research-article

Public Access

Measuring the Separability of Shape, Size, and Color in Scatterplots

Authors:

Danielle Albers SzafirAuthors Info & Claims

CHI '19: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

Paper No.: 669, Pages 1 - 14

https://doi.org/10.1145/3290605.3300899

Published: 02 May 2019 Publication History

All formats PDF

Abstract

Scatterplots commonly use multiple visual channels to encode multivariate datasets. Such visualizations often use size, shape, and color as these dimensions are considered separable--dimensions represented by one channel do not significantly interfere with viewers' abilities to perceive data in another. However, recent work shows the size of marks significantly impacts color difference perceptions, leading to broader questions about the separability of these channels. In this paper, we present a series of crowdsourced experiments measuring how mark shape, size, and color influence data interpretation in multiclass scatterplots. Our results indicate that mark shape significantly influences color and size perception, and that separability among these channels functions asymmetrically: shape more strongly influences size and color perceptions in scatterplots than size and color influence shape. Models constructed from the resulting data can help designers anticipate viewer perceptions to build more effective visualizations.

Supplementary Material

MP4 File (paper669.mp4)

Download
252.93 MB

References

[1]

Daniel Acevedo and David Laidlaw. 2006. Subjective quantification of perceptual interactions among some 2D scientific visualization methods. IEEE Transactions on Visualization and Computer Graphics 12, 5 (2006), 1133--1140. Measuring the Separability of Shape, Size, and Color in Scatterplots WOODSTOCK'97, July 1997, El Paso, Texas USA

Digital Library

[2]

Danielle Albers, Michael Correll, Steve Franconeri, and Michael Gleicher. 2014. A task driven framework for visualizing time series data. In Proc. 2014 ACM Human Factors in Computing Systems. ACM.

[3]

Eric Alexander, Chih-Ching Chang, Mariana Shimabukuro, Steven Franconeri, Christopher Collins, and Michael Gleicher. 2016. The biasing effect of word length in font size encodings. In Poster Compendium of the IEEE Conference on Information Visualization.

[4]

Lawrence D Bergman, Bernice E Rogowitz, and Lloyd A Treinish. 1995. A rule-based tool for assisting colormap selection. In Proceedings of the 6th conference on Visualization'95. IEEE Computer Society, 118.

Digital Library

[5]

David Borland and Russell M Taylor Ii. 2007. Rainbow color map (still) considered harmful. IEEE computer graphics and applications 27, 2 (2007).

Digital Library

[6]

Michael Bostock, Vadim Ogievetsky, and Jeffrey Heer. 2011. D3 datadriven documents. IEEE Transactions on Visualization and Computer Graphics 17, 12 (2011), 2301--2309.

Digital Library

[7]

Nadia Boukhelifa, Anastasia Bezerianos, Tobias Isenberg, and JeanDaniel Fekete. 2012. Evaluating sketchiness as a visual variable for the depiction of qualitative uncertainty. IEEE Transactions on Visualization and Computer Graphics 18, 12 (2012), 2769--2778.

Digital Library

[8]

D.H. Brainard and B.A. Wandell. 1992. Asymmetric color matching: how color appearance depends on the illuminant. Journal of the Optical Society of America A 9, 9 (1992), 1433--1448.

[9]

Cynthia A Brewer, Geoffrey W Hatchard, and Mark A Harrower. 2003. ColorBrewer in print: a catalog of color schemes for maps. Cartogr. Geogr. Inf. Sci. 30, 1 (2003), 5--32.

[10]

Alzbeta Brychtová and Arzu Çöltekin. 2017. The effect of spatial distance on the discriminability of colors in maps. Cartography and Geographic Information Science 44, 3 (2017), 229--245.

[11]

Roxana Bujack, Terece L Turton, Francesca Samsel, Colin Ware, David H Rogers, and James Ahrens. 2018. The good, the bad, and the ugly: A theoretical framework for the assessment of continuous colormaps. IEEE transactions on visualization and computer graphics 24, 1 (2018), 923--933.

[12]

David Burlinson, Kalpathi Subramanian, and Paula Goolkasian. 2018. Open vs. Closed Shapes: New Perceptual Categories? IEEE transactions on visualization and computer graphics 24, 1 (2018), 574--583.

[13]

Tara C Callaghan. 1984. Dimensional interaction of hue and brightness in preattentive field segregation. Perception & psychophysics 36, 1 (1984), 25--34.

[14]

Robert C Carter and Louis D Silverstein. 2010. Size matters: Improved color-difference estimation for small visual targets. Journal of the Society for Information Display 18, 1 (2010), 17--28.

