探索恐怖袭击和武装冲突环境背景的新型空间感知深度学习方法

IF 4.2 1区 地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY International journal of disaster risk reduction Pub Date : 2024-11-01 DOI:10.1016/j.ijdrr.2024.104921
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引用次数: 0

摘要

恐怖袭击和武装冲突(TAACs)的定量评估是全球公共安全研究的重要组成部分,对社会稳定和国家安全至关重要。本研究探讨了此类事件的空间依赖性,即事件爆发与环境之间的关系。基于地理大数据和人工智能(AI),我们提出了一种考虑到事件环境影响的空间特征利用模式,并在事件位置和空间邻域联合范围内建立了特征深度学习(DL)框架,以提高定量评估的精度。结果表明,在 14 种社会、自然和地理驱动因素共同作用下的场景中,包含空间特征的模型在训练和测试阶段的表现均优于仅使用位置特征的模型。此外,同时考虑位置和空间特征的模型在各种评价指标上都优于只使用单一特征的模型。全局归因分析进一步证实了事件的空间依赖性,表现为相邻城市间事件发生可能性的相互影响,以及与各种环境因素的相关性,尤其是与人类活动和生活环境相关的因素。我们发现,繁荣的城市中心和欠发达的农村地区都是 TAAC 的热点地区,而且这类事件更有可能发生在以高温和低降水为特征的恶劣气候模式中。这增强了我们对管理和预防此类事件的理解和准备。
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A novel spatial-aware deep learning approach for exploring the environmental context of terrorist attacks and armed conflicts
The quantitative assessment of terrorist attacks and armed conflicts (TAACs) is a crucial component of global public safety research and is vital for societal stability and national security. This study addresses the spatial dependency of such events, i.e., the relationship between the outbreak of an event and its environment. Based on geographic big data and artificial intelligence (AI), we propose a spatial feature utilization pattern that takes into account the impact of the event environment, and established a deep learning (DL) framework of features within the joint event location and space neighborhood to improve the precision of the quantitative assessment. The results demonstrate that in scenarios under a combination of 14 social, natural, and geographic driving factors, models that incorporate spatial features outperform those that only use location features during both the training and testing phases. Furthermore, models that consider both location and spatial features outperform models using only a single feature across various evaluation metrics. Global attribution analysis further confirms the spatial dependency of events, manifested in the mutual influence on the likelihood of events occurring among adjacent cities and the correlation with various environmental factors, particularly elements related to human activities and living environments. We find that both prosperous urban centers and underdeveloped rural areas are hotspots for TAACs, and that such events more likely to occur in harsh climatic patterns characterized by high temperatures and low precipitation. This enhances our understanding and preparedness for managing and preventing such events.
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来源期刊
International journal of disaster risk reduction
International journal of disaster risk reduction GEOSCIENCES, MULTIDISCIPLINARYMETEOROLOGY-METEOROLOGY & ATMOSPHERIC SCIENCES
CiteScore
8.70
自引率
18.00%
发文量
688
审稿时长
79 days
期刊介绍: The International Journal of Disaster Risk Reduction (IJDRR) is the journal for researchers, policymakers and practitioners across diverse disciplines: earth sciences and their implications; environmental sciences; engineering; urban studies; geography; and the social sciences. IJDRR publishes fundamental and applied research, critical reviews, policy papers and case studies with a particular focus on multi-disciplinary research that aims to reduce the impact of natural, technological, social and intentional disasters. IJDRR stimulates exchange of ideas and knowledge transfer on disaster research, mitigation, adaptation, prevention and risk reduction at all geographical scales: local, national and international. Key topics:- -multifaceted disaster and cascading disasters -the development of disaster risk reduction strategies and techniques -discussion and development of effective warning and educational systems for risk management at all levels -disasters associated with climate change -vulnerability analysis and vulnerability trends -emerging risks -resilience against disasters. The journal particularly encourages papers that approach risk from a multi-disciplinary perspective.
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