# Statistical Modeling and Visualizing Open Big Data Using a Terrorism Case Study

**Authors:** Narasimha Rao Vajjhala (Computer Science Department, University of New York Tirana, Tirana, Albania) — ORCID 0000-0002-8260-2392; Kenneth David Strang (School of Business and Economics, State University of New York, Queensbury, USA; APPC Non-Profit Research, Australia) — ORCID 0000-0002-4333-4399; Zhaohao Sun (Department of Business Studies, PNG University of Technology, Lae, Papua New Guinea)
**Type:** Conference paper
**Source:** 2015 3rd International Conference on Future Internet of Things and Cloud (FiCloud), Rome, Italy, pp. 489–496, IEEE
**Published:** 2015-08
**DOI:** https://doi.org/10.1109/FiCloud.2015.15
**Canonical page:** https://www.narasimharao.net/research/open-big-data-correspondence-analysis-global-terrorism/
**Indexing:** Scopus · Web of Science
**Keywords:** open big data analytics; correspondence analysis; global terrorism open meta big dataset; statistical modeling; data mining; data warehouse

## Summary

**Question.** How could qualitative big data be collected and analyzed, using readily available statistical software, to identify hidden factor relationships that support strategic decision making?

**Finding.** Applying simple correspondence analysis in SPSS 21 to an open meta big dataset of 125,087 global terrorism records (133 fields, 16,636,571 data points, 1970–2013), the authors related nine terrorist attack methods to 13 world regions. The first two dimensions captured 94.5% of total inertia (0.687 and 0.258), justifying a two-dimensional symmetric plot, while the overall association between attack type and region was low (0.21).

**Why it matters.** The study shows that qualitative big data can be analyzed with desktop statistical software to reveal hidden relationships that business managers could use when selecting marketing partners and supply chain providers or deciding to scale down operations in risky areas. The authors aim to generalize the methodology rather than the specific results, and call for more empirical big data analytics papers and a methodology guidebook for practitioners and researchers.

## Key findings

1. The study analyzed a global terrorism open meta big dataset of 125,087 records with 133 fields (16,636,571 data points) covering 209 countries grouped into 13 regions from 1970 to 2013.
2. The terrorism big data study coded attack method into nine nominal types: assassinations, armed assaults, bombing/explosions, hijacking, barricading, kidnapping, infrastructure, unarmed assaults and unknown/mixed.
3. Correspondence analysis of attack method by region found that the first dimension captured 0.687 and the second 0.258 of total inertia, together 94.5% (0.945).
4. The third correspondence analysis dimension added only 3.4% (0.034) of inertia, which the authors used to justify a two-dimensional symmetric plot of terrorist attack type by region.
5. The estimated overall association (correlation) between terrorist attack type and geographic region in the global terrorism big data was 0.21, described as low.
6. The study identified armed assault, bombs and assassinations as the most lethal terrorist attack methods of concern to organizations.
7. The authors argue that correspondence analysis is preferable to cluster analysis or chi-square tests for nominal big data because it estimates the strength and direction of associations among factors without losing attribute detail.

## Study at a glance

| Item | Detail |
|---|---|
| Research question | How can qualitative big data be collected and analyzed to identify factor relationships that support strategic decision making? |
| Design | Positivist, exploratory empirical case study using descriptive non-parametric statistics |
| Data | Global terrorism open meta big dataset: 125,087 records, 133 fields, 16,636,571 data points, 209 countries in 13 regions, 1970–2013 |
| Methods | Simple correspondence analysis (symmetrical normalization, Euclidean distance) in SPSS version 21 of attack method by region |
| Main result | Two dimensions explained 94.5% of inertia (0.687 + 0.258); overall attack-type–region association was 0.21 (low) |
| Implication | Correspondence analysis with common software can turn qualitative big data into visual relationship maps for decision makers |

## Abstract

This study addressed the literature gap, identified by other researchers, that there are too few examples of applied empirical open big data analytics. Using correspondence analysis as a big data analytical technique, this study demonstrates how qualitative big data type could be analyzed to identify hidden factor relationships that may assist strategic decision making. We use a 64MB open meta big dataset developed by summarizing terrorist activity as keyword frequencies collected from trillions of public news articles published during a 43 year period from 1970-2013 and readily available statistical software, SPSS, to visually summarize the findings on a global terrorism big dataset. The approach in this paper might facilitate the research and development of open big data, big data analytics against global terrorism.

## Key terms

- **Correspondence analysis:** A multivariate exploratory technique that estimates the interdependence (inertia) between categorical variables in a contingency table and plots them as distances in a low-dimensional map.
- **Open big data:** Very large datasets made publicly available, here a meta dataset summarizing terrorist activity as keyword frequencies from public news articles.
- **Inertia:** In correspondence analysis, the measure of association between row and column factors, whose proportion accounted for by each dimension indicates how much of the relationship a plot captures.

## How to cite

Vajjhala, N. R., Strang, K. D., & Sun, Z. (2015). Statistical Modeling and Visualizing Open Big Data Using a Terrorism Case Study. In 2015 3rd International Conference on Future Internet of Things and Cloud (FiCloud), Rome, Italy (pp. 489–496). IEEE. https://doi.org/10.1109/FiCloud.2015.15

```bibtex
@inproceedings{vajjhala2015open,
  title = {Statistical Modeling and Visualizing Open Big Data Using a Terrorism Case Study},
  author = {Vajjhala, Narasimha Rao and Strang, Kenneth David and Sun, Zhaohao},
  booktitle = {2015 3rd International Conference on Future Internet of Things and Cloud (FiCloud), Rome, Italy},
  pages = {489--496},
  year = {2015},
  publisher = {IEEE},
  doi = {10.1109/FiCloud.2015.15},
  url = {https://doi.org/10.1109/FiCloud.2015.15}
}
```