[15]

Haidong Chen, Wei Chen, Honghui Mei, Zhiqi Liu, Kun Zhou, Weifeng Chen, Wentao Gu, and Kwan-Liu Ma. 2014. Visual abstraction and exploration of multi-class scatterplots. IEEE Transactions on Visualization and Computer Graphics 20, 12 (2014), 1683--1692.

[16]

Lin Chen and Wu Zhou. 1997. Holes in illusory conjunctions. Psychonomic Bulletin & Review 4, 4 (1997), 507--511.

[17]

Patricia W Cheng and Robert G Pachella. 1984. A psychophysical approach to dimensional separability. Cognitive Psychology 16, 3 (1984), 279--304.

[18]

W.S. Cleveland and R. McGill. 1984. Graphical perception: Theory, experimentation, and application to the development of graphical methods. J. Amer. Statist. Assoc. 79, 387 (1984), 531--554.

[19]

William S Cleveland and William S Cleveland. 1983. A color-caused optical illusion on a statistical graph. The American Statistician 37, 2 (1983), 101--105.

[20]

William S Cleveland and Robert McGill. 1984. Graphical perception: Theory, experimentation, and application to the development of graphical methods. J. Amer. Statist. Assoc. 79, 387 (1984), 531--554.

[21]

Michael Correll, Danielle Albers, Steven Franconeri, and Michael Gleicher. 2012. Comparing averages in time series data. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 1095--1104.

Digital Library

[22]

Michael Correll, Dominik Moritz, and Jeffrey Heer. 2018. ValueSuppressing Uncertainty Palettes. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, 642.

Digital Library

[23]

Michael A Correll, Eric C Alexander, and Michael Gleicher. 2013. Quantity estimation in visualizations of tagged text. In Proceedings of the SIGCHI conference on human factors in computing systems. ACM, 2697--2706.

Digital Library

[24]

Commision Internationale de l'ZEclairage. 1978. Recommendations on uniform color spaces, color-difference equations, psychometric color terms. Paris: CIE (1978).

[25]

Cagatay Demiralp, Michael S Bernstein, and Jeffrey Heer. 2014. Learning perceptual kernels for visualization design. IEEE Transactions on Visualization and Computer Graphics 20, 12 (2014), 1933--1942.

[26]

Ronak Etemadpour, Robson Carlos da Motta, Jose Gustavo de Souza Paiva, Rosane Minghim, Maria Cristina Ferreira de Oliveira, and Lars Linsen. 2014. Role of human perception in cluster-based visual analysis of multidimensional data projections. In Information Visualization Theory and Applications (IVAPP), 2014 International Conference on. IEEE, 276--283.

[27]

Johannes Fuchs, Fabian Fischer, Florian Mansmann, Enrico Bertini, and Petra Isenberg. 2013. Evaluation of alternative glyph designs for time series data in a small multiple setting. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 3237--3246.

Digital Library

[28]

Wendell R Garner. 1976. Interaction of stimulus dimensions in concept and choice processes. Cognitive psychology 8, 1 (1976), 98--123.

[29]

Michael Gleicher, Michael Correll, Christine Nothelfer, and Steven Franconeri. 2013. Perception of Average Value in Multiclass Scatterplots. IEEE TVCG 19, 12 (2013), 2316--2325.

Digital Library

[30]

Richard L Gottwald and WR Garner. 1975. Filtering and condensation tasks with integral and separable dimensions. Perception & Psychophysics 18, 1 (1975), 26--28.

[31]

Lane Harrison, Fumeng Yang, Steven Franconeri, and Remco Chang. 2014. Ranking Visualizations of Correlation Using Weber's Law. IEEE Transactions on Visualization and Computer Graphics (2014).

[32]

J. Heer and M. Bostock. 2010. Crowdsourcing graphical perception: using mechanical turk to assess visualization design. In Proceedings of the 28th international conference on Human factors in computing systems. ACM, 203--212.

Digital Library

[33]

Jeffrey Heer, Nicholas Kong, and Maneesh Agrawala. 2009. Sizing the horizon: the effects of chart size and layering on the graphical perception of time series visualizations. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. ACM, 1303-- 1312.

Digital Library

[34]

Michael Jenkins, Anna Grubert, and Martin Eimer. 2017. Target objects defined by a conjunction of colour and shape can be selected independently and in parallel. Attention, Perception, & Psychophysics 79, 8 (2017), 2310--2326.

[35]

Younghoon Kim and Jeffrey Heer. 2018. Assessing effects of task and data distribution on the effectiveness of visual encodings. In Computer Graphics Forum, Vol. 37. Wiley Online Library, 157--167.

