Building a Risk Map of New Zealand's Roads

In this workflow, we explore how Edge can be used to automatically reveal outliers and hidden patterns from across a dataset's many dimensions, and accelerate exploratory data analysis to ultimately build a personalised risk dashboard within minutes.

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Fusing and Comparing Heterogeneous Transport Datasets

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In this workflow, we explore how Edge can be used to automatically reveal outliers and hidden patterns from across a dataset's many dimensions, and accelerate exploratory data analysis to ultimately build a personalised risk dashboard within minutes

Having built a risk dashboard of a collision database (workflow above) we now delve deeper by integrating additional diverse data sources into our risk mapping analysis.

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Do the most “scenic” spots around London also experience fewer road traffic accidents?

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In this workflow, we fuse three heterogeneous data sources to ask the question "Do the highest-risk traffic collisions take place at scenic locations around London?".

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Predicting taxi fare costs

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In this example workflow, we follow the New York City Taxi Fare Prediction Kaggle competition in which a we are tasked with predicting the fare amount (inclusive of tolls) for a taxi ride in New York City given the pickup and dropoff locations.

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