| Series |
ASA-CRC series on statistical reasoning in science and society
ASA-CRC series on statistical reasoning in science and society. ^A1418113
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| Contents |
Chapter 1: Introduction: The Role of Statistics in Debunking Terrorism Myths -- Chapter 2: Myth No 1: We Know Terrorism When We See It; 2.1 Introduction: the necessity to interpret terrorism data with caution; 2.2 No consensus on the definition; 2.3 Discrepancies among databases; 2.4 Side effects of distinguishing targets; 2.5 State repression and non-state terrorism: insight from the Democratic Republic of Congo; 2.6 Political and non-political terrorism: lessons learned from Pakistan; 2.7 Conclusion -- Chapter 3: Myth No 2: Terrorism Only Aims At Killing Civilians; 3.1 Introduction: a note of caution on the validity of the analysis of terrorism data; 3.2 Half of the terrorist attacks do not kill; 3.3 Measuring and interpreting terrorism casualty is affected by data classification; 3.4 Witnessing levels of terrorism violence: a focus on the Islamic State; 3.5 Conclusion: terrorism does not ineluctably equate with the death of civilians -- Chapter 4: Myth No 3: The Vulnerability of the West to Terrorism; 4.1 Introduction: Asia and Africa in the line of fire; 4.2 One quarter of all attacks worldwide occur in Iraq; 4.3 The most targeted city by terrorism: Baghdad, Iraq; 4.4 Conclusion: The most vulnerable regions to terrorism are in Asia and Africa -- Chapter 5: Myth No 4: An Homogeneous Increase of Terrorism Over Time; 5.1 Introduction: identifying terrorism trends beyond visualization; 5.2 Rise of terrorism in Asia and Africa; 5.3 No temporal pattern in the West?; 5.4 Rise of deadly casualties in Asia and Africa; 5.5 No temporal pattern in terrorism deaths in the Americas and Oceania?; 5.6 High levels of terrorism persist in very few countries; 5.7 Dynamics of terror events and death toll in the world's most targeted city; 5.8 Conclusion: an uneven temporal variability of terrorism across continents, countries, and cities -- Chapter 6: Myth No 5: Terrorism Occurs Randomly; 6.1 Introduction: spatial patterns of terrorism rely on spatial scales and lenses to view spatial data; 6.2 Is terrorism spatially random?; 6.3 Why should we care about spatial autocorrelation?; 6.4 Choosing relevant lenses to explore spatial data; 6.5 Terrorism is spatially correlated at various spatial scales; 6.6 Spatial inaccuracy: what does that mean in practice?; 6.7 In the bull's eye!; 6.8 No dice rolling for target selection: the Iraqi example; 6.9 Conclusion: terrorism is clustered at various spatial scales -- Chapter 7: Myth No 6: Hotspots of Terrorism are Static; 7.1 Introduction: the dynamic nature of hotspots of terrorism; 7.2 Contagious and non-contagious factors that cause the spread of terrorism; 7.3 Type of terrorism diffusion is associated with tactical choice; 7.4 Scale and magnitude of the clustering process associated with ISIS attacks perpetrated in Iraq (2017); 7.5 Localizing and quantifying the reduction of ISIS activity from January to December 2017; 7.6 Explaining and visualizing diffusion of ISIS activity from January to December 2017; 7.7 Conclusion: change is the only constant in terrorism -- Chapter 8: Myth No 7: Terrorism Cannot be Predicted; 8.1 Prediction of terrorism: statistical point of view; 8.2 Stochastic models for the statistical prediction of terrorism patterns; 8.3 Predicting terrorism: limitations, opportunities, and research direction; 8.4 Artificial intelligence to serve counterterrorism?; 8.5 Machine learning algorithms to predict terrorism in space and time: a case study; 8.6 Conclusion: predicting terrorism is a promising but bumpy avenue of research -- Chapter 9: Terrorism: Knowns, Unknowns, and Uncertainty |
| Bibliography note | Includes bibliographical references and index. |
| Biographical note | 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. |
| Issued in other form | ebook version : 9781000093728 |
| ISBN | 0367472244 |
| ISBN | 9780367472245 |
| ISBN | 9780367472283 hardcover |
| ISBN | 0367472287 hardcover |
| ISBN | electronic publication |
| ISBN | electronic book |
| ISBN | Mobipocket electronic book |
| ISBN | electronic book |