[36]

J.H. Krantz. 2001. Stimulus delivery on the Web: What can be presented when calibration isn't possible. Dimensions of Internet Science (2001), 113--130.

[37]

Jeongmi Lee, Carly J Leonard, Steven J Luck, and Joy J Geng. 2018. Dynamics of Feature-based Attentional Selection during Color--Shape Conjunction Search. Journal of cognitive neuroscience (2018), 1--15.

Digital Library

[38]

H Legrand, G Rand, and C Rittler. 1945. Tests for the detection and anlysis of color-blindness I. The Ishihara test: An evaluation. Journal WOODSTOCK'97, July 1997, El Paso, Texas USA Stephen Smart and Danielle Albers Szafir of the Optical Society of America 35 (1945), 268. Issue 4.

[39]

Stephan Lewandowsky and Ian Spence. 1989. Discriminating strata in scatterplots. J. Amer. Statist. Assoc. 84, 407 (1989), 682--688.

[40]

Jing Li, Jarke J van Wijk, and Jean-Bernard Martens. 2009. Evaluation of symbol contrast in scatterplots. In Visualization Symposium, 2009. PacificVis' 09. IEEE Pacific. IEEE, 97--104.

Digital Library

[41]

Sharon Lin, Julie Fortuna, Chinmay Kulkarni, Maureen Stone, and Jeffrey Heer. 2013. Selecting Semantically-Resonant Colors for Data Visualization. In Computer Graphics Forum, Vol. 32. Wiley Online Library, 401--410.

Digital Library

[42]

Yang Liu and Jeffrey Heer. 2018. Somewhere Over the Rainbow: An Empirical Assessment of Quantitative Colormaps. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, 598.

Digital Library

[43]

Luana Micallef, Gregorio Palmas, Antti Oulasvirta, and Tino Weinkauf. 2017. Towards perceptual optimization of the visual design of scatterplots. IEEE transactions on visualization and computer graphics 23, 6 (2017), 1588--1599.

Digital Library

[44]

K.T. Mullen. 1985. The contrast sensitivity of human colour vision to red-green and blue-yellow chromatic gratings. The Journal of Physiology 359, 1 (1985), 381--400.

[45]

Tamara Munzner. 2014. Visualization Analysis and Design. CRC Press.

[46]

B. Oicherman, M.R. Luo, B. Rigg, and A.R. Robertson. 2008. Effect of observer metamerism on colour matching of display and surface colours. Color Research and Applications 33, 5 (2008), 346--359.

[47]

Lace Padilla, P Samuel Quinan, Miriah Meyer, and Sarah H CreemRegehr. 2017. Evaluating the Impact of Binning 2D Scalar Fields. IEEE Transactions on Visualization and Computer Graphics 23, 1 (2017), 431--440.

Digital Library

[48]

Stephen E Palmer. 1999. Vision science: Photons to phenomenology. MIT press.

[49]

Anshul Vikram Pandey, Josua Krause, Cristian Felix, Jeremy Boy, and Enrico Bertini. 2016. Towards understanding human similarity perception in the analysis of large sets of scatter plots. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 3659--3669.

Digital Library

[50]

Susan Petry and Glenn E Meyer. 2012. The perception of illusory contours. Springer Science & Business Media.

[51]

James R Pomerantz and Lawrence C Sager. 1975. Asymmetric integrality with dimensions of visual pattern. Perception & Psychophysics 18, 6 (1975), 460--466.

[52]

Khairi Reda, Pratik Nalawade, and Kate Ansah-Koi. 2018. Graphical Perception of Continuous Quantitative Maps: the Effects of Spatial Frequency and Colormap Design. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems. ACM, 272.

Digital Library

[53]

Katharina Reinecke, David R Flatla, and Christopher Brooks. 2016. Enabling Designers to Foresee Which Colors Users Cannot See. In Proceedings of the 2016 CHI Conference on Human Factors in Computing Systems. ACM, 2693--2704.

Digital Library

[54]

Ronald A Rensink. 2017. The nature of correlation perception in scatterplots. Psychonomic bulletin & review 24, 3 (2017), 776--797.

[55]

Ronald A Rensink and Gideon Baldridge. 2010. The perception of correlation in scatterplots. In Computer Graphics Forum, Vol. 29. Wiley Online Library, 1203--1210.

Digital Library

[56]

P. Rizzo, A. Bierman, and M.S. Rea. 2002. Color and brightness discrimination of white LEDs. In International Symposium on Optical Science and Technology. International Society for Optics and Photonics, 235--246.

