Team Name:
ANU Hacky Bois
Team Members:
ANU Hacky Bois
Our project aims to help young adults better understand and visualise their career oportunities by providing tools to explore how geographic regions interact with jobs.
Users have two main ways of interacting with our site. Users can choose an industry they are interested in (such as "medicine" or "science") and explore the markets for these jobs in different cities. Alternitively, users can instead choose a city they are interested in (e.g. "Canberra" or "Perth") and get information about the jobs market, including median salaries, cost of living, and predicted 5-year growth. We use these factors to rank different fields to give the users suggestions for growing or profitable industries within their city of choice.
Emboiable works by combining data drawn from the Australian Tax Office, the Department of Employment, Skills, Small and Family Business, and crowd-sourced Cost of Living (COL) data into a cohesive description of current and future labour markets, and visualises them through a Flask webapp. This data can be easily updated as times goes on, and could be feasibly expanded to include more cities than the 9 largest included in this trial.
Note that we are currently using dummy data for our demo search, but we have real data set up for linking. To make this tool live, we would simply need to link our backend into the front-end.
Our project uses three main sources of data.
First, we use the ATO Taxation Statistics dataset (currently the 2016-2017 financial year) to get the median salary for a wide range of industries and jobs in different states. Next, we use crowd-sourced cost of living data to adjust salaries to find earnings after living costs. Finally, we use the Labour Market Information Portal's 2018 Employment Projections to provide data to the user about the future growth of potential industries over the next 5 years.
Description of Use We use this data to estimate the cost of living in different cities. We provide the option to our users to either look at city centre rents or outside centre rents, which we do by drawing both costs from this dataset.
Description of Use We use this dataset to provide predictions of how much a queried job will grow over the next 5 years. Since this data is predicted on a national level, we assume the trends will be relatively universal to allow us to use this data with our state-level predictions.
Description of Use Our project uses this data to provide salary estimates for different jobs depending on their states. We make the assumtion that the majority of the income for high-education jobs (e.g. scientists) is earned in major citites, allowing us to extrapolate the state-level data to their respective major cities.
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