Hybrid Sankey diagrams: Visual analysis of multidimensional data for understanding resource use

Lupton, Richard C.; Allwood, Julian M.

doi:10.1016/j.resconrec.2017.05.002

Cited by 130 publications

(61 citation statements)

References 22 publications

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“…We then calculate cross-tabulations, showing for each level and domain of the EQ-5D-3L the corresponding level and domain for the EQ-5D-5L. We then depicted these cross-tabulations graphically using 'Sankey' diagrams [15,16].…”

Section: Response Changementioning

confidence: 99%

A Comparison of the EQ-5D-3L and EQ-5D-5L

Thompson

Turner

2020

PharmacoEconomics

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Introduction The EQ-5D-3L (3L) and EQ-5D-5L (5L) are both frequently used measures of health status. Previous studies have found the EQ-5D-5L to have superior measurement properties but no study has compared the two measures in a large general population survey using matched respondents. Methods Using data from the GP Patient Survey, coarsened exact matching was used to match individuals completing the 3L in 2011 with those completing the 5L in 2012. Measurement properties were assessed for a general population and multimorbid population (chronic conditions ≥ 2), with ceiling effects, informativity and distribution of response compared. Changes in the direction of response, as well as the impact on utility distributions, were quantified. Results Matching resulted in a cohort of 1, 023,218 respondents (2011: 511,609; 2012: 511,609) for analysis. Ceiling effects for the 5L were lower than the 3L (43.8% vs. 54.4%). The 5L had improved informativity and broader spread of responses than the 3L (5L top 50 profiles: 77.4% vs. 3L: 98.8%). Overall, there was an upwards shift in utility values for the 5L versus the 3L as respondents using the 5L reported ill health more frequently but with less severity. Measurement improvements and effects on utility values were more pronounced for the multimorbid population. Conclusion The 5L had superior measurement properties than the 3L and should be preferred in general population surveys and for use in individuals with multimorbidity. At increasing levels of morbidity, the 5L is currently associated with higher utility values than the 3L. Electronic supplementary material The online version of this article (https ://doi.The EQ-5D-5L has superior measurement properties to the EQ-5D-3L and should be preferred in general population and multimorbid populations.The EQ-5D-5L leads respondents to report more illhealth. However, it is less severe than when using the EQ-5D-3L.For higher-levels of multimorbidity, use of the EQ-5D-5L in England will currently lead to higher utility values than the EQ-5D-3L, irrespective of the tariff used.

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Section: Response Changementioning

confidence: 99%

A Comparison of the EQ-5D-3L and EQ-5D-5L

Thompson

Turner

2020

PharmacoEconomics

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“…Time is wasted that could be better used for correct application of methods, quality control, and sensitivity and uncertainty analysis of model results. Data integration and access to data have been a longstanding topic in the IE literature, both in case studies and in theory and database development (Davis, Chmieliauskas, Dijkema, & Nikolic, , ; Frischknecht, ; Hertwich et al., ; Lenzen et al., ; Lupton & Allwood, ; Merciai & Schmidt, ; Murakami, Oguchi, Tasaki, Daigo, & Hashimoto, ; Myers, Fishman, Reck, & Graedel, ; Pauliuk, Majeau‐Bettez, Hertwich, & Müller, ; Weidema, ). For example, the integration of material flow data with input–output (IO) models has a strong tradition (Hoekstra & van den Bergh, ; Merciai & Schmidt, ; Nakamura & Kondo, ; Nakamura, Nakajima, Kondo, & Nagasaka, , ; Ohno, Nuss, Chen, & Graedel, ).…”

Section: Data Integration For Industrial Ecology Research: State Andmentioning

confidence: 99%

“…All data can be linked to general classifications, which again can be linked to the Semantic Web. The tuple‐based data model presented above is represented as OLAP cube, where OLAP stands for “online analytical processing.” An OLAP cube is a multidimensional array representation of tuple‐based datasets, also applicable to IE data (Gray, Bosworth, Lyaman, & Pirahesh, ; Lupton & Allwood, ). In applications and databases, OLAP cubes can be implemented in different ways, for example, multidimensional arrays, 2D tables with multi‐indices, or a list of tuples.…”

