Modeling coordinated multiple views of heterogeneous data cubes for urban visual analytics

Ivo Widjaja, Patrizia Russo, Chris Pettit, Richard Sinnott, Martin Tomko
2014 International Journal of Digital Earth  
With the explosion of digital data, the need for advanced visual analytics, including coordinated multiple views (CMV), is rapidly increasing. CMV enable users to discover patterns and examine relationships across multiple visualizations of one or multiple datasets. CMV have been implemented in a web-based environment through the Australian Urban Research Infrastructure Network (AURIN) project. AURIN offers a platform providing seamless and secure access to an extensive range of distributed
more » ... of distributed urban datasets across Australia. Visual exploration of these datasets is essential to support research endeavors. This paper focuses on the challenges in dealing with complexity and multidimensionality of datasets used in CMV. We rely on the concept of multidimensional data cubes as the theoretical framework for coordination across visualizations. Using the concept of data cubes and hierarchical dimensions, we present strategies to automatically build render groups. This provides an implicit coordination based on cube structures and a framework to establish links between a dataset with its aggregates in a one-to-many fashion. The CMV approach is demonstrated using aggregate-level data, which is provided through federated data services. The paper discusses the issues around our CMV implementation and concludes by reflecting on the challenges in supporting spatio-temporal urban data exploration. With the explosion of digital data the need for advanced visual analytics, such as coordinated multiple views (CMV), is rapidly increasing. CMV enable users to discover patterns and examine relationships across multiple visualizations of one or multiple datasets. CMV have been implemented in a web-based environment known as the Australia Urban Research Infrastructure Network (AURIN) portal, a platform developed to support the visual exploration of urban datasets from distributed, heterogeneous sources in Australia. Specifically, the paper responds to the challenges in dealing with complexity and multidimensionality of datasets used in CMV. We rely on the concept of multidimensional data cubes as the theoretical frame for coordination across data cubes that underlie multiple visualizations. Using the concept of data cubes and hierarchical dimensions, we introduce strategies to automatically build render groups. This provides an implicit coordination based on cube structures and a framework to establish links between a dataset with its aggregates in one-to-many fashion. The CMV approach is demonstrated using aggregate-level data, which is provided through federated data services from across Australia. The paper discusses the issues around our CMV implementation and concludes by reflecting on the challenges in supporting spatio-temporal urban data exploration.
doi:10.1080/17538947.2014.942713 fatcat:lddyetozejawppeeesow66q3wi