| dc.description.abstract | Due to the immense growth of available data spurred by digitalization, data science has emerged as the field in charge of transforming this information into knowledge that can be readily used by other disciplines. This knowledge transfer, however, is not as smooth as desired, data science types, methodologies and processing algorithms are all new and alien to domain experts from disciplines shaped in modern times. Conversely, data scientist, in general, are not instructed in the fundamental concepts of their target disciplinary fields. This gap, which is not only communicational but epistemological, has been observed by the visualization community and taken as part of the role of interdisciplinary visualization.
In this work, we look at the growing intersection between urbanism and data science in mobility and urban behavior from the standpoint of visualizaing trips and flows with mode split and geotagged multidimensional clusterings to efficiently communicate information for urban, planning and implement these techniques in visual data interfaces that can be used as analytical tools by data scientists and domain experts as well. | |
