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The movements of individuals within and among cities influence criticalaspects of our society, such as well-being, the spreading of epidemics, and thequality of the environment. When information about mobility flows is notavailable for a particular region of interest, we must rely on mathematicalmodels to generate them. In this work, we propose the Deep Gravity model, aneffective method to generate flow probabilities that exploits many variables(e.g., land use, road network, transport, food, health facilities) extractedfrom voluntary geographic data, and uses deep neural networks to discovernon-linear relationships between those variables and mobility flows. Ourexperiments, conducted on mobility flows in England, Italy, and New York State,show that Deep Gravity has good geographic generalization capability, achievinga significant increase in performance (especially in densely populated regionsof interest) with respect to the classic gravity model and models that do notuse deep neural networks or geographic data. We also show how flows generatedby Deep Gravity may be explained in terms of the geographic features usingexplainable AI techniques.

Deep Gravity: enhancing mobility flows generation with deep neural networks and geographic information