Landscape pattern and car use: Linking household data with satellite imagery

Abstract

Abstract:Landscape pattern has long been hypothesized to influence automobile dependency.Because choices about land development tend to have long-lasting impacts that span over decades, understanding the magnitude of this influence is critical to the design of policies to reduce emissions and other negative externalities associated with car use. Combining household survey data from Germany with satellite imagery and other geo-referenced data sources, we undertake an econometric analysis of the relation between landscape pattern and automobile dependency. Specifically, we employ a two-part model to investigate two dimensions of car use, the discrete decision to own a car and, conditional upon ownership, the continuous decision of how far to drive. Results indicate that landscape pattern, as captured by measures of both land cover (e.g. the extent of open space and landscape diversity) and land use (e.g. the density of regional businesses) are important predictors of car ownership and use. Other policy-relevant variables, such as fuel prices and public transit infrastructure, are also identified as correlates. Based on the magnitude of our estimates, we conclude that carefully considered land development and zoning measures -ones that encourage dense development, diverse land cover and mixed land use -can have beneficial impacts in reducing car dependency that extend far into the future.

The geography of car ownership in Mexico City: a joint model of households’ residential location and car ownership decisions

“…Across a wide variety of methodological approaches, and at the county, city, neighborhood, and household levels, higher income correlates with higher car ownership and use (for a diverse range of methodologies and units of analysis, see Ingram and Liu, 1999;Holtzclaw et al, 2002;Keller and Vance, 2013;Newman and Kenworthy, 2006;Potoglou and Kanaroglou, 2008). At the correct unit of analysis-generally thought to be the household or individual-income is usually the strongest predictor of car ownership (for examples from a range of geographies and contexts, see Zhang, 2004;Bento et al, 2005;Dissanayake and Morikawa, 2010;Zegras, 2010).…”

Relationship between Rural Built Environment and Household Vehicle Ownership: An Empirical Analysis in Rural Sichuan, China

“…Household car ownership decreases within creased built environment density [6,[19][20][21][22][23]. Diversity is negatively correlated with car ownership [6,8,[22][23][24][25][26]. Ewing et al [21] and Hong et al [25] found that road network density is also negatively correlated with household car ownership; however, the effect of design on car ownership is weaker than the effects of density and diversity [26].…”

“…However, most empirical studies have focused on the relationship between the built environment and car ownership. Household car ownership decreases within creased built environment density [6,[19][20][21][22][23]. Diversity is negatively correlated with car ownership [6,8,[22][23][24][25][26].…”

The Influence of Public Policies on Urban Mobility: A Comparative Study between Porto Alegre (Brazil) and Washington D.C. (United States)

“…The urban horizontalization and sprawl, a result of low constructed densities, increases the distances of daily mobility as well as decreases the efficiency of public transportation systems, which require demand agglomeration to enable high capacity systems [10,21,22]. Such context aligns one of the factors that makes individuals prefer motorized modes of mobility, and it is one of the causes of traffic saturation in central roads [23].…”