Data from: Common statistical patterns in urban terrorism

The underlying reasons behind modern terrorism are seemingly complex and
intangible. Despite diverse causal mechanisms, research has shown that
there exists general statistical patterns at the global scale that can
shed light on human confrontation behaviour. Whilst many policing and
counter-terrorism operations are conducted at a city level, there has been
a lack of research in building city-level resolution prediction engines
based on statistical patterns. For the first time, the paper shows that
there exists general commonalities between global cities under terrorist
attacks. By examining over 30,000 geo-tagged terrorism acts over 7000
cities worldwide from 2002 to today, the results shows the following. All
cities experience attacks $A$ that are uncorrelated to the population and
separated by a time interval $t$ that is negative exponentially
distributed $\sim \exp(-A^{-1})$, with a death-toll per attack that
follows a power law distribution. The prediction parameters yield a high
confidence of explaining up to 87\% of the variations in frequency and
89\% in the death-toll data. These findings show that the aggregate
statistical behaviour of terror attacks are seemingly random and
memoryless for all global cities. The enabled the author to develop a
data-driven city-specific prediction system and we quantify its
information theoretic uncertainty and information loss. Further analysis
show that there appears to be an increase in the uncertainty over the
predictability of attacks, challenging our ability to develop effective
counter-terrorism capabilities.