A hybrid model for high spatial and temporal resolution population distribution prediction

Abstract

The accurate prediction of population distribution is crucial for numerous applications, from urban planning to epidemiological modelling. Using one-week data collected from open and multiple sources, including telecommunication activity, weather, point of interest, buildings, roads, and land use in Milan, Italy, we develop a hybrid method combining cellular automata (CA) and long short-term memory (LSTM) to predict population distribution with fine temporal and spatial granularity. Specifically, the convolutional autoencoder and LightGBM are applied to identify missing building types based on the pedestrian shed. The LSTM learns the transition rules of CA and Shapley additive explanations value is used for variable importance analysis. Results demonstrate that the combination of convolutional autoencoder and LightGBM is effective in building type prediction. The proposed model for population distribution prediction outperforms LSTM, the combination of CA and neural network, and the combination of CA and LightGBM by at least 5–10%. A variable importance analysis reveals that temporal variables are the most significant for prediction, followed by spatial and natural variables. The order of hour, housing-related variables, and types of precipitation are the most important variables in each category.