[57]

Bahador Saket, Alex Endert, and Cagatay Demiralp. 2018. Task-Based Effectiveness of Basic Visualizations. IEEE Transactions on Visualization and Computer Graphics (2018).

[58]

A. Sarkar, L. Blondé, P. Le Callet, F. Autrusseau, P. Morvan, J. Stauder, et al. 2010. A color matching experiment using two displays: design considerations and pilot test results. In Proceedings of the Fifth European Conference on Color in Graphics, Imaging and Vision.

[59]

Michael Sedlmair and Michaël Aupetit. 2015. Data-driven Evaluation of Visual Quality Measures. In Computer Graphics Forum, Vol. 34. Wiley Online Library, 201--210.

Digital Library

[60]

Vidya Setlur and Maureen C Stone. 2016. A linguistic approach to categorical color assignment for data visualization. IEEE transactions on visualization and computer graphics 22, 1 (2016), 698--707.

Digital Library

[61]

Varshita Sher, Karen G Bemis, Ilaria Liccardi, and Min Chen. 2017. An Empirical Study on the Reliability of Perceiving Correlation Indices using Scatterplots. In Computer Graphics Forum, Vol. 36. Wiley Online Library, 61--72.

Digital Library

[62]

Drew Skau and Robert Kosara. 2016. Arcs, angles, or areas: individual data encodings in pie and donut charts. In Computer Graphics Forum, Vol. 35. Wiley Online Library, 121--130.

Digital Library

[63]

M. Stokes, M.D. Fairchild, and R.S. Berns. 1992. Precision requirements for digital color reproduction. ACM Transactions on Computer Graphics 11, 4 (1992), 406--422.

Digital Library

[64]

Maureen Stone, Danielle Albers Szafir, and Vidya Setlur. 2014. An engineering model for color difference as a function of size. In Color and Imaging Conference, Vol. 2014. Society for Imaging Science and Technology, 253--258.

[65]

Danielle Albers Szafir. 2018. Modeling Color Difference for Visualization Design. IEEE transactions on visualization and computer graphics 24, 1 (2018), 392--401.

[66]

Danielle Albers Szafir, Maureen Stone, and Michael Gleicher. 2014. Adapting color difference for design. In Color and Imaging Conference, Vol. 2014. Society for Imaging Science and Technology, 228--233.

[67]

Anne Treisman and Sharon Sato. 1990. Conjunction search revisited. Journal of experimental psychology: human perception and performance 16, 3 (1990), 459.

[68]

Lothar Tremmel. 1995. The visual separability of plotting symbols in scatterplots. Journal of Computational and Graphical Statistics 4, 2 (1995), 101--112.

[69]

André Calero Valdez, Martina Ziefle, and Michael Sedlmair. 2018. Priming and anchoring effects in visualization. IEEE transactions on visualization and computer graphics 24, 1 (2018), 584--594.

[70]

Colin Ware. 2000. Information visualization (3 ed.). Morgan Kaufmann.

Digital Library

[71]

Colin Ware. 2009. Quantitative texton sequences for legible bivariate maps. IEEE Transactions on Visualization and Computer Graphics 15, 6 (2009).

Digital Library

[72]

Liang Zhou and Charles Hansen. 2016. A survey of colormaps in visualization. IEEE Transactions on Visualization and Computer Graphics 22, 8 (2016).

Digital Library

Cited By

Dufresne-Camaro CSakamoto YIrani P(2024)Evaluating the effects of colour blending on optical-see-through displays for ubiquitous visualizationsProceedings of the 50th Graphics Interface Conference10.1145/3670947.3670982(1-13)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3670947.3670982
Yan RSun JDavis A(2024)Chromaticity Gradient Mapping for Interactive Control of Color Contrast in Images and VideoProceedings of the 37th Annual ACM Symposium on User Interface Software and Technology10.1145/3654777.3676340(1-16)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3654777.3676340
Oppenlaender JAbbas TGadiraju U(2024)The State of Pilot Study Reporting in Crowdsourcing: A Reflection on Best Practices and GuidelinesProceedings of the ACM on Human-Computer Interaction10.1145/36410238:CSCW1(1-45)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3641023
Show More Cited By

Index Terms

Measuring the Separability of Shape, Size, and Color in Scatterplots
1. Human-centered computing
  1. Visualization
    1. Empirical studies in visualization

Recommendations

Crowdsourcing graphical perception: using mechanical turk to assess visualization design
CHI '10: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Understanding perception is critical to effective visualization design. With its low cost and scalability, crowdsourcing presents an attractive option for evaluating the large design space of visualizations; however, it first requires validation. In ...
On the perceptual influence of shape overlap on data-comparison using scatterplots▪
Highlights
- A measurement for overlap in multiclass scatterplots based on human perception.
Graphical abstract

