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Debunking Seven Terrorism Myths Using Statistics [Pehme köide]

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Püsilink: https://www.kriso.ee/db/9780367472245.html
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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.

Arvustused

"The book presents incredibly fascinating research, and can be interesting and useful not only to specialists but to general public for understanding and making informed judgments on terrorism and its debunking with help of statistical data analysis and prediction to prevent future attacksEach chapter suggests mathematical definitions, glossary, and additional reading sources. Besides those, the book supplies with bibliography of 153 most recent works and multiple links to the internet sites.." ~Technometrics

Foreword xi
Preface xiii
Author xv
Acknowledgment xvii
Chapter 1 Introduction: The Role Of Statistics In Debunking Terrorism Myths
1(4)
Chapter 2 Myth No 1: We Know Terrorism When We See It
5(18)
2.1 Introduction: the necessity to interpret terrorism data with caution
5(1)
2.2 No consensus on the definition
6(3)
2.3 Discrepancies among databases
9(1)
2.4 Side effects of distinguishing targets
10(5)
2.5 State repression and non-state terrorism: insight from the Democratic Republic of Congo
15(1)
2.6 Political and non-political terrorism: lessons learned from Pakistan
16(2)
2.7 Conclusion
18(5)
Chapter 3 Myth No 2: Terrorism Only Aims At Killing Civilians
23(12)
3.1 Introduction: a note of caution on the validity of the analysis of terrorism data
23(1)
3.2 Half of the terrorist attacks do not kill
24(1)
3.3 Measuring and interpreting terrorism casualty is affected by data classification
25(3)
3.4 Witnessing levels of terrorism violence: a focus on the Islamic State
28(2)
3.5 Conclusion: terrorism does not ineluctably equate with the death of civilians
30(5)
Chapter 4 Myth No 3: The Vulnerability Of The West To Terrorism
35(10)
4.1 Introduction: Asia and Africa in the line of fire
35(1)
4.2 One quarter of all attacks worldwide occur in Iraq
36(3)
4.3 The most targeted city by terrorism: Baghdad, Iraq
39(3)
4.4 Conclusion: The most vulnerable regions to terrorism are in Asia and Africa
42(3)
Chapter 5 Myth No 4: An Homogeneous Increase Of Terrorism Over Time
45(16)
5.1 Introduction: identifying terrorism trends beyond visualization
45(2)
5.2 Rise of terrorism in Asia and Africa
47(2)
5.3 No temporal pattern in the West?
49(1)
5.4 Rise of deadly casualties in Asia and Africa
50(1)
5.5 No temporal pattern in terrorism deaths in the Americas and Oceania?
51(1)
5.6 High levels of terrorism persist in very few countries
51(2)
5.7 Dynamics of terror events and death toll in the world's most targeted city
53(2)
5.8 Conclusion: an uneven temporal variability of terrorism across continents, countries, and cities
55(6)
Chapter 6 Myth No 5: Terrorism Occurs Randomly
61(18)
6.1 Introduction: spatial patterns of terrorism rely on spatial scales and lenses to view spatial data
61(1)
6.2 Is terrorism spatially random?
62(1)
6.3 Why should we care about spatial autocorrelation?
63(2)
6.4 Choosing relevant lenses to explore spatial data
65(2)
6.5 Terrorism is spatially correlated at various spatial scales
67(1)
6.6 Spatial inaccuracy: what does that mean in practice?
68(1)
6.7 In the bull's eye!
69(4)
6.8 No dice rolling for target selection: the Iraqi example
73(2)
6.9 Conclusion: terrorism is clustered at various spatial scales
75(4)
Chapter 7 Myth No 6: Hotspots Of Terrorism Are Static
79(18)
7.1 Introduction: the dynamic nature of hotspots of terrorism
79(1)
7.2 Contagious and non-contagious factors that cause the spread of terrorism
80(2)
7.3 Type of terrorism diffusion is associated with tactical choice
82(1)
7.4 Scale and magnitude of the clustering process associated with ISIS attacks perpetrated in Iraq (2017)
83(2)
7.5 Localizing and quantifying the reduction of ISIS activity from January to December 2017
85(2)
7.6 Explaining and visualizing diffusion of ISIS activity from January to December 2017
87(3)
7.7 Conclusion: change is the only constant in terrorism
90(7)
Chapter 8 Myth No 7: Terrorism Cannot Be Predicted
97(16)
8.1 Prediction of terrorism: statistical point of view
97(1)
8.2 Stochastic models for the statistical prediction of terrorism patterns
98(2)
8.3 Predicting terrorism: limitations, opportunities, and research direction
100(2)
8.4 Artificial intelligence to serve counterterrorism?
102(2)
8.5 Machine learning algorithms to predict terrorism in space and time: a case study
104(4)
8.6 Conclusion: predicting terrorism is a promising but bumpy avenue of research
108(5)
Chapter 9 Terrorism: Knowns, Unknowns, And Uncertainty
113(4)
Bibliography 117(14)
Index 131
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.