  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.
