MAGALHÃES, L; REIS, V.; MACÁRIO, R.
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17TH ATRS WORLD CONFERENCE PAPER
FACTOR AND CLUSTER ANALYSIS APPLIED TO FLEXIBLE AIRPORTS’
PERFORMANCE DATA
Liliana Magalhães, Vasco Reis, Rosário Macário
Instituto Superior Técnico – Universidade de Lisboa, DECivil, CESUR
Avenida Rovisco Pais, 1049-001 Lisboa, Portugal
+351-218418424
Presenter name: Liliana Magalhães
ABSTRACT
Airport operators have to anticipate future possible scenarios which might occur so that
their airport can face the changes. Unpredictable changes on the circumstances lead
forecasts often to fail as the future is not foreseeable. Flexibility helps to maintain or
increase airport’s performance levels by improving the adaptability of the airport’s
functions to external changes. The objective of this work is to understand if flexible airports
are at least able to keep their perform results towards external changes. Flexible airports
were identified based on the literature review and a survey launched to worldwide airports.
To achieve this, the ATRS Airport Benchmarking Report from 2004 until 2011 (except
2010) was used as data sample, for the following performance categories: productivity and
efficiency, costs and financial. A factorial analysis was performed to reduce the variables,
as most of them were highly correlated, to observe how airport evolved regarding the
obtained factors. Using this data, a cluster analysis was conducted to explore whether
flexible airport are similar among them or not during that time interval. Flexible airports
performance seems to be similar to the other airports. However, there are some evidences
supporting the advantages of flexibility but more studies are required.
KEYWORDS: Airport Flexibility, Factor Analysis, Cluster Analysis
MAGALHÃES, L; REIS, V.; MACÁRIO, R.
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CLASSIFICATION: Airport Strategy, Management and Operations; Airport and Airline
Performance
INTRODUCTION
The current economic turmoil has generated a high level of uncertainty in the expected
evolution of air transport markets. Airlines are able to change their network structures
overnight. The oil price, flu epidemics, and financial and economic woes further add to the
volatility of aviation demand development. Combined with tensions between economic and
environmental impacts, this makes airport strategic planning a challenging task. As de
Neufville (2008, p.36) points out “airport planning paradigm is shifting from the traditional
pattern, which is determined by high standards, established customers and long-term
forecast, to that of recognizing great uncertainty at forecasts, broad range standards and
potential for a rapidly changing customer’s base.” Airports’ expansion became increasingly
uncertain and risky, which has led many authors to advocate the need of airports being
increasingly flexible: that it is, able to adjust towards necessity.
Additionally, the uncertainty regarding the composition of demand is highly important
nowadays. Low-cost carriers have been growing and contribute to increase the dynamic of
aviation market due to their strategy of minimizing costs. Their routes change with high
frequency, creating new ones and promoting the development of regional airports.
Moreover, they require specific features at airports in order to fulfil their goals (e.g. quick
turnaround times and simple passenger terminals). Therefore, airports must be able to adapt
to their clients’ needs – airlines requirements – without high investments on it.
The theoretical roots of flexibility can be traced back to the late 1950s (Shuchi et al., 2012).
Flexibility has been applied in other fields besides airport infrastructures: manufacturing
engineering (Suarez & Cusumano, 1991; Taylor, 1991; Schulz et al., 2000; Ross et al.
2008); engineering design, e.g. bridges, oil platforms (Neufville and Scholtes, 2011); and
building design (Till and Schneider, 2005; Schneider and Till, 2007) to name a few.
However, the need for flexibility in airport design is a recent recognition (de Neufville and
Belin, 2002; Edwards, 2005; de Neufville, 2008). Only few authors have been studied the
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concept of flexibility and no universal definition was accepted so far (Morlok and Chang,
2004; Edwards, 2005; Burghouwt, 2007; de Neufville, 2008; Gil & Tether, 2011; Shuchi et
al., 2012).
Only few authors present their own definition of this concept. For Morlok and Chang
(2004) flexibility is described as system’s ability to adapt to external changes but keeping
the system’s performance levels satisfactory. This definition is similar to our understating
of flexibility and it matches with the advantage of flexibility that we are analysing in this
work. de Neufville (2008, p.53) defines flexibility from a design perspective, as a “group of
technical features that enable the owners to change, easily and inexpensively, the
configuration of their facility to meet new needs”. Edwards (2005) states that flexible
design is used to reply to particular changing requirements. This second definition differs
from the previous one since it does not mention expenses or the easiness of changing. It
does not consider the way of doing the change. It is just concerned with the ability to
change. For Burghouwt (2007), flexibility is the same as re-adaptability and is defined as an
ability to perform constant adjustments towards changing circumstances. For Shuchi et al.
(2012, p. 350) flexibility is also consider as the same as adaptability, and they define it as
“the ability to adapt to the environment without making any permanent change to the
environment”. Gil and Tether (2011) associate flexibility with design and risk management.
This approach differs from all the others founded for airport flexibility.
Some similarities can be found among these definitions. One can conclude that for the four
authors, flexibility is associated with some sort of change, as all of them use the word
“change” or a variation of it. Moreover, all of the authors consider flexibility as an ability
of the airport, directly or indirectly. For two of them this is explicitly mentioned in the
definition. Edwards (2005) defines flexibility as a response and de Neufville (2008) as a
feature, so our understanding is that these words can be considered as synonyms of ability.
Our understanding of flexibility is that it is the ability of having an infrastructure as
mutable as possible to adapt to future requirements and able to, at least, keep its
performance results. Moreover, from our point of view flexibility is closely linked with
optimizing the investment and the infrastructure’s performance by reducing its idleness.
Moreover, the application of flexibility can be performed at strategic or operational levels
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and for each level different flexible options are required. For example, at operational level
it is common to use moveable walls and at strategic level the most common options is the
land saving for future expansions (Magalhães et al., 2013)
Airports have been embedded with flexibility to deal with volatile market environment and
uncertainty about future traffic demand and composition. Magalhães et al. (2013) present
several examples of flexible airports all over the world: Dublin International Airport,
Niagara Falls International Airport, Vancouver International Airport, Amsterdam Schiphol
International Airport, Southampton Airport and Bangkok Suvarnabhumi Airport.
Flexibility is very important for infrastructures with long life-cycle such as airports, so that
they can be able to change their functions and processes to respond to external necessities
with minimum costs. Moreover, as de Neufville (2008, p. 54) states, “flexible designs
incorporate capabilities to adjust easily to different scenarios. They create ‘real options’,
similar to financial options (such as puts and calls) that give their owners the right, but not
an obligation, to take an action, now or in the future”. This means that the infrastructure is
embedded with the ability to adapt or change its function but only if necessary managers
“activate” this feature. More than keeping the costs at their minimum, flexibility has the
advantage of keeping the system’s performance levels satisfactory whenever it is necessary
to adapt to external changes (Morlok and Chang, 2004), which is for us the key advantage.
Building an airport so large that it can deal with higher future demand does not mean to be
flexible. Being flexible is related with exploring the infrastructure until the operation
performance reaches its maximum that is when we reach the maximum efficiency.
The objective of this work is to understand how the so-called flexible airports’ performance
differs from the other airports. We want to assess whether flexible airport are at least able
to keep their performance results through time, when compared with the other airports. We
want to explore how flexible airports performance evolved over time. Since flexible
airports are by definition better prepared to deal with the uncertain, we expect that these
airports are more resilient and as such, their performances should be almost stable over
time. To achieve this, we performed a factorial analysis on the ATRS Airport
Benchmarking Report of 2004 in order to reduce the provided variables which are highly
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correlated. The obtained factorial structure was applied to other years (2011 to 2005 except
2010) in order to analyse how these configuration of factors evolved through this time
interval. As the factors represent different typologies configuration of variables, the
analysis over this time period will allow us to identify changes through time. Then, a
cluster analysis was performed for each year of the time period in order to observe how
flexibility has been influencing the airports development for each obtained factor.
Moreover, we also wanted to observe whether flexible airports are similar among them or
not for the period under analysis.
DATA SAMPLE AND FLEXIBLE AIRPORTS CONSIDERED
The base data sample used is the ATRS Airport Benchmarking Report of 2004 that
considers airports from North America, Europe and Asia. Then, we also used the ATRS
Airport benchmarking Reports of 2011, 2009, 2008, 2007, 2006, 2005 but we only
considered the information related with those 140 airports. The data provided at the reports
is two years lagged from the year of publication. For instance, the report of 2011 contains
data from 2009 and so on. Our designation for the years will be hereinafter related with the
data presented at the report to avoid misperceptions.
Deciding if an airport is flexible or not is not a trivial task since so far, there is no common
definition as explained above. We considered as flexible airports the ones pointed by
Magalhães et al. (2013). This classification result from a literature review on airport
flexibility and also a survey released on 2012 to worldwide airports. The goals of this
survey were: to understand whether an airport is flexible, and also which are the flexible
options that the airport has. Flexible options can be applied to different levels of
development: strategic, tactical and operational. Examples of flexible options at the three
levels are: land saving for future expansion ate strategic level, moveable partition walls at
tactical level and, for instance, moving systems such as check-in counters at operational
level. The airports that were considered as flexible are the following:
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Table 1 - Airports considered as being flexible
North America Europe Asia
AUS AMS BKK
BNA ARN WLG
DEN ATH
DTW BRU
JAX CDG
MEM DUB
PDX LIS
RNO
YEG
YUL
YVR
The Airport Benchmarking Reports provided by ATRS present performance indicators for
the following aspects of airport operation: productivity and efficiency, unit costs and cost
competitiveness, financial results and airport charges. The airport charges have no
implications for our objective so we decided to not consider this type of indicators.
Regarding the other three groups of data we consider only the following indicators:
Productivity and Efficiency
o Passenger per employee;
o Aircraft movements per employee;
o Passengers per gate;
o Passengers per square meter of terminal space;
o Aircraft movements per runway;
Unit Costs and Cost Competitiveness
o Labour cost per passenger;
o Labour cost per movement;
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o Variable cost per passenger;
o Variable cost per aircraft movement;
Financial results
o Aeronautical revenue per aircraft movement;
o Concession revenue per passenger;
o Operational revenue per passenger;
o Operational revenue per movement;
o Operational revenue per employee.
These indicators were chosen based on the following rational: (1) to have indicators from
the three performance groups; (2) to have indicators which are not repeated and that
characterize the group to avoid overlap of information. These fourteen indicators will
hereinafter be designated as variables.
