Conditional Visualisation for Statistical Models

It is difficult to understand data and statistical models in high-dimensional space. One way to approach the problem is conditional visualisation, but methods in this area have lagged behind the considerable advances in statistical modelling in recent decades. This thesis presents a new approach to conditional visualisation which uses interactive computer graphics, and supports the exploration of a broad range of statistical models. The new approach to conditional visualisation consists of visualising a single lowdimensional section at a time, showing fitted models on the section, and enhancing the section by displaying observed data which are near the section according to a similarity measure. Two ways of choosing sections are given |choosing sections interactively using data summary graphics, and choosing sections programmatically according to some criteria. The visualisations in this thesis necessitate interactive graphics, which are implemented in the condvis package in R

Web-based Tools for the Analysis of DNA Microarrays

End of project reportDNA microarrays are widely used for gene expression profiling. Raw data resulting from microarray experiments, however, tends to be very noisy and there are many sources of technical variation and bias. This raw data needs to be quality assessed and interactively preprocessed to minimise variation before statistical analysis in order to achieve meaningful result. Therefore microarray analysis requires a combination of visualisation and statistical tools, which vary depending on what microarray platform or experimental design is used.Bioconductor is an existing open source software project that attempts to facilitate analysis of genomic data. It is a collection of packages for the statistical programming language R. Bioconductor is particularly useful in analyzing microarray experiments. The problem is that the R programming language’s command line interface is intimidating to many users who do not have a strong background in computing. This often leads to a situation where biologists will resort to using commercial software which often uses antiquated and much less effective statistical techniques, as well as being expensively priced. This project aims to bridge this gap by providing a user friendly web-based interface to the cutting edge statistical techniques of Bioconductor

Pulsar data analysis with PSRCHIVE

PSRCHIVE is an open-source, object-oriented, scientific data analysis software library and application suite for pulsar astronomy. It implements an extensive range of general-purpose algorithms for use in data calibration and integration, statistical analysis and modeling, and visualisation. These are utilised by a variety of applications specialised for tasks such as pulsar timing, polarimetry, radio frequency interference mitigation, and pulse variability studies. This paper presents a general overview of PSRCHIVE functionality with some focus on the integrated interfaces developed for the core applications.Comment: 21 pages, 5 figures; tutorial presented at IPTA 2010 meeting in Leiden merged with talk presented at 2011 pulsar conference in Beijing; includes further research and development on algorithms for RFI mitigation and TOA bias correctio

JLIN: A java based linkage disequilibrium plotter

BACKGROUND: A great deal of effort and expense are being expended internationally in attempts to detect genetic polymorphisms contributing to susceptibility to complex human disease. Techniques such as Linkage Disequilibrium mapping are being increasingly used to examine and compare markers across increasingly large datasets. Visualisation techniques are becoming essential to analyse the ever-growing volume of data and results available with any given analysis. RESULTS: JLIN (Java LINkage disequilibrium plotter) is a software package designed for customisable, intuitive visualisation of Linkage Disequilibrium (LD) across all common computing platforms. Customisation allows the user to choose particular visualisations, statistical measures and measurement ranges. JLIN also allows the user to export images of the LD visualisation in several common document formats. CONCLUSION: JLIN allows the user to visually compare and contrast the results of a range of statistical measures on the input dataset(s). These measures include the commonly used D' and r(2 )statistics and empirical p-values. JLIN has a number of unique and novel features that improve on existing LD visualisation tools

Remote processing of firm microdata at the Bank of Italy

Providing the possibility to run personalised econometric/statistical analyses on the appropriate data sets by remote processing allows greater flexibility in the production of economic information. Binding confidentiality requirements are required with business survey data. The Bank of Italy's infrastructure allows its business survey data to be exploited, while preserving anonymity of individual data. The system is based on the LISSY platform and has been already adopted by the Luxembourg Income Study (LIS) and other research centres. Firms' privacy is safeguarded by forbidding potentially confidentiality-breaking programme statements and by denying the visualisation of individual data. Data confidentiality is protected by removing key identifiers from the database and by trimming data in the right tail of the distribution. The platform provides its services through plain-text e-mails. The authorised user sends an e-mail containing an identifying header followed by a statistical programme to a predetermined address. The system checks the validity of the header, strips out the code and submits it in a batch to one of the econometric/statistical packages available (SAS and Stata). The outputs are mailed back to the user after passing an array of automatic and manual checks.microdata, confidentiality, remote access

Hierarchical progressive surveys. Multi-resolution HEALPix data structures for astronomical images, catalogues, and 3-dimensional data cubes

