title: A disclosure index to measure the quality of annual reporting by museums in New Zealand and the UK

authors: Wei, Ting Ling

subjects: annual reports

abstract: Purpose – The purpose of this paper is to examine the reporting practice of leading museums in New Zealand (NZ) and the UK to develop a museums' performance accountability disclosure index.

Design/methodology/approach – The paper reports a study of annual reporting by 16 museums in NZ and the UK. Because of the unique nature of the industry the disclosure index uses a framework which draws on the balanced scorecard (BSC). The BSC provides a link between internal reporting of key performance issues and the need of a community organisation to report to the public.

Findings – Overall, the quality of reporting in each country is found to be of a similar standard. The strongest parts of reports are those dealing with internal processes and objectives, and the weakest are those reporting on learning and growth.

Originality/value – Museums are a key repository of tangible cultural heritage of huge intrinsic value both to the community now, but of more importance to future generations. However, the performance and accountability of this sector is basically without research or commentary, and this paper addresses this deficiency.

text: A disclosure index to measure the quality of annual reporting by museums in New Zealand and the UK Purpose – The purpose of this paper is to examine the reporting practice of leading museums in New Zealand (NZ) and the UK to develop a museums' performance accountability disclosure index.

Design/methodology/approach – The paper reports a study of annual reporting by 16 museums in NZ and the UK. Because of the unique nature of the industry the disclosure index uses a framework which draws on the balanced scorecard (BSC). The BSC provides a link between internal reporting of key performance issues and the need of a community organisation to report to the public.

Findings – Overall, the quality of reporting in each country is found to be of a similar standard. The strongest parts of reports are those dealing with internal processes and objectives, and the weakest are those reporting on learning and growth.

Originality/value – Museums are a key repository of tangible cultural heritage of huge intrinsic value both to the community now, but of more importance to future generations. However, the performance and accountability of this sector is basically without research or commentary, and this paper addresses this deficiency.

year: 2008

title: Pan-Antarctic map of near-surface permafrost temperatures at 1 km2 scale

authors: Obu, Jaroslav

abstract: Permafrost is present within almost all of the Antarctic's ice-free areas, but little is known about spatial variations in permafrost temperatures except for a few areas with established ground temperature measurements. We modelled a temperature at the top of the permafrost (TTOP) for all the ice-free areas of the Antarctic mainland and Antarctic islands at 1 km2 resolution during 2000–2017. The model was driven by remotely sensed land surface temperatures and downscaled ERA-Interim climate reanalysis data, and subgrid permafrost variability was simulated by variable snow cover. The results were validated against in situ-measured ground temperatures from 40 permafrost boreholes, and the resulting root-mean-square error was 1.9 ∘C. The lowest near-surface permafrost temperature of −36 ∘C was modelled at Mount Markham in the Queen Elizabeth Range in the Transantarctic Mountains. This is the lowest permafrost temperature on Earth, according to global-scale modelling results. The temperatures were most commonly modelled between −23 and −18 ∘C for mountainous areas rising above the Antarctic Ice Sheet and between −14 and −8 ∘C for coastal areas. The model performance was good where snow conditions were modelled realistically, but errors of up to 4 ∘C occurred at sites with strong wind-driven redistribution of snow.

text: Pan-Antarctic map of near-surface permafrost temperatures at 1 km2 scale Permafrost is present within almost all of the Antarctic's ice-free areas, but little is known about spatial variations in permafrost temperatures except for a few areas with established ground temperature measurements. We modelled a temperature at the top of the permafrost (TTOP) for all the ice-free areas of the Antarctic mainland and Antarctic islands at 1 km2 resolution during 2000–2017. The model was driven by remotely sensed land surface temperatures and downscaled ERA-Interim climate reanalysis data, and subgrid permafrost variability was simulated by variable snow cover. The results were validated against in situ-measured ground temperatures from 40 permafrost boreholes, and the resulting root-mean-square error was 1.9 ∘C. The lowest near-surface permafrost temperature of −36 ∘C was modelled at Mount Markham in the Queen Elizabeth Range in the Transantarctic Mountains. This is the lowest permafrost temperature on Earth, according to global-scale modelling results. The temperatures were most commonly modelled between −23 and −18 ∘C for mountainous areas rising above the Antarctic Ice Sheet and between −14 and −8 ∘C for coastal areas. The model performance was good where snow conditions were modelled realistically, but errors of up to 4 ∘C occurred at sites with strong wind-driven redistribution of snow.

year: 2020

title: Visualising categorical data: Linguistic case studies from te Reo Māori and New Zealand English

authors: Trye, David

subjects: visualisation

abstract: Categorical variables are prevalent in real-world datasets across numerous domains, yet few visualisation techniques accommodate them effectively. This is especially true of datasets comprising three or more categorical variables, termed multivariate categorical data. Visualising such data is challenging due to the lack of inherent ordering of nominal categories, the so-called ‘curse of dimensionality’, and the potential variability in the number of categories per variable. Corpus linguistics, which involves the study of large digital collections of naturally occurring language, serves as the primary application domain in this thesis. This domain was chosen because it is rich in multivariate categorical data and, at the same time, is often visualised using only basic techniques.

