Wednesday, April 16, 2014

How Can Satellite Imagery Help Reduce Care Use, Congestion and Emissions?

Per capita responsibility for current atmosphe...
Per capita responsibility for current atmospheric CO 2 level, including land-use change (Photo credit: Wikipedia)

Landscape Pattern and Car Use: Evidence Linking Household Data with Satellite Imagery (Abstract, Rose Keller, Colin Vance, Journal of Transport Geography, Jan. , 2013) 

Also discussed here: Urban planning could change driving behaviour (1 page pdf, Science for Environment Policy, European Commission DG Environment News Alert Service, Mar. 27, 2014) 

And here: Factors influencing commute distance: a case study of Toronto’s commuter shed (Abstract, Axisa, Jeffrey J.; Scott, Darren M.; Bruce Newbold, K., Journal of Transport Geography, Sep. 2012) 

Today we review research that used satellite imagery along with geographical information system data (GIS) to find out what are the main pre-determinants for car use. Results indicate that diversity of land uses which includes the mix of open space with built-up space and the mix of regional businesses with residential are most highly linked to less care use, along with two more publicly-accepted notions about higher fuel prices and availability of public transit. This suggests that urbn planners would do well to compare digital maps of land use as part of their efforts to effectively reduce car use, congestion and carbon dioxide emissions (which make up 12% of all greenhouse gas emissions in Europe) 

Key Quotes: 

“as diversity of the landscape increased – indicating an increase in the integration of different land uses – household mileage decreased, suggesting that people drive less when land use is more mixed. The probability of owning a car and the distance driven each week was higher in less built up areas.” 

“Households with young children were less likely to own cars, but drove greater distances when they did.” 

“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 and composition 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 important correlates.” 

 “carefully considered land development measures, ones that encourage dense development and mixed use, can have beneficial impacts in reducing car dependency that extend far into the future” 

“Their results hint that combining different land uses in highly developed urban areas could reduce emissions through reduced car use. They also suggest that offering preferential tax rates on properties in urban areas could help lower emissions by reducing car driving.”  
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