Display Omitted

Abstract
Scatterplots can be used for a wide range of visual analysis tasks, for example comparing correlations or variances of clusters across potentially multiple classes of data, in order to find answers to higher-level questions. Comparing ...
Interactive Context-Preserving Color Highlighting for Multiclass Scatterplots
CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems

Color is one of the main visual channels used for highlighting elements of interest in visualization. However, in multi-class scatterplots, color highlighting often comes at the expense of degraded color discriminability. In this paper, we argue for ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

CHI '19: Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems

May 2019

9077 pages

ISBN:9781450359702

DOI:10.1145/3290605

General Chairs:
Stephen Brewster
University of Glasgow, Scotland, UK
,
Geraldine Fitzpatrick
TU Wien, Austria
,
Program Chairs:
Anna Cox
University College London, UK
,
Vassilis Kostakos
University of Melbourne, Australia

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

SIGCHI: ACM Special Interest Group on Computer-Human Interaction

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 02 May 2019

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Funding Sources

National Science Foundation

Conference

CHI '19

Sponsor:

SIGCHI

CHI '19: CHI Conference on Human Factors in Computing Systems

May 4 - 9, 2019

Glasgow, Scotland Uk

Acceptance Rates

CHI '19 Paper Acceptance Rate 703 of 2,958 submissions, 24%;

Overall Acceptance Rate 6,199 of 26,314 submissions, 24%

Upcoming Conference

CHI '25

Sponsor:
sigchi

CHI Conference on Human Factors in Computing Systems

April 26 - May 1, 2025

Yokohama , Japan

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

32
Total Citations
View Citations
1,803
Total Downloads

Downloads (Last 12 months)557
Downloads (Last 6 weeks)64

Reflects downloads up to 22 Nov 2024

Other Metrics

View Author Metrics

Citations

Cited By

Dufresne-Camaro CSakamoto YIrani P(2024)Evaluating the effects of colour blending on optical-see-through displays for ubiquitous visualizationsProceedings of the 50th Graphics Interface Conference10.1145/3670947.3670982(1-13)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3670947.3670982
Yan RSun JDavis A(2024)Chromaticity Gradient Mapping for Interactive Control of Color Contrast in Images and VideoProceedings of the 37th Annual ACM Symposium on User Interface Software and Technology10.1145/3654777.3676340(1-16)Online publication date: 13-Oct-2024
https://dl.acm.org/doi/10.1145/3654777.3676340
Oppenlaender JAbbas TGadiraju U(2024)The State of Pilot Study Reporting in Crowdsourcing: A Reflection on Best Practices and GuidelinesProceedings of the ACM on Human-Computer Interaction10.1145/36410238:CSCW1(1-45)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3641023
Quadri GWang AWang ZAdorno JRosen PSzafir D(2024)Do You See What I See? A Qualitative Study Eliciting High-Level Visualization ComprehensionProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642813(1-26)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642813
Strain GStewart AWarren PJay C(2024)Effects of Point Size and Opacity Adjustments in ScatterplotsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642127(1-13)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642127
Fuchs JDennig FHeinle MKeim DDi Bartolomeo S(2024)Exploring the Design Space of BioFabric Visualization for Multivariate Network AnalysisComputer Graphics Forum10.1111/cgf.1507943:3Online publication date: 6-Jun-2024
https://doi.org/10.1111/cgf.15079
Wang JLi XLi CPeng DWang AGu YLai XZhang HXu XDong XLin ZZhou JLiu XChen W(2024)AVA: An automated and AI-driven intelligent visual analytics frameworkVisual Informatics10.1016/j.visinf.2024.06.0028:2(106-114)Online publication date: Jun-2024
https://doi.org/10.1016/j.visinf.2024.06.002
Li CGuo HYin MZhou XZhang XJi Q(2023)A Systematic Review of Factors Influencing Signage Salience in Indoor EnvironmentsSustainability10.3390/su15181365815:18(13658)Online publication date: 13-Sep-2023
https://doi.org/10.3390/su151813658
Tseng CQuadri GWang ZSzafir D(2023)Measuring Categorical Perception in Color-Coded ScatterplotsProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581416(1-14)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581416
Zeng ZBattle L(2023)A Review and Collation of Graphical Perception Knowledge for Visualization RecommendationProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581349(1-16)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581349
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

HTML Format

View this article in HTML Format.

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents