This document summarizes an analysis of island hopping routes in Greece. It found that the top destinations based on searches were Santorini, Mykonos, Syros, Paros, and Sifnos. A network was modeled showing connections between islands, particularly in the Cyclades. A linear regression model was developed to predict arrivals to destinations based on search traffic from Openseas, achieving good accuracy on test examples. Future work proposed identifying missing routes that prevent daily island hopping and predicting demand for those new routes.

1. ISLAND
HOPPING
K. Ζυμάρα, Χ. Μπαρνασάς,
E. Μπουγιουκλή, Κ. Ξύστρα,
Ν. Τριανταφύλλου
Blue Hacathon 2015

2. BLUE HACKATHON 2015
ISLAND HOPPING
Island hopping is the crossing of
an ocean by a series of shorter journeys
between islands, as opposed to a single
journey directly to the destination.
In Greece, getting there really is
half the adventure and island
hopping remains an essential
part of the Greek experience.

3.  First of all, we analyzed our data in order to find out
which are the top destinations,
 based on the performed queries at Openseas,
http://www.openseas.gr/
 using the R project for Statistical Computing,
http://www.r-project.org
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4. 0
20000
40000
60000
80000
100000
120000
140000
160000
Τop 20 destinations
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Blue Hackathon 2015: FORTH crs data (2014)
Analysis: Top 20 destinations - Total requests (per island) via
www.openseas.gr

5. Top 20 destinations – Some statistics
Using the R code
summary(names_df$the_values)
We obtained the following results
Min. 1st Qu. Median Mean 3rd Qu. Max.
1.0 88.5 596.0 4698.0 3290.0 139300.0
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6. 16,7
6,3
5,7 5,3
4,4 4,0
3,4 3,4 3,2
2,6 2,4 2,3 2,0 2,0 1,9 1,7 1,4 1,3 1,3 1,3
0,0
2,0
4,0
6,0
8,0
10,0
12,0
14,0
16,0
18,0
piraeus
all
santorini
mykonos
syros
paros
sifnos
naxos
aegina
rafina
lavrio
tinos
ios
milos
kea
kythnos
heraklion
rodos
folegandros
serifos
% requests via Openseas (per destination)
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Blue Hackathon 2015: FORTH crs data (2014)
Analysis: Top 20 destinations - % requests (per destination)
via www.openseas.gr

7. 0
5000
10000
15000
20000
25000
30000
35000
40000
45000
50000
santorini
mykonos
syros
paros
sifnos
naxos
tinos
ios
milos
kea
kythnos
folegandros
serifos
Top requests for Cyclades
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Blue Hackathon 2015: FORTH crs data (2014)
Analysis: Top 20 destinations - Total requests (per island) via
www.openseas.gr for Cyclades

8. Network modeling and visualization
 Our aim is to build and visualize a network based on the
connections of these islands (Islands = nodes, Routes = edges),
 with focus on Cyclades
 so that we can see which of the islands are connected
– through a route – and which are not!
 For this we used the Cytoscape visualization tool
www.cytoscape.org
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9. Network modeling and visualization
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From To Total
piraeus-athens sifnos 18312
sifnos piraeus-athens 16045
lavrio-athens kea 13770
kea lavrio-athens 11575
santorini-thira piraeus-athens 11402
piraeus-athens santorini-thira 11199
piraeus-athens paros 10117
piraeus-athens syros 9909
syros piraeus-athens 8432
paros piraeus-athens 8213
piraeus-athens mykonos 8146
mykonos rafina-athens 7074
piraeus-athens milos 6970
rafina-athens mykonos 6967
mykonos santorini-thira 6925
rafina-athens tinos 6839
lavrio-athens kythnos 6187
piraeus-athens naxos 6141
piraeus-athens serifos 6075
milos piraeus-athens 5999
mykonos piraeus-athens 5982
tinos rafina-athens 5398
rafina-athens andros 5374
serifos piraeus-athens 5120
kythnos lavrio-athens 4945
santorini-thira mykonos 4936
piraeus-athens ios 4893
naxos piraeus-athens 4885
ios piraeus-athens 4848
paros syros 4373
mykonos paros 4268
andros rafina-athens 4077
paros mykonos 3877
mykonos syros 3779
syros paros 3775
piraeus-athens kythnos 3506
mykonos tinos 3501
santorini-thira ios 3465
mykonos naxos 3322
Top 136 routes (1 - 40)

10. piraeus-athens folegandros 3315
syros mykonos 3282
naxos mykonos 3251
ios santorini-thira 3202
tinos mykonos 3052
naxos syros 2914
piraeus-athens patmos 2798
santorini-thira naxos 2768
tinos syros 2727
syros tinos 2633
folegandros piraeus-athens 2583
naxos santorini-thira 2566
kythnos piraeus-athens 2553
santorini-thira anafi 2546
syros naxos 2435
paros naxos 2242
folegandros tinos 2217
naxos paros 2195
santorini-thira milos 2134
patmos piraeus-athens 2129
piraeus-athens sikinos 2042
piraeus-athens koufonisia 2003
milos santorini-thira 1951
piraeus-athens amorgos-katapola 1909
santorini-thira folegandros 1871
folegandros santorini-thira 1817
paros santorini-thira 1760
santorini-thira amorgos-katapola 1741
santorini-thira paros 1726
tinos piraeus-athens 1617
anafi santorini-thira 1616
piraeus-athens anafi 1574
milos sifnos 1544
paros sifnos 1499
sifnos milos 1416
rafina-athens paros 1404
amorgos-katapola santorini-thira 1334
sikinos piraeus-athens 1326
paros rafina-athens 1324
sifnos paros 1313
Top 136 routes (41 - 80)
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Network modeling and visualization

