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Deakin Talent


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CoBuild

Project Info

Deakin Talent thumbnail

Team Name


Deakin Talent


Team Members


Amritjot Singh , Srijana , Binil Tom Jose

Project Description


CoBuild: Enabling Better Community Housing and Infrastructure Planning

A smarter way to plan Victoria’s growth – keeping neighbourhoods liveable, inclusive, and connected.

The Challenge

Victoria is growing faster than our infrastructure can keep up. Every housing approval brings opportunity, but also risk:

  • Overcrowded schools
  • Congested roads and transport networks
  • Rising rents and housing stress
  • Strained community services

Right now, housing approvals are often made *without a full picture** of how they affect schools, transport, and community wellbeing. The result? Pressure on residents, councils, and governments that could have been predicted and prevented.*

User Persona

1. Council Urban Planner

  • Profile: Works in a local government planning department.

  • Goals: Ensure sustainable growth, balance housing approvals with schools, transport, and services.

  • Pain Points: Data is siloed, decisions reactive, limited tools for forecasting community impact.

  • How CoBuild Helps: Provides forecasts, visual maps, and resource capacity indicators so they can approve or decline projects with evidence.


2. Community Resident

  • Profile: Homeowner or renter concerned about livability of their suburb.

  • Goals: Affordable housing, good schools, less congestion, quality community services.

  • Pain Points: Feels left out of planning, lacks voice in big decisions, fears overdevelopment.

  • How CoBuild Helps: Can give anonymous feedback, see sentiment scores, and track how planned projects might affect their area.


3. Property Developer / Investor

  • Profile: Builder, developer, or investor wanting to know project feasibility.

  • Goals: Understand whether a housing project will be supported by infrastructure and community sentiment.

  • Pain Points: Risk of community backlash, project delays, hidden infrastructure shortages.

  • How CoBuild Helps: Runs simulations (“add 200 houses → see needed schools/transport”) and generates feasibility reports with data + sentiment.


4. State Government Policy Maker

  • Profile: Works at a state or federal agency overseeing housing, infrastructure, and sustainability.

  • Goals: Long-term planning, balanced growth, reduce urban strain.

  • Pain Points: Limited ability to aggregate local data, difficulty prioritizing funding allocations.

  • How CoBuild Helps: Provides big-picture regional forecasts, identifies high-risk areas early, integrates community voice into policy decisions.


5. Community Advocate / NGO

  • Profile: Works with local community groups, NGOs, or social services.

  • Goals: Ensure vulnerable communities have access to affordable housing, transport, and services.

  • Pain Points: Lack of transparent data, difficult to argue with councils/developers.

  • How CoBuild Helps: Offers clear visualizations, word clouds of major community concerns, and data-backed advocacy material.

    Our Solution: CoBuild

A centralised, data-driven dashboard that combines housing, transport, schools, and community services into one clear view.

Key Features:

  • Crystal Ball Forecasting → Anticipates where pressure will build before it happens.
  • RYG Indicators → Simple, visual signals: Green = safe, Yellow = caution, Red = critical.
  • Community Voice → AI-powered sentiment analysis + resident feedback loop.
  • Equity Lens → Spotlights vulnerable communities most at risk of being left behind.

How It Works

  • See today’s reality → Housing costs, school crowding, bus frequency, community services, and what residents are saying through sentiment analysis.
  • Forecast tomorrow → “Your suburb will add 300 homes → +50 students → transport at 90% load → rising concern about school access.”
  • Act early → Councils, residents, and planners get alerts before crisis hits, backed by both hard data and community voice.

Why It Matters

  • For Residents: Clear picture of how neighbourhood changes affect daily life.
  • For Councils: Tools to plan roads, schools, and services before the pressure.
  • For State Government: Holistic data for smarter policy.
  • For Developers: Build with transparency and community trust.

---

Case Study Example

“500 new homes approved in Brunswick → School capacity hits red, transport at 95%, childcare full within 2 years.”

