Its NOT That Complex!

This visualization gives a general overview of the trends before we examine the data in further depth. The GTD data contained a row for each terrorist attack from to , and a extensive number of variables, including information about the time, place, terrorist group, weapons used, number of victims, type of victims, and so on, for each attack. After verifying the reliability and correctness of the dataset, we cleaned and filtered the data, reducing the number of variables, in order to focus only on the most important ones for our research.

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The goal of this analysis was to understand and visualize the countries that are most frequently attacked, in terms of the number of victims and number of attacks, as well as the evolution of these numbers through time. We decided to visualize the distribution of the attacks using a world map. Doing so, let us show the number of victims per terrorist attack grouped by country, as well as allowing the reader to compare the two historical periods identified in the trend overview: We decided to use a bubble chart to display the number of victims, with clear relative proportions.

In order to visualize the bubble chart on the map, we used Carto , a software that allows to create geo-located visualization using json data. We cleaned and filtered the data in order to create two different datasets for the two different time periods and the software provided us the first drafts of the visualizations. We then modified these drafts using Adobe Illustrator, creating the two overlapping layers on a fixed background map. The challenge of this process was to create an intuitive and clear overlapping, allowing the reader to compare the data as well as filter them.

For this reason, we decided to use two linear textures with opposite orientation, which creates a pattern when overlapped. After examining the geographical visualization of terrorism, we wanted to focus on the protagonists of those attacks: Which are the main groups? What ideological roots could be considered more dangerous in terms of numbers of victims and attacks?

In order to visualize a general answer to these questions we decided to use an interactive bubble chart. The main reason for using this kind of chart was to maintain a visual coherence with the other visualizations. Here again, we used the three main colours to distinguish the three ideological roots and the dimension of the bubbles to represent the number of victims per terrorist group.

The draft of this visualization was created using RawGraphs, an open source web application for the creation of static data visualizations. The initial visualization produced by RawGraphs was modified using Adobe Illustrator in order to change the colours and the distribution of the bubbles. The visualization was made interactive using Javascript. In this case, the interactivity of the visualization was designed to be an tool for exploration; in fact, it allows the reader to navigate among the bubbles and visualize the number of victims and the extended name of the groups.

What's Hot

After this first general visualization, we decided to focus our attention on the most dangerous groups, as measured by the number of their victims. We created a flowchart that shows the countries that are attacked most frequently by these groups, as well as the kinds of weapons most frequently used.

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This visualization is interactive, allowing the user to filter the information. Again, the colours refer to the three ideological roots of the groups. The creation process for this visualization was the same of the previous one - starting with RawGraphs, we edited the visual layout using Adobe Illustrator and coded the interactivity with Javascript. Finally, we wanted to understand the victims of these groups. We visualized different types of information using our data on the victims, such as number, and demographics.

Intuitive Arithmetic With Complex Numbers

We analyzed the number of people killed from to and categorized this into the two periods identified from before, using two different visualizations. Peter Mark Roget and his insidious thesaurus. Complicated problems can be hard to solve, but they are addressable with rules and recipes, like the algorithms that place ads on your Twitter feed. They also can be resolved with systems and processes, like the hierarchical structure that most companies use to command and control employees.

How to classify complex numbers (example) (video) | Khan Academy

Complex problems involve too many unknowns and too many interrelated factors to reduce to rules and processes. A technological disruption like blockchain is a complex problem. A competitor with an innovative business model — an Uber or an Airbnb — is a complex problem. This could be dismissed as an exercise in semantics, except for one thing: When facing a problem, says Nason, managers tend to automatically default to complicated thinking.

With the surge of design thinking focused on try-learn-adapt approaches, I argue there is a risk that organisations will increasingly suffer from an imbalance and ineffectuality, arising from an excessive lean towards right-side creativity at the expense of left-side insight.

Also, when framed as a complex problem situation, most problems are not wholly complicated, or wholly complex, but both. We cannot identify distinct systems. Consider something like obesity.