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The Visual Display of Quantitative…
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The Visual Display of Quantitative Information (original 1983; edição 2006)

por Edward R. Tufte (Autor)

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4,574521,897 (4.42)29
This book deals with the theory and practice in the design of data graphics and makes the point that the most effective way to describe, explore, and summarize a set of numbers is to look at pictures of those numbers, through the use of statistical graphics, charts, and tables. It includes 250 illustrations of the best (and a few of the worst) statistical graphics, with detailed analysis of how to display data for precise, effective, quick analysis. Also offered is information on the design of the high-resolution displays, small multiples, editing and improving graphics, and the data-ink ratio. Time-series, relational graphics, data maps, multivariate designs, as well as detection of graphical deception: design variation vs. data variation, and sources of deception are discussed. Information on aesthetics and data graphical displays is included. The 2nd edition provides high-resolution color reproductions of the many graphics of William Playfair (1750-1800), adds color to other images where appropriate, and includes all the changes and corrections during the 17 printings of the 1st edition.… (mais)
Membro:thekimfamily
Título:The Visual Display of Quantitative Information
Autores:Edward R. Tufte (Autor)
Informação:Graphics Press USA (2006), Edition: Reprint
Colecções:A sua biblioteca
Avaliação:
Etiquetas:Nenhum(a)

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The Visual Display of Quantitative Information por Edward R. Tufte (1983)

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This is Classic in the field of Visualization. I highly recommend anyone into UX/UI to go through this book. It's simple read and won't take much time.

Outline:

Part I — Graphical Practice:

1 Graphical Excellence
2 Graphical Integrity
3 Sources of Graphical Integrity and Sophistication

Part II — Theory of Data Graphics:

1 - Data Ink and Graphical Redesign
2 – Chart junk: Vibrations, Grids and Ducks
3 - Data Ink Maximization and Graphical Design
4 - Multifunctioning Graphical Elements
5 - Data Density and Small Multiples
6 - Aesthetics and Technique in Data Graphical Design

Part III — Design for Display of Information
1 - Epilogue



Part I — 1) Graphical Practice: Graphical Excellence:

Tufte’s summarizes by saying, it is a matter of substance, statistics, and of design. It gives the viewer the greatest number of ideas in the shortest time with least amount of ink space. It consists of communicating complex ideas communicated with clarify, precision and efficiency.

- It is about clarity in communicating with precision, efficiency that shows data, induce user to think about substance of visualization, avoid distortion, present many numbers in small space, make large datasets coherent, encourage eye to compare various pieces of data, reveal data at several layers of detail, serve clear purpose, be closely integrated with statistical and verbal description of data.

Graphics reveal about data.

-Every Graph or Visualization should let the user to think about the data, not the methodology or technique
-Time Series Data

Two Greatest Scientist of Modern Graphical Design are J.H Lambert, Swiss German Mathematician and William Playfair, Scottish political economist.
Playfair preferred Graphics.

-Descriptive Chronology is not casual expression.

Charles Minard, the French Engineer who explained Napoleon’s army — combination of data-map and time series.

Most Modern Graphics are relational — x, y that encourages to find out casual relationship.

Part 1 — 2) Graphical Practice: Graphical Integrity:

For many of us, we constantly think of lies when we look at a graphic or statistic.
Around 1960, John Turkey made Graphical Practice respectable.

What is distortion in Data Graphic?

Lie Factor = size of effect shown in graphic/ size of effect in data
If it’s greater than 1.05 and less than .95, then it’s substantial

Show data variation not design variation.

Context is important for Graphical Integrity — compared to what?

Lying Graphic cheapen graphical art everywhere.


The Six principles of Graphical Integrity:

- Representation of numbers present in the graphic should be directly proportional to numerical quantities reported
- Clear, detailed and thorough labeling should be used to defeat graphical distortions and ambiguity
- Show data variations, not design variations
- In time series display, use standardized monetary units
- The number of information carrying dimensions should not exceed number of dimensions in data
- Graphics must not quote data out of context



Part 1 —3) Graphical Practice: Sources of Graphical Integrity and Sophistication

Why do they lie?
-Lack of Quantitive skills, the doctrine that statistical data is boring

Many believe that graphics are there to entertain unsophisticated readers. Japan has the highest use of statistical graphics in their newspaper.

Part II — 1) Theory of Data Graphics: Data Ink and Graphical Redesign:

Data Graphics should draw viewers attention to substance of data. It should form quantitive contents.

Fundamental principle is, “Above all else, show the data.” This is the principle for a theory of data graphics.

Data Ink ratio is data ink/total ink used in graphic. Remember to maximize the data ink ratio devoted to the data. Other side of data ink ratio is to erase non-data-ink, within reason.



