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D5.1 Report on City Understanding

This document describes the research and development work on visual analytics support to analysis of data related to the transportation services and population mobility in a city. Visual analytics develops methods and techniques to support human analysts in gaining understanding of data and real-world phenomena reflected in data. An essential feature of visual analytics approaches is the involvement of interactive visual interfaces that supply information to the human in an effective way and support reasoning and problem solving. Interactive visualizations are combined with computational analysis and modeling methods that facilitate and enhance the work of humans.


In the reported period, a number of papers on project-relevant topics have been published. In the papers, we studied the state of the art in the visual analytics research related to transportation and mobility, analyzed and classified the types of errors and problems that may exist in mobility and transportation data, presented approaches to analyzing data  available in the form of origin-destination moves or aggregate flows, to semantic analysis of movement data, and to supporting spatial modeling and prediction with the use of regression trees. We include the materials from these papers in the document.


Besides the published work, the document describes the ongoing research and development work performed on the basis of data provided by the City Council of Warsaw. We report our efforts on exploring the data quality and present the quality issues identified. Another part of the work is development of visual analytics approaches supporting the understanding of connectivity between places in a city and reachability of different places with respect to  key places, such as the train station, airport, business areas, and shopping areas. We have developed a prototype that computes the routes and travel times from a selected place to all others or from all places to a selected place. This can be done for different chosen times of the departure from a place or arrival to a place. The tool used data derived from public transport timetables or from real tracks of public transport vehicles. The data are transformed into a collection of moves between public transport stops, where each move has its start and end time. The computation results are visually represented in detailed and aggregated forms. The user can see the spatial distribution of the trip durations to or from different places and identify the places where much time is spent when changing from one line or transportation mode to another. The interactive visual interfaces support comparisons of the reachability based on timetables and real data as well as between the reachability at different times (i.e., depending on when the trips start or end). We provide numerous illustrations demonstrating the visualizations and interactive techniques meant to support the connectivity and reachability exploration. In the future, we plan to extend this work to supporting what-if analysis of possible consequences of introducing changes to timetables.