Multi-scale spatial analysis of household car ownership using distance-based Moran's eigenvector maps: Case study in Loire-Atlantique (France). (January 2022)

• Record Type: Journal Article
• Authors: Hankach, Pierre; Gastineau, Pascal; Vandanjon, Pierre-Olivier
• Is Part Of: Journal of transport geography. Volume 98(2022)
• Publication Date: 2022-01
• Subjects: Distance-based Moran's eigenvector maps -- Car ownership -- Spatial structure -- Spatial vectors -- Spatial scales -- Multi-scale spatial analysis -- Sociodemographic factors -- Variation partitioning; Transportation -- Periodicals; Telecommunication -- Periodicals; Transport -- Périodiques; Télécommunications -- Périodiques; Telecommunication; Transportation; Periodicals; 388
• DOI: 10.1016/j.jtrangeo.2021.103223
• Languages: English
• ISSNs: 0966-6923

Abstract

Abstract : Highlights: Multi-scale spatial analysis of the spatial layout of car ownership. Constructing spatial variables with Distance-based Moran's eigenvector maps method. Spatial variation of car ownership is depicted at three distinct spatial scales. Identified spatial structures are correlated with various explanatory variables. Apportioning car ownership variation between spatial and sociodemographic variables. Abstract: 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.