METHDOLOGY
To achieve the objectives of this work we conducted a factorial analysis on the ATRS
Airport Benchmarking data of 2002 to reduce the provided variables which are highly
correlated. The obtained factorial structure was applied to the data for 2009 to 2003 except
2008, in order to analyse how these configuration of factors evolved through this time
interval. After that, a cluster analysis was performed for each year in order to observe how
the airports have been developing for each obtained factor. Methodology will be explained
with more details below. The software used to perform these analyses were the SPSS
Statistics and MS Excel.
Firstly, we conducted a correlation analysis over the base data sample (2002) and
concluded that most of the variables are significantly correlated. The obtained significance
values were higher than 0.25 for most of the compared pairs of variables. As such, we
decided to perform a Factorial Analysis to reduce the number of variables to take into
account. The goal with this reduction is to be more efficient and parsimonious.
Factorial Analysis
Factor analysis or principal components analysis is used to reduce a multivariate data set
and to interpret data. As Washington et al. (2003) explains, this method provides a more
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parsimonious description of the data and uses linear combinations of the original variables
to clarify the variance-covariance structure. The method builds factors which are not
directly observed in the sample by grouping the variables provided with a certain
“explanation” score for each of them (Marôco, 2011).
To measure the adequacy of the extracted factors from the sample we focus on the Kayser-
Meyer-Olkin (KMO) value and also on the percentage of the total variance explained by the
factors. According to Marôco (2011) the KMO value should be equal or higher than 0.8 to
consider the factorial structure as a good one. However, we considered a KMO value equal
or higher than 0.7 already a good value. We used the principal components method and the
number of factors retained are based on the Kaiser criteria, which only retains factors with
an eigenvalue higher than 1.The rotation method used was the Varimax whose outcome is a
factorial structure where one or more variables are highly related with only one factor and
little associated with the others. The scores estimation was based on the Regression method
and the analysis was performed over the correlation matrix. This analysis was conducted on
SPSS Statistics software. To apply the factorial structure obtained for 2002 to the other
years it was necessary to determine the standardize variables for each year.
Cluster Analysis
Cluster analysis is an exploratory technique that groups variables into homogeneous groups
with one or more common characteristics. Each observation from a certain cluster is similar
to the others which belong to that same cluster but different from the observations which
belong to other clusters (Marôco, 2011). The clusters identification is based on the measure
of the similarity which is usually based on a metric distance. We used as hierarchical
method the Ward’s method, which groups observations in order to minimize the sum of the
squared errors. For the interval we used the Square Euclidean Distance. This analysis was
conducted on SPSS Statistics software.
The clusters analysis is based on the values obtained for each airport and each factor for
each year, which result from the application of the factorial structure obtained for 2002 to
the other years. Based on this, we were able to obtain a cluster analysis for each airport
considering the outcomes of the factorial analysis.
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RESULTS AND DISCUSSION
The correlation matrix is presented in Appendix 1. It possible to observe that only two of
the variables are not correlated with the other ones: passengers per square meter of terminal
space and aircraft movements per runway. The majority of the variables are highly
correlated as the significance values are higher than 0.25. We highlighted those cells in red
to help their identification.
Factorial Analysis
The rotated component matrix obtained for the factorial analysis is presented in Table 2.
Four factors were obtained. By looking at the colours in the table is possible to observe
which variables are more relevant for each factor and they are in green. The obtained KMO
was 0.708 and the total variance explained was 83.499%.
Table 2 - Rotated Component Matrix
Factor 1 Factor 2 Factor 3 Factor 4
Passengers per employee (2002) (thousands) -,062 -,690 -,119 ,558
Aircraft movement per employee (2002) -,272 -,679 -,468 ,291
Passengers per Gate (2002) ,113 -,049 ,887 -,022
Passengers per M2 of Terminal Space (2002) -,244 ,062 ,598 ,107
Aircraft Movement per Runway (2002) -,041 ,034 ,089 ,913
Labour Cost per Passenger (2002) (US$) ,161 ,911 -,153 ,122
Labour Cost per Aircraft Movement (2002) (US$) ,557 ,751 ,075 ,152
Variable Cost per Passenger (2002) (US$) ,868 ,391 -,125 -,055
Variable Cost per Aircraft Movement (2002) (US$) ,968 ,174 ,023 -,030
Aeronautical Revenue per Aircraft Movement (2002) ,940 ,144 -,040 -,033
Concession Revenue per Passenger (2002) ,846 ,188 ,103 -,027
Operational Revenue per Passenger (2002) (US$) ,870 ,338 -,128 -,132
Operational Revenue per Aircraft Movement (2002) (US$) ,969 ,131 ,042 -,066
Operational Revenue per Employee (2002) (US$) ,729 -,277 -,337 ,126
Factor 1 can be designated as the financial factor as this factor is mainly explained by
costs and revenues: variable cost per passenger, variable cost per aircraft movement,
aeronautical revenue per aircraft movement, concession revenue per passenger, operational
MAGALHÃES, L; REIS, V.; MACÁRIO, R.
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revenue per passenger and per movement. Factor 2 can be designated as the labour cost
factor since the more relevant variables to characterize this factor are the labour cost per
passenger and the labour cost per aircraft movement. Factor 3 is the terminal’s
performance factor as the more relevant variables for this factor are passengers per gate
and passengers per square meter of terminal space. Lastly, Factor 4 can be designated as the
airport performance factor as the more relevant variables are aircraft movements per
runway and passengers per employee.
Flexibility has been directly associated with performance as flexible airports are more able
to adapt to changes and remain competitive. Moreover, definitions like the one provided by
de Neufville (2008) lead us to associate flexibility with the terminal’s performance since it
mentions a group of technical features to change the facility’s configuration. Our
understanding is that being flexible is also a synonymous of being resilient – a flexible
airport should be able to, at least, keep is performance results towards new external
changes. From the four previous factors, our understanding is that Factor 3 is the one which
better characterizes its gains since most interventions are performed at the terminal.
However, Factors 1 and 4 can also characterize the performance results of flexible airports
either.
Appendix 2 presents for each factor and airport, the differences between each year and
2002. Comparing 2004 with 2002 for Factors 3 and 4 one can conclude that most airports
had a negative evolution and this situation kept for 2005. However, for the further years
they have better results. The year 2004 seems to be a difficult year for most of the airports
when compared with 2002 regarding Factor 1 as their results got worse especially for Asian
airports. This situation slightly changes for the further years of analysis. Factor 2 is the one
for which more airport presents bad results during more years, except for 2002.
Focusing on the airports, on Factor 1 FRA constantly appears as one of the airports with
lower results, except for 2004-2002, 2007-2002 and 2009-2002. This led us to believe that
this airport probably was able to invert its financial situation. In opposition, ICN airport
constantly appears with the highest results for this factor. For 2007-2002 and 2009-2002
CGD, which is a flexible airport, appears right next to ICN with better financial results.
MAGALHÃES, L; REIS, V.; MACÁRIO, R.
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Factor 2 is headed by ARN, which is a flexible airport, except for 2007-2002 and 2009-
2002 where FRA starts to lead. Despite the increase in the labour costs per passenger and
aircraft movement, FRA was able to increase its revenues (Factor 1) during the same
period. Except for 2003-2002 and 2004-2002, JFK is the airport which presents lower
values for Factor 2 which means lower labour costs per passenger and aircraft movement.
Most of the leaders of Factor 3 are North American airports. SAN and OAK appear on the
top for all the intervals in analysis, but others like CLT, SNA and SJC appears most of the
times. This is the factor where more airports present better results. Nevertheless, MDW
constantly present the worst results.
Factor 4 is headed mostly by the same airports as Factor 3 except for 2004-2002, where
only NRT is the leader. For 2009-2002 one flexible airport presents the highest results:
ATH. For this factor, ATL present the lowest results for 2004-2002, 2005-2002 and 2006-
2002. Despite the good results of few of the airports considered as being flexible, the
majority do not present significant higher results for one particular factor.
Magalhães et al. (2013) concluded from the survey results that most of the flexible
solutions used nowadays are applied at the terminal. Since Factor 3 is the one which
describes the terminal’s performance, we decided to plot only the results for this factor.
Figure 1 presents the average results obtained by flexible and non-flexible airports through
the years. The objective of this figure is to help to analyse if flexible airports are able to
keep their performance results through time. Observing the figure two aspects can be
highlighted: non-flexible airports present better average performance results, and on
average the flexible airports were not able to keep their performance results. This general
overview of this factor is not favourable to our hypothesis for flexible airports. However,
we analysed the situation for each flexible airport in North America (Figure 2), Europe
(Figure 3) and Asia (Figure 4). The averages, the maximums and minimums for each year
were calculated considering only the airports from each continent.
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Figure 1 - Average results for Factor 3 for Flexible and Non-Flexible Airports
For North America is possible to observe that YEG is always above the average for this
factor and since 2004, so has been AUS. YUL was above the average until 2007 but in
2009 it dropped. It is important to notice that airports like MEM and RNO present almost
constant results since 2005. This is consistent with the perspective that a flexible airport
should be more resilient to external changes and at least, able to keep its performance.
In Europe, CDG, AMS and DUB are always above the average except in 2005 for the last
two airports. Also here, the behaviour of BRU, ARN and CDG can be considered as quite
stable. DUB is the only flexible airport which has been close to the maximum values since
2006. Moreover, its increase from 2005 to 2006 is quite high. BRU, ARN and LIS are the
only airports that present negative values for 2009.
In Asia we only have two airports. However, we can observe that WLG is increasing its
results since 2004 and possibly soon will leave the negative zone. As for BKK, despite the
slightly decrease in 2007 it seems to be growing and possibly it is already above the
average.
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Figure 2 - Results for Factor 3 for Flexible Airports in North America
Figure 3 - Results for Factor 3 for Flexible Airports in Europe
MAGALHÃES, L; REIS, V.; MACÁRIO, R.
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Figure 4 - Results for Factor 3 for Flexible Airports in Asia
The behaviour of each flexible airport is quite specific and no common performance results
on a particular factor were found. However, this study launches a new research topic as
some of the flexible airport present a few evidences of being more resilient than the non-
flexible airports. This is based on their constant performance results but requires further
analysis.
