Flexible Urban Region Representation Learning

What is it about?

Urban region representation learning aims to transform urban regions into vector representations, commonly referred to as embeddings. In this paper, we highlight the limitations in flexibility faced by existing methods in handling region features, region formations, and downstream tasks. We then introduce emerging approaches designed to address these challenges and present experimental results that demonstrate how our proposed model performs in real-world settings.

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Photo by Katie Simmons on Unsplash

Photo by Katie Simmons on Unsplash

Why is it important?

We propose a model named FlexiReg to generate effective and flexible region representations. FlexiReg requires only publicly accessible data, facilitating broader adoption. It learns fine-grained grid cell embeddings that are independent of the region partitions used in downstream tasks, thereby achieving region formation flexibility. These cell embeddings can then be aggregated into representations of arbitrary target regions without the need to retrain the model. Moreover, FlexiReg incorporates a prompt enhancer to extract task-specific information and integrate it into the region embeddings, enabling flexibility across downstream tasks.

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