Multi-scale spatial analysis of household car ownership using distance-based Moran's eigenvector maps: Case study in Loire-Atlantique (France)
| Autor: | Pascal Gastineau, Pierre Hankach, Pierre-Olivier Vandanjon |
|---|---|
| Přispěvatelé: | Laboratoire Auscultation, Modélisation, Expérimentation des infrastructures de transport (MAST-LAMES ), Université Gustave Eiffel, Systèmes Productifs, Logistique, Organisation des Transports et Travail (AME-SPLOTT ) |
| Rok vydání: | 2022 |
| Předmět: |
0106 biological sciences
Spatial scales CARTE Scale (ratio) Spatial structure media_common.quotation_subject Geography Planning and Development Transportation DEMOGRAPHIE 010603 evolutionary biology 01 natural sciences 11. Sustainability Range (statistics) Variation partitioning General Environmental Science media_common Multi-scale spatial analysis Transportation planning Variables Spatial vectors Car ownership 010604 marine biology & hydrobiology VECTEUR Autocorrelation Distance-based Moran's eigenvector maps Sociodemographic factors REPRESENTATION SPATIALE [SHS.GEO]Humanities and Social Sciences/Geography VEHICULE INDIVIDUEL Geography ANALYSE Spatial ecology Spatial variability SOCIOLOGIE Cartography |
| Zdroj: | Journal of Transport Geography Journal of Transport Geography, Elsevier, 2022, 98, pp.103223. ⟨10.1016/j.jtrangeo.2021.103223⟩ |
| ISSN: | 0966-6923 |
| DOI: | 10.1016/j.jtrangeo.2021.103223 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
Full Text from ScienceDirect