If so, education could really fix the crime.
The results were staggering.
Turned out, each recent year had had the same number of crimes of the same degree in the same regions, thus, meaning that facts of moral order are subject to invariable laws.
The Statistique morale de l’Angleterre comparée avec la statistique morale de la France, written by Guerry was illustrated by charts comparing the data on the map.
Guerry’s discovery was one of the earliest uses of data and its visual depiction to make social changes and impact the society on a national scale.
Charles Joseph Minard, an engineer and one of the earliest data visualizers used only one chart to report the investigation of a collapsed bridge:Later, he flexed his dashboard muscle on visualizing the Napoleon’s invasion of Russia and the dynamic of his defeat:In 1854, doctor John Snow developed a map of London cholera outbreak.
It was noted that some areas of the city had a lot more people dying from cholera than other places.
The contamination mechanism of cholera was unknown at a time, making it hard to take action against.
John Snow took the data in bodily counts of critical areas and put them on the map.
He then used different variables to find dependencies.
One of them shone through:It was the Broad Street public well with a set of pumps around the area that was the main drinking-water source and a source of cholera infection.
With no medical proof for this theory, Snow’s assumption repeated results in other cities and managed to anticipate the cure.
The true statistics and data visualization genius came in Émile Cheysson.
His 1880s’ graphs showcase all the modern techniques:Cheysson developed a wide variety of recursive multi-mosaic maps that those who could appreciate the data science immediately jumped on.
All these historic charts have two gigantic commonalities that modern data designers can learn from:They focus on the phenomena, not the numbers.
Whether it’s fixing the minds of the criminals or protecting the city from a deadly epidemic, the most impactful charts come from the problem.
If we focus on aesthetic and treat dashboards as an art object, they won’t work.
They are interocular.
“A good graph hits you in between the eyes” — Michael Friendly.
If you have to explain the graph and it doesn’t speak up at first glance, it is weak and worthless.
The functional art“Our fantasy self tends to be very visual, whereas our actual experiences are corporeal.
” — Yuval Noah Harari, Sapiens: A Brief History of HumankindFor a human, to see means to understand.
Not only do we visualize what we want, but we also create our own symbols in line with our inner language, as well as adopt the common symbolism.
This mechanism helped us survive as species and also developed our ingenuity.
With time, we faced a new challenge — surviving what Richard Saul Wurman called the “tsunami” of bits of information that was “cresting the horizon”.
He also introduced the concept of the “black hole between data and knowledge.
” The more knowledge we discover, the wider the gap between what we know and what we think we should know.
Alberto Cairo, a Spanish information designer, professor, and author of The Functional Art: An Introduction to Information Graphics and Visualization uses the term “wisdom” as the final point of the transformation of reality into knowledge:“We reach wisdom when we achieve a deep understanding of acquired knowledge, when we not only “get it,” but when new information blends with prior experience so completely that it makes us better at knowing what to do in other situations, even if they are only loosely related to the information from which our original knowledge came” — Alberto Cairo.
To make this transformation, words are not enough.
We have to rely on the effective visuals.
What we recognize as effective and functional automatically becomes beautiful as well.
This has brought us to a fine line between aesthetic value and actual problem-solving.
On the forefront of the functional and truthful data visualization is a group of designers with science degrees.
The principles they follow are as close to those of Guerry, Minard, and Snow.
They visualize data to squeeze the signal out of the complicated matters.
They are solving problems of human well-being.
It is our visual nature that also sees beauty in the graphs they create.
13 years ago Manuel Lima founded VisualComplexity.
com, a resource space for anyone interested in the visualization of complex networks.
Today, it indexes over 1000 projects depicting the diffusion of information regardless of the industry and the field of knowledge.
What Lima managed to create is as fascinating as the discoveries he made about the principles of visualization.
Manuel points out these visualization insights:NatureUsing nature for inspiration to construct visuals of data can bring outstanding results.
On the left is a human eye.
On the right is the circle visualizing a year of human life with healthy levels of blood sugar and insulin.
“All art is but imitation of nature.
” — Lucius Annaeus SenecaGenuine metaphorsMost of the visualization techniques known today are a reinvented wheel.
As Lima points out, the forgotten metaphors are on par with those that go back not decades but millenniums.
This is a visual analysis of member profiles of an Internet community, created by Felix Heinen.
This is a visualization of Music Theory by Monochord.
From the 13th century.
NetworkismWe not only find inspiration for data visualization in nature but give it back as an artform.
Networkism is an artistic movement deriving from the principles of visualization.
Because the connections are so natural, it’s easy to find correlations and understand visualization on a deeper level.
