Data Story
NebulaNest begins by ingesting diverse public datasets from Victorian government sources:
Building Permits – to identify planned developments.
SEIFA & Housing Affordability – to locate housing stress zones.
School Enrolment Data – to assess capacity and pressure points.
Transport Usage – to detect congestion hotspots.
Community Services Locations – to map support availability.
These datasets are often siloed, but NebulaNest brings them together.
Using spatial and statistical analysis tools:
Geospatial overlays (via PostGIS) align datasets by location.
Python analytics (Pandas, Scikit-learn) detect patterns and correlations.
Temporal filters allow users to explore trends over time.
This step transforms fragmented data into a unified, queryable format.
The processed data powers:
Interactive dashboards with heatmaps and filters.
Development impact forecasts showing how new permits affect infrastructure.
Scenario simulations for councils to model future growth.
These visual tools make complex data accessible and actionable.
Residents interact through:
Feedback forms with targeted questions.
Chatbot interface for real-time dialogue.
Sentiment aggregation to capture community mood and priorities.
This layer ensures that lived experiences complement hard data.
The final output is community intelligence:
Councils receive data-backed recommendations.
Residents gain clarity and voice in planning.
Policymakers can prioritize investments based on real needs.
NebulaNest turns raw data into resilient, inclusive planning decisions.
