An attraction-based cellular automaton model for generating spatiotemporal population maps in urban areas

We develop a cellular automaton (CA) model to produce spatiotemporal population maps that estimate population distributions in an urban area during a random working day. The resulting population maps are at 50 m and 5 minutes spatiotemporal resolution, showing clearly how the distribution of population varies throughout a 24-hour period. The maps indicate that some areas of the city, which are sparsely populated during the night, can be densely populated during the day. The developed CA model assumes that the population transition trends follow dynamics and propagation patterns similar to a contagious disease. Thus, our model designed to change the states of each grid cell (stable or dynami…