JOBY - Prosper the Labour Market

Project Info

Opportunity Monitor thumbnail

Team Members


2 members with unpublished profiles.

Project Description


What is "JOBY"? The project video can be watched here.

JOBY is not just an interactive data visualisation tool.

JOBY is not just an APP.

JOBY is not just an integration of multi-source data and multiple advanced methods.

JOBY is a knowledge and data-driven decision-making system, including four parts:

(1) An interactive data visualisation tool for spatial and temporal labour market data presentation.
(2) A user-friendly APP that can be used on both computers and mobile phones for data search and update, geovisualisation decision making, and performing any analysis in terms of users' needs.
(3) A spatial and temporal data analysis toolbox for exploring temporal and geographically local data patterns and regional clusters, and investigating regional economic development, industry and population factors.
(4) A future labour market prediction toolbox for predicting future labour market scenarios using multi-source data, including socio-economic statistical data, satellite remote sensing data and future global climate change scenarios. Economic models, machine learning and spatial and temporal models are integrated for more accurate prediction.

1. Objectives

This project aims at developing a comprehensive a knowledge and data-driven labour market decision-making system. The comprehensive decision system can help government to improve the capacities of in-depth data analysis and strategic management.

Significance

A knowledge and data-driven decision making system
is not just a single data analysis method
or a visualisation tool,
it is a comprehensive system
including both experts' knowledge and data-driven research,
both methods and tools,
and both data analysis and serving for strategic decision making.

Innovations

  • A series of advanced dynamic visualisation and interactive geovisualisation approaches are used to investigate historical labour market data.

  • Sophisticated mathematical and spatial statistical methods are applied in exploring labour market variations. They are presented in an easy understanding way.

  • Multi-source data are integrated to assess the potential factors of the labour market and their regional effects.

  • Economic models, machine learning and spatial and temporal models are integrated to predict future scenarios of labour market, including the unemployment rate, participation rate and employment rate in different regions across Australia.

2. JOBY: knowledge and data-driven labour market decision-making system

2.1 Dynamic data visualisation tool

The monthly unemployment rate in different regions from 2004 to 2015 are visualised using a dynamic data visualisation tool.

2.2 Interactive geovisualisation decision making APP

The geovisualisation decision making APP can be visited via three optional ways.

(1) Directly use it on the website

(2) Scan QR code to use it on your phone

(3) Click this link to visit the website on your computer

2.3 Spatial and temporal data analysis toolbox

Spatial and temporal variation trends analysis.

Enterprise statistics by industries.

2.4 Future labour market prediction toolbox

Multi-source data are integrated to assess the potential factors of the labour market and their regional effects. The explanatory variables include socio-economic statistical data, satellite remote sensing data and future global climate change scenarios.

Economic models, machine learning and spatial and temporal models are integrated to predict future scenarios of labour market, including the unemployment rate, participation rate and employment rate in different regions across Australia.

3. Summary

Practical contributions to government and communities

Output Summary

  • New Datasets: Labour market reorganised historical and predicted future spatial and temporal data across Australia

  • New Visualisation Approaches and Tools: Dynamic visualisation and interactive geovisualisation

  • An APP: Interactive geovisualisation decision making APP

  • Innovative Findings: National, state/territory and local trends, variations, characteristics, hotspots and potential factors of labour market. The findings can be directly applied in accurate national and regional management.

  • Practical Applications: The new datasets, visualisation approaches and tools, the APP, and innovative findings are integrated for comprehensive, accurate and effective labour market management. The predictive and strategic management is critical for prospering the labour market.


Evidence of Work

Video

Homepage

High-Res Image

Team DataSets

Global future climate change scenarios data

Data Set

NASA Nighttime light remote sensing data

Data Set

Labour Market Information Portal

Description of Use For statistical analysis about the relationship between employment or unemployment rate with enterprise in different companies.

Data Set

LMIP Portal

Description of Use Following data in this website are used for presenting Australian labour market: - Regional employment condition: http://lmip.gov.au/default.aspx?LMIP/Downloads/EmploymentRegion; - Local employment condition: http://lmip.gov.au/default.aspx?LMIP/Downloads/ABSLabourForceRegion.

Data Set

Challenges

Training AI models to deliver better human outcomes

For an outcome create two AI models based on contrasting incentive systems and examine the differing impacts on the defined outcome.

Go to Challenge | 12 teams have entered this challenge.

Why is “placemaking” important for local communities?

A strong community is created through creating a “sense of place” through engagement with the community, partnerships with businesses and industry

Go to Challenge | 4 teams have entered this challenge.

ATO for individuals

How can ATO and other Australian public data be used to help the community fill employment opportunities?

Go to Challenge | 27 teams have entered this challenge.

🌟 The Class of 2025

Considering the growing and emerging economic industries, how can data be used to assist tertiary education providers in developing courses that are relevant to, and supportive of future job creation?

Go to Challenge | 10 teams have entered this challenge.

Efficient Visualisation

Immersive visualisation to understand data

Go to Challenge | 11 teams have entered this challenge.

Leveraging innovation precincts to drive economic prosperity.

How might we build on interaction between businesses and research institutions in Australian innovation precincts, to boost business capability, growth, and job creation?

Go to Challenge | 23 teams have entered this challenge.

Australia’s Future Employment

Choose one of the following questions to address: 1. How can recent and future changes in the labour market help prepare young people for job opportunities? 2. What can we learn from case studies of regional labour markets? For example, what does rapid change in the industries or occupations within a region tell us about the needs of employers/workers in other regional labour markets

Go to Challenge | 38 teams have entered this challenge.