There’s five principles in theory of data graphics produce substantial changes in graphical design.
-Above all else show the data
-Maximize the data-ink ratio
-Erase non-data-ink
-Erase redundant data-ink
-Revise and edit


Part II — 1) Theory of Data Graphics: Chartjunk, Vibrations, Grids and Duck

Interior decoration of a graphic produces a lot of raw ink that does not tell the viewer anything new. The Grid might include a lot of chart-junk.

When Graphics are taken over design or styles rather than quantitative data, it is called as Big Duck.

Part II — 2) Theory of Data Graphics: Data Ink Maximization and Graphical Design.

Reducing ink ratio in some of the charts might induce changes.

Part II — 3) Theory of Data Graphics: Multifunctioning Graphical Elements

The Same Ink should be used for more than one graphical purpose, it carries data information and performs a design function usually left to non-data-ink. Data based grid is shrewd graphical devise.

Sometimes, the puzzle and hierarchy of multifunctioning graphical elements can data graphics into visual puzzles, crypto graphical mysteries. Colors sometimes generate graphical puzzles. The shades of gray gives us more easier comprehension. This is the key.

Part II — 4) Theory of Data Graphics: Data Density and Small Multiples

How many statistical graphics take advantage of ability to detect large amounts of information in small space? Let’s begin with empirical measure of graphical performance and data density.

Data density of graphic = number of entries in data matrix / area of data graphic

More information is better than less information. Maximize data density and size of data matrix within reason. High volume data must be designed with care. The cost of chart junk, non-data-ink, and redundant data-ink is even more costly in data rich design. We apply shrink principle, which means graphics can be shrunk way down. Bertin’s crisp and elegant line displays small scale graphics in a single page.

Small Multiples, resemble frame of movies, series of graphs, series combination of variables.



A Well designed Small Multiple will contain:

- inevitability comparative
- deftly multivariate
- shrunken high density graphics
- based on large matrix
- drawn exclusively with data ink
- efficient in interpretation
- often narrative in content.



Part III — Theory of Data Graphics: 1) Aesthetic and Technique in Data Graphical Design:

-Graphical Elegance is often found in simplicity of design and complexity of data

Visually attractive graphics gather power from content. Basic structure for showing data are sentence, table and graphic. Often two or three of this should be combined. Make Complexity accessible.

Graphics should prefer towards horizontal, greater in length than height.
Lines in Data Graphic should be thin. Graphical elements look better when their proportions are in balance.

( )
1 vote gottfried_leibniz | Jun 25, 2021 |
A slightly dated text with very modern adaptations.

This (classic?) book focuses on what makes a good graphical display of data and has a lot of examples of how to do, including the mandatory horror examples of how to not to do.

I agree with almost everything he says though some of it doesn't really matter that much in computer generated graphics (saving ink to save drawing time). Unfortunately it becomes quite obvious that a lot of graphical display of data in blogs, newspapers or other media has been designed by people that never found this book.

If I'm to summarize his advice it's "draw less". Draw less to not confuse the reader with mixed signals and keep it simple for the same reason. He has a number of examples of what can be removed from a "default" graph without losing any information and all and in the progress enhancing the visualization of the actual data. ( )
  bratell | Dec 25, 2020 |
A really interesting book, still useful despite being dated. A lot of Tufte's lessons have been internalized by contemporary designers, though there are certainly lots of terrible charts out there. Still, the use of moiré he inveighs against is thankfully long since abandoned. The book is not as useful today as it would have been to read 30 years ago, but still informative. ( )
  dhmontgomery | Dec 13, 2020 |
An absolutely essential manual for anyone working in graphics who wants to use data analysis or quantitative information in any way. The book doesn't offer concepts, just information, and it is clear and comprehensive. ( )
  ephemeral_future | Aug 20, 2020 |
A classic.
A beautiful piece of work.
A minimal primer.
A coherent aesthetic philosophy.
An amulet of protection against data-imbued confusions presented as "infoviz"es.
A 'start here'.
The last word. ( )
  GirlMeetsTractor | Mar 22, 2020 |
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Excellence in statistical graphics consists of complex ideas communicated with clarity, precision, and efficiency.
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This book deals with the theory and practice in the design of data graphics and makes the point that the most effective way to describe, explore, and summarize a set of numbers is to look at pictures of those numbers, through the use of statistical graphics, charts, and tables. It includes 250 illustrations of the best (and a few of the worst) statistical graphics, with detailed analysis of how to display data for precise, effective, quick analysis. Also offered is information on the design of the high-resolution displays, small multiples, editing and improving graphics, and the data-ink ratio. Time-series, relational graphics, data maps, multivariate designs, as well as detection of graphical deception: design variation vs. data variation, and sources of deception are discussed. Information on aesthetics and data graphical displays is included. The 2nd edition provides high-resolution color reproductions of the many graphics of William Playfair (1750-1800), adds color to other images where appropriate, and includes all the changes and corrections during the 17 printings of the 1st edition.

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