| terrorism | R Documentation |
The Global Terrorism Database (GTD) "is a database of incidents of terrorism from 1970 onward". Through 2020, this database contains information on 209,706 incidents.
terrorism provides a few summary
statistics along with an ordered
factor 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.
data(terrorism)
data(incidents.byCountryYr)
data(nkill.byCountryYr)
incidents.byCountryYr and
nkill.byCountryYr are matrices giving
the numbers of incidents and numbers of deaths
by year and by location of the event for 204
countries (rows) and for all years between
1970 and 2060 (columns) except for 1993, for
which the entries are all NA, because the raw
data previously collected was lost (though
the total for that year is available in
the data.frame
terrorism).
NOTES:
1. For 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
have been.
2. incidents.byCountryYr and
nkill.byCountryYr are NA for 1993,
because the GTD data for that year were lost.
terrorism is a data.frame
containing the following:
integer year, 1970:2020.
an 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 more recent data.
a character vector consisting of
the first character of the levels
of methodology:
c('p', 'c', 'i', 's')
integer number of incidents identified each year.
NOTE:
sum(terrorism[["incidents"]]) =
214660 = 209706 in the GTD database
plus 4954 for 1993, for which the
incident-level data were lost.
integer number of incidents identified
each year with country_txt =
"United States".
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
with country_txt =
"United States".
number of confirmed fatalities for
incidents in the given year, including
attackers =
sum(nkill, na.rm=TRUE) in the
GTD incident data.
NOTE: 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 NA when
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 extent 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
substantive undercount.
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.
NOTES:
1. This is subject to the same likely
modest undercount discussed with
nkill.)
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
nkill.)
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
nkill.)
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 nkill.us
and nwound.us, which exceed
90 percent for most of the period
with 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 2020.
NOTE: USdeathRate to 2009 is to
two significant digits only. Other death
rates carry more 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
https://www.mortality.org/Country/Country?cntr=USA
from the Human Mortality Database; see sources below.
terrorism deaths per million population worldwide and in the US =
nkill / (0.001*worldPopulation)
nkill.us / (0.001*USpopulation)
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.
Spencer Graves
National Consortium for the Study of Terrorism and Responses to Terrorism (START). (2017). Global Terrorism Database [Data file]. Retrieved from https://start.umd.edu/gtd [accessed 2022-10-08].
See also the Global Terrorism Database maintained by the National Consortium for the Study of Terrorism and Responses to Terrorism (START, 2015), https://www.start.umd.edu/gtd.
The world and US population figures came from "Total Population - Both Sexes", World Population Prospects 2022, published by the Population Division, World Population Prospects, of the United Nations, accessed 2022-10-09.
Human Mortality Database. University of California, Berkeley (USA), and Max Planck Institute for Demographic Research (Germany), accessed 2022-10-11.
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).
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)