Explanipedia

pandemonium: High Dimensional Analysis in Linked Spaces Open

Gabriel McCoy, Ursula Laa, G. Valencia · 2025

polarisR: Non-Linear Dimensionality Reduction Visualization Tool Open

Dharmendra Kumar Yadav, Ursula Laa, Jayani P. G. Lakshika, Eun‐Kyung Lee · 2025

Evaluating transdisciplinary methods: a new scale for measuring knowledge integration Open

Cornelia Fischer, Katharina Gugerell, Ursula Laa, Janet Jacobsen, Marianne Penker · 2025

Demonstrating the Capabilities of the lionfish Software for Interactive Visualization of Market Segmentation Partitions Open

Matthias Medl, Dianne Cook, Ursula Laa · 2025

Market segmentation partitions multivariate data using some clustering algorithm, resulting in some number of homogeneousclusters of consumers for marketing purposes. Often this type of data has no clear cluster structure, that is, no sepa…

Is This Normal? A New Projection Pursuit Index to Assess a Sample Against a Multivariate Null Distribution Open

Annalisa Calvi, Ursula Laa, Dianne Cook · 2025

Comparing Traditional Methods and Modern Statistical Techniques for Tree Height Prediction Open

Jakob Hobiger, Ursula Laa, Sonja Vospernik · 2025

Forest mensuration is important to gain knowledge and information about forest stands. Because tree height often proves more difficult to measure than diameter, different statistical models are used for their estimation instead. In this pa…

Is this normal? A new projection pursuit index to assess a sample against a multivariate null distribution Open

Annalisa Calvi, Ursula Laa, Dianne Cook · 2025

Many data problems contain some reference or normal conditions, upon which to compare newly collected data. This scenario occurs in data collected as part of clinical trials to detect adverse events, or for measuring climate change against…

Meteorological factors control debris slides and debris flows in a high-Arctic glacier basin (Ny-Ålesund, Svalbard) Open

Erik Kuschel, Florian Tölle, Vinzent Klaus, Ursula Laa, Alexander Prokop , et al. · 2024

A Tidy Framework and Infrastructure to Systematically Assemble Spatio-temporal Indexes from Multivariate Data Open

H. Sherry Zhang, Dianne Cook, Ursula Laa, Nicolas Langrené, Patricia Menéndez · 2024

Indexes are useful for summarizing multivariate information into single metrics for monitoring, communicating, and decision-making. While most work has focused on defining new indexes for specific purposes, more attention needs to be direc…

<b>cubble</b>: An <i>R</i> Package for Organizing and Wrangling Multivariate Spatio-Temporal Data Open

H. Sherry Zhang, Dianne Cook, Ursula Laa, Nicolas Langrené, Patricia Menéndez · 2024

Multivariate spatio-temporal data refers to multiple measurements taken across space and time. For many analyses, spatial and time components can be separately studied: for example, to explore the temporal trend of one variable for a singl…

Meteorological Factors Control Landslide Processes in a High-Arctic Glacier Basin (Ny-Ålesund, Svalbard) Open

Erik Kuschel, Florian Tölle, Vinzent Klaus, Ursula Laa, Alexander Prokop , et al. · 2024

Frame to frame interpolation for high-dimensional data visualisation using the woylier package Open

Zoljargal Batsaikhan, Dianne Cook, Ursula Laa · 2023

The woylier package implements tour interpolation paths between frames using Givens rotations. This provides an alternative to the geodesic interpolation between planes currently available in the tourr package. Tours are used to visualise …

Clustering and visualisation tools to study high dimensional parameter spaces: B anomalies example Open

G. Valencia, Ursula Laa · 2023

We describe the applications of clustering and visualization tools using the so-called neutral B anomalies as an example. Clustering permits parameter space partitioning into regions that can be separated with some given measurements. It p…

Clustering and visualization tools to study high dimensional parameter spaces: B anomalies example Open

Ursula Laa, G. Valencia · 2023

We describe the applications of clustering and visualization tools using the so-called neutral B anomalies as an example. Clustering permits parameter space partitioning into regions that can be separated with some given measurements. It p…

Investigating the effects of meteorological conditions on landslide formation in a high-arctic glacier basin using terrestrial laser scanning (Ny-Ålesund, Svalbard) Open

Erik Kuschel, Christian Zangerl, Ursula Laa, Vinzent Klaus, Alexander Prokop , et al. · 2023

Landslide processes are one of the dominant agents of erosion and sediment transport in alpine terrain, which often pose a significant risk to communities and infrastructure around the world. Climate change generates a wide range of proces…

Index construction: a pipeline approach for transparency and diagnostics Open

H. Sherry Zhang, Dianne Cook, Patricia Menéndez, Ursula Laa, Nicolas Langrené · 2023

Indexes are commonly used to combine multivariate information into a single number for monitoring, communicating, and decision-making. They are applied in many areas including the environment (e.g. drought index, Southern Oscillation Index…

