Power Entrances and Exits: A Global Survey

Charts & Graphs, Lab Reports
Sino-Japanese War, 1895, KOBAYASHI IKUHIDE | CC0 via Art Institute of Chicago


In looking at global power from a far vantage point, where have the entrances and exits to power been more tumultuous or in some cases regular and predictable? Are there any notable patterns with certain entrances and exits? What is the gender distribution for power through time and through a global view?


Archigos : A Database of Political Leaders version 4.1 (1 March 2016), a .txt format, served as the primary dataset for this visualization.(GOEMANS et al., 2009) It derives from research published by Henk E. Goemans, Kristian Skrede Gleditsch, and Giacomo Chiozza in 2009, whose main goal was to identify and amass a more empirical leaning data source that highlighted the intersection of changes of power from one leader to the next. The research  gathered information about leaders of independent states from the time period of 1875 to 2004, which had since been updated until December 31, 2015 in the version 4.1. It includes data points of basic information about the leader’s birth and death dates, their gender, and even limited information of their time after office. Some caveats to note with dataset creation: challenges with coding multiple heads of states in office simultaneously, exit coding efforts subjective but deriving from primary/secondary sources, and gaps in data due to lack of information in some instances.

Since the primary dataset did not recognize the country abbreviations as geographic data types I needed to include the dataset: list of independent states that had the country abbreviation with the full country name. It first opened in ascii format, which was then prepared in excel to include (column headers) that would map to the primary dataset.

Additionally, I wanted to be able to focus on the continents individually. This necessitated another dataset that included continent data, which mapped that to the country data source by connecting on “country”, which was represented in both those files.

Light data processing and cleaning through excel and OpenRefine in the form of editing column titles for later joining data sources in Tableau.


Color served as a unifying element between the dashboards, representing the following sentiments across the worksheets:
-Red and similar tones indicating out of “normal” range or more negative situations
-Green indicating “normal” range and situations
-Gray tones reserved for more neutral results and Gender (scale of size through the count of leaders was leveraged to indicate differences)

Scale was leveraged across the dashboards to indicate amount/count of leaders by various dimensions and also respective time in office.

Influential dashboards/ visuals/ and datasets were mostly from the training playlist from Tableau Public, notably the training module #14 with the global view (influencing the Gender Dashboard and the chart filtering another chart action on the Exit Context Dashboard).

Getting creative: The most challenging data to show, but I thought told the most interesting narrative, was the power shifts was the Successions Dashboard.

This is Spain, A graph showing bars cascading from left to right indicating time in office by length of the bar and color referencing exit situation. Green is regular and red is irregular, grey indicates death natural causes.
As shown in this screenshot, Spain has has sprinklings of irregular exits of leaders in power. Notably different is the long light grey bar which is Franco’s rule from 1939-1975. Even with the grey indicating his exit was due to death, natural causes, the scale stands out as vastly different than what Spain has experienced in dataset’s timeline.
This is USA, A graph showing bars cascading from left to right indicating time in office by length of the bar and color referencing exit situation. Green is regular and red is irregular, grey indicates death natural causes.
In comparison to USA ( because everything seems to draw that comparison), the steps of succession cascade at a more consistent frequency, apart from a few deaths in offices and assassinations.

This view was accomplished by creating a gantt chart of the start dates of leaders coming into power with the bar width referencing office duration. The gantt chart allows the viewer to see on a timeline left to right how each leader came to power sequentially. The color played an important role as it indicated how the leaders left power(irregular, regular, foreign, natural death, retired due to ill health, suicide, and still in office). Maintaining the color theme mentioned above, there were gaps in the data when it came to the earlier range of time for the data set (later 19th century to early 20th century). I am assuming this was due to what the researchers were able to find from the decided upon start date for the dataset. Also consideration to the probability of monarchies in power, that data element did not make the criteria to be included , i.e. The United Kingdom is listed in the data, but listing prime ministers as representing leaders in power. I tried to mitigate this space by offering a date slider filter so the viewer could adjust to focus on more current timeframes(thus enlarging the view on succession steps). 

Another challenge to contend with was the varying number of leaders for the respective countries (a total of up to 141 with one occurrence and many occurrences with 1). In order to create the stepped look in the gantt chart, I needed to list all the leaders in the row shelf, but this cluttered the gantt chart visual. I opted to keep the dimension , but to change the text to white, rendering it invisible to the viewer. 


Exit Details:

With green indicating a more “normal” exit of power and red indicating an “irregular” exit, seeing the volume over time highlights durations of peace and turmoil. Additionally, it highlights the persistence of irregular exits over time globally. Selecting an exit type in the chart below filters the chart above. In selecting “Removed by Military, without Foreign Support”, the highest occurrence of irregular exits, many of the spikes on the timeline map to times of war.


The view of the exits of power over time by country provided a sneak peek at a county’s general political state. The various lengths of duration in office paired with the red=bad / green=good color scheme enabled the viewer to ascertain a country’s history over time. Green bars that cascaded evenly from oldest date to a more current date generally reflected a more stable political climate, where as irregular sized bars, which fluctuated from green to red, highlighted a instability.


It was interesting to go through various iterations of building workbooks to orient the distribution of leaders, their reasons for entry and exits, gender, and amount of leaders per country. It was no surprise that the there was only about 2.2% of women holding office during the whole duration of the data set (1875-2015). In looking at the global view of gender distribution, it highlights how much more diversity is needed in the leaders’ profile.

Detail from “Gender” Dashboard


I found it really challenging to not include an over abundance of information and tried my best to keep a tight edit of what key takeaways I wanted to impart the viewer with. Saying that, I do desire to deep dive further on this topic.
Since the dataset’s timeline ended at 2015, the pursuit of updated data would also be a logical next step to expand the project. Although, some of the coding performed by the authors have encapsulated this data with a certain context of time which may not be easily replicated by others picking up the project. Conversely, it might also enrich the coding.

I would have loved more time to pursue additional research to augment or provide more historical context to the changes of power for each country. I find myself asking “why” when I see disruptions of red during the timeline of successions. As an added way to explore contextualizing the visuals by another means, I’m curious how a Sankey graph would highlight the movement of entry to power to exit of power. Within the dataset itself, there are also interesting relationships to explore and further details about the leaders post-office tenure. There is even one specifically about relationships, e.g. familial relationships between the leaders. After a cursory look, there have been a notable amount of “keep in the family”, so it seems….


Goemans, H. E., Gleditsch, K. S., & Chiozza, G. (2009). Introducing Archigos: A Dataset of Political Leaders. Journal of Peace Research, 46(2), 269–283. https://doi.org/10.1177/0022343308100719