SmartMaps

Team Name

BeFrank

Team Members

Project Description

Our solution comes in two parts: a React frontend application for displaying map visualisations, and a backend Python API service for data retrieval and developers.
Given a mode of transport, our app provides you with human centric data about possible routes between your location and another. In particular, it measures the urban heat and greenery coverage and provides a selection of the best routes. Empowering you to make informed pathing decisions.
The datasets Urban Heat & Green Cover dataset(s) from the SEED portal provide heat deviation and amount of green cover. Combining this georeferenced data with Google Maps APIs, we integrated routing information to quantify the amount of heat deviation and vegetation cover that can be experienced along a route.
Displaying the metrics for a route and also providing a rich visualisation and interactive display options for the data can help provide members of the public with a better understanding and options of possible routes.
Since our solution makes it very easy for integration with other arbitrary datasets with geographic position information, other open datasets — such as the Canberra’s street light location dataset — can be included to provide further options for route customisation. For example, this data could be used for determining a well lit route for commuting safety at night.
The API enables quick and easy querying of the joined Government and Maps datasets. Further, the queries made by users can be anonymised and stored by the backend. This provides a live feed of locations in the city that are in need of attention.

Data Story

To provide routing information, we used the Google Maps API to first geocode the user specified origin and destination locations into geographic coordinates which we could then integrate with the Graphhopper service to provide possible routes. This allows us to still optimise for distance and duration, while taking heat and green cover as additional heuristics.
To retrieve data for the expected temperature deviation and green cover, we integrated with the ArcGIS MapServer REST API. Using a list of geographic coordinates along the route, we query the GIS dataset to retrieve temperature and green cover data. In particular, we used the following fields:
- Mean Urban Heat Index temperature deviation from vegetated areas
- Percentage of green cover on a block area
- Ratio of green cover to block area
in addition to geographical data about the area the data applies to.
Once we have retrieved the data, this is aggregated for each route alternative, and metrics such as the average is calculated.