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Found 6 papers, 2 papers with code
GEO-BLEU: Similarity Measure for Geospatial Sequences
In recent geospatial research, the importance of modeling large-scale human mobility data and predicting trajectories is rising, in parallel with progress in text generation using large-scale corpora in natural language processing.
Mobility-based contact exposure explains the disparity of spread of COVID-19 in urban neighborhoods
The rapid early spread of COVID-19 in the U. S. was experienced very differently by different socioeconomic groups and business industries.
Quantifying Spatial Homogeneity of Urban Road Networks via Graph Neural Networks
We apply the method to 11, 790 urban road networks across 30 cities worldwide to measure the spatial homogeneity of road networks within each city and across different cities.
Quantifying the Economic Impact of Extreme Shocks on Businesses using Human Mobility Data: a Bayesian Causal Inference Approach
Quantifying the economic cost of local businesses after extreme shocks is important for post-disaster assessment and pre-disaster planning.
Learning Fine Grained Place Embeddings with Spatial Hierarchy from Human Mobility Trajectories
Place embeddings generated from human mobility trajectories have become a popular method to understand the functionality of places.
City2City: Translating Place Representations across Cities
Large mobility datasets collected from various sources have allowed us to observe, analyze, predict and solve a wide range of important urban challenges.
