Back to Projects

Team Name:

Team Tom


Team Members:


Evidence of Work

ACT Walking Distances to Governmnet Primary and High Schools

Project Info

Team Name


Team Tom


Team Members


Tom

Project Description


Further information about this project can be found at: https://github.com/Gippers/actSchoolWalking.


Data Story


Visit this webpage for the full data story.

ACT School Walking Analysis

As part of Govhack 2025, the Australian Captial Territory (ACT) set the following challenge:

How can we leverage graph analytics, generative AI and other data approaches to optimise public school transport networks to make it simple to get the next generation of students to school with less hassle?

An important and potentially under appreciated aspect of travelling to school is walking. In 2011-12 less than one-quarter (23 per cent) of children aged 5–14 undertook the recommended 60 minutes of physical activity every day (AIHW). Walking to school can and does helps children stay physically active, reduces traffic and helps to reduce our carbon footprint (NSW Education).

A key consideration in children being able to walk to school is the distance that they need to travel. In 2018 in the ACT approximately 40 per cent of children were reported to usually walk or cylce to school (Healthy Schools ACT). If local communities in the ACT don't have a local primary or high school, then children won't have the same opportunity to walk to school. This is particulary important if as the ACT matures and families start to live in different parts of Canberra, the school system does not adapt to these shifting demands.

Questions

  • How has the geographic distribution of school aged children changed since 2001?
  • How long would it take on average for school aged children in the ACT to walk to their nearest ACT public primary or high school?
  • If a new primary or high school were built today, where would be the optimal place to build it to reduce the distance?

Population Changes

Analysis of how school-aged population has shifted across ACT regions since 2001.

Average Walk Time

Current walking times to nearest schools and how they've changed over time.

Future School Optimisation

Where new schools should be built to minimize walking distances for students.

Method and Assumptions

Data sources, methodology, and limitations of this analysis.

Cuation: This analysis was conducted by one person in a single weekend, as a proof of concept with limited testing. There are definitely errors that have not yet been picked up.

Furthermore, all findings and opinions are my own and do not represent the view of any institutions I am associated with.


Evidence of Work

Video

Homepage

Team DataSets

Open Street Maps (OSM) Walking Network

Description of Use The OSM walking network is utilized by the dodgr package to created a graph network that can be used to calculated walking distance and speed.

Data Set

2001-2021 ERP Data with ASGS3 boundaries

Description of Use Provides population estimates by age group and sa2 from 2001 to 2024. Used to determine the school aged population in the ACT.

Data Set

ACT Schools Wikipedia

Description of Use I used this to supplement the other school opening and closing data sets. This one appeared to be a little more up to date, however the other was more reputable.

Data Set

Schools in ACT

Description of Use I used google maps to geocode each school. The key part form this data set was the closing and starting dates of schools which enable longitudinal analysis.

Data Set

SA2 Shapefile Geoboundaries

Description of Use Used for the SA2 boundaries on maps

Data Set

Challenge Entries

Optimising Transport Networks for School Kids

How can we leverage graph analytics, generative AI and other data approaches to optimise public school transport networks to make it simple to get the next generation of students to school with less hassle?

#Reimagining-school-transport-networks

Eligibility: Open to all, although special consideration will be given to teams with a lead based in ACT. Contestants are strongly encouraged to use multiple sources of data including datasets outside of those listed below.

Go to Challenge | 13 teams have entered this challenge.