The UC Davis Data Lab hosted this data challenge in efforts for teams to submit their work making data visualizations that present publicly available data relating to the 2020 California Ballot Initiatives. This post is a written version of the oral presentation used during the data challenge.
Authors: Darien Satterfield, Katherine Corn, Erin Calfee
Above is a screenshot of the interactive web app map created by myself, Katherine Corn, and Erin Calfee. To interact with the webb app version click Here.
The Challenge: Select one of the ballot initiatives up for vote in California's 2020 election. Use publicly available data to clearly tell a story and to indicate the implications of a "yes" vote verus a "no" vote. The data visualization must be reproducible. Meaning it was required that all code used must be able to be run by the judges.
The Ballot Initiave Chosen:
*The following information is directly from the 2020 CA Ballot*
Proposition 20- Restricts parole for certain offenses currently considered to be non-violent. Authorizes felony sentences for certain offenses currently treated only as misdemeanors.
A "yes" vote supported this initiative to add crimes to the list of violent felonies for which early parole is restricted; recategorize certain types of theft and fraud crimes as wobblers (chargeable as misdemeanors or felonies); and require DNA collection for certain misdemeanors.
A "no" vote opposed this initiative to add crimes to the list of violent felonies for which early parole is restricted; recategorize certain types of theft and fraud crimes as wobblers (chargeable as misdemeanors or felonies); and require DNA collection for certain misdemeanors.
The Data Processing and Coding: We used the total prison populations going back to the first prison in California in 1851. These reports came from the dept of corrections and rehabilitation. The prison population by individual prisons were available as single pdfs for each year. These were used to calculate the percent capacity of each prison. The format of the PDFs for prison population data varied substantially from year to year, so we used R to organize and compile the data into one nice and neat data set.
We also wanted to know what percent of the Total California population was IN PRISON historically and how that has changed through time. So we needed an estimate of how many people were residents of California for each year. Luckily these data were readily available and already in a nice format from the census data and from the dept of finance. To cover the whole timeline we compiled the census data and department of finance data into one longer data set.
We decided our results would best be displayed as a map so we needed the latitude and longitude coordinates of each prison. For this we used R to scrape the addresses from the dept of corrections and rehabilitation website, and then used TidyGeocoder to get the latitude and longitude values. Some of the data presented us with challenges, mainly in the form of missing or incorrect information. For example, a handful of the latitude and longitude values for the prisons were incorrect and needed to be manually fixed.
Finally we wanted to align prior legislation with changes in historical prison population and percent capacity. So we built a data set with the name of each event and its date gathered from a publication produced by California department of corrections and rehabilitation.
The next step of our approach was to process all of the data we had collected, much of which involved using R to automatically read the relevant numbers from the PDFs we collected from our sources and then creating data frames in R.
Once our data were collected and processed we created a map which plots each prison and shows the percent capacity of that prison. We made a version of this map for each year. In addition to this map we created a line graph of the change in the number of inmates per 100,000 California residents through time. On this graph we marked the dates of historically relevant events.
We wanted our figure, to be accessible, simple to interpret and follow and to be interactive, so we used R-shiny, a package in R for making interactive web-based visualizations, to publish our map and graph such that the viewer can watch the map play through time or toggle to any year of interest.
The final output can be found HERE. And the github repository containing our code to reproduce our visual can be found HERE.
Our Interpretations: Proposition 20 if passed would have changed certain shoplifting, theft, and fraud charges from misdemeanors to fellonies. Prop 20 would have also made 51 crimes ineligible for early parole and allow parole decisions to be appealed by prosecutors. Proponents said Prop 20 would disincentivize store theft and make California safer. Opponents said Prop 20 would undo effective criminal justice reforms and lead to unjust prison conditions while wasting a large amount of taxpayer dollars.
Our map of the changes in total prison population and percent occupancy through time show that prior legislation has made significant contributions to lowering the prison population to below 137.5% of capacity, the legal maximum set by the US Supreme Court in 2011 (Brown v. Plata). As Prop 20 would repeal parts of these prior laws, our figure indicates that Prop 20 has the power to reverse the course of criminal justice reform efforts, which have so far only partially solved overcrowding in California’s prisons.
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