Capital One Software Engineer Summer Summit Challenge. This challenge gave us public information law enforcement statistics in San Francisco. Our goal was to create a web app which allows the data to be visualized in a creative way and also determine the type of dispatch from a user inputted address and time.
-
Data Visuals: Display or graph 3 metrics or trends from the data set that are interesting to you.
-
Given an address and time, what is the most likely dispatch to be required?
-
Which areas take the longest time to dispatch to on average? How can this be reduced?
- Preparing for the future: Which areas are experiencing the greatest increase in dispatch calls? Where and what type of dispatch service would you place to help with the rate of increasing calls?
Data Visualization: Canvas.js, MapBox
-
Pie Charts
a. Number of incidents for each of the Neighborhoods/Districts.
b. Number of incidents based off of the call type of each call.
-
Bar Charts
a. Average dispatch time for each call type.
b. Number of incidents per unit type dispatched.
-
Map
a. Top 3 call types in different colors with all others as yellow.
Data Computation: Javascript, Google Maps Using the user's inputted address and time, a prediction was made of the call type and also the type of dispatch. Using the 5 closest points in terms of distance and time, the call type and unit type were outputted.
Data Extraction: Python, Pandas, NumPy
Front End: HTML, CSS, JavaScript, Bootstrap, MapBox
Computations for the csv data can be found in jupyter notebook [jupyter-notebooks] (https://github.com/nbabra/Capital-One-SFPD/tree/master/jupyter-notebooks)
All other computations can be found in address.js
- Navneeth Babra - (https://github.com/nbabra)