Clusters Analysis
The cluster membership is presented on Appendix 3. We obtained the analysis from five to
twelve clusters, but we decided to base our conclusions for the eight clusters grouping as
the results are more balance for this case. One of the aspects that capture our attention was
the fact that apart from 2006, Cluster 8 is constantly almost filled only by Japanese airports.
This means that these two airports (NRT and KIX) are very similar but different from the
others. Moreover, ATL and the airports in New York area (JFK, EWR and LGA) are
constantly in a specific cluster with no more than 3 or 4 other airports in some years, expect
for 2004 and 2005. So, these airports must have common performance characteristics that
differentiate them from the others.
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There is no specific cluster that gathers all flexible airports. However, CDG and DUB are
usually on the same cluster or in different clusters but with few members. This is not the
case of LIS which only appears more or less isolated for 2002. The Canadian flexible
airports always appear in the same cluster but sometimes, like for 2002 and 2009, in a
cluster that is different from the one where non-flexible Canadian airports are. Apart from
2004, the flexible airports MEM and PDX are also appearing together.
We estimated the average for each cluster regarding each factor and built graphics that are
presented on Figure 5, Figure 6 and Figure 7 for Factors 1, 3 and 4 only as we consider
Factor 2 as not appropriate to analyse flexibility.
For Factor 1 is possible to observe that Cluster 8 presents the highest results. This cluster is
composed only by Japanese airports. Only for 2006 the Japanese airports are substitute by
CDG, MUC and VIE, and as we can observe, the results dropped significantly. Cluster 1,
despite being constant, presents the lowest results. This cluster in 2002 contains airports
from all continents, including the flexible airports AUS, BNA, JAX, RNO and ATH, but
this scenario changes and in 2009 it has only three European airports (MXP, PRG and
RIX). The others are from North America and Asia.
Figure 5 - Annual Average of Factor 1 for each Cluster
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For Factor 3, three clusters present positive results for all years: 4, 5 and 7. However, only
cluster 7 presents a tendency to increase its results. This cluster is composed only by
European airports for 2009, including CDG and DUB. Cluster 4 does not present any
flexible airport for 2009 and it is composed by two American airports (MDW and CLT)
and eight Asian airports. Regarding Cluster 5, it has 32 members for 2009 from which two
of the airports are flexible: DEN and BKK.
Figure 6 - Annual Average of Factor 3 for each Cluster
Regarding Factor 4, the clusters 2, 4 and 6 always present positive results. However, cluster
6 is more constant than the other ones and it is composed by fourteen European airports and
one airport from North American for 2009. None of them are flexible. Cluster 2 is
composed by several airports until 2005 but since 2006 this clusters is mainly taken by
ATL and the three airports in New York (JFK, EWR and LGA), and in 2009 they are the
only members of the cluster. Cluster 4 is mainly a North American and Asian cluster but is
possible to find a few European airports in some years (2002, 2003, 2004 and 2006). And
here, several flexible airports can be found in different years but in 2009 there is none.
MAGALHÃES, L; REIS, V.; MACÁRIO, R.
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Figure 7 - Annual Average of Factor 4 for each Cluster
No major conclusion can be drawn for all the flexible airports in terms of cluster
membership. However, some flexible airports constantly appear in the same cluster. For
instance, AUS, BNA, JAX and RNO are constantly in Cluster 1. YEG, YUL and YVR are
also usually on Cluster 2. Lastly, AMS, ARN and ATH constantly appear together in
Cluster 5 or Cluster 3.
CONCLUSIONS
This work represents an adding contribute to increase the knowledge on the advantages of
flexibility applied to airports. Our goal was to understand if the so-called flexible airports
were able to at least keep its performance results through time, comparing with the non-
flexible airports. The used data did not allow us to validate our hypothesis. This might be
related with two aspects: the performance variables that we choose may not be the most
adequate to analyse the advantages of flexibility or; the advantages of flexibility may not be
related with our hypothesis, which is having a resilient performance towards change. More
studies on this topic are needed.
It is also important to notice that the results obtained leads to conclude that flexible airports
do not present higher performance results when compared to the other ones. This is
consistent with Morlok and Chang (2004) perspective that flexibility is related with
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keeping satisfactory performance results towards external changes. This does not
necessarily mean higher performance results when compared to the non-flexible airports,
but being resilient. However, we were not able to validate this hypothesis with the data
used. Still, some of the flexible airports present higher results on variables related with
financial results and terminal’s performance when compared with the non-flexible airports.
But these results cannot be generalized to all the flexible airports in the data sample.
We also wanted to explore how flexible airport have been developing through time.
Regarding this aspect, we were able to notice that some of the flexible airports present a
stable behaviour in terms of performance results. This is consistent with our view of
flexible airport’s ability of keeping its performance results through time. This result is a
valuable and new insight on the study of airport flexibility that should be explored with
more detail for each flexible airport.
The classification of which airports are flexible is based on a previous work that identified
20 airports as being flexible. However, we are convinced that there are more than 20
airports that can be considered as flexible but it was not possible to identify them so far.
With a higher sample of flexible airports, especially for Europe and Asia, more conclusions
can be drawn regarding their performance results. It is our understanding that the next step
on the topic of airport flexibility is to develop a new tool to classify an airport as flexible or
not.
ACKNOWLEDEGMENTS
We would like to acknowledge to our colleague Luís Martinez, Ph.D. in Transportation,
who kindly provide valuable insights related with the factorial and clusters analysis to our
work.
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MAGALHÃES, L; REIS, V.; MACÁRIO, R.
22
APPENDIX 1 – Correlation matrix for the 14 variables
Passengers per
employee (2002)
(thousands)
Aircraft
movement per
employee (2002)
Passengers
per Gate
(2002)
Passengers per
M2 of Terminal
Space (2002)
Aircraft
Movement
per Runway
(2002)
Labour Cost
per Passenger
(2002) (US$)
Labour Cost per
Aircraft
Movement
(2002) (US$)
Variable
Cost per
Passenger
(2002)
Variable Cost per
Aircraft Movement
(2002) (US$)
Aeronautical
Revenue per
Aircraft
Movement (2002)
Concession
Revenue per
Passenger
(2002)
Total
Revenue per
Passenger
(2002) (US$)
Total Revenue
per Aircraft
Movement
(2002) (US$)
Total
Revenue per
Employee
(2002) (US$)
Passengers per employee (2002)
(thousands) 1
Aircraft movement per
employee (2002) 0,74468545 1
Passengers per Gate (2002) 0,166086211 -0,156519034 1
Passengers per M2 of Terminal
Space (2002) 0,076580412 0,079753953 0,315223928 1
Aircraft Movement per Runway
(2002) 0,423589696 0,291199427 0,162425583 0,097480519 1
Labour Cost per Passenger (2002)
(US$) -0,44911902 -0,420234573 -0,12029893 0,048248702 -0,04990617 1
Labour Cost per Aircraft
Movement (2002) (US$) -0,39083947 -0,550419357 0,080458866 0,023645085 0,07862574 0,81470685 1
Variable Cost per Passenger
(2002) (US$) -0,327778007 -0,378638032 -0,070043 -0,060426654 -0,036260867 0,73040652 0,783584028 1
Variable Cost per Aircraft
Movement (2002) (US$) -0,16759974 -0,355793171 0,097724186 -0,098817228 0,034205491 0,343176406 0,66457071 0,83382791 1
Aeronautical Revenue per
Aircraft Movement (2002) -0,184113186 -0,341044521 0,041864844 -0,200543938 -0,043860683 0,233438319 0,569022394 0,77935245 0,963269999 1
Concession Revenue per
Passenger (2002) -0,237766125 -0,442378068 0,08623718 -0,152187583 0,02710992 0,230506857 0,57035312 0,70848097 0,844178695 0,835449172 1
Total Revenue per Passenger
(2002) (US$) -0,41838214 -0,512476688 -0,01324494 -0,226714574 -0,115874363 0,625788375 0,735760343 0,91311245 0,79908281 0,767302649 0,769063785 1
Total Revenue per Aircraft
Movement (2002) (US$) -0,222320538 -0,42039739 0,151181422 -0,204423304 0,001174542 0,218061552 0,580344336 0,76723126 0,955355468 0,908810485 0,865388078 0,847916688 1
Total Revenue per Employee
(2002) (US$) 0,319963109 0,285685698 -0,04693745 -0,189608705 0,067911174 -0,124461888 0,101496488 0,41670989 0,572407476 0,5192045 0,384154458 0,368438119 0,542179853 1
MAGALHÃES, L; REIS, V.; MACÁRIO, R.
