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

Abstract

In this paper, a cellular automaton model is developed to generate spatio-temporal population maps that estimate population distributions in an urban area in a random working day. The resulting population maps are at 50 by 50 meter spatial resolution and 5 minutes temporal resolution, showing clearly how the distribution of population varies throughout a 24-hour period. Places that are sparsely populated during night-time can be densely populated during day-time. The generated maps can be used to estimate population-at-risk in the wake of major disasters when they occur in an urban area at any time of a day. In addition to assessing exposure to hazards, the resulting maps also reveal the movement patterns, transition trends, peak hours, activity levels, etc. Possible applications, thus, range from public safety, disaster management, transport modeling and urban growth studies to strategic energy distribution planning. The developed cellular automata model assumes that the population transition trends follow similar dynamics and propagation patterns of a contagious disease. Thus the cellular automaton is designed to change the states of each grid cell (Stable/Dynamic) similarly as state changes of an individual being exposed to an infective disease (Susceptible/Infected). The modeling space is further informed by several geographic features, such as the transport routes, land use categories, population attraction points, etc.The model is geosimulated for the city of Trondheim in Norway, where the synthetic day population could be validated upon an estimated day-population map based on the registered work place addresses and employee statistics.

Estimating temporary populations: a systematic review of the empirical literature

“…Finally, a number of studies employ some form of modelling or simulation. These include agent-based simulation (Walker and Barros, 2012;Kashiyama et al, 2017;Crols and Malleson, 2019), cellular automaton models (Khakpour and Rød, 2016), and neural networks (Liu et al, 2018;Chen et al, 2018).…”

Human mobility data and analysis for urban resilience: A systematic review

“…Population distribution data are human mobility data that are derived, analyzed, or simulated on a grid basis using datasets representing a population, such as census data. They tend to be combined with other grid data, such as hazard maps (Liu et al, 2021; Renner et al, 2018) and digital elevation maps (Khakpour and Rød, 2016). Population distribution analysis is better suited for detecting large spatiotemporal scale patterns and changes, such as daily, hourly, or seasonal fluctuations (Chen et al, 2020; Freire and Aubrecht, 2012; Renner et al, 2018).…”

“…Necessary for such assessment, population data based on administrative units are usually available and are often collected through conventional methods, such as national censuses. However, the spatial and temporal scales are not sufficient for timely and accurate risk assessment (Khakpour and Rød, 2016; Renner et al, 2018). For example, national censuses record populations based on residences, but business districts generally have fewer residences.…”

“…To refine our understanding of human mobility across temporal scales, models can be used to improve the estimates of daily, weekly, and seasonal changes in population distribution (Chen et al, 2020; Freire and Aubrecht, 2012; Renner et al, 2018) or human flow (Solmaz and Turgut, 2017). For example, Khakpour and Rød (2016) built a cellular automation model assuming that the human mobility pattern is similar to that of a contagious disease. They produced spatiotemporal maps at resolutions of 50 m and 5 min that could estimate population distribution changes between daytime and nighttime.…”