CoBuild flags this before approvals, giving decision-makers time to adapt—expanding capacity, adding bus routes, or rethinking approvals.


Technical Overview

  • Frontend: React.js and React Bootstrap are used to create a clean, responsive user interface.

  • Mapping: Leaflet.js and heatmaps are implemented to visualise transport networks, housing density, and service coverage.

  • Backend: Python with Flask or FastAPI powers the API endpoints and AI-driven analysis.

  • Data Processing: Pandas and NumPy are used for dataset cleaning and aggregation.

  • AI & Sentiment Analysis: The OpenAI API is integrated for summarisation and feedback clustering.

  • Visualisation: Red-Yellow-Green (RYG) metrics are displayed using Leaflet heat layers and progress bars.

  • Hosting: The frontend is hosted on GitHub Pages

  • Open Source: The full source code is available on GitHub.

What’s Next for CoBuild?

  • Pilot Partnerships

    We plan to launch pilot partnerships with councils and state agencies, making CoBuild a living tool that delivers smarter, fairer planning outcomes.

  • Scaling Up

    We aim to scale from single suburbs to entire states, integrating housing, transport, schools, and community sentiment into one unified platform.

  • Future Innovation

    We plan to add real-time data feeds, AI-driven forecasts, and API integrations, enabling councils, developers, and governments worldwide to co-plan sustainable communities.

The Vision

  • CoBuild empowers Victoria to grow sustainably, fairly, and intelligently—so every new street, school, and home strengthens the fabric of our communities.

  • Let’s make growth future-ready. Together.

  • Join Us in Building Future-Ready Communities

Let’s make Victoria’s growth sustainable, fair, and community-driven.


#community #buildtogether #cobuild #govhack

Data Story


CoBuild Data Story

  • Data Ingestion

    • Collects multi-source datasets: transport networks, housing density, demographic trends, and service coverage.
    • Integrates community feedback (survey responses, open text feedback, digital engagement tools).
  • Data Cleaning & Preparation

    • Uses Pandas and NumPy for standardisation, missing value handling, and aggregation.
    • Geo-tagging ensures spatial datasets align with suburb and regional boundaries.
    • Normalisation ensures comparability across metrics (e.g., housing approvals vs. service gaps).
  • Spatial Processing & Mapping

    • Leaflet.js visualises urban layouts with interactive layers.
    • Heatmaps highlight transport availability, housing pressure zones, and service coverage gaps.
    • Region-to-subregion drill-down allows both macro (council-level) and micro (suburb-level) analysis.
  • AI & Community Insights

    • OpenAI API performs sentiment analysis on community feedback.
    • Feedback is clustered into themes (e.g., transport delays, school shortages, green space concerns).
    • Summarisation converts raw responses into digestible insights for decision-makers.
  • Scoring & Metrics

    • Uses Red-Yellow-Green (RYG) scales to benchmark regions against capacity, growth, and service adequacy.
      Progress bars and traffic-light indicators simplify complex planning metrics for quick decision-making.
  • Backend Processing

    • Flask/FastAPI serves as the API layer, connecting cleaned datasets and AI outputs to the frontend.
    • Modular design ensures new datasets (e.g., live transport feeds) can be plugged in without re-architecture.
  • Frontend Experience

    • React.js + React Bootstrap deliver a clean, responsive interface.
    • Users explore maps, filter by region, and view AI-driven summaries side by side with raw metrics.
    • Feedback collection integrated within the dashboard keeps the loop continuous.
  • Deployment & Open Source

    • Frontend hosted on GitHub Pages for accessibility.
    • Source code available on GitHub, enabling open collaboration and transparency.

