The Global Terrorism Database (GTD) "is a database of incidents of terrorism from 1970 onward". Through 2015, this database contains information on 141,966 incidents.
terrorism provides a few summary
statistics along with an
methodology, which Pape et al.
insisted is necessary, because an increase
of over 70 percent in suicide terrorism
between 2007 and 2013 is best explained by
a methodology change in GTD that occurred
on 2011-11-01; Pape's own
Suicide Attack Database
showed a 19 percent decrease over
the same period.
1 2 3
nkill.byCountryYr are matrices giving
the numbes of incidents and numbers of deaths
by year and by location of the event for 206
countries and for all years between 1970 and
2015 except for 1993, for which the raw data
nkill.byCountryYr and for
terrorism[c('nkill', 'nkill.us')], NAs
in GTD were treated as 0. Thus the actual
number of deaths were likely higher, unless
this was more than offset by incidents being
classified as terrorism, when they should not
nkill.byCountryYr are NA for 1993,
because the GTD data for that year were lost.
terrorism is a
containing the following:
integer year, 1970:2014.
ordered factor giving the
methodology / organization responsible for
the data collection for most of the given
year. The Pinkerton Global Intelligence
Service (PGIS) managed data collection
from 1970-01-01 to 1997-12-31. The
Center for Terrorism and Intelligence
Studies (CETIS) managed the project from
1998-01-01 to 2008-03-31. The Institute
for the Study of Violent Groups (ISVG)
carried the project from 2008-04-01 to
2011-10-31. The National Consortium
for the Study of Terrorism and
Responses to Terrorism (START) has
managed data collection since
2011-11-01. For this variable,
partial years are ignored, so
methodology = CEDIS for
1998:2007, ISVG for 2008:2011, and
START for 2012:2014.
a character vector consisting of
the first character of the levels
c('p', 'c', 'i', 's')
integer number of incidents identified each year.
146920 = 141966 in the GTD database
plus 4954 for 1993, for which the
incident-level data were lost.
integer number of incidents identified
each year with
integer number of incidents classified
as "suicide" by GTD variable
suicide = 1. For 2007, this
is 359, the number reported by
Pape et al.
For 2013, it is 624, which is 5 more
than the 619 mentioned by Pape et al.
Without checking with the SMART
project administrators, one might
suspect that 5 more suicide incidents
from 2013 were found after the data
Pape et al. analyzed but before the
data used for this analysis.
Number of suicide incidents by year
number of confirmed fatalities for
incidents in the given year, including
sum(nkill, na.rm=TRUE) in the
GTD incident data.
nkill in the GTD incident
data includes both perpetrators
and victims when both are available.
It includes one when only one is
available and is
neither is available. However, in
most cases, we might expect that the
more spectacular and lethal incidents
would likely be more accurately
reported. To the exent that this is
true, it means that when numbers are
missing, they are usually zero or
small. This further suggests that
the summary numbers recorded here
probably represent a slight but not
number of U.S. citizens who died as a
result of incidents for that year =
sum(nkill.us, na.rm=TRUE) in the
GTD incident data.
1. This is subject to the same likely
modest undercount discussed with
2. These are U.S. citizens killed
regardless of location. This explains at
least part of the discrepancies between
terrorism[, 'nkill.us'] and
nkill.byCountryYr['United States', ].
number of people wounded. (This is
subject to the same likely modest
undercount discussed with
Number of U.S. citizens wounded in
terrorist incidents for that year =
sum(nwound.us, na.rm=TRUE) in
the GTD incident data. (This is
subject to the same likely modest
undercount discussed with
proportion of observations by year
with missing values. These numbers
are higher for the early data than
more recent numbers. This is
particularly true for
nwound.us, which exceed
90 percent for most of the period
methodology = 'PGIS',
prior to 1998.
Estimated de facto population in thousands living in the world and in the US as of 1 July of the year indicated, according to the Population Division of the Department of Economic and Social Affairs of the United Nations; see "Sources" below.
Crude death rate (deaths per 1,000 population) worldwide and in the US, according to the World Bank; see "Sources" below. This World Bank data set includes USdeathRate for each year from 1900 to 2014.
