How many news reports have you come across in your lifetime about a missing or murdered Indigenous woman? Most Americans aren’t aware that Native American women have a 1.7 times higher risk of experiencing violence within the last year compared to non-Hispanic white women. Why isn’t this disparity seen across the media? Because the methods of data collection do not accurately represent the rates of missing or murdered Indigenous women in the U.S.
In order to understand how to reduce the rate of missing Indigenous people going unrecorded, I need to look more closely at the national databases that already exist, and what makes them inaccurate. The five main databases relevant to collecting information on violent crimes against Alaskan Native and Native Americans are the Urban Indian Health Institute (UIHI), National Incident-Based Reporting System (NIBRS), National Missing and Unidentified Persons System (NamUS), National Violent Death Reporting System (NVDRS), and the National Crime Information Center (NCIC). Ideally, I want to analyze the differences in reporting to each of these institutions, what data is excluded, and how representative they are of the Indigenous population. For the scope of this project I am focusing on NamUS's dataset, and comparing its national database to a statewide one as well as the Bureau of Indian Affairs. I am using Python to conduct a case study on the discrepancy in data on missing Native American men and women in South Dakota. Dataset #1 from NamUS was a downloadable csv file, whereas datasets #2 and #3 from the BIA and SD Missing Persons Clearinghouse I had to make myself through coding using the first and last names as a mode of comparison to the NamUS dataframe. I have created a visualization which shows the percent of overlap between the three databases, as well as how many cases each organization has logged. Then, I created a storymap with a sample of the missing people from all three databases for users to interact with and learn more about each missing person. This is important for my project because it highlights the fact that each of these pieces of data are real humans with real families who are suffering amid their disappearance. I will evaluate my work based on web accessibility factors such as color schemes and font size, and interactivity. My story map is interactive but my graphs aren’t so I plan on fixing that if possible.