A Systematic Review of The Recent Geospatial Approach in Addressing Spatially-Related Radicalism And Extremism Issues

Authors

  • Juhaida Jamal Geospatial Imaging and Information Research Group, Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia
  • Mohd Faisal Abdul Khanan Geospatial Imaging and Information Research Group, Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia
  • Ami Hassan Md Din Geospatial Imaging and Information Research Group, Faculty of Built Environment and Surveying, Universiti Teknologi Malaysia, 81310 Johor Bahru, Johor, Malaysia
  • Hafiz Aminu Umar Department of Environmental Sciences, Faculty of Science, Federal University Dutse, P.M.B 7156, Dutse, Nigeria
  • Mohd Mizan Mohammad Aslam Department of International Relations, Security and Law, Faculty of Defense Studies and Management, Universiti Pertahanan Nasional Malaysia, 57000 Sungai Besi, Kuala Lumpur, Malaysia

DOI:

https://doi.org/10.11113/ijbes.v10.n3.1112

Keywords:

Systematic Review, Geospatial Issues, Radicalism, Extremism

Abstract

This systematic review article focuses on the geospatial issues of radicalism and extremism. The scholar has intensified the application of geospatial in radicalism and extremism study to understand better the causes, patterns, and trends of the radicalism and extremism incidents. The advanced geospatial approach provides more spatio-temporal information on radicalism and extremism incidents'. It improves the conventional study method that only focuses on fundamentals and theory. Unfortunately, some geospatial issues from previous radicalism and extremism studies have been found. Hence, the present study reviewed past studies on geospatial applications in radicalism and extremism. Meanwhile, the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method were used to review the current research. This systematic review utilises two major journal databases, Scopus and Web of Science. Searching works found in a total of 24 articles can be analysed systematically. The selected article was separated into four corresponding geospatial analysis types: distribution pattern analysis, cluster analysis, statistical and prediction analysis, and 3D technology. Finally, several recommendations were offered after this study for future scholars' consideration.

References

Ahmad, S., Asghar, M. Z., Alotaibi, F. M., & Awan, I. (2019). Detection and classification of social media-based extremist affiliations using sentiment analysis techniques. Human-Centric Computing and Information Sciences, 9(1): 24. https://doi.org/10.1186/s13673-019-0185-6

Allan, H., Glazzard, A., Jesperson, S., Reddy-Tumu, S., & Winterbotham, E. (2015). Drivers of violent extremism: Hypotheses and literature review. Serial Report, October, 35. https://assets.publishing.service.gov.uk/media/57a0899d40f0b64974000192/Drivers_of_Radicalisation_Literature_Review.pdf

Bahgat, K., & Medina, R. (2013). An Overview of Geographical Perspectives and Approaches in Terrorism Research. Perspectives on Terrorism, 7(1): 38–72.

Baisagatova, D. B., Kemelbekov, S. T., Smagulova, D. A., & Kozhamberdiyeva, A. S. (2016). Correlation of concepts “extremism” and “terrorism” in countering the financing of terrorism and extremism. International Journal of Environmental and Science Education, 11(13): 5903–5915.

Basu, A., Saha, S., Aladi, H., Kurian, A., & Basu, A. (2017). Future Terrorist Attack Prediction using Machine Learning Techniques. PESIT South Campus: Bengaluru, India, 2017. https://doi.org/10.13140/RG.2.2.17157.96488

Bötticher, A. (2017). Towards academic consensus definitions of radicalism and extremism. Perspectives on Terrorism, 11(4): 73–77.

Brown, D., Dalton, J., & Hoyle, H. (2004). Spatial forecast methods for terrorist events in urban environments. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3073: 426–435. https://doi.org/10.1007/978-3-540-25952-7_33

Caplan, J. M., Kennedy, L. W., & Miller, J. (2011). Risk Terrain Modeling: Brokering Criminological Theory and GIS Methods for Crime Forecasting. Justice Quarterly, 28(2): 360–381. https://doi.org/10.1080/07418825.2010.486037

Chen, L., & Mu, F. (2021). Spatiotemporal characteristics and driving forces of terrorist attacks in Belt and Road regions. PLoS ONE, 16(3 March): 1–24. https://doi.org/10.1371/journal.pone.0248063

