SmartChicago

SmartChicago Logo

Inspiration

We are a group of mostly local students from the Chicago area and have grown tired of hearing about Chicago's crime rate and inefficiency as a city. We want to help bring Chicago into the new age by taking advantage of its vast public database; Database.

What it does

We used multiple API's to develop a user-friendly website displaying patterns of almost-realtime crime data. Allowing us to pinpoint locations where criminal activity is more prominent.

How we built it

Using multiple API's and massive datasets from the Chicago database and web scraping using Beautiful Soup, we gathered the most relevant pieces of data for each occurring crime. This included the type of crime, the time it occurred, and the location where it occurred. We parsed this data and stored it in a geojson file. We could add the file to our map system powered by MapBox. We then embedded our customized map system onto our website.

Challenges we ran into

Parsing through the vast database became very time consuming considering our limited computing power. Some of the data sets consisted of more than one hundred thousand rows with around 20 elements in each row. For that reason, we would need a little more time to develop visual representations for the pollution and energy usage datasets.

Accomplishments that we're proud of

As a group, this was everyone's first hackathon and we all had little to no coding experience. Over the last day together, we all grew as programmers as did our interest in computer science. We are most proud of the progress we made as a team, working to come up with a relevant idea and executing it together.

What we learned

We all learned a lot of good programming habits and behaviors. We learned to manipulate data by filtering it, parsing it and then displaying it in a useful way. Additionally, we became well versed in HTML5 and css3, as we developed and published our own website displaying our idea and data.

What's next for SmartChicago

We know that we have barely scratched the surface when it comes to data analysis. We will extrapolate on our experience by learning more about Deep learning and Neural Networks to make predictions about Chicago's crime patterns. Secondly, after developing our pollution and energy usage datasets, we can work with the City of Chicago to make the city cleaner and more efficient. Our team has really come together over the past 24+ hours and will stay in touch with Skype and text to continue to work on our project.