A Multimodal and Multitask Approach for Adaptive Geospatial Region Embeddings

Abstract

Geospatial region embeddings are vital in developing predictive models tailored to urban environments. Such models enable critical applications, including crime rate prediction and land usage classification. However, state-of-the-art methods typically generate embeddings based on fixed administrative regions. These regions may not always align with specific tasks or areas of user interest. Creating fine-grained embeddings tailored to specific tasks and regions of user interest is labor-intensive and requires substantial resources. In this paper, we propose MAGRE – a novel approach that generates fine-granular adaptive geospatial region embeddings by leveraging multimodal and multitask learning. The embeddings generated by MAGRE can be flexibly aggregated to suit various region boundaries, rendering them effective in diverse urban applications. Our experimental results demonstrate that MAGRE’s embeddings outperform state-of-the-art embedding baselines, resulting in a 25.73% reduction in root mean squared error for crime rate prediction and a 19.08% reduction for check-in count prediction.