1.
Visualizing the
Global Terrorism Dataset
Saurav Jha
Machine Learning Engineering, FactSet
mail@sauravjha.com.np

2.
Data set
• Available at - https://www.start.umd.edu/gtd/
• 135 columns, 181691 entries (1970-2017)

3.
Visualizing geographical data - plotly

4.
Asia & Europe by Attack and Target typeAsiaEurope

5.
Top ranked countries & cities – bar plot
Nations Cities

6.
Top ranked regions & weapons – bar plot
Regions Weapons

7.
Top ranked groups & targets – bar plot
TargetsGroups

8.
Visualizing dependencies - Correlation heatmap
Observations:
• Suicide – Ransom amount paid
• Ransom amount paid – Weapon types
• Ransom amount – Individual
• Weapon type – Claim mode
• Gun certain – Ransom amount, etc.

9.
Evaluating the correlations - jointplot
• Weapon type 5 = Firearms with most kills
• Weapon type 7 = Fake weapons with least kills

10.
Frequency visualization – Histogram plot
• Region 6 (South Asia) and 10 (Middle East &
North Africa) = most sensitive.
• Region 4 (East Asia) = least sensitive.
• Attack type 3 (Bombing / Explosion) = most
prevalent.
• Attack type (Hijacking) = least prevalent.

11.
Region vs Weapon Type - boxplot
Observations:
• Weapon type 2 = Chemical, Prevalent in region 6,
i.e., South Asia
• Weapon type 9 = Melee, Prevalent in region 8,
i.e., Western Europe

12.
• Jupyter notebook - https://github.com/Saurav0074/ds-
101/blob/master/globalTerror.ipynb
• Further Read – Chasing the Trajectory of Terrorism
THANK YOU!