23
APPENDIX 2 – Differences for each year and 2002 for airport and factor (North America)
dF1 03-02 dF2 03-02 dF3 03-02 dF4 03-02 dF1 04-02 dF2 04-02 dF3 04-02 dF4 04-02 dF1 05-02 dF2 05-02 dF3 05-02 dF4 05-02 dF1 06-02 dF2 06-02 dF3 06-02 dF4 06-02 dF1 07-02 dF2 07-02 dF3 07-02 dF4 07-02 dF1 09-02 dF2 09-02 dF3 09-02 dF4 09-02
ABQ -0,02936 -0,03878 0,306614 0,034927 -1,08196 -0,17633 1,648847 1,329488 0,514913 -0,20327 1,744634 1,141413 0,495162 -0,40707 1,505567 1,297221 0,382578 -0,40041 1,456632 1,294182 0,437556 -0,22349 1,75065 0,857085
ALB -0,01723 0,026198 0,303137 0,025124 -1,08996 -0,07586 1,144118 1,288216 0,429046 0,080171 1,053489 1,256998 0,452035 0,212034 1,002426 0,658588 0,430143 0,038628 1,042018 0,540917 0,268502 -0,11035 1,022222 0,950682
ATL -0,23388 0,679067 -0,20375 -1,06244 -0,01949 0,601239 -0,13791 -2,77232 -0,28697 0,574466 0,070764 -2,47522 -0,37454 0,071933 -0,18813 -1,13212 -0,30662 0,296245 -0,23091 -1,09345 -0,2486 0,234477 -0,05281 -0,94903
AUS -0,03574 -0,05707 0,267927 -0,00011 -1,01848 -0,1782 1,810091 1,352962 0,421676 -0,3472 1,730272 1,619066 0,461164 -0,42851 2,018828 1,114422 0,393105 -0,54239 2,078911 1,203188 0,395354 -0,37295 2,213101 1,128866
BNA 0,398129 -0,16895 1,172697 1,028521 -1,00541 -0,06028 1,040154 1,313653 0,289052 -0,15698 0,989167 1,211393 0,364148 0,033908 1,276398 0,4491 0,336539 -0,08619 1,314562 0,460219 0,350144 0,0567 1,530868 0,5329
BOS 0,260406 -0,30007 1,151353 -0,06392 -0,43801 -0,37788 1,703734 0,074093 0,23373 -0,32559 1,325253 -0,09897 0,265433 -0,28571 1,209416 -0,18792 0,209692 -0,51139 1,123589 -0,28844 0,280339 -0,47761 1,155912 -0,3428
BWI -0,03544 0,058608 0,094458 -0,08545 -0,50289 0,142247 0,188115 0,041035 -0,05231 0,165876 -0,09056 -0,04397 -0,00866 0,129218 -0,13592 -0,18801 -0,02379 0,048589 -0,15888 -0,22402 -0,01627 -0,03753 0,085745 -0,2123
CLE 0,025288 0,088368 1,35598 1,200385 -0,65134 0,061782 1,166493 1,485944 0,045007 0,100294 1,133617 1,315066 -0,02884 0,110047 1,146062 1,176665 -0,10171 0,145313 1,236204 1,472444 -0,12275 0,254029 1,392582 1,334844
CLT 0,053949 0,167711 2,052017 2,694205 -0,5992 0,294159 2,125772 1,825149 0,087928 -0,17854 1,972782 2,366468 -0,01583 0,178913 2,491804 2,780207 -0,05782 0,332501 2,575311 2,925361 0,015824 -0,12542 2,823186 2,663912
CVG -0,10658 -0,01238 -0,01919 0,280677 -0,66011 -0,0976 -0,12487 -0,67497 -0,13344 -0,08468 -0,35878 -0,59855 -0,23125 0,413613 -0,46847 -0,96625 -0,25238 0,433419 -0,45338 -1,08831 -0,25059 0,607317 -0,8086 -2,07108
DCA 0,011135 -0,08901 0,376216 0,24916 -0,32324 -0,1612 0,62579 0,264401 -0,01487 -0,29712 0,837124 0,238985 -0,07005 -0,29784 1,151852 0,452852 -0,09109 -0,40864 0,835031 0,522173 -0,19355 -0,27583 1,110077 0,430747
DEN -0,12094 -0,11702 0,028238 -0,31066 0,080028 -0,29094 0,232653 -0,30534 -0,15284 -0,31768 0,742175 -0,29843 -0,20385 -0,35057 0,464374 -0,10011 -0,39264 -0,19841 -0,21464 -0,06181 -0,18272 -0,54201 0,796383 0,102495
DFW -0,02653 -0,08746 0,062663 -0,05401 -0,67639 -0,33637 0,089012 -0,21656 0,176427 -0,35131 0,41376 -0,40728 0,232381 -0,18612 0,388551 -0,11921 0,155646 -0,17285 0,224777 -0,22923 0,146531 -0,21731 0,52436 -0,27792
DTW 0,098973 -0,14978 0,107747 0,177458 -0,62691 -0,27778 0,290129 0,439353 0,070509 -0,24288 -1,04863 -0,07957 0,023812 -0,18459 -0,86948 -0,0373 -0,05627 -0,20469 -1,01118 -0,12484 0,035959 -0,36057 -1,22646 0,002602
EWR 0,175556 -0,56978 0,126687 0,55227 1,255692 -0,84725 0,165468 -0,12983 0,044959 -0,62822 0,552292 -0,00671 -0,07688 -0,74874 0,576737 0,718048 -0,42816 -0,4277 0,667931 0,519887 -0,23264 -0,54163 0,560816 0,66423
FLL -0,01023 0,010183 -0,13919 0,025076 -0,42942 -0,03079 0,076584 -0,07015 0,145816 -0,33228 0,061274 -0,19744 0,167558 -0,22333 0,315735 -0,14577 0,062345 -0,21451 0,290846 -0,06133 0,006757 -0,09525 0,085563 -0,19749
HNL 0,173291 -0,04496 0,219415 -0,04246 -0,47261 -0,07854 0,439817 0,01459 8,43E-05 -0,06677 0,675992 0,008783 0,009718 -0,02327 0,323546 -0,09551 -0,10523 -0,0028 0,266874 -0,066 -0,00187 -0,00692 0,242036 -0,00807
IAD 0,284667 -0,06395 -0,3106 -0,47075 -0,46608 -0,41007 -0,2916 -0,68179 0,125942 -0,85531 -0,43328 0,014217 0,230629 -0,50494 -0,3721 -0,37936 0,093906 -0,37788 -0,18996 -0,5355 0,078736 -0,51914 -0,44488 -0,19568
IAH 0,114634 0,144502 1,809757 1,689298 -0,58967 0,371973 1,848335 1,508547 0,113609 0,483461 1,716234 1,608486 0,156118 0,006454 2,367068 2,151055 0,08774 0,160585 1,861278 2,108265 0,066163 0,143905 1,58181 2,015511
IND 0,141425 0,094101 0,153238 0,01053 -0,81761 0,005111 0,159025 0,247068 0,158985 -0,16896 0,241074 0,18784 0,138857 -0,03782 0,136709 0,115981 0,13521 -0,09665 0,173613 -0,12118 0,165152 0,103326 -0,17133 -0,17981
JAX -0,01747 0,048889 0,290212 0,065393 -1,07543 0,065536 1,178608 1,374671 0,552644 0,12436 1,493829 1,2504 0,527045 0,000539 1,532234 0,948261 0,478649 -0,18182 1,590377 0,931844 0,403781 -0,27037 2,089794 0,943691
JFK 0,359519 -0,76129 0,552284 0,45893 1,669798 -1,17611 0,63565 0,650404 0,201981 -1,39514 1,456223 1,008559 -0,06233 -1,48855 1,202764 1,162945 -0,44669 -1,75856 0,888651 1,791092 0,00523 -2,15468 0,880151 1,975656
LAS -0,08796 -0,02794 -0,36587 -0,23377 -0,34159 -0,1708 -0,07871 -0,51117 -0,06095 -0,33646 -0,20209 -0,44375 -0,11521 -0,08142 0,188782 -0,1222 -0,14269 -0,04162 0,058955 -0,20415 -0,15492 0,13968 -0,25695 -0,27615
LAX -0,14902 0,064815 0,07067 -0,14674 -0,33998 -0,15552 0,252734 -1,00288 0,076367 -0,24638 0,539761 -0,80315 0,084809 0,063397 0,647735 0,000645 -0,059 0,133956 -0,01304 -0,00433 0,135658 0,150667 0,128682 -0,25712
LGA 0,09134 -0,55433 0,307005 -0,26993 0,283903 -0,95084 -0,43802 -0,97171 0,224262 -1,23541 -0,31582 -0,14381 -0,00689 -0,9959 -0,10881 0,865908 -0,1376 -0,81147 -0,31746 0,702236 -0,05986 -0,66306 -0,35953 0,655059
MCI -0,01017 0,109316 0,019703 -0,09395 -0,66741 0,215213 -0,13418 0,122098 -0,10115 0,271095 -0,18404 0,020197 0,031144 0,18706 -0,28933 -0,1788 -0,08044 0,159342 -0,04672 -0,15484 -0,0804 0,183162 -0,02492 -0,21719
MCO 0,059873 -0,01795 0,017996 -0,42223 -0,1472 0,042161 0,130172 -0,28808 -0,04102 -0,12489 0,661052 -0,13724 -0,04791 -0,09371 0,346393 -0,17581 -0,22681 0,038486 -0,07069 -0,1726 -0,04246 -0,20621 0,385575 -0,17893
MDW 0,086021 -0,32394 -2,16375 0,241774 -0,44397 -0,68248 -2,59104 0,92785 1,012426 -0,77782 -2,43337 0,436683 0,139164 -0,47147 -2,55227 0,184553 0,052289 -0,41538 -2,5965 0,11259 0,220928 -0,8343 -2,80863 0,362362
MEM -0,02264 -0,25228 0,486298 -0,00518 -0,60919 -0,25617 0,363562 -0,05518 -0,07024 -0,27182 0,164209 -0,07897 -0,0754 -0,22188 0,241346 -0,18558 -0,13577 -0,18955 0,209128 -0,22655 -0,20451 -0,12609 0,273238 -0,25968
MIA 0,107933 -0,22348 -0,07544 -1,0966 -0,00205 -0,35898 0,110099 -1,17638 0,048116 -0,40981 0,072951 -1,14832 0,095129 -0,67938 0,164941 -1,17971 0,016857 -0,92429 0,361458 -1,15203 -0,10569 -1,00058 0,507399 -1,22444
MKE -0,03298 0,000674 0,384492 0,01805 -1,01115 -0,13107 0,833148 1,370648 0,32917 -0,26932 0,812621 1,454913 0,347926 -0,26196 0,800545 0,997616 0,269874 -0,25746 0,901648 1,008372 0,23173 -0,12831 1,202669 0,978743
