Rationale

As of 31 December 2015, how many refugees and migrants will UNHCR report as having arrived in Europe by sea? Europe is facing a refugee crisis […]. Question will be resolved using the data available on UNHCR’s Emergency Response Page for the Mediterranean as of 31 December 2015 (http://data.unhcr.org/mediterranean/regional.html ).

– from an Open Good Judgment Question

The code is in the public domain and I encourage you to play! (CC BY 3.0) Roland Kofler. Any Idea how to improve? Contact me via Gmail: my name separated by a dot.

Source on GitHub

Found the freaking raw data

Its all about Lesbos!

Facts

October=10 
September=9   #you get the idea
reportedAmount=723221 #1 November
aMillion=10^6
N=10000;
projectionMonth=30/20;
arrivals2014= c(3270, 4369, 7283, 17084, 16627, 26221, 28303, 33478, 33944, 23050, 13318, 9107)
arrivals2015= c( 5546,  7343,   10184,  29441,  40117,  53987,  75483,  130837, 172843, 197440,101000*projectionMonth)
lesbosArrivals2014= c(290, 413, 555, 620, 461, 824, 873, 1064, 1778, 2072, 959, 802)
lesbosArrivals2015= c(737, 1002, 3348, 4990, 7228, 14796, 23721, 56579, 95384, 124698, 63400*projectionMonth)

syrianRefugees=4000000

Data Analysis

arrivalsFor2Years <- c(arrivals2014, arrivals2015)
arrivalTimeseries <- ts(arrivalsFor2Years, start=c(2014, 1), end=c(2015, 11), frequency=12)

lesbosArrivalsFor2Years <- c(lesbosArrivals2014, lesbosArrivals2015)
lesbosTimeseries <- ts(lesbosArrivalsFor2Years, start=c(2014, 1), end=c(2015, 11), frequency=12)

Seasonplots

They are looking as nobody can stop the trend

suppressMessages(library(forecast, quietly = TRUE, warn.conflicts = FALSE, verbose=F))
#monthplot(arrivalTimeseries)
#library(forecast)
seasonplot(arrivalTimeseries) 

#seasonplot(lesbosTimeseries)

Extreme situation in Lesbos

Lesbos is the main door to the EU, being the greatest Island of the EU only a few km ashore of Turkey it is reachable with a dingi boat

plot(lesbosArrivals2015/ lesbosArrivals2014[1:11], type = 'o', main="Lesbos 2015 vs Lesbos 2014")

changeRateLesbosArrivalsYoY <- lesbosArrivalsFor2Years/arrivalsFor2Years
plot(changeRateLesbosArrivalsYoY, type = 'o', main="How Lesbos became the door to Europe", xlab="month since Jan 2014", ylab="*100=% of Lesbos Refugees to Total")

If you are a Refugee Maximalista – someone who believes that more than a million will come – its worth to take a look at How Greek Island behaved in Winter 2014

Monte Carlo Simulation

model parameters

if refugee inflow behave the same like last year in Lesbos

changeMoM= lesbosArrivals2014[12]/lesbosArrivals2014[11]

we get changeMoM changeMoM=0.8362878

BIG OPEN Question: how to estimate our Standard Deviation? If I understand the bell curve correctly I shoud set a SD so that no event lays outside of the plausible range. so not below already reportedAmount If thats the case > 1mm is so imporbable.

arrivalsImputedFuture= c(arrivalsFor2Years, arrivals2015[11] * changeMoM)
arrivalTimeseriesWithImputedFutureValues <- ts(arrivalsImputedFuture, start=c(2014, 1), end=c(2015, 12), frequency=12)

seasonplot(arrivalTimeseriesWithImputedFutureValues);

standardDeviation= 0.18
weight= rnorm(N, mean=changeMoM, sd=standardDeviation)

Calculations

arrivalsNovember = arrivals2015[11]
arrivalsDecember = arrivalsNovember * weight


futureArrivals= arrivalsNovember + arrivalsDecember
refugeesAmount = reportedAmount + futureArrivals
plot(refugeesAmount)
abline(h=aMillion, col="red");
abline(h=reportedAmount, col="green");

overAmillion = refugeesAmount > 10^6
ProbabilityOverMillion= sum(overAmillion)/N

Probability of more than a Million Refuges

print(ProbabilityOverMillion * 100)
## [1] 51.99