New and simplified manual controls for projection and slice tours, with application to exploring classification boundaries in high dimensions Open

Ursula Laa, Alex Aumann, Dianne Cook, G. Valencia · 2023

This paper describes new user controls for examining high-dimensional data using low-dimensional linear projections and slices. A user can interactively change the contribution of a given variable to a low-dimensional projection, which is …

New and simplified manual controls for projection and slice tours, with application to exploring classification boundaries in high dimensions Open

Ursula Laa, Alex Aumann, Dianne Cook, G. Valencia · 2023

This paper describes new user controls for examining high-dimensional data using low-dimensional linear projections and slices. A user can interactively change the contribution of a given variable to a low-dimensional projection, which is …

Casting multiple shadows: interactive data visualisation with tours and embeddings Open

Stuart Lee, Ursula Laa, Dianne Cook · 2022

Non-linear dimensionality reduction (NLDR) methods such as t-distributed stochastic neighbour embedding (t-SNE) are ubiquitous in the natural sciences, however, the appropriate use of these methods is difficult because of their complex par…

cubble: An R Package for Organizing and Wrangling Multivariate Spatio-temporal Data Open

H. Sherry Zhang, Dianne Cook, Ursula Laa, Nicolas Langrené, Patricia Menéndez · 2022

Multivariate spatio-temporal data refers to multiple measurements taken across space and time. For many analyses, spatial and time components can be separately studied: for example, to explore the temporal trend of one variable for a singl…

Hole or Grain? A Section Pursuit Index for Finding Hidden Structure in Multiple Dimensions Open

Ursula Laa, Dianne Cook, Andreas Buja, G. Valencia · 2022

Multivariate data is often visualized using linear projections, produced by techniques such as principal component analysis, linear discriminant analysis, and projection pursuit. A problem with projections is that they obscure low and high…

Pandemonium: a clustering tool to partition parameter space—application to the B anomalies Open

Ursula Laa, G. Valencia · 2022

We introduce the interactive tool to cluster model predictions that depend on a set of parameters. The model predictions are used to define the coordinates in observable space which go into the clustering. The results of this partitioning …

The state‐of‐the‐art on tours for dynamic visualization of high‐dimensional data Open

Stuart Lee, Dianne Cook, Natalia da Silva, Ursula Laa, Nicholas Spyrison , et al. · 2021

This article discusses a high‐dimensional visualization technique called the tour, which can be used to view data in more than three dimensions. We review the theory and history behind the technique, as well as modern software developments…

Burning Sage: Reversing the Curse of Dimensionality in the Visualization of High-Dimensional Data Open

Ursula Laa, Dianne Cook, Stuart Lee · 2021

In high-dimensional data analysis the curse of dimensionality reasons that points tend to be far away from the center of the distribution and on the edge of highdimensional space. Contrary to this, is that projected data tends to clump at …

A Review of the State-of-the-Art on Tours for Dynamic Visualization of High-dimensional Data Open

Stuart Lee, Dianne Cook, Natalia da Silva, Ursula Laa, Earo Wang , et al. · 2021

This article discusses a high-dimensional visualization technique called the tour, which can be used to view data in more than three dimensions. We review the theory and history behind the technique, as well as modern software developments…

Pandemonium: a clustering tool to partition parameter space --\n application to the B anomalies Open

Ursula Laa, G. Valencia · 2021

We introduce the interactive tool pandemonium to cluster model predictions\nthat depend on a set of parameters. The model predictions are used to define\nthe coordinates in observable space which go into the clustering. The results\nof thi…

A Slice Tour for Finding Hollowness in High-Dimensional Data Open

Ursula Laa, Dianne Cook, G. Valencia · 2021

Taking projections of high-dimensional data is a common analytical and visualization technique in statistics for working with high-dimensional problems. Sectioning, or slicing, through high dimensions is less common, but can be useful for …

Visual Diagnostics for Constrained Optimisation with Application to Guided Tours Open

Sherry Zhang H., Dianne Cook, Ursula Laa, Nicolas Langrené, Patricia Menéndez · 2021

A guided tour helps to visualise high-dimensional data by showing low-dimensional projections along a projection pursuit optimisation path. Projection pursuit is a generalisation of principal component analysis, in the sense that different…

Casting Multiple Shadows: High-Dimensional Interactive Data Visualisation with Tours and Embeddings Open

Stuart Lee, Ursula Laa, Dianne Cook · 2020

Non-linear dimensionality reduction (NLDR) methods such as t-distributed stochastic neighbour embedding (t-SNE) are ubiquitous in the natural sciences, however, the appropriate use of these methods is difficult because of their complex par…

Burning sage: Reversing the curse of dimensionality in the visualization\n of high-dimensional data Open

Ursula Laa, Dianne Cook, Stuart Lee · 2020

In high-dimensional data analysis the curse of dimensionality reasons that\npoints tend to be far away from the center of the distribution and on the edge\nof high-dimensional space. Contrary to this, is that projected data tends to\nclump…

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