Evidence of Work

Video

Homepage

Project Image

Team DataSets

Rental Report 2011-12 - Quarterly

Description of Use The Rental Report 2011–12 – Quarterly, published by Homes Victoria, provides summary-level data about the private rental market in Victoria, covering trends in affordability, rental costs, and availability across different regions. Although originally published in June 2013, the dataset has been continuously updated, with the most recent refresh on 6 September 2024, and is licensed under Creative Commons Attribution 4.0 International. In CoBuild, this dataset is vital for understanding and visualising rental stress within communities. By combining rental affordability insights with building permits, school enrolments, and transport accessibility data, CoBuild can show where new housing growth may either relieve or worsen rental pressures. For example, if a large number of dwellings are approved in an LGA with already high rental stress, CoBuild flags this for councils and residents, highlighting the potential affordability challenges. This allows users to see not just where homes are being built, but whether those developments are aligned with rental market realities, ensuring that planning decisions support affordability and community wellbeing.

Data Set

Victorian Public Transport Lines and Stops

Description of Use In CoBuild, this dataset is used to map accessibility and service coverage around new housing developments. By overlaying transport lines and stops with building permits, rental stress, and school enrolment data, CoBuild highlights whether new residential growth areas are adequately connected to public transport. For example: a) If 400 new dwellings are approved in Casey, CoBuild checks the nearest train/tram/bus stops and calculates coverage gaps. b) If schools in Wyndham are projected to become overcrowded, CoBuild also verifies whether transport lines exist to support travel to nearby schools. c) Residents using the simulator can see: “If this many new homes are built, will transport connections need to expand too?” By integrating this dataset, CoBuild doesn’t just model housing stress—it reveals the infrastructure readiness of neighbourhoods, enabling councils to prioritize investments in transport services alongside housing growth.

All Schools FTE Enrolments - February 2025, Victoria

Description of Use In CoBuild, this dataset is crucial for understanding capacity vs demand when planning new housing developments. By linking enrolment counts with school location data, CoBuild can flag suburbs where schools are already nearing or exceeding their enrolment thresholds. When new housing permits are issued, CoBuild’s simulation projects how many additional students will enter the system and whether existing schools can absorb them. This makes it possible for councils to test scenarios like: “If 500 new dwellings are added, will local schools remain balanced or face overcrowding?” By integrating enrolment data, CoBuild transforms abstract population growth into concrete education service needs, ensuring planning decisions are backed by real, measurable impacts.

Data Set

School Locations 2025

Description of Use In CoBuild, these datasets help us link housing growth directly to education service demand. By overlaying new dwelling data from building permits with nearby school locations and their enrolments, we can identify areas where student populations are likely to exceed capacity. This integration allows the simulation tool to show, for example, how approving 500 new homes in a suburb could result in local schools going beyond their enrolment thresholds. Councils and residents can visualise not just where new schools may be needed, but also how future developments affect classroom sizes, accessibility, and equitable distribution of education services. By combining school locations with enrolment data, CoBuild ensures that housing and infrastructure planning is grounded in real, data-driven insights about community capacity.

Data Set

Building Permit Activity Data 2025

Description of Use For CoBuild, this dataset serves as the foundation layer of our housing and infrastructure planning tool. It enables us to map where housing growth is occurring by aggregating new permits at the LGA and suburb level, estimate population inflows based on the number of new dwellings, and anticipate future service demand across schools, transport, health, and social housing. It also powers our simulation feature, where users can “add” or “remove” housing within the CoBuild interface and instantly see the recalculated impacts on community services and infrastructure. By linking building permit data with other key datasets—such as school enrolments, transport stops, rental stress, and housing registers—CoBuild provides both residents and councils with a clear, interactive view of how development pressures will shape their communities.

Data Set

Challenge Entries

Enabling Better Community Housing and Infrastructure Planning

How might we use multiple public datasets, including building permits, to guide communities in planning for housing and services in their local communities— from understanding areas of housing stress to anticipating the impact of future developments on community access, services, and social connection?

#Building-Victoria's-future

Eligibility: Open to all, but preference given to teams with a lead in VIC. At least one dataset from data.vic.gov.au must be used.

Go to Challenge | 7 teams have entered this challenge.