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

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.