Project Info

28eyes thumbnail

Team Name


Team Members

Emma , Jy , Jun , Sean , Dawin , Lite , Mark

Project Description

Problems we found

  • On the 17th of August 2022, the slip from floods also caused damage to water pipes in Nelson, leading to failure in restoring the main water supply line from the Maitai Reservoir to Nelson city. It took two days since the damage to supply water through a secondary pipe with a much-reduced flow rate and volume (Stuff, 2022)
  • 411,000 buildings, 19,000km of road, 15,000km of railway, 20 airports, 3,400km of electrical transmission lines and 21,000km of water pipes in flood hazard zones across the whole of New Zealand (NIWA, 2019).
  • It is estimated that 675,000 people are at risk of surface or river flooding (The Spinoff, 2019)

We can solve the problems by

  • Providing users the flow performance to monitor water flow rates and identify low-performing pipes and abnormalities
  • Colour coding the pipes based on their flow performance.
  • Offering an early warning system using the sensors that detects the water flow and level.
  • Identifying and fixing bottlenecking pipelines that have low water flow performance.
  • Helping users to make informed decisions to respond and recover from flood events effectively and efficiently.






Source code URLS:

Data Story

We used the Three Waters pipelines data to be able to identify the pipe fixtures around the Christchurch/Canterbury region.
This in combination with Google Maps allows us to visualise where all the pipes are.

Evidence of Work



Project Image

Team DataSets

Litter Intelligence

Description of Use Waterway drainage network information and litter information retrieved from the dataset, to show on the application.

Data Set

Canterbury Stormwater Pipelines

Description of Use Stormwater pipeline network information retrieved from the dataset, to show on the application.

Data Set

Challenge Entries

Community design for flood mitigation

How might we use data to identify and improve water management for our regions?

Go to Challenge | 1 team has entered this challenge.

Reduce waste dumped in our waterways

How can we use data to create actionable insights that reduce pollution around our city waterways, improve water quality, protect our sea life and positively impact the food chain?

Go to Challenge | 2 teams have entered this challenge.

Best Creative Use of Data in Response to ESG (NZ)

How can you showcase data in a creative manner to respond to ESG challenges? How can we present and visualise data to stimulate conversation and promote change?

Go to Challenge | 4 teams have entered this challenge.