Re: ARIA Graphics Module -- proposed roles hierarchy & data properties

Jason,

Jason wrote -
The underlying principle which comes to mind here is that the user
agent/assistive technology should perform the analyses that an informed
observer could intuitively and easily carry out by inspecting the data as
visually presented. The level of accuracy should be the same as what an
informed recipient of the visual representation would be able to achieve.

Of course, if the author provides the actual data (as we’ve proposed to
allow but not to require), then more accurate analyses can be undertaken.

A good visualization will allow a sighted user to get the important
information in a few milliseconds, via the the visual process. If done
correctly, the important information is easy to see and a very low level
part of the brain does low level feature analysis and provides your brain
the cue to look at the important data instantaneously. Often the important
information is an overall impression - "oh the bars in the chart are all
about even - not a lot of difference in this data set" or "wow look at that
one in the upper right corner, it is not near any other data point" or "
the tread is always downward".  These things can be hard to covey to a user
by providing numbers and it will certainly take a user longer to dig out
the information. Note, if the important information were easy to see by
comparing numbers, then the intelligent author would have used a table
instead of a chart.

So what can sighted users get from a chart? I am giving WAG's for a sighted
user error of when estimating a continuous value. Sighted users don't
extrapolate values from locations on empty fields with any precision - my
guess is 10% error is normal. Using color with continuous scales gives you
a touchy-feely sense of relative value, but it is pretty difficult to
interpolate, my guess is 15% error. Size is difficult to estimate,
especially if the size is tied to area and not length. My estimate for
visually guessing linear size of symbol is 15% error and for areas 40%
error - people are really bad at comparing areas - especially circles. Are
folks really that bad at guessing and extrapolating, YES and it gets worse
as we get older or more impatient and it is a real pain to to do too. In
creating SVG examples I tried to interpolate values to add the information
in descriptions and in every case, I gave up and went back to the chart's
spec and read the data. For charts that I could not find the chart spec
for, I abandoned the effort and those charts do not appear in the examples.
What makes it really bad is a person's error in estimations are not
systematic and precise, rather they are random.

Another factor that affects interpretability of a chart is how well does
work with the data set to convey the information the author wants conveyed?
Choosing the right visualization is an art and for a few folks a science.
Scientific article authors will search for the optimal visualization for
their data and audience. But mostly what a business user will get are
automated reports that have a bar chart or whatever kind of chart an admin
decided on months ago, in your report and the chart may not be the optimal
choice for the particular data set. So the obvious message in the chart may
be "things are normal" and "this guy is the worst of the lot and this gal
is the best of the lot".

I think we should empower the author to pass along the information they
believe should be important to the user as they see fit.  After all the
author knows better than the user agent, what the context for the chart is
and what the role of the chart in the product is.

                                                              
                                                              
                    Regards,                     Fred         
                                                              
                   Fred Esch                                  
     Accessibility Focal, Watson Solutions                    
    AARB Complex Visualization Working Group                  
                     Chair                                    
        W3C SVG Accessibility Task Force                      
                   IBM Watson                                 
                                                              
                                                              






From: "White, Jason J" <jjwhite@ets.org>
To: Fred Esch/Arlington/IBM@IBMUS
Cc: Amelia Bellamy-Royds <amelia.bellamy.royds@gmail.com>,
            "public-svg-a11y@w3.org" <public-svg-a11y@w3.org>
Date: 09/01/2015 11:24 AM
Subject: Re: ARIA Graphics Module -- proposed roles hierarchy & data
            properties



The underlying principle which comes to mind here is that the user
agent/assistive technology should perform the analyses that an informed
observer could intuitively and easily carry out by inspecting the data as
visually presented. The level of accuracy should be the same as what an
informed recipient of the visual representation would be able to achieve.

Of course, if the author provides the actual data (as we’ve proposed to
allow but not to require), then more accurate analyses can be undertaken.


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