Using Topological Analysis to Support Event-Guided Exploration in Urban Data

The explosion in the volume of data about urban environments has opened up opportunities to inform both policy and administration and thereby help governments improve the lives of their citizens, increase the efficiency of public services, and reduce the environmental harms of development. However,...

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Bibliographic Details
Published in:IEEE transactions on visualization and computer graphics 2014-12, Vol.20 (12), p.2634-2643
Main Authors: Doraiswamy, Harish, Ferreira, Nivan, Damoulas, Theodoros, Freire, Juliana, Silva, Claudio T.
Format: Article
Language:English
Subjects:
Computer Graphics
Data visualization
Event detection
Geographic Information Systems
Humans
Leisure Activities
Maps as Topic
New York City
Spatio-Temporal Analysis
Subways
Terrain mapping
Topology
Urban areas
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Summary:The explosion in the volume of data about urban environments has opened up opportunities to inform both policy and administration and thereby help governments improve the lives of their citizens, increase the efficiency of public services, and reduce the environmental harms of development. However, cities are complex systems and exploring the data they generate is challenging. The interaction between the various components in a city creates complex dynamics where interesting facts occur at multiple scales, requiring users to inspect a large number of data slices over time and space. Manual exploration of these slices is ineffective, time consuming, and in many cases impractical. In this paper, we propose a technique that supports event-guided exploration of large, spatio-temporal urban data. We model the data as time-varying scalar functions and use computational topology to automatically identify events in different data slices. To handle a potentially large number of events, we develop an algorithm to group and index them, thus allowing users to interactively explore and query event patterns on the fly. A visual exploration interface helps guide users towards data slices that display interesting events and trends. We demonstrate the effectiveness of our technique on two different data sets from New York City (NYC): data about taxi trips and subway service. We also report on the feedback we received from analysts at different NYC agencies.
ISSN:1077-2626
1941-0506
DOI:10.1109/TVCG.2014.2346449