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MicroLant


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Evidence of Work

GreenWay - Making Public Transport More Comfortable

Project Info

MicroLant thumbnail

Team Name


MicroLant


Team Members


2 members with unpublished profiles.

Project Description


GreenWay is a live functional web app that navigates shows you between any two points in the Greater Sydney Area with routes that prioritise greenery and avoid the urban heat of the built up in environment. It also shows you heat maps for the area showing the levels of vegetation cover and temperature, block by block. Making the route more comfortable encourages out of their air conditioned cars on hot days.


Data Story


We used NSW Department of Planning, Industry & Environment's open data via SEED (Urban Heat Island and Urban Vegetation Cover) and created visual heat maps for visualisation, and integrated it into our navigation routing engine to upweight trees and cool regions while still considering fast routes.


Evidence of Work

Video

Homepage

Project Image

Team DataSets

Greater Sydney Region Urban Vegetation Cover to Modified Mesh Block 2016

Description of Use Used in our Greenway project to provide navigation routes which favour vegetated areas. The idea being that a slightly longer route, but which has more green vegetation would be a more pleasant walk, than the shorter but non-green route.

Data Set

NSW Urban Heat Island to Modified Mesh Block 2016

Description of Use Used in our Greenway project to provide navigation routes which favour cooler areas bypassing areas with a high urban heat island affect. The idea being that a longer route though a cooler area (based on the urban heat island data) would be more favourable than a shorter route where it's hotter due to the urban heat island affect.

Data Set

OpenStreetMap

Description of Use We've used OpenStreetMap data to power our navigation routing engine, for our project Greenway - a navigation app that prefers cooler, greener routes.

Data Set

Challenge Entries

Public Transport for the Future

How might we combine data with modern technologies - such as AI/ML, IoT, Analytics or Natural Language interfaces - to better our public transport services. Outcomes could take the form of new commuter experiences, reduced environmental impact, or helping plan for the future.

Go to Challenge | 45 teams have entered this challenge.