Early in April, some of our team  participated in the Auckland Space Apps Challenge organized by NASA, KiwiSpace and AUT.

Our Team:

• Jerry Peng - Software Engineer
• Isaac Johnston - Software Engineer
• Martijn Van Buuren - Senior UX Designer

This was a great learning experience and helped with our team building and collaboration having fun along the way.

The challenge we chose was visualising changes in streamflow data through time on the United States river network. Our objective was to find a way to translate raw data provided by U.S. Geological Survey into something the general public could understand. This was important as the data that water stations provide is very broad, and could be useful to all kinds of people. For Example:

• Scientists could get insights into trends over time;e.g, if you know when and where a dam was built you can find this date and see the effect it had on the water in the area.
• Water sports enthusiasts could use it to help them find good conditions. For example huge velocity and speed with big boulders for kayaking. A calm river with blue water and a nice temperature to go swimming. Or even find the perfect conditions to go fishing based on river width and fish population.

We wanted to display parameters like width, depth, velocity and speed in an intuitive way so it would be  accessible to a broad audience.

The video below shows how we wanted to visualise a river, based on water station measurements. River width was based on actual width. Colour on actual colour. Depth was represented by a dark middle, the darker, the deeper. Velocity was represented by dots, more dots meant a higher velocity. The speed at which the dots moved represented the water speed. The direction of the dots represented the direction the water was moving.

Our final product shows a map of the United States with some controls. At the bottom of the screen is a timeline. When you click on any day, month or year time jumps to that particular date.

We used the following technology for this challenge:

• Clojure - as a dynamic functional language, Clojure is extremely powerful for writing data transformation and aggregation logic and you can easily parallelize the process.
• Elasticsearch - thanks to the powerful geo-location searching and aggregating capabilities of Elasticsearch, we could implement an API that can quickly provide aggregated water quality data based on geo locations.

Author: Isaac Johnston, a previous Movio Developer