The WorldDeathRate here were read manually from a plot on that web page, except for the the number for 2015, which was estimated as a reduction of 0.73 percent from 2014, which was the average rate of decline (ratio of two successive years) for 1990 to 2014. The same method was used to estimate the USdeathRate for 2015 as the same as for 2014.
NOTE: USdeathRate is to two significant digits only, unlike WorldDeathRate, which has four significant digits.
number of deaths by year in the world and US
worldDeaths = worldPopulation * worldDeathRate.
USdeaths were computed by summing across age groups in "Deaths_5x1.txt" for the United States, downloaded from http://www.mortality.org/cgi-bin/hmd/country.php?cntr=USA&level=1 from the Human Mortality Database; see sources below.
terrorism deaths per million population worldwide and in the US =
0.001 * nkill / worldPopulation
terrorism deaths as a proportion of total deaths worldwide and in the US
pkill = nkill / worldDeaths
pkill.us = nkill.us / USdeaths
As noted with the "description" above, Pape et al. noted that the GTD reported an increase in suicide terrorism of over 70 percent between 2007 and 2013, while their Suicide Attack Database showed a 19 percent decrease over the same period. Pape et al. insisted that the most likely explanation for this difference is the change in the organization responsible for managing that data collection from ISVG to START.
If the issue is restricted to how incidents are classified as "suicide terrorism", this concern does not affect the other variables in this summary.
However, if it also impacts what incidents are classified as "terrorism", it suggests larger problems.
The Global Terrorism Database maintained by the National Consortium for the Study of Terrorism and Responses to Terrorism (START, 2015), downloaded 2015-11-28.
The world and US population figures came from "Total Population - Both Sexes", World Population Prospects 2015, published by the Population Division of the Department of Economic and Social Affairs of the United Nations, accessed 2016-09-05.
The World and US death rates came from the World Bank, accessed 2016-09-05.
Robert Pape, Keven Ruby, Vincent Bauer and Gentry Jenkins, "How to fix the flaws in the Global Terrorism Database and why it matters", The Washington Post, August 11, 2014 (accessed 2016-01-09).
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data(terrorism) ## ## plot deaths per million population ## plot(kill.pmp~year, terrorism, pch=method, type='b') plot(kill.pmp.us~year, terrorism, pch=method, type='b', log='y', las=1) # terrorism as parts per 10,000 # of all deaths plot(pkill*1e4~year, terrorism, pch=method, type='b', las=1) plot(pkill.us*1e4~year, terrorism, pch=method, type='b', log='y', las=1) # plot number of incidents, number killed, # and proportion NA plot(incidents~year, terrorism, type='b', pch=method) plot(nkill.us~year, terrorism, type='b', pch=method) plot(nkill.us~year, terrorism, type='b', pch=method, log='y') plot(pNA.nkill.us~year, terrorism, type='b', pch=method) abline(v=1997.5, lty='dotted', col='red') ## ## by country by year ## data(incidents.byCountryYr) data(nkill.byCountryYr) yr <- as.integer(colnames( incidents.byCountryYr)) str(maxDeaths <- apply(nkill.byCountryYr, 1, max) ) str(omax <- order(maxDeaths, decreasing=TRUE)) head(maxDeaths[omax], 8) tolower(substring( names(maxDeaths[omax[1:8]]), 1, 2)) pch. <- c('i', 'g', 'f', 'l', 's', 'c', 'u', 'p') cols <- 1:4 matplot(yr, sqrt(t( nkill.byCountryYr[omax[1:8], ])), type='b', pch=pch., axes=FALSE, ylab='(square root scale) ', xlab='', col=cols, main='number of terrorism deaths\nby country') axis(1) (max.nk <- max(nkill.byCountryYr[omax[1:8], ])) i.nk <- c(1, 100, 1000, 3000, 5000, 7000, 10000) cbind(i.nk, sqrt(i.nk)) axis(2, sqrt(i.nk), i.nk, las=1) ip <- paste(pch., names(maxDeaths[omax[1:8]])) legend('topleft', ip, cex=.55, col=cols, text.col=cols)
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