By Emily Gong published on Jul 27th, 2023
Kate McInerny graduated summa cum laude from UCLA with a BA in Public Affairs and a minor in Digital Humanities. Her story with the Data Science Center (DSC) started in Fall 2019 when she took African American Studies 188 with Dr. Kelly Lytle Hernandez, the Thomas E. Lifka Endowed Chair in History and African American Studies at UCLA. The course was designed for 2 quarters. To encourage more hands-on exploration with real-life data, Dr. Hernandez actively engaged with Librarians at the DSC, including Tim Dennis, Leigh Phan, Jamie Jamison, and Kristian Allen, who taught GIS mapping and R programming in the classroom. The innovative class dedicated 50% of the time to lectures and the other 50% for hands-on data analysis and visualization, where Kate started her programming journey.
Kate stated, “[s]ince I took the AA Studies course, I have relied on DSC as a very approachable resource for coding help.” Later in her college career, Kate became involved in researching the patterns of law enforcement with helicopter surveillance in Los Angeles. She and her team specifically examined how surveillance is racialized and its adverse effects on residents, particularly related to noise and its effect on people’s sleep. After suing the LAPD for helicopter data, Kate got access to mapping and analyzing flight trajectory information. While I had a little coding experience, I still felt very new to it all.” Kate stated. She reached back out to the DSC early 2022. Over 2022, Kate met with DSC and DataSquad consultants over 30 times as she progressed through the project.
To help her get started, Kristian Allen and our DataSquad alumni Ethan Allavarpu designed various functions to shape the nested data into a flat file for easier exploratory analysis. Kate could then draw insights from the 12 separate datasets she had, each with around 1 million rows, for each month of the year. “The DSC made the process even easier when [they] set me up with the deep learning machine – then I was able to combine all 12 months of data and perform the analysis in one fell swoop,” Kate stated.
“Both Ethan and Kristian have shown me how much coding just requires practice,” Kate mentioned. Through her engagement with the DSC, Kate says she now better understands how to approach data. The long-term engagement with DSC enabled Kate to take the research project further as her skills grew. When asked for what tips she would give to other students who are currently doing research and may need technical help, Kate said to “start with the DSC!” She also encourages them to seek out help and really ask questions if they don’t understand. She emphasized the importance of breaking down codes and understanding the logic behind every step – “that is how to engrain lessons from this valuable resource,” Kate highlighted. We love working with Kate and seeing how impactful her work is is wonderful. If you’re interested in reading more about helicopter surveillance in LA, please check out their research results, recently covered by the LA Times.
https://milliondollarhoods.pre.ss.ucla.edu/
Q. Could you please describe your helicopter project? What’s the goal of the project? What are some methods you used? And what are your results? If you have a presentation/website that you’d like us to link to, feel free to let me know as well!
A. We are studying the patterns of law enforcement helicopter surveillance in Los Angeles. By mapping and analyzing flight trajectory data, we are examining how surveillance is racialized and its adverse effects on residents, particularly related to noise and its effect on people’s sleep.
Q. You mentioned your project evolved over time. Could you please give an example of how DSC might have supported you through those transitions?
A. My initial work for the project involved data cleaning and exploratory analysis, for which the DSC helped set me up. The spatial data included flights with nested information that needed to be separated, turned into a flat file, and marked with a flight ID. Ethan wrote much of the code to do this and subsequent data analysis – which I performed on 12 separate datasets (each with around 1 million rows) for each month of the year. The DSC made the process even easier when Tim set me up with the deep learning machine – then I was able to combine all 12 months of data and perform the analysis in one fell swoop.
Q. During the call, you said DSC was really friendly and great for researchers who are relatively new to coding. Could you share an episode of how DSC might have helped you to become a better programmer? You can feel free to talk about your engagement with Ethan if you’d like :)
A. Since I took the AA Studies course, I have relied on DSC as a very approachable resource for coding help. While I had a little coding experience, I still felt very new to it all. Both Ethan and Kristian have shown me how a lot of coding just requires practice. It also starts with thinking through what I want to do with the data, and then using the tools I already know to start approaching the question. For this project, Ethan’s initial demonstrations of writing code to find certain outputs with the data allowed me to take it steps further – taking the base code and slightly changing it in different functions depending on what I was looking for.
Q. What tips would you give to other students who are currently doing research and may need technical help?
A. Start with the DSC! And don’t be afraid to seek out help and really ask questions if you don’t understand. There were so many times I asked DSC folks to break down what we were doing so I could understand the logic behind every step in the coding process – that is how to engrain lessons from this valuable resource.
In her research she uses geospatial data analysis and digital mapping to address social and urbanistic questions, with a specific focus on racial (in)justice. For ICW, she is working to digitize maps and synthesize historical records for a multi-layered narrative on LA’s Old Chinatown neighborhood. In a separate, UCLA-based project, she is analyzing the racialized impacts of Los Angeles law enforcement helicopter surveillance. Kate is committed to research that is accountable to impacted community members and those resisting systems of oppression.
In her research she uses geospatial data analysis and digital mapping to address social and urbanistic questions, with a specific focus on racial (in)justice. For ICW, she is working to digitize maps and synthesize historical records for a multi-layered narrative on LA’s Old Chinatown neighborhood. In a separate, UCLA-based project, she is analyzing the racialized impacts of Los Angeles law enforcement helicopter surveillance. Kate is committed to research that is accountable to impacted community members and those resisting systems of oppression.