MSP -0,07627 0,205663 0,232021 -0,31872 -0,4338 0,04807 -0,01819 -1,36548 -0,04615 0,071274 0,201118 -1,47904 -0,09499 0,18458 0,305937 -1,40512 -0,15987 0,247764 0,070053 -1,57194 -0,1597 0,252361 0,162581 -1,58365
MSY 0,521708 -0,28339 1,361951 0,831547 -0,90805 -0,0888 1,439577 1,397089 0,207789 0,2152 1,188118 0,925479 0,413289 0,074703 0,958412 0,204913 0,478567 -0,63151 0,774814 0,93784 0,481962 -0,86914 0,774118 1,35
OAK 0,702465 -0,38424 3,579543 2,469051 -0,7909 -0,45271 2,923898 1,515221 0,809474 -0,53013 3,270131 1,347873 0,67947 -0,50747 4,222811 1,57719 0,448541 -0,07841 3,24567 1,149718 0,684745 0,145888 2,992421 1,014536
ONT -0,01707 0,247772 0,214983 0,107402 -1,01153 0,126224 2,612493 1,795844 0,560665 -0,00027 1,550858 1,470152 0,576938 0,182597 1,26936 1,29475 0,444644 0,131527 1,178745 1,254299 0,480657 0,370186 1,021621 1,144543
ORD -0,08171 -0,05433 -0,098 0,000103 -0,27175 -0,26785 -0,07573 -0,52227 -0,07492 -0,2918 0,200331 -0,37741 -0,13234 -0,13173 0,289168 0,317236 -0,13327 -0,11138 0,101504 0,227153 -0,06617 -0,29802 -0,11899 0,297898
PBI -0,04907 -0,07136 0,299199 -0,03191 -0,81991 -0,32625 1,675132 1,318418 0,504971 -0,65396 1,309524 1,67444 0,579978 -0,34919 1,454599 0,672 0,447948 -0,43727 1,548097 0,709081 0,474111 -0,2686 1,481543 0,588443
PDX 0,000843 0,075258 0,359065 0,034689 -0,22507 0,249008 0,552422 -0,1757 -0,09048 0,163601 0,583274 -0,24696 -0,11889 0,177056 0,481204 -0,35448 -0,25361 0,230051 0,634934 -0,48031 -0,23072 0,317743 0,547777 -0,52791
PHL 0,001379 -0,06802 0,302823 -0,07936 -0,53143 -0,15479 0,134624 -0,38656 -0,0384 -0,25515 0,404965 -0,134 -0,04739 -0,18495 0,492482 0,224295 -0,10257 -0,15127 0,44821 0,186812 -0,15993 0,098892 0,537318 0,326973
PHX -0,02225 -0,03383 0,577504 0,112356 -0,4666 -0,19956 0,268883 -1,16295 -0,03797 -0,06167 0,429823 -1,04562 -0,03358 0,089569 0,35344 -0,35979 -0,19187 0,078646 0,133073 -0,24703 -0,09743 0,084323 0,418876 -0,29246
PIT 0,149515 0,260019 1,179812 1,567562 -0,62776 0,361661 1,724197 1,531952 0,044321 0,823069 1,313928 1,020154 0,093396 0,885012 1,0939 0,763982 0,087687 0,901604 0,765373 0,543636 0,199638 0,953849 1,13382 0,370961
RDU -0,02515 0,119211 0,177352 -0,02667 -0,60686 0,100959 -0,55735 0,019055 0,130483 0,024842 0,075804 0,008743 -0,07151 0,223825 0,005629 -0,41353 -0,12605 0,168884 0,163412 -0,32889 -0,08941 0,198521 0,470553 -0,4835
RIC -0,03974 -0,13878 0,361342 -0,0781 -1,0962 -0,17775 1,185414 1,218338 0,355765 -0,28877 0,89578 1,113639 0,344086 -0,41742 0,844555 0,625583 0,287597 -0,49179 0,99809 0,551595 0,296277 -0,53601 1,128991 0,612569
RNO -0,01721 0,004246 0,288601 0,024392 -1,0991 -0,14888 1,197199 1,179238 0,333797 -0,22623 1,318074 1,206931 0,385798 -0,0824 1,396117 0,656018 0,328691 -0,22509 1,360445 0,549431 0,404961 0,023757 1,298054 0,644283
SAN 0,505868 0,216647 2,438608 3,723538 -0,76033 1,579285 2,795503 9,968269 0,426642 0,38243 2,019959 2,45731 0,406474 0,616668 2,492683 3,650005 0,346842 0,614315 2,773068 3,491221 0,274888 0,734041 3,068312 3,530672
SAT -0,02623 -0,0486 0,277648 0,0138 -1,09867 -0,07248 2,069446 1,370615 0,257395 -0,08634 1,608957 1,619798 0,30167 -0,10118 1,902963 1,06506 0,264484 -0,11177 1,787905 1,102767 0,364663 -0,05627 1,939596 1,203119
SDF -0,02054 0,029275 0,279191 0,02941 -1,12458 0,095556 1,08492 1,365447 0,431996 0,042368 0,977789 1,257365 0,521867 -0,04131 0,956086 0,996182 0,435883 -0,06833 0,970015 0,973836 0,460892 -0,0792 1,001889 1,052025
SEA 0,010194 -0,02856 -0,03657 -0,12068 -0,35229 -0,34476 -0,32805 -1,28008 0,153826 -0,39016 -0,04096 -1,06131 0,176874 -0,28501 -0,12466 -0,2894 0,049607 -0,23189 -0,14475 -0,22252 -0,00328 -0,48478 0,066775 -1,14639
SFO 0,19543 0,053321 0,145358 -0,2925 0,1427 -0,36318 0,698648 -0,37685 0,040644 -0,57367 0,797731 -0,39005 -0,01097 -0,40075 0,611259 -0,38395 -0,08786 -0,57266 0,514812 -0,46778 -0,14361 -0,66484 0,659205 -0,24495
SJC 0,548136 -0,03149 2,547983 1,844595 -0,79237 -0,04098 1,777869 1,185324 0,506316 -0,12864 1,988604 1,401346 0,497627 0,045092 2,435135 1,148065 0,373803 -0,05508 2,294313 0,715736 0,583173 -0,04876 2,502905 0,983404
SLC -0,0447 0,056365 -0,67746 -0,23165 -0,70553 0,104664 -0,54883 -0,36215 -0,08437 -0,15519 -1,01086 -0,34983 -0,13863 -0,12481 -0,66825 -0,07813 -0,15942 -0,0835 -0,72116 -0,37204 -0,18185 0,054426 -0,55093 -0,03824
SMF 0,324109 -0,13632 4,224934 2,08524 -1,00678 -0,1926 2,110637 1,605028 0,472963 -0,29378 2,194767 1,416762 0,924608 -0,18415 1,627985 1,305954 0,443485 -0,49916 1,681939 1,600895 0,627688 -0,43945 1,803729 1,243408
SNA 0,011407 -0,14657 0,285136 0,223122 -0,80683 -0,7811 2,270983 1,803812 0,867182 -1,09436 2,895003 3,797841 0,930823 -0,60472 3,884964 2,03347 0,808758 -0,47528 3,738755 2,009651 0,800161 -0,35096 3,632872 1,768323
STL 0,057271 0,236919 -0,45859 -0,135 -0,6641 0,459759 -0,26145 0,094523 -0,04195 0,440325 -1,06924 -0,32261 -0,07186 0,50043 -1,0342 -0,59091 -0,04432 0,397127 -1,08908 -0,63694 -0,04383 0,574283 -1,08981 -0,81543
TPA 0,069013 0,096745 0,160902 -0,03663 -0,46435 0,235545 0,00951 -0,06877 -0,03767 0,175557 0,369221 -0,08192 -0,02779 0,271228 0,594412 -0,16855 -0,11449 0,265849 0,539438 -0,18905 -0,12636 0,319838 0,569735 -0,33672
YEG 0,217 -0,06711 -0,1878 -0,09076 -0,88662 0,027498 -0,03179 0,576918 0,184888 -0,06607 -0,06344 0,481443 0,270032 -0,2939 -0,43028 0,55162 0,241125 -0,23158 -0,15738 0,405779 0,107068 -0,19785 -0,1239 0,179356
YHZ 0,713155 -0,94934 4,458021 0,743323 -0,85614 0,172699 0,258248 1,189595 0,13735 0,146041 0,305923 1,202888 0,220965 0,152417 -0,0409 0,774136 0,142554 0,160064 0,036837 0,751318 0,106862 0,160587 0,110643 0,919761
YOW 0,028595 0,027892 -0,2678 0,206268 -0,60723 0,121813 -0,32767 0,96326 0,115552 0,160253 -0,16572 0,781452 0,179263 0,179993 -0,54919 0,277421 0,065597 0,150977 -0,41437 0,195309 -0,00016 -0,06292 -0,54431 0,410533
YUL 0,117308 0,071517 -0,18426 0,172531 -0,73512 -0,04372 -0,2935 0,176453 0,371067 -0,1741 -0,02865 0,308627 0,144756 0,134927 -0,46299 0,051443 0,183331 0,029185 -0,32093 0,080057 0,563354 -0,15439 0,025662 0,268581
YVR 0,063408 0,020303 0,275036 -0,30122 -0,27608 0,082649 0,595455 -0,09052 0,067469 0,196239 0,62386 -0,24156 0,203529 0,169295 0,322764 -0,01039 -0,00437 0,383516 0,224422 -0,06723 -0,01338 0,44188 0,257634 -0,49098
YWG -0,03311 0,112431 0,29747 0,050229 -1,1016 0,212873 0,961801 1,321003 0,271869 0,280602 1,326028 1,188349 0,371832 0,314489 0,999913 1,388314 0,304033 0,284349 1,066444 1,436089 0,322621 0,264864 1,096769 1,526153
YYC 0,157357 -0,42654 -0,16035 0,35662 -0,45518 -0,21597 0,159748 0,585042 0,181852 -0,24394 0,564212 0,448897 0,208973 -0,29493 -0,19609 0,567112 0,161072 -0,37264 -0,10763 0,745345 0,246912 -0,49219 0,083091 0,760826
YYZ 0,016039 0,133118 -0,10999 -0,10043 -0,50325 0,035614 -0,19453 -0,10549 0,378797 0,296945 0,110942 5,443893 -0,19004 0,183982 -2,04664 -1,49628 0,215028 0,185301 -1,83417 -1,62091 0,636122 -0,38851 -0,30569 0,364119
MAGALHÃES, L; REIS, V.; MACÁRIO, R.