On the left is a red thread art installation by Chiharu Shiota and on the right is the Beetroot Designgroup poster which illustrates the connections between the occurrences of the Romeo and Juliet names in the Shakespeare’s play with 55,440 red lines.
The visual gimmickBecause we are a visual species, we are easily manipulated.
When visualization becomes nothing but a tool to draw attention and make an impression, it’s a gimmick.
Everyone is used to the complex simplicity of Hollywood UI.
This is how to tell a real data visualization from an agenda-driven trumpery according to Alberto Cairo:Proud statsWhen the graph is representing one clear-cut assumption, it is orchestrated.
Non-data journalists simplify the information and add validity by putting it in charts.
People unfamiliar with the theory of probability get easily misinformed because we are not used to checking the information we get.
Data is all around us we are only learning to work with it properly.
One of the ways to fix this is to add explanatory notes to the graph and let the people figure things out on their own.
Shameful falsehoodWords like ‘everyone’, ‘all’, ‘proven’ if not followed by proof are there to falsify truth and trick you into believing a lie.
People twist the data and only display a part of it to make up facts confirming their agenda.
Visual complexity only adds up to it.
The more you are confused, the more you trust confident guidance no matter where it’s from.
Keeping the data back is no different from lying.
What’s worse is this can be unintentional and due to the lack of data.
For that reason, research is as important as is the visualization itself.
Less public — more honestA common trap to fall into when dealing with data visualization is a truth bias.
When the data confirms our own beliefs, we tend to trust it more.
However, we must check not only our own data but also the data we use to confirm or dismiss assumptions.
Before quoting something, it’s better to do the math and not quote it if it confirms your falsehood.
Being truthful is bigger than being right.
Ironically, the most truthful data is often the hardest to find.
The dashboard mantraBehind every visual graph, there is a principle that has to tell.
We want the image to express what otherwise can’t be expressed in the same fashion.
The core of every dashboard or a chart is the contrived impact.
In other words,Squeeze the signal out of the data.
The least impact comes from pointing out what people are already thinking.
It might seem to be a safe choice to use visual data to confirm what people know, but in reality, it’s a waste.
As we said, it has to be about the phenomena and the impact it makes on people rather than another angle of the same problem.
OstensivenessEven though representing data to its fullest makes charts and dashboards look complex, the purpose of the chart has to be obvious within the first five seconds.
This might contradict a lot of what’s been said above but bear with me.
The first five seconds is what we need to get familiar with a visual piece in front.
This is the time to grab their attention and motivate people to study further.
Dashboard by uixNinja2.
The inverted pyramid logicIn order to speak within the first five seconds, the graph has to showcase a strong logic.
We’ve spoken about the inverted pyramid model before.
It came from journalism where attention scarcity is a real issue.
The first thing to notice has to be the most substantial and bear the most of the information people came for.
The second layer is details confirming the first part and adding to the general understanding of it.
At the bottom is all the background details taking the problem of the graph to a deeper level of understanding.
Health Tracker by CubertoThis one screams something is not alright.
Shows the graphic with a critical issue, then presents the data to prove that, and then adds the details to complete the picture.
Choosing the right type of visualizationThe choice of visualization type depends on the type of information to be shown.
This is where beginner-level designers studying information visualization mess up.
Starting from the looks here is wrong.
Depending on the type of impact you are looking to make with your data, you have to consider the specific types of graphs to go with.
Confuse and mislead are the worst two things visualized data can cause apart from the blatant lie serving someone’s interests.
The best way to avoid these is to research which type of data visualization works best for your type of data.
Geospatial dataChoropleth mapCartogramDot distribution mapDasymetric mapContour lineTravel time map by Kohei SugiuraTemporal dataTimelineGantt chartThemeRiverPie chartSankey diagramSankey diagram by datavizblog.
comMultidimensional dataHistogramTreemapScatter plotHeat mapSpider chartMarimekko chartNadieh Bremer’s Radar Chart RedesignHierarchical dataDendrogramRadial treeHyperbolic treeWedge stack graphIcicle chartOrgOrgChart: The Evolution of an OrganizationNetwork dataMatrixNode-link diagramDependency graphAlluvial diagramTube mapThe dependency hell: the runtime dependency graph of Mozilla FirefoxFor further exploration, check out the Visual Complexity website and the books by Manuel Lima, Alberto Cairo, and Michael Friendly.
A lot of modern designers get into data visualization in their dashboard designs.
Most of them apply the principles of ostensiveness, inverted pyramid logic, and select the appropriate data visualization types for specific data.
Some still see dashboards as a purely artistic means.
We believe the power of data is worth trying to combine the two approaches and promote truth and objectivity as well as an aesthetic pleasure.