larakaracasu / indigent-burial-analysis

Project analyzing trends in U.S. indigent burial for the 2022 Public Interest Technology Data Science Corps.

Team Burial Biographies

Lara Karacasu: I am a rising junior at Columbia's School of Engineering studying computer science! In my spare time, I love trying out new restaurants in NYC, spending time outdoors, and writing poetry.

Simon Ogundare: I'm a rising junior at Columbia College, majoring in Neuroscience and Behaviour. Outside of school, I play piano and jazz saxophone. I'm interested in data science for public interest because I'm interested in using data to share meaningful stories about particular underrepresented demographics. In the future, I'm looking to pursue an MD-PhD program in Neurobiology when I graduate!

Abdur-Rasheed Syed: I am a rising senior at Macaulay Honors College at Lehman, and I am majoring in computer science. I enjoy driving and am very passionate about cars. I also love traveling and spending time with family

Bryan Brugal: I'm a rising senior at Lehman College studying computer science. My joys are writing code and trying out new technologies. When I'm not coding, I like watching movies, ice skating, exercising, playing or watching basketball, and reading about technology and finance.

Sovira Chaudhry: I am a graudate student at Lehman College studying Biological Sciences. I like to read and bake on my spare time. I am interested in entering the medical field in the future but I want to continue helping my local community in any way I can. Hence, learning coding and more about data science.

Project Description

Over Summer 2022, our team built a database consisting of 121,840 indigent burial records from 12 different jurisdictions across the US. The database includes features such as name, age, sex, race, date of death, state of residence, and county where the person was found. We used the data scraped from various local and state agencies that deal with such cases, to extract relevant information about persons who were lost, unclaimed or unidentified. We then cleaned, standardized, and compiled all records into a CSV file, and ran analyses (using Python Pandas) on both the final dataset and individual jurisdictions' datasets to reveal trends in the records. The analyses, including tables and plots describing these trends, can be found in Jupyter and Python files. The final CSV with records from all jurisdictions is named indigent_burials.csv.