Team Name:
Data Barbies
Team Members:
Data Barbies
EVe is a solution that helps us accelerate the implementation of the National EV strategy.
It is a national interactive mapping tool to support optimal investment in – and deployment of – EV charging infrastructure. Through our user centric approach, we aim to provide insight to which type of chargers, where to build and more importantly, WHEN to build it.
By opening the portal to current or potential EV owners, EVe empowers end users to actively take part in the shaping of our future EV landscape and provide market driven insights to charger providers.
In the bustling world of electric vehicles (EVs), there's a growing need for charging stations. But where should they be located to best serve the community? Let's dive into data to find out. The "Population" data offers a lens into where the majority reside, highlighting potential hotspots for EV charging demand in the future. With "Commute Distance" data, we understand daily travel habits. If most people are covering long distances, charging stations on main routes become essential. Conversely, shorter commutes suggest a need for stations in city centers or residential areas. The "EV buyers" data, showcasing current and projected EV registrations, acts as a barometer for future charging demand.
But it's not just about demand; supply-side considerations are equally crucial. The "Electricity Infrastructure" dataset ensures that when we place a charging station, it's powered efficiently, tapping into existing transmission lines and renewable energy sources. Beyond commuting, the "Places of Interest" data underscores the importance of integrating charging into daily life, suggesting stations near shopping centers, parks, and other hubs. Economic dynamics, captured in the "Income data", hint at regions where EV adoption might be rapid, necessitating more stations. The "Road routes" data ensures we address range anxiety, placing stations strategically on popular routes. And to truly optimize placement, the "Traffic Volumes" dataset guides us to the busiest roads, ensuring maximum utility for EV drivers.
By meticulously analyzing these datasets, each offering a unique piece of the puzzle, we can craft a comprehensive strategy. Implementing a recursive algorithm, we ensure even remote areas have access, and maintain an optimal EV to Charging Station Ratio. The result? Precise locations, accurate to less than 300 meters, each with a priority score, directing us where to build first to best serve the EV community.
Please find a diagram of the datastory in the Google Drive folder. (https://drive.google.com/file/d/1K6szzodK3a9FaTCnsuM2ELtWw210MlW6/view?usp=sharing)
Description of Use Gain insight into the EV landscape in Australia
Description of Use Contains data on road routes to find optimal locations and deal with range anxiety and distance inconvenience
Description of Use Provide insights for the parameters in the recommendation algorithm
Description of Use Provide insights for the parameters in the recommendation algorithm
Description of Use use api to create the EV owner interface
Eligibility: Must use a least one Australian relevant dataset - combining datasets preferred.
Go to Challenge | 33 teams have entered this challenge.
Eligibility: Must use at least one ACT Government data set, either from the Open Data Portal (data.act.gov.au) or the Geospatial Catalogue (actmapi-actgov.opendata.arcgis.com).
Go to Challenge | 11 teams have entered this challenge.
Eligibility: Use at least 1 dataset from Data.Vic list.
Go to Challenge | 12 teams have entered this challenge.
