Awesome periodic table with aligned bars per cell

Periodic tables of the atoms are great visualizations. Much has been written about Mendeleev’s periodic table and other tables that organize atomic data. The periodic table is a powerful tool because the elements are organized and aligned by commonalities, enabling prediction of unknown elements in the early usage of periodic tables.

While looking at various tables regarding use of text to visualize data in tables, I stumbled across this periodic table by Henry Hubbard and William Meggers (1963) at the Smithsonian:


Data dense periodic table from Hubbard and Meggers 1963.

Most periodic tables show only a few attributes per element, such as the atomic symbol, the atomic number, and the name. But there are many more data attributes per element such as expansivity, compressibility, ionization potential, atomic weight, isotopes, crystal form, orbits, magnetism, state at room temperature, melting point, boiling point, atomic radius, and so on. What’s really interesting in Hubbard & Megger’s table is that they pack in all of this information into each cell using various visual cues, as shown in this blurry legend from an earlier edition:



Each table cell is packed with data.

Cell’s have text and numbers like many modern periodic tables, but they also have bars around the perimeter and triangular markers indicating quantitative values, plus dots, symbols and diagrams.  One may wonder:

Why is the quantitative data represented as bars around the cell, and not just numerical data?

Recall that the periodic table is organized so that rows and columns organize elements by commonality. By using bars, visual comparisons can be made along a row or column. Here is a redrawn simplification of the first column from this chart:


Closeup drawing from Column I showing bars and triangles around the perimeter and an overlay line showing trend.

This redrawn closeup is focused on the quantitative graphics around the perimeter of the cells. For example, the bottom bar on a cell shows the ionization potential in bright orange. A viewer visually attending to these orange bars can compare this quantity within a column by scanning vertically (as shown by the overlaid dashed orange line). In effect, this creates an embedded bar chart that spans across the cells – as shown by the overlaid orange dotted line. It is highest for Hydrogen (H) at the top of column, then decreases down successive elements in the column to Cesium (Cs). The next element in column, Francium (FR), has no bar, as presumably this value has not been measured when this chart was published; however, by observing the trend, one might predict the value for Francium.

Similarly, the top bar per cell can be visually scanned to show a trend (as shown by the overlaid dashed green line). In addition to the four perimeter bars around the cell, there are also tiny triangles that float along each edge, showing other quantitative variables. For example, the triangle on the right edge indicates specific heat by its vertical position. These can similarly be compared across cells.

Note that horizontally oriented bars better facilitate comparison within a column than across a row. That is, horizontally oriented bars share a common baseline along the left edge of the column. A common baseline allows for more accurate comparisons of quantities than bars that do not share a common baseline (Cleveland and McGill 1984, or Heer and Bostock 2010). It is unknown how Hubbard and Meggers specifically chose which variables to place horizontally and which to place vertically to facilitate columnar comparison and row-based comparisons.

The notion of creating these aligned marks in the context of other data seems to be an interesting idea for both packing a lot of data into the visualization while at the same time organizing the data to facilitate visual comparisons and projections.


About richardbrath

Richard is a long time visualization designer and researcher. Professionally, I am one of the partners of Uncharted Software Inc. I have recently completed a PhD in data visualization at LSBU. The opinions on this blog are related to my personal interests in data visualization, particularly around research interests related to my PhD work- this blog is about exploratory aspects of data visualization not proven principles.
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