Abstract
Terrorism has wreaked havoc on today’s society and people. The discovery of the regularity of terrorist attacks is of great significance to the global counterterrorism strategy. In this study, we improve the traditional location recommendation algorithm coupled with multi-source factors and spatial characteristics. We used the data of terrorist attacks in Southeast Asia from 1970 to 2016, and comprehensively considered 17 influencing factors, including socioeconomic and natural resource factors. The improved recommendation algorithm is used to build a spatial risk assessment model of terrorist attacks, and the effectiveness is tested. The model trained in this study is tested with precision, recall, and F-Measure. The results show that, when the threshold is 0.4, the precision is as high as 88%, and the F-Measure is the highest. We assess the spatial risk of the terrorist attacks in Southeast Asia through experiments. It can be seen that the southernmost part of the Indochina peninsula and the Philippines are high-risk areas and that the medium-risk and high-risk areas are mainly distributed in the coastal areas. Therefore, future anti-terrorism measures should pay more attention to these areas.
Highlights
IntroductionThe spatial risk assessment of the terrorist attacks in Southeast Asia is of great significance to the implementation of both the One Belt One Road Initiative and the counterterrorism strategy
Minu et al used the wavelet neural network (WNN) for prediction and applied it to the nonstationary nonlinear time-series of the terrorist attack time-series; the results revealed that the WNN is the best model for analyzing the time-series of terrorist attacks [16]
The method that is most suitable for convergtheenscpeatoiaflpdoiviinsitosninoftthheis tsetusdt yspisaocbet.aiTnhede. distance between any two points of the same cluster is less than the distance between any two points of different clusters [24]
Summary
The spatial risk assessment of the terrorist attacks in Southeast Asia is of great significance to the implementation of both the One Belt One Road Initiative and the counterterrorism strategy. According to the statistics of the Global Terrorism Database (GTD), 1078 terrorist attacks occurred in Southeast Asia in 2016 alone, resulting in 533 deaths and causing great panic within the society. Because of the unprecedented advancement of the global digitization and the application of various advanced material collection methods, the terrorist attack assessment can obtain more types and larger volumes of related data from various angles than ever before, requiring researchers to have smarter, more efficient complex data processing capabilities. The machine learning-based terrorist attack assessment model can be widely accommodated and integrated with unstructured data, and we have the ability to find discernable patterns from clutter and mixed data [18]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
