Different-sized discrete crosses placed in an organized lattice pattern can assist the human eyes to read numerical values on statistical graphs, enabling more precise interpretation and enlarging the utility of statistical graphs that visually represent numerical quantities. This paper presents a novel graph-plotting method that places roughly ten thousand of separated grids on a graph, providing human data analysis with an easy access to arbitrary numerical readouts from a statistical graph. At present, this functionality has been lacking in the existing graph-plotting softwares.

1. IEEE PacificVis, Kobe, 2018-04-12
To Make Graphs Such As Scatter Plots
Numerically Readable Toshiyuki Shimono
The grids are crucial to understand the meaning.
Background : The conventional graphs usually lack the
numerical precision to be read meaningfully enough, although
they are useful to show numeric meanings such as the value
ranges and the correlation of variables which bare numbers
cannot do.
New way to plot: The author proposes putting various-
sized gridding-crosses in the graph background to allow human
eyes reading the number within an error of 0.5% of the graph
expansion without impairing the graph legibility.
Number data only A conventional graph New method
Numerical
readability
Each value can have
arbitrary precision.
The error in 5% of the max minus
min is usual w.r.t. the
coordinates (>_<)
The error in reading is within 0.5%
of the graph width or heights.
Readability
of meaning
Definitely behind the
graphs. Difficult to read
the whole quickly. (>_<)
Easy to see the up/down trends
by a line chart, the extension and
correlation from a scatter plot.
Almost good enough as the
conventional graphs to see the
numerical patterns.
0.0 0.2 0.4 0.6 0.8 1.0
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y*0.7+x*0.3
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y*0.7+x*0.3
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0.00 0.25 0.50 0.75 1.00
0.00
0.25
0.50
0.75
1.00
(1) No grid (2) “Line” grids (3) “Cross” grids (4) 4x4 vs. 5x5
Can you read out the
coordinates of the 24 points? The grids with 3 different
densities help to read the
number with an error < 0.5%
The darkness reduced and the
distinction to other graph
objects becomes clearer.
Which intervals are simpler to
see: 20% or 25% on the axes?
Lorenz curve
Accumulative population rate
What percentage of money is used
by the top paying persons by credit cards?
Accumulativepaymentoccupation
TwitterFollowernumber
The Following number
The inverse function of
accumulative empirical
distribution function
Blue, green, orange : JPY paid
in 2016 via the credit card at the
age of 20, 40, 60 in Chugoku Region.
Thin black : all who are living in
Tokyo metropolitan area.
“Percentile Rank” in card number
PercentileinJapaneseYen(¥)paidin2016AD
The walls of
following
2,000
|←
Twitter
Celeb
habitat


96.2% of money is
from half of buyers.
76.8% of all is paid by
only 20% top buyers.
1e-06
1e-05
1e-04
0.001
0.01
0.1
0.5
0.9
0.99
0.001
0.01
0.1
0.5
0.9
0.99
0.999
0.9999
0.99999 Logit transformed
Lorenz curve
10% is from
0.55% users
1.1% is from
0.01% users
Lower10%
pays0.03%
GNU R plotted this graph
from cumulatively added
sum from 1.6x108 records
on a table on BigQuery.
Both ends of the curve
are hidden to keep
privacy of the
customers.