Before UniHack the team met up and discussed ways that data-driven insights could create tangible benefits in our local communities. A problem we identified was the lack of efficiency in the planning process for urban environments, where some of the most important infrastructure would be created and utilised.

What the tool does

Our tool is a web app that uses AI and data analytics to streamline the urban planning process.

The application displays layers of economic data – such as population density, schools, and hospitals – on a map, using a coloured heatmap to intuitively visualize the distribution of these factors across any geographical location worldwide.

In addition, our tool has a built-in AI engine that is then able to learn from gaps in these layers, and provide recommendations for the optimal locations of new infrastructure to maximise amenity. For example, consider a local council that is considering investing $500,000 for the development of a new hospital. Currently, there is no robust, quantitative method of deciding where this hospital should be located. Our tool will fill this gap by recommending the best location based on interactions with other the other data layers – providing a reliable method of resource allocation which accounts for all factors.

What we learned

Cleaning data is an incredibly complex and time-consuming process! Our chief frustration was allowing for viewing and calculations off of comparable datasets. This necessitated drawing from a vast range of sources with different syntaxes.

Population Density

School Locations Labelled

Hospital Locations Labelled

Recommendation Engine Suggested Hospitals

Schools and Hospitals

What's next for UrbanPlanner.AI

By integrating different data sources for other economic indicators – such as traffic congestion, crime rates, and public spaces – we can train our machine learning model to calculate a liveability index, that will quantify the level of amenity in urban environments. This will enable more robust and efficient decision making for a large number of stakeholders at any level of granularity: whether it be state government deciding where to invest funds, or a local property developer deciding the best location for a new construction project.

In addition, our AI-based recommendation engine will undergo further training, in order to make more pertinent and insightful recommendations for urban planning decisions. This provides a clear benefit for all levels of government in particular – given a set amount of investment, our tool will be able to recommend where to allocate those funds to achieve maximal amenity.

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