Pipe - CS50 Fair 2019

Key Takeaways

hi so we have another student here with us who's gonna tell us about his project but first yeah can you introduce yourself a little bit yeah hi I'm Yao I'm a first year from New Jersey and I live in Canada haha and I know you freshmen you you don't have to design a concentration but were you aiming so right now I'm thinking about taking the data science track in the Gulf Department as I was always interested in computer science and also government so I want to combine the two to like do something together okay great that's actually a perfect segue into his projects it's also worried about that yeah yeah of course so for my project I combine my cs50 final project with my Gulf 1005 final project which is learning about data visualization and data cleaning so I wanted to make like a data science blog so or I could like showcase some of my cool projects so for my current project I was looking at gun violence decrease in stamps just go to Oakland so based on this a Guardian article a couple months ago they're talking about how gun violence decrease in Oakland and San Francisco by 50% while an increase in other US cities so we wanted to figure out why it was by looking at data making some cool visualizations and yeah can you walk us through some of the visualizations how you went about gathering the data right yeah results you found worse yeah so we started up I like trying to graph out some data points looking at it from like a bird's eye perspective so we use the Google Maps API and some gun violence datasets from the police city department police departments so we looked at that so we can graph out each dot represents for San Francisco and aggravated assault crime and for Oakland gun violence crime yeah why they separated the dot meanings because there were different data sets so every Police Department codes crimes differently so for example just got a code it just differently they were more specific with their coding yeah so for violent crimes we start started by looking at the visualizations but I'm some data points and just seeing like if the what the article said was true and like what specific years and I were supposed to focus on so when we look at our data I started by graphing it but then I realized that it was kind of hard to tell because of Chicago's large gun violence crime rate so then we looked at filter by crook per capita so we can actually see some of the trends so from 2013 onwards we can see that Oakland had a decrease in gun violence and Tim's just go how to decrease in gun lines while other cities like st. Louis Baltimore and Chicago at increases yeah so after that we want to see like why this was we're trying to explain why there's a decrease in these two cities and increase in other cities so we started off by looking at imprisonment data so like prison reform other prison related activities and we certainly hit a brick road because we couldn't get a lot of data from private prisons we're not really willing to like release our data so we ended up scratching that because like San Francisco just were there a lot of scratchy data points like some years weren't even reported so okay unfortunately we couldn't get the data for that there's systemic reasons why like is it underfunding maybe not having the administrative capacity to keep track yeah I think like it's just like a lot of people don't a lot of persons don't wanna release your data and they're private about it of course but also like they're just actually prisons oh yeah private prisons yeah some people just like just don't have their data like publicly available fortunately yeah so then we looked at some regressions to look at like gun violence increases throughout the years in San Cisco Oakland and then looking at gun violence laws especially got gun control laws in California specifically so we noticed that from there's been an increase in the past few years which is good and in 2012 to 2013 there was a big spike of like four new laws in California so this is roughly the same time that gun violence started decreasing in California in these two cities so then we want to see what these four laws were and if they were specific to California so looking at these four laws we found one law called domestic violence restraining order removal so basically if this person has a domestic violence restraining order then the police would go in and remove their guns from their home and this is all laws only been adopted in four states Massachusetts being the earliest one in 1994 and in California you have Nick okay I'm gonna gotcha yeah and then in 2013 California 2014 Minnesota and 2017 New Jersey which is run from so bit late to the game but were there so because this law was implemented so recently and with just within the past decade and with varying degrees of intensity like California is one of the more intense version of the law more strict versions while like Minnesota has a bit more toned down version so we're really comparing apples to apples so we couldn't really chaff I know conclusion and with the law being so recent than what just wasn't a lot of data to fruit like show that it correlated this law with a specific decrease so in our conclusion to be an interesting point yes yes that's what we send their conclusion basically how is one factor out of many and that we should provide more research and do more research in the future so you mentioned you want to do data science going on into your next four years do you first see yourself using this blog yeah of course like right now on the home page you can see that I only have one project unfortunately on first year so I'm slack but yeah definitely adding more I'm taking more Gulf data science classes okay great and I just want to re-emphasize I think this is one of the really cool things about CS has been intersectionality yeah I was able to take a scuf project the CS project combined them learn something really interesting do a deep dive into the data and then the second cool part is for someone who's just interested about this topic but may not have the resources or the time to do all this work you've displayed it in a very user friendly format I'm sure you know it looks pretty but you can forget how much work has to go into scraping the data cleaning at formatting it and then making really hard-hitting visualizations like this so really great job and I think I get to see where else to go thank you very much yeah