We show that a recently proposed model generates accurate commuting networks on 80 case studies from different regions of the world (Europe and United-States) at different scales (e.g. municipalities, counties, regions). The model takes as input the number of commuters coming in and out of each geographic unit and generates the matrix of commuting flows between the units. The single parameter of the model follows a universal law that depends only on the scale of the geographic units. We show that our model significantly outperforms two other approaches proposing a universal commuting model [1], [2], particularly when the geographic units are small (e.g. municipalities).

Many different simulation frameworks, in different topics, need to treat realistic datasets to initialize and calibrate the system. A precise reproduction of initial states is extremely important to obtain reliable forecast from the model. This paper proposes an algorithm to create an artificial population where individuals are described by their age, and are gathered in households respecting a variety of statistical constraints (distribution of household types, sizes, age of household head, difference of age between partners and among parents and children). Such a population is often the initial state of microsimulation or (agent) individual-based models. To get a realistic distribution of households is often very important, because this distribution has an impact on the demographic evolution. Usual techniques from microsimulation approach cross different sources of aggregated data for generating individuals. In our case the number of combinations of different households (types, sizes, age of participants) makes it computationally difficult to use directly such methods. Hence we developed a specific algorithm to make the problem more easily tractable. We generate the populations of two pilot municipalities in Auvergne region (France) to illustrate the approach. The generated populations show a good agreement with the available statistical datasets (not used for the generation) and are obtained in a reasonable computational time.

This study examines the evolution of Schwartz’s Basic Human Values during the COVID-19 outbreak, and their relationships with perceived threat, compliance with movement restrictions and social distancing. An online questionnaire was administered to a heterogeneous sample of French citizens (N= 1025) during the first French lockdown related to the outbreak. Results revealed a significant evolution of values; the conservation value was higher during the outbreak than usual, and both self-enhancement and openness-to-change values were lower during the COVID-19 outbreak than usual. Conservation and perceived threat during the outbreak were robustly and positively related to both compliance with movement restrictions and social distancing. Conservation during the outbreak emerged as a significant partial mediator of the relationship between perceived threat and outcomes (i.e., compliance with movement restrictions and social distancing). Implications of these results for the malleability of values and the COVID-19 modelling are discussed.