NEXTSTOP

Project Info

Team Name

Big 4

Team Members

4 members with unpublished profiles.

Project Description

NEXTSTOP, an interactive dashboard supported by machine learning to help ACT government decision-making process for public transport development.

Data Story

Our project uses several datasets from ACT open data portal, such as bus stops point locations, bus stops - group by suburb, bus stops, ACT population projections by suburb (2015 to 2020).

Team DataSets

Canberra Map on Google Maps

Description of Use Our project uses Google Maps and its API to access all location data point within suburbs in Canberra.

Data Set

Bus Stops Point Locations

Description of Use Our project uses this data to access the point location data of ACTION bus stops within the ACT as of July 2017.

Data Set

Bus Stops - group by suburb

Description of Use Our project uses this data to access the information of bus stops in the ACT, grouped by suburb.

Data Set

Bus Stops

Description of Use Our project uses Bus Stops data from the open data portal (dataACT) to get the tabular data of ACTION bus stops within the ACT.

Data Set

ACT Population Projections by Suburb (2015 - 2020)

Description of Use Our project uses this dataset to predict the growing number of Canberrans population, based on the suburbs.

Data Set

Challenge Entries

Canberra 2029 – First Hackers: Inclusive; Progressive; Connected

How do we use data from the past to predict a better future for Canberra? How do we best support the diversity of our community? Optimise the way we travel and transport goods throughout our city? Predict the jobs of the future – and the skills needed for them? Connect our citizens with their environment?

Eligibility: Majority of team members need to have never competed in a GovHack previously and Must use at least ONE relevant/related dataset from www.data.act.gov.au

Go to Challenge | 10 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

Eligibility: Use one or more datasets from the Labour Market Information Portal

Go to Challenge | 38 teams have entered this challenge.

🌟 Canberra 2029 – Inclusive; Progressive; Connected

How do we use data from the past to predict a better future for Canberra? How do we best support the diversity of our community? Optimise the way we travel and transport goods throughout our city? Predict the jobs of the future – and the skills needed for them? Connect our citizens with their environment?

Eligibility: Must use at least ONE relevant/related dataset from www.data.act.gov.au

Go to Challenge | 21 teams have entered this challenge.

Public Transport for the Future

How might we combine data with modern technologies - such as AI/ML, IoT, Analytics or Natural Language interfaces - to better our public transport services. Outcomes could take the form of new commuter experiences, reduced environmental impact, or helping plan for the future.

Eligibility: Use any open dataset to support your entry.

Go to Challenge | 45 teams have entered this challenge.

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