Landscape pattern and car use: Linking household data with satellite imagery
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Date
2013-08-30
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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.
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Keywords
Germany, Landscape pattern, Satellite imagery, Two-part model