SOPHIA

The Iraqi Insurgency (2003–2011) has commonly been characterized as demonstrating the tendency for violence to cluster and diffuse at the local level. Recent research has demonstrated that insurgent attacks in Iraq cluster in time and space in a manner similar to that observed for the spread of a disease. The current study employs a variety of approaches common to the scientific study of criminal activities to advance our understanding of the correlates of observed patterns of the incidence and contagion of insurgent attacks. We hypothesize that the precise patterns will vary from one place to another, but that more attacks will occur in areas that are heavily populated, where coalition forces are active, and along road networks. To test these hypotheses, we use a fishnet to build a geographical model of Baghdad that disaggregates the city into more than 3000 grid cell locations. A number of logistic regression models with spatial and temporal lags are employed to explore patterns of local escalation and diffusion. These models demonstrate the validity of arguments under each of three models but suggest, overall, that risk heterogeneity arguments provide the most compelling and consistent account of the location of insurgency. In particular, the results demonstrate that violence is most likely at locations with greater population levels, higher density of roads, and military garrisons

Using casualty assessment and weighted hit rates to calibrate spatial patterns of Boko Haram insurgency for emergency response preparedness

Since the beginning of the current millennium, Boko Haram has terrorised the residents of Northern Nigeria with devastating and high profile campaigns resuming in 2010. First responders struggle to cope with planning for and responding to the aftermath of these attacks. This paper describes analysis that can help emergency services pre-empt the geography and magnitude of susceptibility to attacks and the potential of the terrorists to generate severe attacks. The data used for the study were five years of terrorist activities. Results suggest that the efficiency of Boko Haram is not necessarily random and that attacks are generally well calculated to hit communities with disproportionate concentrations of vulnerable residents. The analysis is the first attempt to examine how a spatial segmentation framework might offer insight and intelligence towards understanding the configuration of terrorism for operational response

Deep Neural Network for Anomaly Detection

학위논문(석사)--서울대학교 대학원 :공과대학 협동과정 기술경영·경제·정책전공,2019. 8. 황준석.Human insurgency is one of the prevalent, incessant, and threatening events happening worldwide. Among many topics of developmental studies, one of the seminal research focuses is to understand and model armed conflicts, which have been suspected to be linked to the capacity of a country in various ways, such as food security, child nutrition, economic welfare, and even environmental issues. Mapping human insurgencies is, therefore, imperative. To cope with the atrocities, there have been previous attempts to uncover the latent patterns of human insurgent incidents. The salient behavior of these insurgencies follows the 'power-law' distribution, which exhibits a heavy-tail. This feature implies that events far from the norm are nontrivial when compared with the normal distribution, where essentially no weight is far from the mean. This pattern indicates that the insurgencies are the few incidents happening with relentless severity, while the majority of the events occur with mere severity. To fully exploit the latent behavior of human insurgencies, this research focuses on the anomalies — the events that have a great number of fatalities but little probability of occurrence, lying on the heavy tail—. To detect such anomalies, a novel approach, variational autoencoder, is used. The seminal essence of this model lies in processing high-volume data and capturing their non-linearity, which makes data-driven detection possible. The results show that the trained model successfully detects anomalies when given test data, showing no false negatives (Type III error) or false positives (Type I error). This predictive model, if well deployed, can provide humanitarian aid agencies and governments the ability to efficiently allocate resources, reducing wastes and mitigating the level of conflict through targeted preventive policies무력 충돌, 급진적인 테러 등을 포함하는 인간 폭등 (Human Insurgency)은 지금껏 만연하고 끊임없이 전 세계적으로 위협을 주는 현상이다. 세계 개발 분야의 여러 주제 중 기관 및 조직의 주요한 관심은 이러한 충돌을 이해하고 모델 하는 것이었다. 이러한 현상은 한 체제의 식량 안보, 아동 영양, 경제적, 그리고 환경적 문제와도 연관이 되는 것으로 간주되고 있다. 따라서 무력 충돌을 포함한 인간 폭등 현상을 매핑하는 것은 필수적으로 요구되고 있다. 기존의 연구들에서는 이런 현상들의 숨어 있는 패턴을 분석하고자 하는 시도들이 있었다. 그중 두드러진 특징은 멱법칙 (power law) 분포를 따른다는 것이다. 멱법칙 분포는 꼬리가 긴 형태를 가지고 있다. 기존 정규분포와는 달리, 멱법칙 분포는 중심에서 멀리 위치하고 있는 현상이 더 이상 간소히 존재하는 것이 아니라, 상당히 존재한다고 볼 수 있다. 이러한 급증 규칙은 소수의 사건들이 작은 확률로 큰 규모로 발생하는 반면, 다수의 사건들은 작은 규모로 발생한다는 것을 암시하고 있다. 이러한 규칙들을 활용하여, 본 연구에서는 폭등을 매핑하기 위해 비이상적 사건들에 초점을 맞춘다. 비이상적 사건들은 멱법칙의 꼬리에 위치하는, 규모가 크지만 작은 확률로 발생하는 사건들을 일컫는다. 이러한 비이상적 사건들을 판별하기 위해, Variational Autoencoder (VAE)라는 새로운 방법론을 도입하였다. 이 모델의 장점은 많은 량의 데이터를 처리하고, 비 선형적인 관계들을 포착할 수 있다는 것이다. 이러한 방법으로 data-driven 판별을 할 수 있게 한다. 이러한 모델로 훈련한 결과, False Negative (Type III error)와 False Positive (Type I error)이 발생하지 않았고, 비이상적인 사건들을 성공적으로 판별할 수 있었다. 본 연구에서는 이러한 모델을 제시함으로 실제 인도주의 단체나 정부에 적용되었을 경우, 제한된 자원을 효율적으로 배치하여 향후 폭등 규모를 완화할 수 있다고 본다.Abstract i Contents iii List of Tables v List of Figures vi Chapter 1. Introduction 1 Chapter 2. Literature Review 5 2.1 Human Insurgency 5 2.1.1 Definition of Human Insurgency 7 2.2 Predictive Models of Human Insurgency 8 2.2.1 The limitation of previous literature 10 2.3 Latent Behavior of Human Insurgency 10 2.4 Anomaly Detection 12 2.4.1 Anomaly Detection Methods 13 Chapter 3. Methodology and Data 15 3.1 Model 15 3.1.1 Variational Inference 15 3.1.2 Autoencoder 17 3.1.3 Variational Autoencoder (VAE) 18 3.2 VAE Anomaly detection 23 3.3 Analysis Sequence 25 3.4 Data Description 26 Chapter 4. Result 31 4.1 Reconstruction Error 31 4.2 Performance Analysis 33 4.2.1 Accuracy 34 4.2.2 ROC (Receiver Operating Characteristics) 35 4.2.3 Precision 36 4.2.4 Recall (Sensitivity) 37 4.2.5 Precision vs Recall 38 Chapter 5. Discussion and Conclusion 40 5.1 Implication 40 5.2 Limitations 41 5.3 Further Research 42 Bibliography 43 Abstract (Korean) 51Maste

