Readings:

Excel (either Mac or Windows): combines spreadsheets and graphing

(one can use other applications, such as SPSS, SAS, etc.)
 
 
 
 
 

2. This leads to a paradoxical quality of graphic thinking: on the one hand, a graph should be transparent enough so that the observer sees data and not design (-> data variation not design variation). YET, the form of the graphic itself shapes the structure of perception: the assumption that there is a relationship between time and a variable, or between different variables, or between space and time, etc. (relate to paradigms).
 
 

4. Inductive vs. deductive: how much to demonstrate a specific point with the graphic vs having the reader/viewer draw their own conclusions and see their own patterns. NOTE: pure inductive data presentation seems impossible: all graphics involve choices over what data to present and not to present.
 
 

5. the connection between good writing and good graphics:
 

6. Compare:

information (data) to communication (presentation).

The first is latent, the latter is actual. In planning theory, there is a shift from the former to the latter (e.g., communicative-based action).
 
 
 

In this age of digital, the issue of information content is often seen as data storage.
That is, we emphasize the amount of digital space needed for data storage:  e.g.,   this Netscape Communicator file (consisting mostly of text) file is about 48,000 bytes.  (Had there been more visuals, the file size would be MUCH larger.)
 
 

"a picture tells a 1000 words."

But there is a difference between data storage vs. effective content

A photo in digital form (a 4*6 inch photo scanned on a scanner at 250 dpi -- dots per inch) may require 6 megabytes of storage, which is 6,000,000 bytes or 1,536,000,000 bits (that is, over 1 billion sets of 0/1 binary bits of data to represent a simple snapshot -- and still at a lower visual quality than the standard drugstore photo print.) Typical digital cameras (as of early 2002) record images 1-2 MB, while the better ones have 4-5 MB. Standard 35mm slide film is generally still more detailed (but digital is catching up).

Therefore:  a picture may tell a thousand words, but require 6 million bytes (6 megabytes) to be stored digitally.  1000 words may require just about 6,000 bytes to store.
In other words, one digital picture requires as much storage space as 1000 words * 1000 = 1,000,000 words (which is equal to about 10 books!)

Another example:  a color pie chart, generated by Excel, depicting the percentage of men vs. women in planning, contains just a single data point.   Yet the pie chart image itself, stored digitally, might require 6,000 bytes, which is 1,536,000 bits (1 or 0 elements).
 

Why the discrepancy between data storage size and effective content?

• redundancy of information:  e.g., it may take 2 megs to simply store a uniform blue sky background in a photo.
• the human eye can't process all that detail stored (or differentiate between the millions of different color possibilities for each pixel)
• there is thus a difference between latent information and usable (or effective) information.

So why digitize images if they are so data intensive and of lower visual quality?
This is the digital age:  allows for images to be standardized, manipulated, and transmitted in ways traditional images cannot.  That text, data, graphs, photographs, drawings, sound, etc., can all be stored and transmitted in a single, standardized format (e.g., CD-rom, modem lines, etc.)

An Example:
 

 

	An 8x10 inch color photograph made from a 35 mm negative (traditional silver-based film processed in a darkroom)	a digital image (e.g., taken with a digital camera;  or a scanned photograph;  or a scanned slide transparency)
Storage	image can be stored as a negative film strip or as a print this "storage" is an inexpensive technology	stored digitally.  thus is treated the same as text, sound, etc. (e.g., ISDN).   with high quality images, a high data storage requirement needed.
Image quality	the image quality is potentially quite high (depending on the quality of the camera optics, the film, the paper and the processing.) easy to increase or decrease the size of the image (through magnification of the enlarger image)	image quality is not as high, though getting better
Modification of the single image	hard to modify the image (except through "dodging" and other darkroom techniques)	much easier and with far more possibilities (e.g., with Photoshop software).
combination of multiple images	not easy:  either through double exposure techniques or collage cut-and-paste.	much easier and with far more possibilities
transference of image     Copying Image	the photo can be mailed the photo can be sent by wire or fax after first converted to dots.   (with loss of quality) each subsequent copy leads to a reduction in quality from the original	quite easy (as easy as any other form of digital information)     one can make an identical copy

That said, I envision a future technological era NOT defined digitally (binary), but one in which data is stored and processed either as a hologram, or neurologically (biological), etc.

Relate to Kant time and space as categories of the mind: the first way we classify sensation. (as paraphrased by Durant):

1. "show the data" [p. 13]

2. "induce the viewer think about substance rather than about methodology, graphic design, technology of graphic production, or something else" [p. 13] (i.e., transparent and revealing)

3. "avoid distorting" [p. 13]

4. "present many numbers in a small space." [p. 13] [data density]

5. "make large data sets coherent" [p. 13] [communication, not just information]

6. "encourage the eye to compare different pieces of data" [p. 13]

7. "reveal the data at different levels: from a broad overview to the fine structure" [p. 13]

8. "serve a reasonably clear function: description, exploration, tabulation, or decoration." [p. 13]
 
 
 
 
 

a table?

a chart?

text?

a photo or slide? (and digital or traditional silver-based film?)

a map?  (and a hand-drawn paper map, a vector-based (polygons) GIS map, or a raster-based (grid) GIS map?)

a drawing?

a site plan?
 
 

? All are forms of representation, with advantages and drawbacks. Don't automatically graph everything: a shortcoming of EXCEL and Lotus: the ease to graph. Create a graph because it communicates something substantial and meaningful that the other formats cannot.
 
