Data has become extremely important. It is now being used to take business decisions. Companies like Google, Amazon, Netflix, etc depend on this data entirely to decide what products to show on front page or maybe decide on which TV Show they should provide.
Thinking about this we could immediately relate ourselves with the traffic analysis. We could actually think on how it can help improve decision making on what services the government should be building and where.
Today its not just about implementing but rather being proactive and data analytics is only a step towards achieving it.
What it does
Provides traffic flow information based on speeds. If the speeds are high enough, it simply means the roads are clear and theres no or minimal traffic. An Yellow color indicates moderate traffic and Red color indicates heavy traffic.
It provides detailed heatmaps showing exactly where the heavy activity is.
Note: The Metropia data has been truncated and then used because of its large size.
How we built it
- We made the UI on HTML and CSS.
- D3 JS library was used to provide functionality with SVG graphs and analytics on the web.
Challenges we ran into
- We wanted to display data for Tucson specifically and there were no datasets available online. So we had to extract maps from SHP files and eventually we could narrow down to Pima County. But later we decided to go for Google Maps.
Accomplishments that we're proud of
- Understanding flow and working of SHP Files
- Getting colored visuals on maps to show traffic flow.
- Generating Heatmaps.
What we learned
- SHP Files
- Maps on Web
What's next for Data Visualisation
- Providing toggles in the webpage which will behave as a time toggle and for every hour change, it should display where rides began.
- Which areas provide highest earnings for cabs?
- Most congested routes
- Day/Hour Heatmaps