On the Risk Assessment of Terrorist Attacks Coupled with Multi-Source Factors

Zhang, Xun; Jin, Min Ho; Fu, Jingying; Yu, Chongchong; Xie, Xiaolan

doi:10.3390/ijgi7090354

Cited by 17 publications

(10 citation statements)

References 25 publications

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“…e paper has claimed that the framework has made accurate prediction of the behavior of the terrorist groups. Zhang et al [23] in 2018 improved the location recommendation algorithm with multisource factors and spatial characteristics using the data of terrorist attack in Southeast Asia from 1970 to 2016. e model was used to build a spatial risk assessment model of terrorist attacks. e paper has claimed to achieve an accuracy of up to 88%.…”

Section: Related Workmentioning

confidence: 99%

Prediction of Future Terrorist Activities Using Deep Neural Networks

et al. 2020

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One of the most important threats to today’s civilization is terrorism. Terrorism not only disturbs the law and order situations in a society but also affects the quality of lives of humans and makes them suppressed physically and emotionally and deprives them of enjoying life. The more the civilizations have advanced, the more the people are working towards exploring different mechanisms to protect the mankind from terrorism. Different techniques have been used as counterterrorism to protect the lives of individuals in society and to improve the quality of life in general. Machine learning methods have been recently explored to develop techniques for counterterrorism based on artificial intelligence (AI). Since deep learning has recently gained more popularity in machine learning domain, in this paper, these techniques are explored to understand the behavior of terrorist activities. Five different models based on deep neural network (DNN) are created to understand the behavior of terrorist activities such as is the attack going to be successful or not? Or whether the attack is going to be suicide or not? Or what type of weapon is going to be used in the attack? Or what type of attack is going to be carried out? Or what region is going to be attacked? The models are implemented in single-layer neural network (NN), five-layer DNN, and three traditional machine learning algorithms, i.e., logistic regression, SVM, and Naïve Bayes. The performance of the DNN is compared with NN and the three machine learning algorithms, and it is demonstrated that the performance in DNN is more than 95% in terms of accuracy, precision, recall, and F1-Score, while ANN and traditional machine learning algorithms have achieved a maximum of 83% accuracy. This concludes that DNN is a suitable model to be used for predicting the behavior of terrorist activities. Our experiments also demonstrate that the dataset for terrorist activities is big data; therefore, a DNN is a suitable model to process big data and understand the underlying patterns in the dataset.

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Section: Related Workmentioning

confidence: 99%

Prediction of Future Terrorist Activities Using Deep Neural Networks

et al. 2020

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“…Thus, a total of 21 kinds of division results are obtained; then, the quality of the division results is evaluated by the CH index. The CH index is a statistical index that can be used to detect clustering effects, the clustering result is optimal when the CH index reaches the maximum value [47]. The final classification is obtained ( Figure 6).…”

Section: Hierarchical Characteristics Of Digital Signagementioning

confidence: 99%

Analysis of Spatial Characteristics of Digital Signage in Beijing with Multi-Source Data

Zhang

Jiang

et al. 2019

IJGI

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Digital signage is an important medium for urban outdoor advertising. Understanding the spatial distribution characteristics and factors that influence the site of digital signage are conducive to the efficient, standardized, and sustainable development of digital signage. The outdoor commercial digital signage within the Sixth Ring Road in Beijing is taken as the research object, and social network check-ins, housing prices, traffic network centrality and the mount of commercial facilities are considered factors that influence digital signage. The spatial distribution characteristics of digital signage are studied by using point pattern analysis methods. Moreover, we use three spatial clustering algorithms to study the hierarchical spatial characteristics of digital signage and test the effectiveness of the results. In addition, the factors that influence the distribution of digital signage are analyzed by Spearman correlation analysis. The results indicate that (1) the digital signage in Beijing generally presents a relatively concentrated distribution with centrality and forms an obvious gathering area and the agglomeration centers are mainly concentrated in the core parts of the central business district (CBD). (2) Digital signage is categorized into three groups, the traffic-oriented, the population-oriented, and the market-oriented. In addition, the spatial distribution of digital signage is consistent with the historical urban development of Beijing. (3) The social network check-ins with dynamic population characteristics had the highest correlation with the operation cost of digital signage. The spatial characteristics of digital signage evaluated in this study can effectively enhance the sustainable management of digital signage and provide a reference for research of the sustainable allocation of digital signage resources.

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“…For example, the field of terrorism research has undergone a geographical shift [89] and geography should provide more support in the security field [90]. Some scholars have used geographical methods to study the relationship between domestic and global terrorism [91], performed geographical profiling by combining locations of various terrorist attack incidents, identified their spatial patterns [74], assessed terrorism risks in a region, and analyzed the spatial distribution patterns of a region's vulnerability [79], [80]. The Institute for Economics and Peace (IEP) has designed a map of global terrorism indicators (GTI) to describe the main spatial distribution patterns and trends of global terrorism [92] (Figure 1).…”

Section: B Security Situation Awareness and Assessmentmentioning

confidence: 99%

Geosocial Media as a Proxy for Security: A Review

Han

Cui

et al. 2019

IEEE Access

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Security issues such as natural disasters and terrorist attacks have attracted increasing global concern and attention. How to effectively detect security events has become worrisome to countries worldwide. Advances in mobile Internet technology have led to hundreds of millions of users using social media daily to post microblogs, text messages and multimedia information, generating enormous amounts of social media data that reflect people's social behaviors. Studies have proven that the timely intelligence can be extracted from these data. In particular, geosocial media when combined with location information can be used as a proxy for security event detection and security situational awareness. This paper provides a synopsis of the geosocial media data and the related processing/analysis methods used for detecting security events, and summarizes the general framework of security-related analyses based on geosocial media. Four major categories of analysis methods and application cases, including natural language processing, social network analysis, location inference and geospatial analysis, and image or video understanding, are discussed in detail. The paper concludes with possible future directions and areas of research that could be addressed and investigated. We hope to provide a clarion call to the scientists, practitioners, and other stakeholders to enhance the capabilities and accuracy of security events detection and security situational awareness and assessment using geosocial media.

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On the Risk Assessment of Terrorist Attacks Coupled with Multi-Source Factors

Cited by 17 publications

References 25 publications

Prediction of Future Terrorist Activities Using Deep Neural Networks

Prediction of Future Terrorist Activities Using Deep Neural Networks

Analysis of Spatial Characteristics of Digital Signage in Beijing with Multi-Source Data

Geosocial Media as a Proxy for Security: A Review

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