[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123650-en":3,"doc-seo-123650-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},123650,549758146520,"Patrick","https://ap-avatar.wpscdn.com/avatar/80002397d8c0411e94?_k=1775819394049821470",8,"Research & Report","Identifying Factors Associated with Terrorist Attack Locations by Data Mining and Machine Learning","While research often examines socio-economic factors of perpetrators to understand terrorism motivations, comparatively less attention focuses on attributes linked to where attacks occur. This study fills that gap through multivariate analysis that merges a global terrorism database with relevant socio-economic variables extracted from literature. An 11-model machine-learning workflow evaluates 75 attributes to predict targeted locations, improving performance for 10 variables. Population and public transportation infrastructure emerge as key drivers, while model selection and hyperparameter optimization elevate a multi-layer perceptron to the best AUC (89.3%). Race, age, gender, marital status, income level, and home values show no predictive gains. The resulting pipeline supports policymakers in quantifying risks and making objective resource-allocation decisions to safeguard public health.","Preprint Manuscript: Bridgelall, R. (2023). Identifying Factors Associated with Terrorist Attack Locations by Data Mining and Machine Learning. International Social Science Journal. (Online) DOI:10.1111/issj.12414 .  \nIdentifying Factors Associated with Terrorist Attack Locations by Data Mining and Machine Learning  \nRaj Bridgelall, Ph.D.  \nAssociate Professor, Transportation, Logistics & Finance, College of Business North Dakota State University  \nPO Box 6050, Fargo ND 58108-6050  \nEmail: [raj@bridgelall.com](raj@bridgelall.com)  \n[ORCiD: 0000-0003-3743-6652](ORCiD: 0000-0003-3743-6652)  \nCompliance with Ethical Standards  \nFunding: The author did not receive support from any organization for the submitted work.  \nDeclarations: The author has no relevant financial or non-financial interests to disclose.  \nData Availability: The sources of the datasets used in this study are cited within the manuscript, and they are all publicly available.  \nIdentifying Factors Associated with Terrorist Attack Locations by Data Mining and Machine Learning  \nAbstract  \nWhile studies typically investigate the socio-economic factors of perpetrators to comprehend terrorism motivations, there was less emphasis placed on factors related to terrorist attack locations. Addressing this knowledge gap, this study conducts a multivariate analysis to determine attributes that are more associated with terrorist attacked locations than others. To tackle the challenge of identifying pertinent attributes, the methodology merges a global terrorism database with relevant socio-economic attributes from the literature. The workflow then trains 11 machine learning models on the combined dataset. Among the 75 attributes assessed, 10 improved the predictability of targeted locations, with population and public transportation infrastructure being key factors. After optimizing hyperparameters, a multi-layer perceptron—a type of artificial neural network—exhibited superior predictive performance, achieving an AUC score of 89.3%, classification accuracy of 88.1%, and a harmonically balanced precision and recall score of 87.3% . In contrast, support vector machines demonstrated the poorest performance. The study also revealed that race, age, gender, marital status, income level, and home values did not improve predictive performance. The machine learning workflow developed can aid policymakers in quantifying risks and making objective decisions regarding resource allocation to safeguard public health.  \nKeywords: Counterterrorism; Data Fusion; Exploratory Spatial Data Analysis; Feature Relevance Scoring; Multivariate Analysis; Population Demographics; Predictive Models; Transportation Security  \nConflict of Interest: none  \n1 Introduction  \nAlthough research frequently explores the socio-economic factors of perpetrators to understand terrorism motivations, there has been less attention to factors associated with terrorist attack locations. Mass shootings, particularly in the United States, often prompt questions about the reasons behind terrorists targeting specific locations (Metzl and MacLeish 2015) . One might assume that perpetrators consistently choose large cities with numerous targets such as public transportation, resulting in severe consequences. However, due to terrorism's adaptive nature and multifaceted motives (Bridgelall 2022), targeted locations can vary widely. Applied intelligence to identify attributes more closely related to attacked locations can assist policymakers in prioritizing risk mitigation and countermeasure resources for locations at elevated risk. It is essential to note that a statistical association between attributes and targeted locations does not necessarily imply latent or causal relationships.  \nThe objective of this research was to leverage data mining (DM) and machine learning (ML) methods to identify statistical associations of various attributes with attacked locations. The approach was to mine the literature on terrorism to identi","cbCais0j9nYQNSGf","https://ap.wps.com/l/cbCais0j9nYQNSGf","pdf",685987,1,30,"English","en",105,"# Abstract\n# Introduction\n## Research objective and rationale\n## Terrorism and attacked-location targeting\n## Prior studies and reported findings","[{\"question\":\"What gap does the study address regarding terrorism research?\",\"answer\":\"It targets the lack of emphasis on factors related to terrorist attack locations, in contrast to the more common focus on perpetrators’ socio-economic attributes.\"},{\"question\":\"How does the study build its dataset and modeling approach?\",\"answer\":\"It combines a global terrorism database with socio-economic attributes drawn from the literature, then trains and evaluates 11 machine-learning models on the merged dataset.\"},{\"question\":\"Which factors show the strongest association with predicting terrorist attack locations?\",\"answer\":\"Among 75 attributes, 10 improve predictability, with population and public transportation infrastructure identified as key factors.\"}]","Identifying Factors Associated with Terrorist Attack Locations by Data Mining and Machine Learning | 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