24
APPENDIX 2 – Differences for each year and 2002 for airport and factor (Europe)
dF1 03-02 dF2 03-02 dF3 03-02 dF4 03-02 dF1 04-02 dF2 04-02 dF3 04-02 dF4 04-02 dF1 05-02 dF2 05-02 dF3 05-02 dF4 05-02 dF1 06-02 dF2 06-02 dF3 06-02 dF4 06-02 dF1 07-02 dF2 07-02 dF3 07-02 dF4 07-02 dF1 09-02 dF2 09-02 dF3 09-02 dF4 09-02
AMS -0,98274 -0,12113 1,542164 1,353069 -0,03574 -0,29014 1,462345 1,619174 0,496903 -0,37145 1,750901 1,11453 0,428844 -0,48532 1,810984 1,203295 0,431094 -0,31588 1,945174 1,128974 0,035739 0,057066 2005,732 0,000108
ARN -1,40354 0,108676 -0,13254 0,285132 0,398129 0,011975 -0,18353 0,182872 -0,03398 0,202861 0,103701 -0,57942 -0,06159 0,082765 0,141866 -0,5683 -0,04799 0,225653 0,358172 -0,49562 -0,39813 0,168953 2005,827 -1,02852
ATH -0,69842 -0,07781 0,552381 0,138015 0,260406 -0,02552 0,1739 -0,03504 0,005027 0,014362 0,058063 -0,12399 -0,05071 -0,21132 -0,02776 -0,22452 0,019933 -0,17754 0,004559 -0,27888 -0,26041 0,30007 2007,849 0,063922
BCN -0,46744 0,083639 0,093657 0,126489 -0,03544 0,107269 -0,18501 0,041488 0,026784 0,07061 -0,23038 -0,10256 0,011652 -0,01002 -0,25334 -0,13856 0,019172 -0,09613 -0,00871 -0,12685 0,035445 -0,05861 -0,09446 0,085454
BHX -0,67663 -0,02659 -0,18949 0,285559 0,025288 0,011926 -0,22236 0,114681 -0,05413 0,021679 -0,20992 -0,02372 -0,127 0,056945 -0,11978 0,272059 -0,14804 0,165661 0,036602 0,134459 -0,02529 -0,08837 -1,35598 -1,20039
BRU -0,65315 0,126448 0,073755 -0,86906 0,053949 -0,34625 -0,07923 -0,32774 -0,06978 0,011202 0,439787 0,086002 -0,11177 0,16479 0,523294 0,231157 -0,03813 -0,29313 0,771168 -0,03029 -0,05395 -0,16771 -2,05202 -2,6942
BUD -0,55353 -0,08523 -0,10569 -0,95564 -0,10658 -0,07231 -0,33959 -0,87922 -0,12468 0,425988 -0,44929 -1,24693 -0,1458 0,445795 -0,43419 -1,36898 -0,14401 0,619692 -0,78941 -2,35176 0,106577 0,012375 0,019189 -0,28068
BTS -0,33437 -0,07219 0,249573 0,015241 0,011135 -0,20811 0,460908 -0,01017 -0,08118 -0,20883 0,775635 0,203691 -0,10222 -0,31963 0,458815 0,273013 -0,20469 -0,18681 0,733861 0,181587 -0,01114 0,089012 -0,37622 -0,24916
CDG 0,200968 -0,17392 0,204415 0,005324 -0,12094 -0,20067 0,713937 0,01223 -0,08291 -0,23356 0,436136 0,210549 -0,2717 -0,08139 -0,24288 0,248848 -0,06178 -0,42499 0,768146 0,413156 0,12094 0,117017 -0,02824 0,310662
CGN -0,64986 -0,24891 0,026349 -0,16255 -0,02653 -0,26385 0,351097 -0,35327 0,258911 -0,09865 0,325888 -0,0652 0,182176 -0,08538 0,162114 -0,17522 0,173061 -0,12985 0,461697 -0,22392 0,026529 0,087465 -0,06266 0,054007
CIA -0,72588 -0,128 0,182383 0,261895 0,098973 -0,0931 -1,15638 -0,25703 -0,07516 -0,03481 -0,97723 -0,21476 -0,15525 -0,05492 -1,11893 -0,3023 -0,06301 -0,2108 -1,33421 -0,17486 -0,09897 0,149778 -0,10775 -0,17746
CPH 1,080137 -0,27747 0,038781 -0,6821 0,175556 -0,05844 0,425605 -0,55898 -0,25243 -0,17896 0,45005 0,165777 -0,60371 0,142072 0,541244 -0,03238 -0,4082 0,028145 0,434129 0,11196 -0,17556 0,569776 -0,12669 -0,55227
DUB -0,41919 -0,04097 0,215776 -0,09523 -0,01023 -0,34247 0,200466 -0,22251 0,177791 -0,23351 0,454927 -0,17084 0,072578 -0,22469 0,430038 -0,08641 0,01699 -0,10543 0,224755 -0,22256 0,010233 -0,01018 0,139192 -0,02508
DUS -0,6459 -0,03359 0,220402 0,057046 0,173291 -0,02181 0,456578 0,051239 -0,16357 0,021689 0,104131 -0,05306 -0,27852 0,042156 0,047459 -0,02355 -0,17516 0,038037 0,022622 0,034386 -0,17329 0,044956 -0,21941 0,042455
EDI -0,75074 -0,34612 0,018999 -0,21104 0,284667 -0,79136 -0,12267 0,484966 -0,05404 -0,44099 -0,0615 0,091394 -0,19076 -0,31393 0,120645 -0,06475 -0,20593 -0,45519 -0,13428 0,275066 -0,28467 0,06395 0,310603 0,47075
FCO -0,70431 0,227471 0,038578 -0,18075 0,114634 0,338959 -0,09352 -0,08081 0,041484 -0,13805 0,557311 0,461757 -0,02689 0,016083 0,051521 0,418967 -0,04847 -0,0006 -0,22795 0,326214 -0,11463 -0,1445 -1,80976 -1,6893
FRA -0,95903 -0,08899 0,005787 0,236538 0,141425 -0,26306 0,087837 0,17731 -0,00257 -0,13192 -0,01653 0,105451 -0,00622 -0,19075 0,020375 -0,13171 0,023727 0,009225 -0,32456 -0,19034 -0,14142 -0,0941 -0,15324 -0,01053
GVA -1,05796 0,016647 0,888397 1,309278 -0,01747 0,075471 1,203617 1,185008 0,54451 -0,04835 1,242022 0,882868 0,496114 -0,23071 1,300166 0,866452 0,421246 -0,31926 1,799582 0,878299 0,017465 -0,04889 -0,29021 -0,06539
HAM 1,310279 -0,41481 0,083366 0,191474 0,359519 -0,63385 0,903939 0,549629 -0,42185 -0,72726 0,65048 0,704015 -0,80621 -0,99727 0,336368 1,332161 -0,35429 -1,39339 0,327868 1,516726 -0,35952 0,76129 -0,55228 -0,45893
HEL -0,25363 -0,14286 0,287162 -0,27741 -0,08796 -0,30851 0,16378 -0,20998 -0,02725 -0,05348 0,554652 0,111569 -0,05473 -0,01367 0,424826 0,029612 -0,06696 0,167622 0,108922 -0,04239 0,087962 0,027942 0,36587 0,233765
IST -0,19096 -0,22034 0,182064 -0,85614 -0,14902 -0,3112 0,469091 -0,65641 0,233834 -0,00142 0,577065 0,147383 0,090027 0,069141 -0,08371 0,142407 0,284683 0,085852 0,058013 -0,11038 0,149025 -0,06482 -0,07067 0,146738
KEF 0,192563 -0,39651 -0,74502 -0,70178 0,09134 -0,68108 -0,62282 0,126116 -0,09823 -0,44157 -0,41582 1,135839 -0,22894 -0,25714 -0,62447 0,972166 -0,1512 -0,10873 -0,66653 0,924989 -0,09134 0,554327 -0,30701 0,26993
LGW -0,65724 0,105897 -0,15388 0,216051 -0,01017 0,161779 -0,20374 0,11415 0,041318 0,077744 -0,30903 -0,08485 -0,07027 0,050026 -0,06642 -0,06089 -0,07023 0,073847 -0,04463 -0,12324 0,010174 -0,10932 -0,0197 0,093953
LHR -0,20708 0,060111 0,112176 0,134152 0,059873 -0,10694 0,643057 0,284989 -0,10778 -0,07576 0,328398 0,246417 -0,28668 0,056436 -0,08868 0,24963 -0,10234 -0,18826 0,367579 0,243297 -0,05987 0,01795 -0,018 0,422229
LIS -0,52999 -0,35853 -0,4273 0,686076 0,086021 -0,45388 -0,26962 0,194909 0,053143 -0,14753 -0,38853 -0,05722 -0,03373 -0,09144 -0,43276 -0,12918 0,134907 -0,51036 -0,64489 0,120587 -0,08602 0,323942 2,163746 -0,24177
LJU -0,58655 -0,00389 -0,12274 -0,05 -0,02264 -0,01954 -0,32209 -0,07379 -0,05276 0,030396 -0,24495 -0,1804 -0,11313 0,062727 -0,27717 -0,22137 -0,18187 0,126193 -0,21306 -0,2545 0,022641 0,252278 -0,4863 0,00518
MAD -0,10998 -0,1355 0,185543 -0,07978 0,107933 -0,18633 0,148395 -0,05171 -0,0128 -0,4559 0,240385 -0,08311 -0,09108 -0,70082 0,436902 -0,05543 -0,21362 -0,77711 0,582843 -0,12783 -0,10793 0,223478 0,075444 1,096601
MAN -0,97817 -0,13174 0,448656 1,352598 -0,03298 -0,27 0,428129 1,436863 0,380909 -0,26264 0,416054 0,979566 0,302857 -0,25813 0,517156 0,990322 0,264713 -0,12898 0,818177 0,960693 0,032983 -0,00067 -0,38449 -0,01805
MLA -0,35753 -0,15759 -0,25021 -1,04675 -0,07627 -0,13439 -0,0309 -1,16032 -0,01872 -0,02108 0,073916 -1,0864 -0,0836 0,042101 -0,16197 -1,25322 -0,08343 0,046697 -0,06944 -1,26492 0,07627 -0,20566 -0,23202 0,318723
MUC -1,42976 0,194584 0,077626 0,565542 0,521708 0,498585 -0,17383 0,093932 -0,10842 0,358088 -0,40354 -0,62663 -0,04314 -0,34812 -0,58714 0,106293 -0,03975 -0,58575 -0,58783 0,518453 -0,52171 0,283385 -1,36195 -0,83155
