Abstract
Abstract. The new data sources give the possibility to answer analytically the questions that arise from mobility manager. The process of transforming raw data into knowledge is very complex, and it is necessary to provide metaphors of visualizations that are understandable to decision makers. Here, we propose an analytical platform that extracts information on the mobility of individuals from mobile phone by applying Data Mining methodologies. The main results highlighted here are both technical and methodological. First, communicating information through visual analytics techniques facilitates understanding of information to those who have no specific technical or domain knowledge. Secondly, the API system guarantees the ability to export aggregates according to the granularity required, enabling other actors to produce new services based on the extracted models. For the future, we expect to extend the platform by inserting other layers. For example, a layer for measuring the sustainability index of a territory, such as the ability of public transport to attract private mobility or the index that measures how many private vehicle trips can be converted into electrical mobility.
Highlights
Track Urban dashboard design and implementationTypically, official demographic data are collected systematically every ten years, during the nationwide official census
Mobility Data Mining research fields have produced a wide set of analytical methods to analyze, transform, aggregate and interpret spatiotemporal data
To leverage the power of Mobility Data Mining (MDM) methods and to guarantee a broad diffusion of the analytical results, we propose a new paradigm capable of bridging the gap between the complex models derived from the analytical methods and the accessibility of content of non-expert users
Summary
Official demographic data are collected systematically every ten years, during the nationwide official census. The broad availability of location-aware services allows the collection of a vast repository of movement data. These new sources of data give an unprecedented opportunity to have a social microscope of the individual, collective, and global behaviors. We focus on mobility data, such as mobile phone data or such as the GPS tracks from car navigation devices, which represent societywide proxies of human activities These big mobility data help us understand human mobility, and discover the hidden patterns and profiles that characterize the trajectories followed by individuals during daily business. We propose a paradigm where complex analytical processes are summarized into a set of quantitative estimators of the main properties of mobility in a territory. We have decided to select some areas of the city, where POIs are present, in which to specialize the analysis
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