The recent spate of terror attacks across Europe has created newer challenges for preventing and neutralising them. This is because the
modus operandi for these attacks does not come from previously known practices or precedents. The same can be said of the attacks on Indian military establishments in Uri and Pathankot airbases, and others to some extent. Since any security system with a foolproof solution is a cost bearing and tedious affair to manage and often difficult to implement, a geographic information system (GIS) based system along with integration of other technologies offers to cover some of the known lacunae, and provide an alternate solution. The four stages of countering terror attack include prediction, prevention, response and mitigation. Application of GIS for border management of India using predictive analysis to forecast infiltration bids would go along way in reducing the already waning attempts.
Predictive analytics with GIS offers a working solution to predict, prevent and mitigate terror attacks. Prediction is built on predictive modelling, machine learning or artificial intelligence — AI — and assessing of large sets of data. The availability of geographic data along with terror attacks related information allows the creation of co-relation between preparations and target selection, including identifying types and signature of certain terror attacks. This correlation, then helps AI modules in GIS platforms to predict temporal range and spatial locations where higher chances of the next attack may take place. To create a predictive system using GIS requires the entire gamut of information regarding the perpetrators of the attack from ideology to personal traits to their previous location haunts. The steps in GIS based predictive analysis starts with observation and monitoring of an area or person (or persons) of interest. Readymade solutions already allow queries to find parallels in trends of recorded activity in a given location and expected activity in other locations. GIS systems allow predictions based on mobility information relating to speed and distance travelled; also, predict shortest and fastest path analysis traffic, and other dependent factors.
Prediction is built on predictive modelling, machine learning or artificial intelligence — AI — and assessing of large sets of data.
GIS based monitoring systems observe an individual or a group of individuals. The AI algorithms mark the geographical location for various parameters like ethnicity, economic standards, education, living standard, troubled history, and precedent of violence, among others, to identify patterns. This allows the GIS system to create an agent-based model for basing the predictive systems and processing the results that pass through statistical data trends. The result of the observation, then throws up certain possible suspects and locations that are vulnerable to a terrorist act. Depending on what is the designated agent in the model, it becomes easier to narrow down the number of possibilities. The entire focus can then be put for prevention of the incident at a particular location or locations.
The other spectrum of the application of GIS deals with response and managing mitigation efforts for the victims of a terror attack. This involves processes like cordoning, barricading, quarantine, coordinating large-scale medical services. There is an increase in efficiency for all areas of response from deployment of security forces to carrying the injured to nearby hospitals, if GIS enabled services are integrated with emergency response mechanisms. These could include information for something as simple as calculating the shortest path, berthing capacity of hospitals or trauma centres, traffic density during a given time among others. It needs to be implemented with the emergency response team of down to the level of district/city administration. Emergency response teams should be provided with surveillance drones that can be launched early so that real time information about perpetrators and victims can reach the proper authorities. The guidelines for standard operating procedures (SOPs) can be built into the response system such that there is no delay in logistical and administrative decisions for any counterterrorist or rescue operations, unlike in 2008 and also seen in later attacks in India. Use of AI for GIS systems in response management assists reducing lag in decision-making process. Thus mapping of data helps remove the fog of confusion during a terrorist attack emergency. Integration of satellite imagery with local drones and ground based mobile platforms already in existence needs to be interconnected into a single seamless grid.
Use of AI for GIS systems in response management assists reducing lag in decision-making process.
A national level mobile-based GIS service could become a mandatory public safety feature inbuilt into cell phones and other mobile platforms. This would enable citizens and officials to disseminate information like location and time of an attack the moment it took place. This in turn would reduce time for dissemination of information while an unexpected attack was occurring. A real-time application with a one-button press facility can be envisaged, that is connected to a central national terror response server and institutions like National Disaster Management Authority (NDMA) and National Investigation Agency (NIA), and other local administration and security institutions like the police. A similar system was built in Israel during Operation Protective Edge in 2014 that intimated the citizens about an impending rocket attack the moment the border sensors of the IDF picked up a projectile fired at Israel. The app was able to give the expected time, location of impact. Though the objective of the Israeli app Red Alert was for a different purpose it was able to provide real time information about impending danger. A similar integrated system for terror attacks in India would help in reducing the response time from the start of the unfortunate event and give the security forces and emergency response teams a quick reaction time to reduce the after effect of the attack.
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