MXP -1,49336 -0,06847 -0,65564 -0,95383 0,702465 -0,14589 -0,30941 -1,12118 -0,02299 -0,12323 0,643269 -0,89186 -0,25392 0,305831 -0,33387 -1,31933 -0,01772 0,530128 -0,58712 -1,45451 -0,70246 0,38424 -3,57954 -2,46905
ORY -0,99445 -0,12155 2,397511 1,688442 -0,01707 -0,24804 1,335875 1,36275 0,59401 -0,06518 1,054378 1,187348 0,461715 -0,11625 0,963763 1,146897 0,497729 0,122414 0,806638 1,037141 0,017072 -0,24777 -0,21498 -0,1074
OSL -0,19003 -0,21352 0,022273 -0,52237 -0,08171 -0,23747 0,298333 -0,37752 -0,05063 -0,07741 0,387171 0,317134 -0,05156 -0,05705 0,199507 0,227051 0,015541 -0,24369 -0,02099 0,297795 0,081712 0,054328 0,098003 -0,0001
PRG -0,77084 -0,25489 1,375933 1,350326 -0,04907 -0,5826 1,010325 1,706348 0,62905 -0,27783 1,155399 0,703908 0,497021 -0,36591 1,248897 0,740989 0,523184 -0,19724 1,182343 0,62035 0,049073 0,071359 -0,2992 0,031908
RIX -0,22592 0,17375 0,193356 -0,21039 0,000843 0,088343 0,224209 -0,28164 -0,11973 0,101798 0,122139 -0,38917 -0,25446 0,154792 0,275868 -0,515 -0,23156 0,242485 0,188712 -0,5626 -0,00084 -0,07526 -0,35907 -0,03469
SOF -0,53281 -0,08678 -0,1682 -0,3072 0,001379 -0,18714 0,102143 -0,05464 -0,04876 -0,11693 0,189659 0,303654 -0,10395 -0,08326 0,145387 0,26617 -0,16131 0,166908 0,234495 0,406332 -0,00138 0,068016 -0,30282 0,079358
STN -0,44435 -0,16572 -0,30862 -1,27531 -0,02225 -0,02784 -0,14768 -1,15797 -0,01134 0,123401 -0,22406 -0,47214 -0,16962 0,112478 -0,44443 -0,35939 -0,07518 0,118155 -0,15863 -0,40481 0,022246 0,033832 -0,5775 -0,11236
TLL -0,77727 0,101642 0,544385 -0,03561 0,149515 0,56305 0,134117 -0,54741 -0,05612 0,624993 -0,08591 -0,80358 -0,06183 0,641585 -0,41444 -1,02393 0,050123 0,69383 -0,04599 -1,1966 -0,14952 -0,26002 -1,17981 -1,56756
TXL -0,58171 -0,01825 -0,7347 0,045728 -0,02515 -0,09437 -0,10155 0,035415 -0,04636 0,104614 -0,17172 -0,38686 -0,1009 0,049674 -0,01394 -0,30222 -0,06425 0,07931 0,293201 -0,45682 0,025154 -0,11921 -0,17735 0,026672
VIE -1,05646 -0,03897 0,824071 1,296442 -0,03974 -0,14999 0,534438 1,191743 0,383827 -0,27864 0,483213 0,703687 0,327338 -0,35301 0,636747 0,629698 0,336019 -0,39724 0,767649 0,690673 0,039742 0,138779 -0,36134 0,078104
WAW -1,08189 -0,15313 0,908598 1,154846 -0,01721 -0,23047 1,029474 1,182539 0,403007 -0,08664 1,107516 0,631626 0,3459 -0,22933 1,071845 0,525039 0,42217 0,019511 1,009453 0,619891 0,017209 -0,00425 -0,2886 -0,02439
ZRH -1,26619 1,362638 0,356895 6,244732 0,505868 0,165783 -0,41865 -1,26623 -0,09939 0,400021 0,054076 -0,07353 -0,15903 0,397668 0,33446 -0,23232 -0,23098 0,517394 0,629704 -0,19287 -0,50587 -0,21665 -2,43861 -3,72354
MAGALHÃES, L; REIS, V.; MACÁRIO, R.
25
APPENDIX 2 – Differences for each year and 2002 for airport and factor (Asia)
dF1 03-02 dF2 03-02 dF3 03-02 dF4 03-02 dF1 04-02 dF2 04-02 dF3 04-02 dF4 04-02 dF1 05-02 dF2 05-02 dF3 05-02 dF4 05-02 dF1 06-02 dF2 06-02 dF3 06-02 dF4 06-02 dF1 07-02 dF2 07-02 dF3 07-02 dF4 07-02 dF1 09-02 dF2 09-02 dF3 09-02 dF4 09-02
ADL -1,07244 -0,02388 1,791798 1,356815 -0,02623 -0,03774 1,331309 1,605998 0,327897 -0,05258 1,625315 1,05126 0,290712 -0,06317 1,510257 1,088967 0,39089 -0,00767 1,661948 1,189319 0,026228 0,0486 -0,27765 -0,0138
AKL -1,10404 0,066282 0,80573 1,336037 -0,02054 0,013093 0,698599 1,227955 0,542409 -0,07058 0,676895 0,966773 0,456426 -0,09761 0,690824 0,944426 0,481435 -0,10847 0,722698 1,022615 0,020542 -0,02927 -0,27919 -0,02941
BKK -0,36248 -0,3162 -0,29148 -1,15941 0,010194 -0,3616 -0,00439 -0,94063 0,16668 -0,25645 -0,08809 -0,16873 0,039413 -0,20333 -0,10818 -0,10184 -0,01348 -0,45622 0,103347 -1,02571 -0,01019 0,02856 0,036571 0,120677
BNE -0,05273 -0,4165 0,55329 -0,08436 0,19543 -0,62699 0,652373 -0,09755 -0,2064 -0,45407 0,465901 -0,09146 -0,28329 -0,62598 0,369454 -0,17528 -0,33904 -0,71817 0,513847 0,047546 -0,19543 -0,05332 -0,14536 0,292496
BOM -1,34051 -0,00948 -0,77011 -0,65927 0,548136 -0,09714 -0,55938 -0,44325 -0,05051 0,076586 -0,11285 -0,69653 -0,17433 -0,02358 -0,25367 -1,12886 0,035037 -0,01726 -0,04508 -0,86119 -0,54814 0,031494 -2,54798 -1,8446
CAN -0,66083 0,048299 0,128623 -0,13049 -0,0447 -0,21155 -0,3334 -0,11818 -0,09394 -0,18118 0,009208 0,153522 -0,11473 -0,13986 -0,0437 -0,14038 -0,13715 -0,00194 0,126525 0,193417 0,044695 -0,05636 0,677456 0,231652
CGK -1,33088 -0,05629 -2,1143 -0,48021 0,324109 -0,15746 -2,03017 -0,66848 0,600499 -0,04784 -2,59695 -0,77929 0,119377 -0,36285 -2,54299 -0,48435 0,303579 -0,30313 -2,42121 -0,84183 -0,32411 0,136317 -4,22493 -2,08524
CHC -0,81824 -0,63453 1,985846 1,58069 0,011407 -0,9478 2,609867 3,574719 0,919416 -0,45815 3,599827 1,810348 0,797351 -0,32871 3,453618 1,786529 0,788754 -0,2044 3,347736 1,5452 -0,01141 0,146567 -0,28514 -0,22312
CNS -0,72137 0,22284 0,197138 0,229524 0,057271 0,203406 -0,61064 -0,18761 -0,12913 0,263511 -0,57561 -0,45591 -0,10159 0,160208 -0,63049 -0,50194 -0,1011 0,337364 -0,63122 -0,68043 -0,05727 -0,23692 0,458591 0,135001
CNX -0,53336 0,1388 -0,15139 -0,03214 0,069013 0,078812 0,208319 -0,04529 -0,0968 0,174483 0,43351 -0,13192 -0,18351 0,169104 0,378536 -0,15242 -0,19538 0,223093 0,408833 -0,30009 -0,06901 -0,09674 -0,1609 0,03663
DEL -1,10362 0,094612 0,156011 0,667679 0,217 0,00104 0,124365 0,572203 0,053032 -0,22679 -0,24248 0,64238 0,024124 -0,16447 0,030426 0,49654 -0,10993 -0,13074 0,063904 0,270116 -0,217 0,067113 0,187801 0,09076
DXB -1,5693 1,12204 -4,19977 0,446272 0,713155 1,095382 -4,1521 0,459565 -0,49219 1,101758 -4,49892 0,030814 -0,5706 1,109406 -4,42118 0,007996 -0,60629 1,109929 -4,34738 0,176439 -0,71316 0,949342 -4,45802 -0,74332
HAK -0,63583 0,09392 -0,05987 0,756992 0,028595 0,132361 0,102085 0,575184 0,150668 0,152101 -0,28139 0,071153 0,037001 0,123084 -0,14657 -0,01096 -0,02875 -0,09081 -0,27651 0,204265 -0,0286 -0,02789 0,267804 -0,20627
HDY -0,85243 -0,11523 -0,10924 0,003922 0,117308 -0,24562 0,155615 0,136096 0,027448 0,06341 -0,27873 -0,12109 0,066023 -0,04233 -0,13667 -0,09247 0,446046 -0,22591 0,209923 0,096051 -0,11731 -0,07152 0,18426 -0,17253
HKG -0,33949 0,062346 0,32042 0,210701 0,063408 0,175936 0,348824 0,059661 0,140121 0,148992 0,047729 0,290833 -0,06778 0,363213 -0,05061 0,233996 -0,07679 0,421578 -0,0174 -0,18976 -0,06341 -0,0203 -0,27504 0,301222
HKT -1,06849 0,100442 0,664331 1,270774 -0,03311 0,168172 1,028557 1,13812 0,404941 0,202059 0,702443 1,338085 0,337143 0,171919 0,768973 1,38586 0,355731 0,152434 0,799299 1,475924 0,03311 -0,11243 -0,29747 -0,05023
ICN -0,61254 0,21057 0,320102 0,228422 0,157357 0,182604 0,724566 0,092276 0,051616 0,131607 -0,03574 0,210492 0,003716 0,053905 0,052727 0,388725 0,089555 -0,06564 0,243445 0,404206 -0,15736 0,426542 0,160354 -0,35662