11. Network modeling and visualization
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Blue Hackathon 2015: FORTH crs data (2014)
Analysis: Network of the top 136 routes - based on the requests via Openseas

12. In order to predict the demand of a destination
we found the relationship between OpenSeas searches and
actual arrivals,
 using as input the searches towards 50 different
destinations, against the actual arrivals
 from the data provided by OpenSeas.gr and the Greek
port Authorities (respectively).
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13. Prediction of the demand of a destination
For this, we trained a linear regression algorithm written in
R so that, given as input:
 the destination
 the number of OpenSeas queries towards that destinations,
 the month in which these queries were conducted
it predicts the actual number of arrivals at that
destination
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14. Prediction of the demand of a destination
 We defined the destinations and the months
as categorical variables, and
 we discovered that the model that best fits the data is of
the following form:
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arrivals = (requests*name)+ name + month + requests^2
+ month*requests + (requests^2*month)
Where in the above equation, requests is a real number denoting the number of Openseas
requests, month is a vector (Jan,…, Dec) and names is a vector containing all the names of
the islands (paros, naxos, …).

15. Prediction of the demand of a destination
The full equation is given below:
Arrivals = 7268.782 -14.4934* requests + 46772.25*aegina + 7807*andros -5824.9*paros -6067.2* kasos + 12261.7 *
kefalonia -4002.41*sami -4907.4*karpathis +4486.72*symi -2767.0*alonnisos -1388.42*skopelos -2649.0*samothraki -
6127.7* portoheli -1000.2*patmos -4112.5*serifos -3288.7*kythnos -1383.4*sifnos -5996.8*kimolos -2289.9*milos -
5798.6*sikinos -4059.5*folegandros -1045.4*ios -5768.6*ermioni + 13201.7*syros + 1330.0*spetses + 1942.81*skiathos -
7182.7*samos -7758.4*ikaria -6549.5*rethymno + 14985.7*poros -6214.7* methana + 17507.3* paros + 6339.5*
antiparos -7825* amorgos + 14674.6* naxos + 25217.4* mykonos -6219.2* agios-efstratios -4283.5* limnos -4389.2*
lefkada -5585.1* lipsi -3618.8* leros -5100.9*nisyros + 4131.8*kos +37472.3*kylini -5990.1*anafi +14003.2*thirasia +
20324.7*zakynthos + 22982.8*August 3047.6*December -3135.7*Februrary -2793.0*January + 15778.5*July
+6383.0*June -3059.2*March + 2634.9*May -2181.4*November -1059.0*October +6694.9*September -2.6481
(requests^2) + 0.4352 *requests*aegina +2.9 * requests*andros -9.09*requests*astypalea -89.8*requests*kasos +
209.8536*requests*kefalonia -28.8*requests*sami -19.9*requests*karpathos + 3.3723 requests*tinos -1.5*requests*symi -
3.46*requests*alonnisos -1.3768 * requests*skopelos -55.92*requests*samothraki -90.9*requests*porto-heli -
4.5273*requests*patmos -2.8035* requests*serifos -2.89*requests*kythnos -1.73*requests*sifnos -14.5*requests*kimolos +
0.0035 requests*milos -8.47*requests*sikinos -3.35*requests*folegandros -0.6253 *requests*ios -39.05*requests*ermioni-
2.44*requests*syros -9.64*requests*spetses + 0.5608 *requests*skiathos -90.0*requests*samos -76.9*requests*ikaria -
27.8*requests*rethymno + 14.9669 requests*poros -23.10*requests*methana + 1.16*requests*paros
8714.4*requests*antimparos -115.47*requests*amorgos + 1.2*requests*naxos + 0.5*requests*mykonos -
77.4*requests*agios-efstratios -5.1*requests*limnos -1553.1*requests*lefkada -28.5*requests*lipsi -14.5*requests*leros-
21.3307 requests*nisyros-3.5376 requests*kos + 151.80*requests*kyllini-8.11*requests*anafi +
418.7*requests*anafi+418.7*requetsts*thirasia + 109.9*requests*zakynthos + 13.08*requests*August -
228.80*requests*December -3702.3*requests*February -1141.6*requests*January+13.443*reqyests*July +
102.18*requests*June + -384.6*requests*March -125.6*requests*May-64.0*requests*November + 16.87*requests*October
+ 15.2*requests*September + 2.64*(requests^2)*August + 2.2*(requests^2) December + 234.7*(requests^2)*Februrary +
28.45*(requests^2)* January + 2.64*(requests^2)* July + 2.5121*(requests^2)*June -33.4*(requests^2)*March +
4.06*(requests^2)*May + 2.8*(requests^2)*November + 2.6*(requests^2)*October + 2.6*(requests^2)*September
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16. Prediction of the demand of a destination
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‘EXPERIMENTS’/EXAMPLES
ISLAND MONTH SEARCHES PREDICTIONS ACTUAL
ARRIVALS
Mykonos October 4000 37545.69 37065
Aegina July 5050 106847.9 107225
Paros August 11092 124534.4 132117
Ios June 74 18303.95 13245
Naxos April 23 20238.17 18758
Antiparos July 3 55527 55527

17. Future Steps: System’s failure
 We will define as failure of the system, not being able to visit one
of these islands on a daily basis (island hopping).
 We will study also the islands which are connected but the existing
routes do not allow the visitor to go/return at a convenient time.
 From the above, we will find out which routes are missing, so that
island hopping will become possible!
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18. Future Steps: Missing routes
 We will predict the demand of the missing routes.
 To achieve this, we will use the prediction of the demand of a
destination, and other parameters, such as the attractiveness of the
islands.
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19. THANK YOU VERY MUCH!