City data dating: Emerging affinities between diverse urban datasets
Cities are complex environments in which digital technologies are more and more pervasive; this digitization of the urban space has led to a rich ecosystem of data producers and data consumers. Moreover, heterogeneous sources differ in terms of data complexity, spatio-temporal resolution and curation/maintenance costs. Do those diverse urban sources reflect the same picture of the city? Do distinct perspectives share some commonalities? In this paper we present our data analytics/empirical experiments on a set of urban sources related to the city of Milano; our investigation is aimed at discovering affinities between datasets by means of different quantitative and qualitative correlation analyses. We also explore the influence of spatial resolution and data complexity on the dependence strength between heterogeneous urban sources, to pave the way to a meaningful information fusion.
information fusion smart city data diversity spatio-temporal data resolution mobile data processing correlation analysis regression analysis clustering analysis