  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.

Applications;Social and Information Networks;Physics and Society