Koala Safety Net

Team Name

GovHack3d

Team Members

Project Description

Global warming has been around for hundreds of years, however due to the introduction of large amounts of carbon emissions due to human intervention, it has become further exacerbated putting Koalas more at risk. In the 2019/20 bushfires in NSW where an estimated 6,382 koalas were killed, representing 15% of the New South Wales koala populations.
For this reason, our team GovHack3d have taken it upon ourselves to develop a solution using our knowledge of data modelling to ensure the safety and survival of Australia's most iconic wildlife.
Koala safety net aims to create an interactive map that can be accessed via website.
The map is to contain:
-location of koalas relative to local bushfire prone areas within Queensland
- as well any highlight major roads that may prevent them from reaching safety
This will provide the government with a tool to help identify where to apply specific solutions such as fauna bridges for access over roadways, easy of locating and relocating Koalas within at-risk locations as well as planning for future bushfire danger. Therefore, allowing for more efficient application of resources and effective mitigation of Koala loss.

Data Story

The Data used was obtained from Ausmaps, Wildnet Koala Locations and Bushfire Hazard Area Mapping Methodology for Queensland. These were chosen to track Koala populations, paired with bushfire tracking and Major Road works to assist in applying safety measures for Koalas.
However, whilst using this data the challenges we faced, was when using R studio to correlate the data to identify the best areas to apply solutions. In addition, we were unable to locate state-wide annual data of yearly bushfires in Queensland.
Yet from the data we were able to use, it was possible to use data tracks over the major entries over the last 50 years for Koala populations along with Major Road works.
Due to our limited experience and knowledge with the software we were unable to expand the program to fit our desired goal.
Future implementation could incorporate more features such as recommended areas where solutions should be applied as well as use of data for other Wildlife populations.