This thesis contributes to the area of categorical data visualisation in several ways. First, we propose a taxonomy of techniques for visualising categorical data, highlight limitations of existing solutions, and identify relevant analysis tasks. Building on this foundation, the thesis introduces novel techniques and enhancements for visualising datasets involving multiple categorical variables. We focus on adapting the layout and interactive capabilities of an existing technique that uses a matrix of heatmaps to represent pairwise category intersections. These modifications show that directly visualising statistical test results for categorical data can be beneficial for exploring bivariate patterns and associations. Furthermore, we contribute the design, implementation and evaluation of a novel technique called MultiCat, which is not restricted to pairwise intersections but rather facilitates analysis of relationships among multiple variables simultaneously. Both these techniques are interactive and offer greater scalability than existing alternatives, thereby affording new possibilities for analysing multivariate categorical data. However, since categorical variables can occur within more complex data structures, we also consider their presence in networks and hypergraphs, which require specialised methods.

To demonstrate the application of these techniques, we draw on two linguistic case studies that focus on languages of special significance in Aotearoa New Zealand. Addressing the low-resource status of Māori, the country’s Indigenous language, we first contribute two related Twitter datasets—a monolingual Māori corpus and a mixed–language Māori–English corpus—together with an architecture for differentiating Māori and English words. Our initial case study uses the monolingual Māori corpus and proposed visualisation techniques to investigate grammatical possession in Māori, offering fresh insights into the linguistic practices of contemporary speakers. The second case study uses networks and hypergraphs with categorical attributes to explore Māori loanword co-occurrence in New Zealand English newspaper articles. We find that loanwords tend not to occur in isolation and that New Zealanders are still importing new (unlisted) borrowings from Māori.

Ultimately, the techniques developed in this thesis have broad applications both within and beyond the corpus linguistics community. By enabling more effective visualisation and analysis of multivariate categorical data, this research has the potential to facilitate deeper insights into domains as diverse as education, healthcare, business and science.

text: Visualising categorical data: Linguistic case studies from te Reo Māori and New Zealand English Categorical variables are prevalent in real-world datasets across numerous domains, yet few visualisation techniques accommodate them effectively. This is especially true of datasets comprising three or more categorical variables, termed multivariate categorical data. Visualising such data is challenging due to the lack of inherent ordering of nominal categories, the so-called ‘curse of dimensionality’, and the potential variability in the number of categories per variable. Corpus linguistics, which involves the study of large digital collections of naturally occurring language, serves as the primary application domain in this thesis. This domain was chosen because it is rich in multivariate categorical data and, at the same time, is often visualised using only basic techniques.

This thesis contributes to the area of categorical data visualisation in several ways. First, we propose a taxonomy of techniques for visualising categorical data, highlight limitations of existing solutions, and identify relevant analysis tasks. Building on this foundation, the thesis introduces novel techniques and enhancements for visualising datasets involving multiple categorical variables. We focus on adapting the layout and interactive capabilities of an existing technique that uses a matrix of heatmaps to represent pairwise category intersections. These modifications show that directly visualising statistical test results for categorical data can be beneficial for exploring bivariate patterns and associations. Furthermore, we contribute the design, implementation and evaluation of a novel technique called MultiCat, which is not restricted to pairwise intersections but rather facilitates analysis of relationships among multiple variables simultaneously. Both these techniques are interactive and offer greater scalability than existing alternatives, thereby affording new possibilities for analysing multivariate categorical data. However, since categorical variables can occur within more complex data structures, we also consider their presence in networks and hypergraphs, which require specialised methods.

To demonstrate the application of these techniques, we draw on two linguistic case studies that focus on languages of special significance in Aotearoa New Zealand. Addressing the low-resource status of Māori, the country’s Indigenous language, we first contribute two related Twitter datasets—a monolingual Māori corpus and a mixed–language Māori–English corpus—together with an architecture for differentiating Māori and English words. Our initial case study uses the monolingual Māori corpus and proposed visualisation techniques to investigate grammatical possession in Māori, offering fresh insights into the linguistic practices of contemporary speakers. The second case study uses networks and hypergraphs with categorical attributes to explore Māori loanword co-occurrence in New Zealand English newspaper articles. We find that loanwords tend not to occur in isolation and that New Zealanders are still importing new (unlisted) borrowings from Māori.

Ultimately, the techniques developed in this thesis have broad applications both within and beyond the corpus linguistics community. By enabling more effective visualisation and analysis of multivariate categorical data, this research has the potential to facilitate deeper insights into domains as diverse as education, healthcare, business and science.

year: 2024