The geo-temporal evolution of violence in civil conflicts

Existing works on diffusion fail to account for the incapacitating effects conflict events may have on the operational capability of the combatant sides and how these effects may determine the evolution of a conflict. I hypothesize that it is those events with losses on the state side that are likely to be associated with geo-temporal spillovers, whereas events with insurgency losses are less likely to be associated with future mayhem in their vicinity. To test my arguments, I first introduce a new, comprehensive and detailed event dataset on the long-running civil conflict in Turkey. The Turkish State–PKK Conflict Event Database (TPCONED) includes the exact date and county-level location for the fatal events of the armed conflict between the Turkish state and the rebel organization PKK since its very beginning in 1984 with detailed information on combatant casualties. I then employ a split population bi-probit model which allows me to comprehensively depict the geotemporal evolution of the conflict by acknowledging, estimating and accounting for the variation in the underlying conflict proneness across locations as a latent variable that shapes the diffusion of events. The results of the statistical analyses offer support for my hypotheses and reveal that how events evolve over space and time is conditioned by the damages suffered by the combatant sides. I demonstrate the robustness of these results on a matched sample I obtain by employing the Coarsened Exact Matching (CEM) on the data

Spatio-temporal patterns of IED usage by the Provisional Irish Republican Army

In this paper, a unique dataset of improvised explosive device attacks during “The Troubles” in Northern Ireland (NI) is analysed via a Hawkes process model. It is found that this past dependent model is a good fit to improvised explosive device attacks yielding key insights about the nature of terrorism in NI. We also present a novel approach to quantitatively investigate some of the sociological theory surrounding the Provisional Irish Republican Army which challenges previously held assumptions concerning changes seen in the organisation. Finally, we extend our use of the Hawkes process model by considering a multidimensional version which permits both self and mutual-excitations. This allows us to test how the Provisional Irish Republican Army responded to past improvised explosive device attacks on different geographical scales from which we find evidence for the autonomy of the organisation over the six counties of NI and Belfast. By incorporating a second dataset concerning British Security Force (BSF) interventions, the multidimensional model allows us to test counter-terrorism (CT) operations in NI where we find subsequent increases in violence