UrbIA - Urban Insights Atlas

The Banico Family proudly presents our GovHack project, UrbIA (Urban Insights Atlas), an innovative tool designed to make geospatial data accessible to everyone, empower decision-making, and promote sustainable urban development across Australia.

Features and Technical Approach

Geospatial Navigation: Users interact with a map interface powered by the Digital Atlas of Australia, which presents localized data across categories such as road safety, sustainability, and public services.

Integration with the Digital Atlas of Australia: UrbIA will dynamically retrieve geolocated data sets as input for insights. (https://digital.atlas.gov.au/api/search/definition/)

Generative AI for Insights: Through OpenAI, UrbIA generates tailored insights based on real-world data. Users can rate these insights and suggest improvements, creating a feedback loop between data-driven analysis and community input.

Crowdsourced Wisdom: By allowing users to react and provide suggestions, UrbIA encourages collective knowledge sharing, empowering both citizens and policymakers to make informed decisions.

Use Case Highlights

Safety Insights: For road safety, UrbIA can analyze Victorian Road Crash Data and generate actionable recommendations, such as improving road lighting and adding guardrails.

Capacity Forecasting: Using traffic volume data, UrbIA can assess roads for capacity needs, recommending maintenance and safety improvements where necessary.

Sustainability Insights: UrbIA examined cycling infrastructure and public transport utilization to propose strategies for reducing car dependency and promoting green mobility.

Meeting the Challenges

UrbIA addresses several critical questions:

How can we use the Digital Atlas of Australia’s API and Generative AI to create user-friendly tools and visualizations that make geospatial data accessible, empowering decision-making and engagement with local and national environments?

Our solution leverages the Digital Atlas of Australia, geolocated data, and AI to offer users an intuitive map-based platform. Users can explore local data (e.g., road safety statistics, traffic patterns, housing demand) while receiving AI-generated insights. These insights guide decision-making by offering actionable suggestions, like improving infrastructure or enhancing road safety.

How might we leverage road crash statistics and multi-agent AI-based applications to enhance road safety and inform policy-making?

For road safety, UrbIA integrates Victorian Road Crash Data to pinpoint accident locations, types, and times. It leverages on AI to generate recommendations such as improved road lighting, safety barriers, or targeted public awareness campaigns. Users can contribute to these insights by providing feedback, fostering a collaborative approach to road safety enhancements.

How might we predict future changes in community dynamics, such as population density, housing demand, traffic patterns, and the demand for public services or amenities?

While UrbIA primarily focuses on current geospatial data, it uses AI to generate capacity insights based on traffic patterns and public service utilization. By analyzing geolocated data, such as road traffic volumes or public transport usage, UrbIA provides AI-driven recommendations on infrastructure capacity. For example, it can suggest where roads might need upgrades or where public services could be expanded based on current usage patterns. These AI-generated insights allow councils and policymakers to make informed decisions to address immediate community needs and optimize urban planning efforts.

How can we use data insights to promote the development of sustainable urban infrastructure and reduce dependency on private vehicles?

UrbIA analyzes geolocated data on bike paths, public transport usage, and traffic patterns, using AI to generate insights that promote sustainable alternatives. For example, the AI can suggest areas where cycling infrastructure could be expanded or where public transport services might be optimized based on current usage patterns. These AI-driven recommendations help urban planners focus on reducing car dependency and encourage greener mobility solutions, fostering the development of more sustainable urban environments.

How can councils leverage climate and movement data from multi-function poles, sensors, and devices to improve asset management, optimize services, and design cleaner, more livable urban spaces?

UrbIA integrates climate and movement data from sensor-equipped urban assets and uses AI to generate real-time insights for better asset management. For example, AI can suggest adjustments to street lighting based on movement patterns or identify areas that would benefit from green infrastructure to mitigate urban heat island effects. These AI-generated recommendations allow councils to optimize services, improve urban livability, and make data-driven decisions that support cleaner, more efficient public spaces.

Conclusion

Our project demonstrates how geospatial data and AI can transform decision-making in urban planning, road safety, and sustainability. With UrbIA, we empower all Australians to engage with their environments and help shape the future of their cities.