Car use generates negative externalities, which are responsible for many health, environmental and economic problems. To tackle this issue, more work is needed to identify better the correlates of car use, especially at the contextual level. In this study, a mobility-focused questionnaire involving 1722 working French adults living in the Paris area (France) was used to explore gender-stratified relationships between residential socioeconomic deprivation and car use as the main transport mode against public transport, after controlling for potential confounders. While the vast majority of similar studies have assumed linear and global statistical relationships, the present work involved a random slope hierarchical generalised additive modelling framework, which revealed both non-linear and territorially-varying relationships. Among women, living in a more deprived neighbourhood was associated with an increase in the odds of reporting car use up to a certain threshold, after which the relationship plateaued, while among men, this relationship is linearly negative. In the most deprived department of the Paris region (Seine-Saint-Denis), living in a more deprived neighbourhood was associated with a lower odds of car use among men while a more complex nonlinear bell-shaped relationship was observed among women. The opposite was found in the wealthiest department (Yvelines), with a negative relationship among women and a U-shaped one among men. In Paris inner city, again a strong opposite trend was distinguished according to sex, with a negative relationship among women. These findings suggest that spatial contexts, characterised by complex interactions between socioeconomic factors, the built environment and the distance to Paris, play the role of moderators in the relationship between residential deprivation and car use. In conclusion, this study reinforces the idea that environment-transport relationships should be understood through local analyses (e.g. random slope multilevel or spatially-varying coefficients models) rather than global ones only, in order to guide specific public policies more effectively.

汽车的使用会产生负面的外部影响，这会导致许多健康，环境和经济问题。为了解决这个问题，需要做更多的工作来更好地确定汽车使用的相关性，尤其是在背景方面。在这项研究中，以流动性为重点的问卷调查了居住在巴黎地区（法国）的1722名在职法国成年人，用于研究在控制潜在潜力之后，住宅社会经济剥夺与汽车使用之间的性别分层关系（作为针对公共交通的主要交通方式）混杂因素。虽然绝大多数类似研究都假设线性和全局统计关系，但目前的工作涉及随机斜率分层广义加性建模框架，该框架揭示了非线性和领土变化的关系。在女性中 居住在更贫困的社区与举报汽车使用率的可能性增加到一定阈值相关，此后这种关系趋于平稳，而在男性中，这种关系呈线性负相关。在巴黎地区最贫困的部门（塞纳-圣但尼），居住在较贫困的社区与男性使用汽车的几率较低相关，而女性之间则观察到更为复杂的非线性钟形关系。相反，在最富有的部门（伊夫林省）中发现，女性之间的关系是负面的，而男性之间的关系是U形的。在巴黎市中心，根据性别再次显示出强烈的相反趋势，女性之间的关系却是消极的。这些发现表明，以社会经济因素之间复杂的相互作用为特征的空间环境，建筑环境和到巴黎的距离，在住宅匮乏和汽车使用之间的关系中扮演主持人的角色。总之，本研究强化了这样一种观念，即应该通过局部分析（例如随机斜率多级或空间变化系数模型）而不是全局分析来理解环境与运输的关系，以便更有效地指导特定的公共政策。