分离个体层面与基于位置的收入揭示社会经济偏好性流动并影响隔离估计
Disentangling individual-level from location-based income uncovers socioeconomic preferential mobility and impacts segregation estimates
AI总结：
本文结合手机流动数据与普查收入数据，分离个体层面与基于位置的收入，发现各收入群体存在访问更高收入区域的偏好性流动，并指出当前流动模型未能捕捉该行为，需纳入年龄、性别和社会经济偏好以改进模型。
摘要：
隔离编码了关于社会的信息，例如社会凝聚力、混合程度和不平等。然而，过去和当前的大多数研究通过分析静态聚合流动网络来处理社会经济（SE）隔离问题，通常没有考虑收入之外的进一步个体特征，最重要的是，没有区分个体层面收入与基于位置的收入。获取个体层面收入可能有助于将宏观行为映射到更细粒度的流动模式，从而影响隔离估计。在此，我们将一个按年龄、性别和收入分层并调整的西班牙各区每日流动手机数据集与各区收入中位数的普查数据相结合。我们为多个人口统计群体构建基于流动的社会经济同配性矩阵，并观察三个收入群体相对于基于位置的社会经济类别的流动模式。我们发现，当分离特定收入群体的流动时，社会经济同配性存在差异：我们观察到各群体更倾向于访问平均收入高于自身收入的区域，我们称之为偏好性流动。我们的分析表明，按年龄类别划分的工作日与周末社会经济同配性存在显著差异，周末的特征是更高的社会经济同配性。我们的建模方法表明，辐射模型通常在再现市际人口流动方面表现最佳，其最适配中等收入和中年群体的流动，而在年轻和低收入群体上表现较差。我们的双重视角方法，聚焦于同配性模式和流动建模，表明最先进的流动模型未能捕捉偏好性流动行为。总体而言，我们的工作表明，考虑社会经济偏好行为、年龄和性别差距相互作用的流动模型可能会显著改善最先进模型的性能。
英文摘要：
Segregation encodes information about society, such as social cohesion, mixing, and inequality. However, most past and current studies tackled socioeconomic (SE) segregation by analyzing static aggregated mobility networks, often without considering further individual features beyond income and, most importantly, without distinguishing individual-level from location-based income. Accessing individual-level income may help mapping macroscopic behavior into more granular mobility patterns, hence impacting segregation estimates. Here we combine a mobile phone dataset of daily mobility flows across Spanish districts stratified and adjusted by age, gender and income with census data of districts median income. We build mobility-based SE assortativity matrices for multiple demographics and observe mobility patterns of three income groups with respect to location-based SE classes. We find that SE assortativity differs when isolating the mobility of specific income groups: we observe that groups prefer to visit areas with higher average income than their own, which we call preferential mobility. Our analysis suggests substantial differences between weekdays and weekends SE assortativity by age class, with weekends characterized by higher SE assortativity. Our modeling approach shows that the radiation model, which typically performs best at reproducing inter-municipal population mobility, best fits middle income and middle-aged flows, while performing worse on young and low income groups. Our double-sided approach, focusing on assortativity patterns and mobility modeling, suggests that state of the art mobility models fail at capturing preferential mobility behavior. Overall, our work indicates that mobility models considering the interplay of SE preferential behavior, age and gender gaps may sensibly improve the state of the art models performance.
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