Cothren, J., Smith, B. L., Roberts, P., & Damphousse, K. R. (2008). Geospatial and Temporal Patterns of Preparatory Conduct among American Terrorists. International Journal of Comparative and Applied Criminal Justice, 32(1): 23–41. https://doi.org/10.1080/01924036.2008.9678776

Donald Brown, Jason Dalton, H. H. (2004). Spatial Forecast Methods for Terrorist Events in Urban Environments. Symposium on Intelligence and Security Informatics, 426–435. https://doi.org/10.1007/978-3-662-19161-3

Ehrlich, P. R., & Liu, J. (2002). Some roots of terrorism. Population and Environment, 24(2): 183–192. https://doi.org/10.1023/A:1020700221602

Freilich, J. D., Chermak, S. M., & Gruenewald, J. (2015). The future of terrorism research: a review essay. International Journal of Comparative and Applied Criminal Justice, 39(4): 353–369. https://doi.org/10.1080/01924036.2014.922321

Gao, P., Guo, D., Liao, K., Webb, J. J., & Cutter, S. L. (2013). Early detection of terrorism outbreaks using prospective space-time scan statistics. Professional Geographer, 65(4): 676–691. https://doi.org/10.1080/00330124.2012.724348

Gui, R., Chen, T., & Nie, H. (2020). Erratum: In-depth analysis of railway and company evolution of Yangtze River Delta with deep learning (Complexity (2020) 2020 (5192861) DOI: 10.1155/2020/5192861). Complexity, 2020. https://doi.org/10.1155/2020/7562682

Haddaway, Neal R., Bethel, A., Dicks, L. V., Koricheva, J., Macura, B., Petrokofsky, G., Pullin, A. S., Savilaakso, S., & Stewart, G. B. (2020). Eight problems with literature reviews and how to fix them. Nature Ecology and Evolution, 4(12): 1582–1589. https://doi.org/10.1038/s41559-020-01295-x

Haddaway, Neal Robert, & Macura, B. (2018). The role of reporting standards in producing robust literature reviews. Nature Climate Change, 8(6): 444–447. https://doi.org/10.1038/s41558-018-0180-3

Hao, M., Jiang, D., Ding, F., Fu, J., & Chen, S. (2019). Simulating spatio-temporal patterns of terrorism incidents on the Indochina peninsula with GIS and the random forest method. ISPRS International Journal of Geo-Information, 8(3): 133. https://doi.org/10.3390/ijgi8030133

Henkin, S., Boyd, M. A., & Martin, A. (2020). Southeast Asia after the Caliphate: Identifying Spatial Trends in Terrorism and Radicalization in Malaysia. Statistics, Politics and Policy, 11(2): 139–165. https://doi.org/10.1515/spp-2020-0001

Jha, M. K. (2009). Dynamic Bayesian Network for Predicting the Likelihood of a Terrorist Attack at Critical Transportation Infrastructure Facilities. Journal of Infrastructure Systems, 15(1): 31–39. https://doi.org/10.1061/(asce)1076-0342(2009)15:1(31)

Kalaiarasi, S., Mehta, A., & Bordia, D. (2019). Using Global Terrorism Database (GTD) and Machine Learning Algorithms to Predict Terrorism and Threat. International Journal of Engineering and Advanced Technology, 9(1): 5995–6000. https://doi.org/10.35940/ijeat.a1768.109119

Kanika, S., Anurag, C., & Parmeet, K. (2019). A Machine Learning Approach for Enhancing Defence Against Global Terrorism. 2019 Twelfth International Conference on Contemporary Computing (IC3), 1–5. https://doi.org/10.1109/IC3.2019.8844947

Khan, A. U. (2005). The terrorist threat and the policy response in Pakistan. SIPRI Policy Paper No. 11, 2007(11): 56. http://www.sipri.org/contents/publications/Policypaper11.pdf/

Khan, I., Hamza, S., Burke, F., & ul-Hud, S. N. (2020). Application of GIS for evaluation of ethnic fault lines of Karachi. Malaysian Journal of Society and Space, 16(4): 15–29. https://doi.org/10.17576/geo-2020-1604-02

Krueger, A. B., & Malečková, J. (2003). Education, poverty and terrorism: Is there a causal connection? Journal of Economic Perspectives, 17(4): 119–144. https://doi.org/10.1257/089533003772034925

