Pedro M. Valero-Mora

Visualizing categorical data in ViSta

The modules in the statistical package ViSta related to categorical data analysis are presented These modules are: visualization of frequency data with mosaic and bar plots, correspondence analysis, multiple correspondence analysis and loglinear analysis. All these methods are implemented in ViSta with a big emphasis on plots and graphical representations of data, as well as interactivity for the user with the system. These provide a system that has shown to be easy, useful, and powerful, both for novice and experienced users.

Is naturalistic driving research possible with highly instrumented cars? Lessons learnt in three research centres

This paper provides an overview of the experiences using Highly Instrumented Cars (HICs) in three research Centres across Europe; Spain, the UK and Greece. The data collection capability of each car is described and an overview presented relating to the relationship between the level of instrumentation and the research possible. A discussion then follows which considers the advantages and disadvantages of using HICs for ND research. This includes the obtrusive nature of the data collection equipment, the cost of equipping the vehicles with sophisticated Data Acquisition Systems (DAS) and the challenges for data storage and analysis particularly with respect to video data. It is concluded that the use of HICs substantially increases the depth of knowledge relating to the drivers behaviour and their interaction with the vehicle and surroundings. With careful study design and integration into larger studies with Low(ly) instrumented Cars (LICs), HICs can contribute significantly and in a relatively naturalistic manner to the driver behaviour research.

Using Interactive Graphics to Teach Multivariate Data Analysis to Psychology Students

This paper discusses the use of interactive graphics to teach multivariate data analysis to Psychology students. Three techniques are explored through separate activities: parallel coordinates/boxplots; principal components/exploratory factor analysis; and cluster analysis. With interactive graphics, students may perform important parts of the analysis “by hand,” using techniques such as pointing at, selecting and changing the colors of the points/observations. Our experience demonstrates that this approach is very useful when teaching an intermediate/advanced course on multivariate data analysis to students of Psychology, who tend to have low to moderate proficiency in Mathematics.

Gossip: The Architecture of SpreadPlots

A spreadplot is a visualization that simultaneously shows several different views of a dataset or model. The individual views can be dynamic, can support high-interaction direct manipulation, and can be algebraically linked with each other, possibly via an underlying statistical model. Thus, when a data analyst changes the information shown in one view of a statistical model, the changes can be processed by the model and instantly represented in the other views. Spreadplots simplify the analysts task when many different plots are relevant to the analysis at hand, as is the case in regression analysis, where there are many plots that can be used for model building and diagnosis. On the other hand, the development of a visualization involving many dynamic, highly interactive, directly manipulable graphics is not a trivial task. This article discusses a software architecture which simplifies the spreadplot developers task. The architecture addresses the two main problems in constructing a spreadplot, simplifying the layout of the plots and structuring the communication between them.

The First (Known) Statistical Graph: Michael Florent van Langren and the “Secret” of Longitude

A 1644 diagram by Michael Florent van Langren, showing estimates of the difference in longitude between Toledo and Rome, is sometimes considered to be the first known instance of a graph of statistical data. Some recently discovered documents help to date the genesis of this graphic to before March 1628, and shed some light on why van Langren chose to display this information in this form. In the process, we discovered three earlier versions of the 1644 graph and one slightly later reproduction. This article describes these early attempts on the solution of “the problem of longitude” from the perspective of a history of data visualization. This article has supplementary material online.

Visualizing parameters from loglinear models

SummaryThis paper presents a graphical display for the parameters resulting from loglinear models. Loglinear models provide a method for analyzing associations between two or several categorical variables and have become widely accepted as a tool for researchers during the last two decades. An important part of the output of any computer program focused on loglinear models is that devoted to estimation of parameters in the model. Traditionally, this output has been presented using tables that indicate the values of the coefficients, the associated standard errors and other related information. Evaluation of these tables can be rather tedious because of the number of values shown as well as their rather complicated structure, mainly when the analyst needs to consider several models before reaching a model with a good fit. Therefore, a graphical display summarizing tables of parameters could be of great help in this situation. In this paper we put forward an interactive dynamic graphical display that could be used in such fashion.

Why women do not use the helmet when riding a bicycle

Women seem to use the helmet when riding a bicycle less frequently than men. Two possible explanations for this behavior are that 1) it is less appalling to them because of lack of comfort or other reasons, or 2) they use bicycles in a more cautious way than men so they feel that they do not need the helmet as much. The present paper explores these two explanations in 5,691 cyclists that responded to an online survey conducted in 17 countries as part of an EU COST project. Answers to questions related to the two aforementioned explanations were analyzed graphically and three questions that showed the most conspicuous differences between males and females were identified. These were: ‘Helmets are a problem because they disturb your hair’, ‘I am a fast rider’, and ‘I am a skilled rider’. The responses to these three questions plus their interactions with the gender of the respondent were used as predictors of the proportion of helmet wear. The results showed that: 1) the three questions predicted the use of the helmet, 2) the interaction between gender and hair disturbance was not significant, and 3) the interactions between gender and being a fast cyclist and being a skilled rider were both statistically significant showing that women that regard themselves as slow riders or skillful riders use relatively less the helmet than men in similar conditions.

Automating statistical diagrammatic representations with data characterization

The search for an efficient method to enhance data cognition is especially important when managing data from multidimensional databases. Open data policies have dramatically increased not only the volume of data available to the public, but also the need to automate the translation of data into efficient graphical representations. Graphic automation involves producing an algorithm that necessarily contains inputs derived from the type of data. A set of rules are then applied to combine the input variables and produce a graphical representation. Automated systems, however, fail to provide an efficient graphical representation because they only consider either a one-dimensional characterization of variables, which leads to an overwhelmingly large number of available solutions, a compositional algebra that leads to a single solution, or requires the user to predetermine the graphical representation. Therefore, we propose a multidimensional characterization of statistical variables that when complemented with a catalog of graphical representations that match any single combination, presents the user with a more specific set of suitable graphical representations to choose from. Cognitive studies can then determine the most efficient perceptual procedures to further shorten the path to the most efficient graphical representations. The examples used herein are limited to graphical representations with three variables given that the number of combinations increases drastically as the number of selected variables increases.

A Strategy for Automating the Presentation of Statistical Graphics for Users without Data Visualization Expertise - A Position Paper.

The growing need to convert the data in databases into knowledge for a public without data visualization expertise requires the ever more precise selection of graphics to be presented to the user for consideration. This can be achieved through a more detailed characterization of the data as well as the data visualization task that the user wishes to accomplish. One way to limit the number of possible graphics based on the data is to characterize the multiple properties that can be described for each variable represented by a column of data. This paper presents seven dimensions with their respective levels that can serve as a framework for classifying statistical graphics such that their effectiveness in performing a given task may then be evaluated.