Our project is inspired by the exhaustive criminal record in Champaign. It includes over four hundred thousand criminal records ranging from the last century to 2019. We think that such detailed records can provide rather valuable information if we can analyze them with our knowledge and make our community a safer place.
What it does
Our algorithm reads the coordinates of criminal and incidental records from the police database and generates a series of safety scores for apartments and houses based on their relative distance towards the crime location. Further analysis is done to illustrate the criminal and incidental records based on time and location. We offer a map directly shows dangerous places and rectangular heatmaps to indicate the frequency of occurrences.
How I built it
We used R studio to do the data collection, data cleaning, and filtering. Then we use QGIS to do geocoding per records to pinpoint their location. Next, Tableau and Google Map API are employed for data visualization. Lastly, we wrote an algorithm to generate the number of certain categories of crime happened around the apartments which we choose to evaluate, and give the community a reference about the safety degree of the properties and its neighborhood.
Challenges I ran into
Accomplishments that I'm proud of
We successfully managed the big data sets and our product was able to generate safety scores for properties all around Champaign-Urbana.
What I learned
We learned how to play with an enormous amount of data and analyzed them for our own good. Also, we learned how to visualize data with various tools and deploy them to our website.
What's next for Safe Home
Next, we are going to polish the user interface of our website to includes more functions. It will be able to read the user input and generate the data accordingly. We also plan to include more categories of criminal records for better and more accurate safety evaluation.