 

GOAL: give the viewer the greatest number of ideas, in the shortest time, with the least amount of ink, in the smallest space.
 
 
 
 

graph: lots of data, to be compared, multivariate; little text/labels.

tables: small, non comparative, highly labeled data sets, often univariate.
 
 

one rule of thumb: what is the Tufte information/ink ratio for the two approaches? which one is less?
 
 
 
 

2. what kind of chart? pie, bar, column, scatter, line, etc.
varies by the number of variables and cases, the amount of labels, the continuity or discontinuity of data over time, etc.
 
 
 
 

3. dimensions of data vs. dimensions of graphs (general rule: don't have more visual dimensions than information dimensions. i.e., avoid 3-d graphs).
 
 
 
 

4. complexity vs. simplicity: how much information does the graph include? how much does the reader readily pick up? What is just chart-junk? (This is Tufte's INK/INFORMATION RATIO) or better:

6. close and far: the first overall look and the second in depth look (graphs should encourage both)
 
 

7. Data density: the eye can pick up fine details; most graphs waste this ability to process fine details. (because they often have so little information in them.) e.g., a bar chart of 3 cases; 1 variable. low density of data there. (and why have a chart as all? for decoration and emphasis?). TUFTE is interested more in representing complex, relational data). Remember: graphics can be shrunk way down in size, and the eye can still comprehend.

Low density: can be well less than 1 data entry/square inch. Or as high as 100- 1000s/square inch). Maps can handle higher density, since the reader can arguably (1) easily relate spatial data side-by-side, and (2) it requires little labeling, since one assumes that the reader can interpret a map without labels. (This may be a potential virtue of GIS: geo-coded and spatially displayed data.)

Compare the data density of the following map and this pie chart:

source: http://www.census.gov/geo/www/mapGallery/images/2k_night.jpg

8. Compare to photographs and drawings. Does a picture tell a 1000 words? or video? when are these effective compared to text and tables? (especially in this interchangeable world of ISDN?)
 
 
 
 

9. Know the difference between:  unit of analysis, case, variable, value (of a variable)
 
 

11. Finally, the current challenge to get computer software to follow the rules of Tufte. Sometimes you may need to import your half-finished graph into a paint or draw program. And: there is nothing wrong with hand-drawn visuals!
 
 
 
 
 
 
 

1. Be sure to use a full title for the graphic (variables, dates, locations, units of analysis). I.e., rather than "Crime and Infant Mortality," use "Crime Rate per 100,000 Population (1991) and Infant Death Rate per 1,000 Live Births (1988) in the Largest 40 U.S. Cities". If you choose to use a shorter title, be sure that somewhere the variables are fully defined.
 
 

2. List the source of the data (just as you would for a data table.). Anticipate that some readers may simply photocopy your chart rather than your whole article or dissertation; the graph should be somewhat self-standing. (Include a descriptive caption at the bottom if useful).
 
 

3. Explain and label missing data. Be sure that the reader knows the difference between a missing value and a zero-value (if you are not careful, statistical software will treat these two as the same).
 
 

4. Order the chart in some useful way. And if the chart has an ordering to it, be sure to state this (e.g., cities ranked by population size).

alphabetical is not always the best:

try instead ordering based on some relevant variable (here simply the variable displayed):

5. If you use a subset of the cases, be sure to explain the logic of the selection (e.g., among the 10 largest U.S. Cities).
 
 

6. Label the x and y axes.
 
 

7. Use a legend or labels to define variables in a multivariate bar or column chart. You do not need a legend for a univariate chart.
 
 

8. Often an x-y scatterplot is preferable to a bar chart (or column chart) with two variables. Scatterplots use less ink, and they usually reveal bivariate relationships (i.e., the relationship between x and y) far better than bar or column charts.

Here is the same bivariate data displayed two ways:
 

9. It is fine to do a regression analysis, but be sure to explain your results.
 
 

10. Do not add the Hispanic population with other racial categories (black, Asian, etc.), since the U.S. Census states that "persons of Hispanic origin may be of any race."
 
 

11. Avoid 3-dimensional graphs unless the data itself is 3-dimensional. Even then, 3-d is hard to read.

13. Avoid column charts with too many data points: the columns become too narrow (and the labels too small or some not showing) to read easily. (This also applies to bar charts). This problem literally multiplies with multiple variables displayed on one chart. Above about 10-15 data points (e.g., columns), I would consider an alternative format (such as scatterplot, a table, grouping data, etc.). Or use several charts, side-by-side, with the same format (e.g., one for each variable). [see Tufte on the use of "small multiples"]. an example of a problematic chart below:

Note how it is really hard to see patterns in the data (with 3 variables and 16 cases). The gray background is distracting too. Best to avoid this type of chart. (Remember: just because Excel can create a chart from your data doesn't mean that it is necessarily a good format for the data.)

14. Overall, show the data; have the view think the patterns in the data, not the graphic design; avoid distortion; encourage the eye to compare data; clearly label the graph.
 
 



 
 
 
 

1. how to determine the denominator: think of a survey result: what to do with nonresponses, etc.

2. also: "the percentage effect": a percentage may go down, when the absolute goes up. How do we interpret? (GIve an example). Well, it depends on whether the actual theory of phenomenon is better explained by absolute or percentage.
 
 
 

-- e.g., bank interest, rabbits reproducing, etc.