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GreenRoutes

Project Info

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Team Name

Mentally Friendly

Team Members

6 members with unpublished profiles.

Project Description

Green Routes' vision is to increase citizens' engagement with their environment.

Along with preparing citizens for urban heat, we believe that we can also tackle congestion, reduce obesity in young Australians, and improve the overall well-being of all citizens.

With more and more Australians walking along shaded pathways road congestion will be reduced and the overall fitness of our population will be improved. Not to mention the improved well-being outcomes from exercise and being in nature.

https://docs.google.com/presentation/d/1G-l08RyczGRc_vJjnHR8WubSSwNCRTTcE6iXxrpfIMk/edit?usp=sharing

Data Story

We use multiple datasets from the Department of Planning & Environment, the Department of Transport, and the CSIRO to provide citizens with a simple and informative app to explore Sydney. Citizens will be able to view which areas of Sydney experience the highest amount of Urban Heat. And which areas have the most tree canopy. We also display cycleways and animals that have been recently spotted in the Greater Sydney region.

We use the Urban Heat and Tree Canopy data in our algorithm to plan an efficient route for citizens that reduce their exposure to the heat but still gets them to where they need to be.

Evidence of Work

Video

Homepage

Project Image

Team DataSets

NSW Urban Vegetation Cover to Modified Mesh Block 2016

Description of Use We are using this dataset to visualise the tree and shrub coverage. In the future, we will feed the data into a machine learning model to automate navigation to choose areas that have a higher than normal amount of tree and shrub coverage.

Data Set

NSW Urban Heat Island to Modified Mesh Block 2016

Description of Use We are using this dataset to visualise the land surface temperatures. In the future, we will feed the data into a machine learning model to automate navigation to avoid areas that have a higher than expected land surface temperature.

Data Set

Atlas of Living Australia via CSIRO

Description of Use We are using this dataset to show which animals have been sighted along the suggested route. We are using the data to share the biodiversity of the urban environment the user is walking through. In the future, we will allow users to report the animals they sighted.

Data Set

Cycle Network - City of Sydney

Description of Use We are using this dataset to visualise the cycleways in the City of Sydney. We are using the information to promote alternatives to walking or public transportation.

Data Set

Challenge Entries

šŸŒŸ What's the coolest way to travel across the city?

Using datasets which map urban heat and green cover across Greater Sydney, we challenge you to develop a tool which visualises green routes through the city. Help people avoid urban heat and move across the city in comfort by mapping out green streets and pathways which connect shopping centres, public transport stops and public spaces.

Go to Challenge | 18 teams have entered this challenge.

šŸŒŸ The three Cā€™s of innovation ā€“ combination, collaboration, and chance.

How can we combine and use environmental data to gain new insights into New South Wales and tell a story of our diverse landscape?

Go to Challenge | 14 teams have entered this challenge.

What are the key levers that can be affected to ease congestion in NSW?

Using open data and other data sources, what can you infer that can be changed by Transport for NSW to help ease congestion? This can be congestion from people, cars, train passengers, on a platform or queuing for a bus or just generally on a road. What has happened in the past? What information can we provide customers, bus drivers or employers to assist in easing congestion? Note: this is not just road congestion. It can be viewed holistically or at a microlevel ā€“ for say an intersection.

Go to Challenge | 7 teams have entered this challenge.

Thrive or survive: how can we adapt for the future?

What will Australia in 2050 look like?

Go to Challenge | 38 teams have entered this challenge.

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