How I engineered legal data to analyze linguistic discrimination against asylum seekers.
Currently, the United States is experiencing what the New Yorker describes as a “translation crisis at the border.” When migrants from South America arrive at the border, they’re met at the border by paramilitary officers and immigration agents. Interpreters are integral to the asylum process, since they facilitate communication between asylum applicants and law enforcement.
However, there aren’t enough interpreters to represent asylum seekers — especially asylum seekers who are indigenous or speak a language with a small speaking population.
As a data engineer for non-profit advocacy organization…
Alberta, Canada, has a continental climate, with balmy summers and freezing winters. Scattered around the province are 11 weather stations, which collect and report meteorological data daily.
Over time, meteorologists can use this data to see trends and make predictions about how climate will change (although not too far into the future!).
By being able to accurately predict where in the province data is being collected from, researchers will be able to pinpoint which communities are being affected by certain effects of climate change — and hopefully lead policy makers in Alberta to better serve their constituents.
The Stanford Open Policing Project, a data journalism enterprise at Stanford University, has been cataloging police traffic stops across the country. Currently, they are are 100 million entries.
One officer in South Carolina makes up almost 30,000 of those entries.
Because the Stanford Open Policing Project, whose data I’m analyzing, encrypted all the police officers’ identities. For the sake of clarity, this officer will be referred to as Officer X is. (More here on why the the Project encrypted police identities.)
However, the data can reveal a bit of information:
Data scientist with a focus on advocacy and public records. Combining data and language to increase public access to information.