Analyzing spatial structures of transportation data at various scales can be of prime interest to transportation planning and governance. In recent years, multi-scale spatial analysis methods have been developed and used in fields like ecology and geography, but only a few studies have applied these methods to transportation data. However, such methods can provide an efficient exploratory tool for: identifying those scales at which transportation data vary spatially; modeling the spatial structures at each scale; and determining the processes at work that explain these spatial structures. This paper describes and demonstrates how a multi-scale spatial analysis method, namely distance-based Moran's eigenvector maps (dbMEM), can be applied to study the spatial layout of car ownership. For this analysis, we rely on aggregated census data for small statistical areas within France's Loire-Atlantique administrative region. At first, 176 spatial vectors representing spatial patterns with a positive autocorrelation are constructed. Among the 176 vectors, only 23 significant ones are retained after performing a regression with car ownership as the dependent variable. Next, we divide these spatial vectors into three sub-models representing three spatial scales: broad scale, medium scale, and fine scale. Lastly, we identify a set of sociodemographic factors capable of explaining the spatial variation at each scale, i.e.: the broad-scale variation is mainly explained by population density, couples with children and income variables; the medium scale by couples with children, share of individuals in the 25–54 year age range and income; and the fine scale by couples with children and income variables.

分析不同尺度的交通数据的空间结构可能是交通规划和治理的首要任务。近年来，多尺度空间分析方法在生态、地理学等领域得到发展和应用，但将这些方法应用于交通数据的研究很少。然而，这些方法可以提供一种有效的探索工具，用于：识别交通数据在空间上变化的那些尺度；对每个尺度的空间结构进行建模；并确定解释这些空间结构的工作过程。本文描述并演示了如何应用基于距离的莫兰特征向量图 (dbMEM) 等多尺度空间分析方法来研究汽车拥有量的空间布局。对于这个分析，我们依赖法国卢瓦尔-大西洋行政区内小型统计区域的汇总人口普查数据。首先，构建了 176 个表示具有正自相关的空间模式的空间向量。在以汽车拥有量为因变量进行回归后，在 176 个向量中仅保留了 23 个显着向量。接下来，我们将这些空间向量划分为代表三个空间尺度的三个子模型：宽尺度、中尺度和精细尺度。最后，我们确定了一组能够解释每个尺度空间变化的社会人口因素，即：大尺度变化主要由人口密度、有孩子的夫妇和收入变量来解释；中等规模的有孩子的夫妇、25-54 岁年龄段的个人比例和收入；