Scientific exploitation of the ever increasing volumes of astronomical data requires efficient and practical methods for data access, visualisation, and analysis. Hierarchical sky tessellation techniques enable a multi-resolution approach to organising data on angular scales from the full sky down to the individual image pixels. Aims. We aim to show that the Hierarchical progressive survey (HiPS) scheme for describing astronomical images, source catalogues, and three-dimensional data cubes is a practical solution to managing large volumes of heterogeneous data and that it enables a new level of scientific interoperability across large collections of data of these different data types. Methods. HiPS uses the HEALPix tessellation of the sphere to define a hierarchical tile and pixel structure to describe and organise astronomical data. HiPS is designed to conserve the scientific properties of the data alongside both visualisation considerations and emphasis on the ease of implementation. We describe the development of HiPS to manage a large number of diverse image surveys, as well as the extension of hierarchical image systems to cube and catalogue data. We demonstrate the interoperability of HiPS and Multi-Order Coverage (MOC) maps and highlight the HiPS mechanism to provide links to the original data. Results. Hierarchical progressive surveys have been generated by various data centres and groups for ~200 data collections including many wide area sky surveys, and archives of pointed observations. These can be accessed and visualised in Aladin, Aladin Lite, and other applications. HiPS provides a basis for further innovations in the use of hierarchical data structures to facilitate the description and statistical analysis of large astronomical data sets.Comment: 21 pages, 6 figures. Accepted for publication in Astronomy & Astrophysic

Development of an Ontology-Based Visual Approach for Property Data Analytics

oai:ojs.pkp.sfu.ca:article/4Real estate is a complex market that consists of many layers of social, financial, and economic data, including but not limited to price, rental, location, mortgage, demographic and housing supply data. The sheer number of real estate properties around the world means that property transactions produce an extraordinary amount of data that is increasing exponentially. Most of the data are presented through thousands of rows on a spreadsheet or described in long paragraphs that are difficult to understand. The emergent data visualization techniques are intended to allow data to be processed and analytics to be displayed visually to enable an understanding of complex information and the identification of new patterns from the data. However, not all visualization techniques can achieve such a thing. Most techniques are able to display only visual low-dimensional data. This paper introduces an ontology visualisation methodology to explore the ontologies of property data behaviour for multidimensional data. The visualisation combines real estate data statistical analysis with several high dimensional data visualisation techniques, including parallel coordinates and stacked area charts. By using six residential suburbs in Sydney as a demonstration, we find that the developed data visualisation methodology can be applied effectively and efficiently to analyse complex real estate market behaviour patterns

A visualisation tool to analyse usage of web-based interventions: The example of Positive Online Weight Reduction (POWeR)

Background: Attrition is a significant problem in web-based interventions. Consequently, research aims to identify the relation between web usage and benefit from such interventions. We have developed a visualisation tool that enables researchers to more easily examine large data sets on intervention usage that can be difficult to make sense of using traditional descriptive or statistical techniques alone. Objectives: This paper demonstrates how the visualisation tool was used to explore patterns in participants’ use of a web-based weight management intervention (POWeR: Positive Online Weight Reduction). We also demonstrate how the visualisation tool can be used to inform subsequent statistical analyses of the association between usage patterns, participant characteristics, and intervention outcome. Methods: The visualisation tool was used to analyse data from 132 participants who had accessed at least one session of the POWeR intervention. Results: There was a drop in usage of optional sessions after participants had accessed the initial, core POWeR sessions, but many users nevertheless continued to complete goal and weight review. POWeR tools relating to the food diary and steps diary were re-used most often. Differences in participant characteristics and usage of other intervention components were identified between participants who did and did not choose to access optional POWeR sessions (in addition to the initial core sessions) or re-use the food and steps diary. Re-use of the steps diary and the getting support tools was associated with greater weight loss. Conclusions: The visualisation tool provided a quick and efficient method for exploring patterns of web usage, which enabled further analyses of whether different usage patterns were associated with participant characteristics or differences in intervention outcome. Further usage of visualisation techniques is recommended in order to 1) make sense of large data sets more quickly and efficiently, 2) determine the likely active ingredients in web-based interventions, and thereby enhance the benefit they may provide and 3) inform (re-)design of future interventions to promote greater use and engagement by enabling users to easily access valued intervention content/tools

Visualisation of multi-dimensional medical images with application to brain electrical impedance tomography

Medical imaging plays an important role in modem medicine. With the increasing complexity and information presented by medical images, visualisation is vital for medical research and clinical applications to interpret the information presented in these images. The aim of this research is to investigate improvements to medical image visualisation, particularly for multi-dimensional medical image datasets. A recently developed medical imaging technique known as Electrical Impedance Tomography (EIT) is presented as a demonstration. To fulfil the aim, three main efforts are included in this work. First, a novel scheme for the processmg of brain EIT data with SPM (Statistical Parametric Mapping) to detect ROI (Regions of Interest) in the data is proposed based on a theoretical analysis. To evaluate the feasibility of this scheme, two types of experiments are carried out: one is implemented with simulated EIT data, and the other is performed with human brain EIT data under visual stimulation. The experimental results demonstrate that: SPM is able to localise the expected ROI in EIT data correctly; and it is reasonable to use the balloon hemodynamic change model to simulate the impedance change during brain function activity. Secondly, to deal with the absence of human morphology information in EIT visualisation, an innovative landmark-based registration scheme is developed to register brain EIT image with a standard anatomical brain atlas. Finally, a new task typology model is derived for task exploration in medical image visualisation, and a task-based system development methodology is proposed for the visualisation of multi-dimensional medical images. As a case study, a prototype visualisation system, named EIT5DVis, has been developed, following this methodology. to visualise five-dimensional brain EIT data. The EIT5DVis system is able to accept visualisation tasks through a graphical user interface; apply appropriate methods to analyse tasks, which include the ROI detection approach and registration scheme mentioned in the preceding paragraphs; and produce various visualisations