Section: Implementation Of the General Data Model In The Ie Data Commonsmentioning

confidence: 99%

A general data model for socioeconomic metabolism and its implementation in an industrial ecology data commons prototype

Pauliuk

Heeren

Hasan

et al. 2019

J of Industrial Ecology

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Until this day, data in industrial ecology (IE) have been commonly seen as existing within the domain of particular methods or models, such as input–output, life cycle assessment, urban metabolism, or material flow analysis data. This artificial division of data into methods contradicts the common phenomena described by those data: the objects and processes in the industrial system, or socioeconomic metabolism (SEM). A consequence of this scattered organization of related data across methods is that IE researchers and consultants spend too much time searching for and reformatting data from diverse and incoherent sources, time that could be invested into quality control and analysis of model results instead. This article outlines a solution to two major barriers to data exchange within IE: (a) the lack of a generic structure for IE data and (b) the lack of a bespoke platform to exchange IE datasets. We present a general data model for SEM that can be used to structure all data that can be located in the industrial system, including process descriptions, product descriptions, stocks, flows, and coefficients of all kind. We describe a relational database built on the general data model and a user interface to it, both of which are open source and can be implemented by individual researchers, groups, institutions, or the entire community. In the latter case, one could speak of an IE data commons (IEDC), and we unveil an IEDC prototype containing a diverse set of datasets from the literature.

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“…It can also be used to explore results in light of the multiple possible supply chain paths (e.g., competing suppliers), for example, via “drag‐and‐drop”‐type tools. Material and energy flow analyses are also becoming increasingly detailed, with data on new and more detailed material and energy flows, agents, and the like (Schmidt ). A way to better visualize such flows is by combining data‐visualization techniques with Sankey diagrams, through so‐called hybrid Sankey diagrams (HSDs) (Lupton and Allwood ). Adding interactive features to HSD presents multiple opportunities to explore its full potential (see, e.g., the “floweaver” tool [ https://github.com/ricklupton/floweaver]).…”

Section: Current and Potential Interactive Visualizations To Illustramentioning

confidence: 99%

Interactive Visualization and Industrial Ecology: Applications, Challenges, and Opportunities

Vivanco

Hoekman

Fishman

et al. 2018

J of Industrial Ecology

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Summary The emergence of increasingly complex data in industrial ecology (IE) has caused scholarly interest in interactive visualization (IV). IV allows users to interact with data, aiding in processing and interpreting complex datasets, processes, and simulations. Consequently, IV can help IE practitioners communicate the complexities of their methods and results, shed light on the underlying research assumptions, and enable more transparent monitoring of data quality and error. This can significantly increase the reach and impact of research, promote transparency, reproducibility, and open science, as well as improve the clarity and presentation of IE research. A review of current IV applications reveals that, while data exploration has received some attention among IE practitioners, IV applications in scientific communication are clearly lacking. With the help of a working example, we explore the value of IV, discuss its operationalization, and highlight challenges that the IE community must face during IV uptake. Such challenges include technical and knowledge limitations, limits on user interaction, and implementation strategies. With these challenges in mind, we outline key aspects needed to lift the IE field to the forefront of scientific communication in the coming years. Among these, we draft the basic principles of a “Hub for Interactive Visualization in Industrial Ecology” (HIVE), a point of encounter where IE practitioners could find an array of data visualization tools that are geared toward IE datasets. IV is here to stay, and its inceptive stage presents many opportunities to IE practitioners to shape its operationalization and benefit from early adoption.

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Hybrid Sankey diagrams: Visual analysis of multidimensional data for understanding resource use

Cited by 130 publications

References 22 publications

A Comparison of the EQ-5D-3L and EQ-5D-5L

A Comparison of the EQ-5D-3L and EQ-5D-5L

A general data model for socioeconomic metabolism and its implementation in an industrial ecology data commons prototype

Interactive Visualization and Industrial Ecology: Applications, Challenges, and Opportunities

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