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2. Designing Adaptable Spatial Cyberinfrastructure for Urban eResearch

Author

Tomko, Martin, primary, Galang, Gerson, additional, Bayliss, Chris, additional, Koetsier, Jos, additional, Greenwood, Phil, additional, Voorsluys, William, additional, Mannix, Damien, additional, Sarwar, Sulman, additional, Widjaja, Ivo, additional, Pettit, Chris, additional, and Sinnott, Richard, additional

Published
2018
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3. The Online What if? Planning Support System

Author

Pettit, Christopher J., Klosterman, Richard E., Nino-Ruiz, Marcos, Widjaja, Ivo, Russo, Patrizia, Tomko, Martin, Sinnott, Richard, Stimson, Robert, Geertman, Stan, editor, Toppen, Fred, editor, and Stillwell, John, editor

Published
2013
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4. Privacy Preserving Geo-Linkage in the Big Urban Data Era

Author

Sinnott, Richard O., Bayliss, Christopher, Bromage, Andrew, Galang, Gerson, Gong, Yikai, Greenwood, Philip, Jayaputera, Glenn, Marques, Davis, Morandini, Luca, Nogoorani, Ghazal, Pursultani, Hossein, Sarwar, Muhammad, Voorsluys, William, and Widjaja, Ivo

Published
2016
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6. The Australia urban research gateway

Author

Sinnott, Richard O, Bayliss, Christopher, Bromage, Andrew, Galang, Gerson, Grazioli, Guido, Greenwood, Philip, Macaulay, Angus, Morandini, Luca, Nogoorani, Ghazal, Nino-Ruiz, Marcos, Tomko, Martin, Pettit, Christopher, Sarwar, Muhammad, Stimson, Robert, Voorsluys, William, Widjaja, Ivo, University of Zurich, and Sinnott, Richard O

Subjects
1712 Software, 10122 Institute of Geography, 1705 Computer Networks and Communications, 1706 Computer Science Applications, 910 Geography & travel, 2614 Theoretical Computer Science, 1703 Computational Theory and Mathematics
Published
2015
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7. Modeling coordinated multiple views of heterogeneous data cubes for urban visual analytics

Author

Widjaja, Ivo, Russo, Patrizia, Pettit, Christopher, Sinnott, Richard, Tomko, Martin, Widjaja, Ivo, Russo, Patrizia, Pettit, Christopher, Sinnott, Richard, and Tomko, Martin

Abstract

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 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.

Published
2015

9. The Australia urban research gateway

Author

Sinnott, Richard O., primary, Bayliss, Christopher, additional, Bromage, Andrew, additional, Galang, Gerson, additional, Grazioli, Guido, additional, Greenwood, Philip, additional, Macaulay, Angus, additional, Morandini, Luca, additional, Nogoorani, Ghazal, additional, Nino-Ruiz, Marcos, additional, Tomko, Martin, additional, Pettit, Christopher, additional, Sarwar, Muhammad, additional, Stimson, Robert, additional, Voorsluys, William, additional, and Widjaja, Ivo, additional

Published
2014
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10. The design of a flexible web-based analytical platform for urban research

Author

Tomko, Martin, primary, Greenwood, Phillip, additional, Sarwar, Muhammed, additional, Morandini, Luca, additional, Stimson, Robert, additional, Bayliss, Christopher, additional, Galang, Gerson, additional, Nino-Ruiz, Marcos, additional, Voorsluys, William, additional, Widjaja, Ivo, additional, Koetsier, George, additional, Mannix, Damien, additional, Pettit, Chris, additional, and Sinnott, Richard, additional

Published
2012
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11. A data-driven urban research environment for Australia

Author

Sinnott, Richard O., primary, Bayliss, Christopher, additional, Galang, Gerson, additional, Greenwood, Phillip, additional, Koetsier, George, additional, Mannix, Damien, additional, Morandini, Luca, additional, Nino-Ruiz, Marcos, additional, Pettit, Chris, additional, Tomko, Martin, additional, Sarwar, Muhammed, additional, Stimson, Robert, additional, Voorsluys, William, additional, and Widjaja, Ivo, additional

Published
2012
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12. The Australia urban research gateway.

Author

Sinnott, Richard O., Bayliss, Christopher, Bromage, Andrew, Galang, Gerson, Grazioli, Guido, Greenwood, Philip, Macaulay, Angus, Morandini, Luca, Nogoorani, Ghazal, Nino‐Ruiz, Marcos, Tomko, Martin, Pettit, Christopher, Sarwar, Muhammad, Stimson, Robert, Voorsluys, William, and Widjaja, Ivo

Subjects
URBAN research, GATEWAYS (Computer networks), CYBERINFRASTRUCTURE, WEB-based user interfaces, DATA visualization
Abstract

The $20m Australian Urban Research Infrastructure Network (AURIN) project () began in July 2010. AURIN has been tasked with developing a secure, Web-based virtual environment (e-Infrastructure) offering seamless, secure access to diverse, distributed and extremely heterogeneous data sets from numerous agencies with an extensive portfolio of targeted analytical and visualization tools. This is being provisioned for Australia-wide urban and built environment researchers - itself a highly heterogeneous collection of research communities with diverse demands, through a unified urban research gateway. This paper describes these demands and how the e-Infrastructure and gateway is being designed and implemented to accommodate this diversity of requirements, both from the user/researcher perspective and from the data provider perspective. The scaling of the infrastructure is presented and the way in which it copes with the spectrum of big data challenges (volume, veracity, variability and velocity) and associated big data analytics. The utility of the e-Infrastructure is also demonstrated through a range of scenarios illustrating and reflecting the interdisciplinary urban research now possible. Copyright © 2014 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Published
2015
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14. Modeling coordinated multiple views of heterogeneous data cubes for urban visual analytics

Author

Patrizia Russo, Richard O. Sinnott, Christopher Pettit, Martin Tomko, Ivo Widjaja, University of Zurich, and Widjaja, Ivo

Subjects
Visual analytics, Computer science, Digital data, 1900 General Earth and Planetary Sciences, Multidimensional data, Data science, Computer Science Applications, Visualization, Urban research, Data cube, 1712 Software, chemistry.chemical_compound, Aurin, 10122 Institute of Geography, chemistry, 1706 Computer Science Applications, General Earth and Planetary Sciences, 910 Geography & travel, Multiple view, Software
Abstract

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 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 ...

Published
2015
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