Debunking Seven Terrorism Myths Using Statistics: 1st Edition (Paperback) book cover

Debunking Seven Terrorism Myths Using Statistics

1st Edition

By Andre Python

Chapman and Hall/CRC

142 pages

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Description

What is terrorism? What can we learn and what cannot we learn from terrorism data? What are the perspectives and limitations of the analysis of terrorism data? Over the last decade, scholars have generated unprecedented insight from the statistical analysis of ever-growing databases on terrorism. Yet their findings have not reached the public. This book translates the current state of knowledge on global patterns of terrorism free of unnecessary jargon. Readers will be gradually introduced to statistical reasoning and tools applied to critically analyze terrorism data within a rigorous framework.

Debunking Seven Terrorism Myths Using Statistics communicates evidence-based research work on terrorism to a general audience. It describes key statistics that provide an overview of the extent and magnitude of terrorist events perpetrated by actors independent of state governments across the world. The books brings a coherent and rigorous methodological framework to address issues stemming from the statistical analysis of terrorism data and its interpretations.

Features

  • Uses statistical reasoning to identify and address seven major misconceptions about terrorism.
  • Discusses the implications of major issues about terrorism data on the interpretation of its statistical analysis.
  • Gradually introduces the complexity of statistical methods to familiarize the non-statistician reader with important statistical concepts to analyze data.
  • Use illustrated examples to help the reader develop a critical approach applied to the quantitative analysis of terrorism data.
  • Includes chapters focusing on major aspects of terrorism: definitional issues, lethality, geography, temporal and spatial patterns, and the predictive ability of models.

Table of Contents

1. Introduction: The Role of Statistics in Debunking Terrorism Myths

2. Myth 1: We Know Terrorism When We See It

INTRODUCTION: THE NECESSITY TO INTERPRET

TERRORISM DATA WITH CAUTION

NO CONSENSUS ON THE DEFINITION

DISCREPANCIES AMONG DATABASES

SIDE EFFECTS OF DISTINGUISHING TARGETS

STATE REPRESSION AND NON-STATE TERRORISM: INSIGHT FROM THE DRC

POLITICAL AND NON-POLITICAL TERRORISM:

LESSONS LEARNED FROM PAKISTAN

CONCLUSION: GUIDING PRINCIPLES OF

THE ANALYSIS OF TERRORISM DATA

3. Myth 2: Terrorism only aims at killing civilians

INTRODUCTION: A NOTE OF CAUTION ON

THE VALIDITY OF THE ANALYSIS OF TERRORISM DATA

HALF OF THE TERRORIST ATTACKS DO NOT KILL

MEASURING AND INTERPRETING TERRORISM CASUALTY IS AFFECTED BY DATA CLASSIFICATION

WITNESSING UNPRECEDENTED LEVELS OF TERRORISM VIOLENCE: A FOCUS ON THE ISLAMIC STATE

CONCLUSION: TERRORISM DOES NOT INELUCTABLY EQUATE WITH THE DEATH OF CIVILIANS

4. Myth 3: The vulnerability of the West to terrorism

INTRODUCTION: ASIA AND AFRICA IN THE LINE OF FIRE

ONE QUARTER OF ALL ATTACKS WORLDWIDE OCCUR IN IRAQ

THE MOST TARGETED CITY BY TERRORISM

CONCLUSION: THE MOST VULNERABLE REGIONS TO TERRORISM ARE IN ASIA AND AFRICA

5. Myth 4: A homogeneous increase of terrorism over time

INTRODUCTION: IDENTIFYING TERRORISM TRENDS BEYOND VISUALIZATION

RISE OF TERRORISM IN ASIA AND AFRICA

NO TEMPORAL PATTERN IN THE WEST?

RISE OF DEADLY CASUALTIES IN ASIA AND AFRICA

NO TEMPORAL PATTERN IN TERRORISM DEATHS IN THE AMERICAS AND OCEANIA?

HIGH LEVELS OF TERRORISM PERSIST IN VERY FEW COUNTRIES

DYNAMICS OF TERROR EVENTS AND DEATH TOLL IN THE WORLD’S MOST TARGETED CITY

CONCLUSION: AN UNEVEN TEMPORAL VARIABILITY OF TERRORISM ACROSS CONTINENTS, COUNTRIES, AND CITIES

6. Myth 5: Terrorism Occurs Randomly

INTRODUCTION: THE DETECTION OF SPATIAL PATTERNS RELIES ON SPATIAL LENSES AND SCALES

IS TERRORISM RANDOM?

WHY SHOULD WE CARE ABOUT SPATIAL AUTOCORRELATION?

CHOOSING RELEVANT LENSES TO EXPLORE SPATIAL DATA

TOBLER’S LAW APPLIED TO TERRORISM

SPATIAL INACCURACY: WHAT DOES THAT MEAN IN PRACTICE?

IN THE BULL’S EYE!

NO DICE ROLLING FOR TARGET SELECTION: THE IRAQI EXAMPLE

CONCLUSION: TERRORISM IS CLUSTERED AT VARIOUS SPATIAL SCALES

7. Myth 6: Hotspots of Terrorism are Static

INTRODUCTION: THE DYNAMIC NATURE OF HOTSPOTS OF TERRORISM

CONTAGIOUS AND NON-CONTAGIOUS FACTORS THAT CAUSE THE SPREAD OF TERRORISM

TYPE OF TERRORISM DIFFUSION IS ASSOCIATED WITH TACTICAL CHOICE

SCALE AND MAGNITUDE OF THE CLUSTERING PROCESS ASSOCIATED WITH ISIS ATTACKS PERPETRATED IN IRAQ ()

LOCALIZING AND QUANTIFYING THE REDUCTION OF ISIS ACTIVITY FROM JANUARY TO DECEMBER

EXPLAINING AND VISUALIZING DIFFUSION OF ISIS ACTIVITY FROM JANUARY TO DECEMBER

CONCLUSION: CHANGE IS THE ONLY CONSTANT IN TERRORISM

8. Myth 7: Terrorism cannot be predicted

PREDICTION OF TERRORISM: A STATISTICAL POINT OF VIEW

STOCHASTIC MODELS FOR THE STATISTICAL PREDICTION OF TERRORISM PATTERNS

PREDICTING TERRORISM: LIMITATIONS, OPPORTUNITIES AND RESEARCH DIRECTION

ARTIFICIAL INTELLIGENCE TO SERVE COUNTERTERRORISM?

MACHINE LEARNING ALGORITHMS TO PREDICT TERRORISM IN SPACE AND TIME: A CASE STUDY

CONCLUSION: PREDICTING TERRORISM IS A PROMISING BUT BUMPY AVENUE OF RESEARCH

9. Terrorism: Knowns, Unknowns, and Uncertainty

 

About the Author

Andre Python is ZJU100 young professor of Statistics at Zhejiang University. His current research interests are in extending statistical models to address policy-relevant issues raised by the spread of phenomena threatening global security and health. In 2017, Andre completed a PhD in Statistics at the University of St Andrews, applying a Bayesian spatiotemporal model to capture fine-scale patterns of non-state terrorism across the world. As postdoctoral researcher at the University of Oxford, he has developed geostatistical models and actively contributed to the design and teaching of Bayesian statistics and R software courses for PhD students and University staff.

About the Series

ASA-CRC Series on Statistical Reasoning in Science and Society

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Subject Categories

BISAC Subject Codes/Headings:
MAT029000
MATHEMATICS / Probability & Statistics / General