Team Name:
GovHike
Team Members:
GovHike
Our project had a two-pronged approach: to both aid in citizen science and local animal awareness from an app, and to help provide greater data awareness and analysis capabilities to the local council.To do so, we have created two outputs: the app HikeAware, and an associated backend data dashboard, accessible directly through the app, or via its own address. Our video explaining our product is available here:
The HikeAware app is built using NextJS. It utilised a responsive mobile-first design to deliver a seamless user experience. Styling ws accomplished using Tailwind CSS. An interactive map is implemented using the Google Maps API.
The HikeAware dashboard was created using the Python Plotly Dash library. It is hosted on an ARDC Nectar cloud virtual machine. It can be accessed here. It utilises several data sets that have been combined into two CSV files that are loaded into the app via Pandas. Data analysis is accomplished using Numpy.
We have entered into the challenge Smart infrastructure for data-driven decision making.
We are utilising the following datasets:
+ Mary Cairncross Scenic Reserve Farmo PIR Counter
+ Mary Cairncross Scenic Reserve NCount WiFi Counter
+ Mary Cairncross Scenic Reserve Atmos Weather Station
+ Mary Cairncross Scenic Reserve Milesight Occupancy Counter
+ Mary Cairncross Scenic Reserve SMT100a Soil Moisture Sensor
+ Sugar Bag Road Recreation Reserve Atmos Weather Station
+ Sugar Bag Road Recreation Reserve Farmo PIR Counter
We have also created some mock data for the app to utilise. This data is in the form of animal sighting counts per hour.
Data was aggregated into two CSV files: ALLDATA.csv, and ALLDATA_sbr.csv. These are located in the code folder.
We are a team of four people: Lachlan McKinnie (PhD student, bioinformatics), Milan Malla (3rd year engineering student), Jack Thorpe (4th year engineering student), and Zeke Pari (1st year IT student).
To complete our project, we accessed several datasets, all taken from the Sunshine Coast council datasets for Mary Cairncross Park and Sugar Bag Road bike trail.
We used seven datasets, which we aggregated into two CSV files: one for Mary Cairncross, and one for Sugar Bag Road. These included environmental data (already aggregated), soil conditions (Mary Cairncross only), digital activity, and people counts (Mary Cairncross). Data was aggregated on MS Excel using xlookup. We also generated mock animal sighting data for use in our dashboard.
Go to Challenge | 16 teams have entered this challenge.
