expectation of life" value that corresponded to the age at which each woman died. In other words, I found the estimate of how many years each woman had remaining. These are, indeed, estimates.

The data visualizations for this project were created in Tableau Public. To create the lost years visualizations, I had to generate a calculated field within Tableau for the cumulative sum of lost years using the "running sum" formula. This allowed me to create the animated linked map and chart that appears towards the end of the Narrative & Data Visualizations. More information regarding the rationale behind the calculations and decisions mentioned here are discussed in detail within the context of the narrative.