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Spatial data science for sustainable mobility

Martin Raubal

Abstract


The constant rise of urban mobility and transport has led to a dramatic increase in greenhouse gas emissions. In order to ensure livable environments for future generations and counteract climate change, it will be necessary to reduce our future CO2 footprint. Spatial data science contributes to this effort in major ways, also fuelled by recent progress regarding the availability of spatial big data, computational methods and geospatial technologies. This paper demonstrates important contributions from Spatial data science to mobility pattern analysis and prediction, context integration, and the employment of geospatial technologies for changing people's mobility behavior. Among the interdisciplinary research challenges that lie ahead of us are an enhanced public availability of mobility studies and their data sets, improved privacy protection strategies, spatially-aware machine learning methods, and evaluating the potential for people's long-term behavior change towards sustainable mobility.

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