Uncovering Global Terrorism Patterns and Trends: An In-Depth Analysis of the Global Terrorism Database (1970-2017)
This repository contains an analysis of the Global Terrorism Database (GTD) from 1970 to 2017. The GTD is a comprehensive dataset that records information about terrorist incidents worldwide, including details such as the location, date, type of attack, casualties, and the responsible groups. This project aims to extract meaningful insights and trends from the data, shedding light on the patterns and dynamics of global terrorism over nearly five decades.
- Dataset used for this analysis: terrorismdata.csv
- Dataset Source: Global Terrorism Database on Kaggle
- Dataset Description: The Global Terrorism Database (GTD) is maintained by the National Consortium for the Study of Terrorism and Responses to Terrorism (START) at the University of Maryland. It provides a wealth of information on terrorist incidents and is widely used for research and analysis in the fields of security studies, data science, and conflict analysis.
-
Geospatial Analysis: Explore terrorist activity hotspots using interactive maps.
-
Top Affected Countries: Identify countries and regions with the highest number of terrorist attacks and casualties.
-
Trends over Time: Analyze how terrorism has evolved over time, including yearly patterns.
-
Group Analysis: Investigate the involvement of various terrorist groups and their activities.
-
Casualty Analysis: Examine the impact of terrorism on human lives, including fatalities and injuries.
And many more.
Explored insights and trends extracted from the Global Terrorism Database through a series of interactive Tableau visualizations. These visualizations offer a comprehensive view of the dataset.
To interact with these visualizations and explore the data-driven narratives, please visit my Tableau profile: Deepak Bhatt's Tableau Profile.
Screenshots of the dashboards I created:
- Dashboard 1
- Dashboard 2
- Dashboard 3
- Dashboard 4
- Dashboard 5
Gain a deeper understanding of global terrorism through these dynamic and informative dashboards.
- Python (Pandas): For data cleaning and formatting.
- Tableau: For data visualization.
Feel free to explore the Jupyter Notebook provided in this repository for details on data cleaning and formatting. To interact with the Tableau visualizations and dashboards, please visit my Tableau profile linked above.