ABSTRACT:Urban research is fundamentally underpinned by heterogeneous, highly varied data. The availability and quantity of digital data sources is increasing rapidly. In order to facilitate decision-making and support processes related to urban policy and management, such data has to be readily analysed, synthesised and the results readily communicated to support evidence based decision-making. In this paper, we consider the current state of play of visualisation as it supports urban research. In doing so we firstly consider visualisation environments such as geographical information systems (GIS) and Cartography tools, digital globes, virtual simulation environments, building information models and gaming platforms. Secondly, we consider a number of visualisation techniques with a focusing on GIS and Cartography tools including space time cubes, heat maps, choropleth maps, flow maps and brushing.This review of visualisation environments and techniques is undertaken in the context of the Australian Urban Research Infrastructure Network project (www.aurin.org.au). AURIN is tasked with developing a portal and associated e-Infrastructure, which provides seamless access to federated data, modelling and visualisation tools to support the urban researcher community in Australia. We conclude by outlining future research and development opportunities in developing the AURIN visualisation toolkit by reflecting on the value of visualisation as a data exploration and communication tool for researchers and decision-makers to assist with the study and management of the urban fabric.

SUMMARYThe $20m Australian Urban Research Infrastructure Network (AURIN) project (www.aurin.org.au) 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.

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The Australian Urban Research Infrastructure Network (AURIN) project (www.aurin.org.au) is tasked with developing an e-Infrastructure to support urban and built environment research across Australia. As identified in [1], this e-Infrastructure must provide seamless access to highly distributed and heterogeneous data sets from multiple organisations with accompanying analytical and visualization capabilities. The project is tasked with delivering a secure, webbased unifying environment offering a one-stop-shop for Australia-wide urban and built environment research. This paper describes the architectural design and implementation of the AURIN data-driven e-Infrastructure, where data is not just a passive entity that is accessed and used as a consequence of research demand, but is instead, directly shaping the computational access, processing and intelligent utilization possibilities. This is demonstrated in a situational context.

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. Modeling Coordinated Multiple Views of Heterogeneous Data Cubes for Urban Visual AnalyticsWith 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.