Use of GIS to Discover the Existence of Terrorism

Threats to national defense can be military or non-military. The greatest unresolved threat and challenge facing the Indonesian state is terrorism. The Indonesian government has dealt with terrorism, but catching terrorists remains difficult. The purpose of this research is to provide an alternative that uses Geospatial Intelligence (GEOINT) to find out where terrorists are hiding. The limitation of this research is the mountainous region in Central Sulawesi Province. The method used in this study is to use the GEOINT approach which is a combination of remote sensing, geographic information systems (GIS) and cartography, to extract information and analyze the results. The analysis was performed using a weighted linear combination method. The quantification process is carried out on all spatial data used for each parameter related to the presence of terrorists in the mountains. Quantification is done by changing each sub-parameter class to a value between 1-5. Each value is then weighted as a coefficient to arrive at the final score. From the results of the analysis and discussion it can be concluded that the GEOINT analysis can be used as an initial research on terrorist hideouts

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

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

Identifying Factors Associated with Terrorist Attack Locations by Data Mining and Machine Learning

Raj Bridgelall is the program director for the Upper Great Plains Transportation Institute (UGPTI) Center for Surface Mobility Applications & Real-time Simulation environments (SMARTSeSM).While 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.https://www.ugpti.org/about/staff/viewbio.php?id=7

Simulating Spatio-Temporal Patterns of Terrorism Incidents on the Indochina Peninsula with GIS and the Random Forest Method

In recent years, various types of terrorist attacks have occurred which have caused worldwide catastrophes. The ability to proactively detect and even predict a potential terrorist risk is critically important for government agencies to react in a timely manner. In this study, a method of geospatial statistics was used to analyse the spatio-temporal evolution of terrorist attacks on the Indochina Peninsula. The machine learning random forest (RF) method was adopted to predict the potential risk of terrorist attacks on the Indochina Peninsula on a spatial scale with 15 driving factors. The RF model performed well with AUC values of 0.839 [95% confidence interval of 0.833–0.844]. The map of the potential distribution of terrorist attack risk was obtained with a 0.05×0.05-degree (approximately 5×5 km) resolution. The results indicate that Thailand is the most dangerous area for terrorist attacks, especially southern Thailand, Bangkok and its surrounding cities. Middle Cambodia and the northern and southern parts of Myanmar are also high-risk areas. Other areas are relatively low risk. This study provides the hotspots for terrorist attacks on a more fine-grained geographical unit. Meanwhile, it shows that machine learning algorithms (e.g., RF) combined with GIS have great potential for simulating the risk of terrorist attacks