Kwan, M. P., & Lee, J. (2005). Emergency response after 9/11: The potential of real-time 3D GIS for quick emergency response in micro-spatial environments. Computers, Environment and Urban Systems, 29(2): 93–113. https://doi.org/10.1016/j.compenvurbsys.2003.08.002

Li, Z., Sun, D., Li, B., Li, Z., & Li, A. (2018). Terrorist Group Behavior Prediction by Wavelet Transform-Based Pattern Recognition. Discrete Dynamics in Nature and Society, 2018. https://doi.org/10.1155/2018/5676712

Maniraj, S. P., Chaudhary, D., Deep, V. H., & Singh, V. P. (2019). Data Aggregation and Terror Group Prediction using Machine Learning Algorithms. International Journal of Recent Technology and Engineering, 8(4): 1467–1469. https://doi.org/10.35940/ijrte.d7590.118419

Millett, S. M. (2009). Trend Analysis as Pattern Recognition. World Futures Review, 1(4): 5–16. https://doi.org/10.1177/194675670900100403

Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009). Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. BMJ (Online), 339(7716): 332–336. https://doi.org/10.1136/bmj.b2535

Okeniyi, C. G., Ayara, O. O., & Akoth, N. N. (2018). Terrorism in Africa: Mapping the Nigeria and Kenya situation. International Journal of Development and Sustainability, 7(3): 993–1014. www.isdsnet.com/ijds

Peace, M., & World, I. N. A. C. (2020). Institute for Economics & Peace. Global Peace Index 2020: Measuring Peace in a Complex World. http://visionofhumanity.org/reports

Rokhman, M., Chalim, A., Sholahuddin, A., Suharnoko, D., & Usman, F. (2020). Geo-spatial of Terrorism and Radicalism in Indonesia -A Preventive Efforts-. 25(7): 1–9. https://doi.org/10.9790/0837-2507010109

Siebeneck, L. K., Medina, R. M., Yamada, I., & Hepner, G. F. (2009). Spatial and temporal analyses of terrorist incidents in Iraq, 2004-2009. Studies in Conflict and Terrorism, 32(7): 591–610. https://doi.org/10.1080/10576100902961789

Smith, B. L., Cothren, J., & Damphousse, K. R. (2008). Geospatial Analysis of Terrorist Activities :The Identification of Spatial and Temporal Patterns of Preparatory Behavior of International and Environmental Terrorists. May 2014, 86.

VanHorn, J. E., & Mosurinjohn, N. A. (2010). Urban 3D GIS Modeling of Terrorism Sniper Hazards. Social Science Computer Review, 28(4): 482–496. https://doi.org/10.1177/0894439309360836

Veldhuis, T., & Staun, J. (2009). Islamist radicalisation: a root cause model. In Clingendael Security Paper 12(October). http://www.clingendael.nl/cscp/publications/papers/?id=7740&&type=summary

Webb, J. J., & Cutter, S. L. (2009). The geography of U.S. terrorist incidents, 1970-2004. Terrorism and Political Violence, 21(3): 428–449. https://doi.org/10.1080/09546550902950308

Yu, X., Zhe, Z., Lei, W., Wei, P., Hechang, C., Zhezhou, Y., & Bin, L. (2020). Deep Ensemble Learning for Human Action Recognition in Still Images. Complexity, 2020. https://doi.org/10.1155/2020/9428612

Zhang, X., Jin, M., Fu, J., Hao, M., Yu, C., & Xie, X. (2018). On the risk assessment of terrorist attacks coupled with multi-source factors. International Journal of Geo-Information Article, 7(9): 354. https://doi.org/10.3390/ijgi7090354

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Published

2023-08-30

How to Cite

Jamal, J., Abdul Khanan, M. F., Md Din, A. H., Umar, H. A., & Mohammad Aslam, M. M. (2023). A Systematic Review of The Recent Geospatial Approach in Addressing Spatially-Related Radicalism And Extremism Issues. International Journal of Built Environment and Sustainability, 10(3), 37–49. https://doi.org/10.11113/ijbes.v10.n3.1112

Issue

Vol. 10 No. 3 (2023): International Journal of Built Environment and Sustainability, Volume 10, Issue 3, 2023

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