KIX -0,51929 -0,0975 -0,08454 -0,00505 0,016039 0,163828 0,220933 5,544324 -0,20608 0,050865 -1,93665 -1,39585 0,198989 0,052183 -1,72418 -1,52048 0,620084 -0,52163 -0,1957 0,464549 -0,01604 -0,13312 0,109991 0,100431
KUL 0,07755 -0,13701 0,318169 0,221965 0,504737 -0,17489 -0,50436 -0,09581 -0,00367 -0,25313 0,265856 0,242669 0,149584 -0,31299 0,21609 -0,20004 0,101336 -0,25042 0,107016 -0,05802 -0,50474 -0,17817 0,289006 0,013435
MEL -1,40562 -0,13717 0,159615 -0,07804 0,345834 -1,47915 0,474033 -0,71004 0,165209 -1,0577 0,507896 -0,4517 0,835545 -2,69681 0,231083 -1,51755 1,203941 -2,60919 0,162996 -1,44739 -0,34583 -3,36672 1,145225 -0,8801
MFM -1,06257 -0,24298 1,003854 1,463889 -0,10971 0,161326 0,984522 2,334356 1,827003 0,212859 0,694573 2,520616 1,838749 -0,19111 0,91166 2,391011 1,792214 0,066636 0,913929 2,827025 0,10971 -0,3572 -0,4318 0,329527
MNL -0,67458 -0,02018 -0,04804 -0,58645 -0,02168 -0,05624 -0,24363 -0,88976 0,341326 -0,04278 0,355351 -0,26975 1,071642 -0,54052 0,094595 -0,18585 0,651153 0,539299 -1,68914 -1,25376 0,021677 -0,35109 0,386093 -0,25453
NRT 0,662461 -0,12136 0,017588 0,310508 -0,6915 -0,30386 -0,2156 0,007294 0,961823 0,103506 -0,44044 0,030138 0,630949 -0,20775 -2,13292 -1,31483 0,3546 -0,55066 0,233781 0,499196 0,691502 -0,00148 -0,46884 0,028982
PEK -0,04004 -0,32644 0,16936 0,020184 -0,01967 -0,18655 -0,00027 0,058159 0,439476 -0,20189 -0,16288 0,092555 0,206138 -0,38892 -0,07358 0,022859 0,532834 -0,71393 0,594341 -0,06474 0,019671 -0,14296 0,050054 -0,07558
PEN -1,19039 0,266923 -0,2949 1,109301 -0,0458 -0,11096 1,165327 1,312355 -0,00427 -0,04194 1,214889 1,3175 1,290446 0,073775 -0,11234 1,203096 1,651687 0,820024 0,674825 1,756036 0,045798 -0,05295 -0,28056 -0,01344
PER -1,19039 0,075083 0,79191 1,236075 -0,0458 -0,1486 1,587417 1,122325 0,137381 -0,39304 2,076786 0,58839 0,788852 1,082331 0,331597 1,17383 0,853903 1,305407 0,23483 1,361802 0,045798 -0,05295 -0,28056 -0,01344
PVG 0,348682 -0,15186 -0,04486 -0,46795 -0,89041 -0,18765 0,143987 -0,5602 1,422728 0,017433 0,333589 0,210169 2,630641 -0,78253 0,104565 -0,18023 3,221328 -0,68249 0,286752 -0,00021 0,890411 -0,66512 0,181347 -0,33417
SEL 0,138583 3,685072 -1,3853 2,303089 -0,16651 3,317991 -0,84212 2,385439 0,605294 3,284449 -1,43018 1,85035 0,736124 2,704058 -1,17662 1,557593 0,643648 2,714036 -1,04125 1,63269 0,166513 4,290046 -1,79049 2,048485
SHA -1,08812 0,015533 0,797503 0,752508 0,08392 -0,58412 1,475337 0,708458 0,39712 -0,71928 3,507463 0,890245 1,297098 -1,41665 3,705828 0,575558 0,53843 -0,93706 1,832589 0,504645 -0,08392 -0,0209 -0,94366 -0,54326
SIN -0,06082 0,03842 -0,00862 0,094749 -0,03032 0,208104 -0,47811 0,011272 0,441193 0,314315 0,039677 0,467904 0,280454 0,261289 0,032419 0,332844 0,157076 0,358999 0,006966 0,17792 0,030319 -0,28453 0,517093 0,030028
SYD -0,62776 0,245139 -0,55481 0,45793 0,121966 -0,22878 -0,67866 0,391114 0,005827 -0,26369 1,452569 0,63337 0,819923 -1,11981 1,389826 1,050503 1,0183 -0,64432 1,502813 1,138488 -0,12197 -0,13319 0,895041 0,279742
SZX 1,017457 -1,56102 0,424518 -0,52117 -0,96519 -0,55003 0,300828 0,030132 1,198561 -0,73312 0,029209 -0,15654 1,779814 -1,39438 0,420622 -0,37457 1,622314 -1,32211 0,649632 0,27873 0,965189 -0,59224 0,226566 -0,32981
TPE -0,85231 -0,13252 0,414398 -0,1139 0,406515 -0,36768 -0,33012 -0,66588 0,025198 -0,60947 0,177285 -0,15054 0,215086 -0,15397 -1,28408 -0,91018 -0,05284 -0,54403 -0,89942 -0,93871 -0,40651 -0,35415 -0,09181 -1,25028
WLG -0,56437 -0,23265 0,545406 0,165027 -0,08986 -0,44005 0,502158 0,034479 0,173086 -0,61803 0,538499 -0,14855 1,039836 -1,35981 0,855151 -0,48035 0,622992 -1,31091 1,064484 0,030572 0,089861 -0,08736 0,280569 -0,08663
XMN 2,037575 -0,11411 -0,24833 -0,74339 -1,5999 -0,11508 -0,07989 -0,44843 -0,08928 0,002786 -0,2931 0,202013 1,590669 4,784065 -1,49289 2,951937 2,031405 4,42294 -1,29117 2,749742 1,599897 -0,44217 -0,0457 -0,33134
MAGALHÃES, L; REIS, V.; MACÁRIO, R.
26
APPENDIX 3 – Cluster membership (2002, 2003, 2004, 2005)
1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8
ABQ ATL BOS LAX MDW MIA IST KIX ABQ ATL BNA DFW LAX MIA YHZ KIX ABQ ALB ATL LAX SAN ARN EDI KIX ABQ ALB ATL BWI MIA YYZ CDG NRT
ALB CVG BWI SEA EDI SFO LIS NRT ALB CLT BOS DTW SEA SFO TXL NRT AUS BNA CLT MIA CDG BOM NRT DCA AUS EWR CLT SFO MAD CGN
AUS EWR DCA BCN PEK CDG MXP AUS CVG BWI FLL BCN ARN SEL BOS CLE CVG ONT CGN MEL DEN BNA JFK CVG AMS DUB
BNA JFK DEN LGW SEL CGN CNX JAX EWR CLE IAH GVA BHX BWI IAD DEN SFO DUB PEK DFW BOS LGA DTW ARN DUS
CLE LGA DFW LHR SHA CPH HKT MKE JFK DCA LAS LGW BRU DCA IND DTW AMS HEL PVG FLL CLE MDW IAD ATH HAM
CLT MSP DTW MAD DUB ICN ONT LGA DEN MDW LHR CDG DFW JAX EWR BCN LJU SEL HNL IND OAK IAH BCN MUC
IAH PHX FLL DUS MEL PBI MSP HNL OAK MAD CIA HNL MCI FLL BHX ORY SHA LAS JAX SNA MEM BHX ORY
JAX HNL FCO MFM RIC PHX IAD ORD STN CPH IAH MEM JFK BRU RIX LAX MCI LHR MKE BRU VIE
MKE IAD GVA PEN RNO SAN IND SJC DUB MCO MKE LAS CIA VIE MCO MSY HKG PBI CIA
MSY IND HEL PER SAT MCI SLC DUS MSY RDU LGA CPH MSP ONT ICN PDX CPH
OAK LAS MAN PVG SDF MCO SMF FCO PBI RIC MDW DUS ORD PIT KIX PHL FCO
ONT MCI MUC TPE SNA MEM AMS HAM PDX RNO MSP FCO PHX RIC MEL RDU GVA
PBI MCO ORY YWG MSY EDI HEL PHL SDF OAK GVA SAN RNO MFM SLC HEL
PIT MEM VIE ATH PDX FRA MAN PIT YEG ORD HAM SEA SAT SIN YVR LGW
RIC ORD WAW BUD PHL MUC ORY SAT YHZ PHX LGW SJC SDF SYD YYC LIS
RNO PDX ZRH BTS PIT AKL VIE SEA YOW SNA LHR SMF STL TPE BNE LJU
SAN PHL KEF RDU BKK WAW SJC YUL YVR MAD TPA YEG WLG MAN
SAT RDU LJU STL BOM ZRH SLC YWG YYC MAN EDI YHZ MLA
SDF SLC MLA TPA HKG SMF BUD ADL OSL FRA YOW OSL
SJC STL RIX YEG ICN STL KEF AKL STN IST YUL PRG
SMF TPA SOF YOW PEK TPA MUC BNE WAW ADL YWG RIX
SNA YEG TLL YUL PVG YYZ SOF HKG ZRH AKL BUD STN
YHZ YUL DXB YVR SHA ATH TXL ICN BKK BTS WAW
YOW YVR HAK YYC SIN BTS CNS PER CAN KEF ZRH
YWG YYC HDY YYZ FRA DXB SYD CHC MXP
ATH YYZ SZX CGN IST HAK TPE DEL SOF
BUD AMS XMN IST LIS XMN WLG KUL TLL
BTS ARN LIS MLA MNL TXL
CIA BHX MXP MXP PEK BOM
KEF BRU OSL PRG PER CGK
LJU FRA PRG TLL PVG CNS
MLA HAM ADL BKK SEL CNX
RIX OSL BNE CAN SHA DXB
SOF PRG CAN CGK SZX HAK
STN AKL CGK CHC HDY
TLL BKK CHC CNX HKT
TXL BNE CNS DEL PEN
ADL CHC CNX HDY XMN
BOM HKG DEL HKT
CAN KUL HKT KUL
CGK SIN KUL MFM
CNS SYD MEL MNL
DEL WLG MFM PEN
DXB MNL SIN
HAK PEN SZX
HDY PER
MNL SYD
SZX TPE
XMN WLG
2002 2003 2004 2005
MAGALHÃES, L; REIS, V.; MACÁRIO, R.
27
APPENDIX 3 – Cluster membership (2006, 2007, 2009)
1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8
ABQ ATL CLT LAX MIA OAK AMS CDG ABQ ATL BOS CLT FLL BCN CDG KIX ABQ ATL BOS CLT DCA ONT CDG KIX
ALB EWR DCA BCN SFO SNA HKG MUC ALB EWR IAD DEN MDW DUB FRA NRT ALB EWR BWI MDW DEN BCN DUB NRT
AUS JFK DEN DUB ARN CIA ICN VIE AUS JFK LAX IAH OAK LGW MUC PER AUS JFK DTW ADL DFW BTS FRA
BNA LGA DFW LGW ATH EDI KIX BNA LGA MIA LAS SNA LHR VIE BNA LGA HNL BNE FLL CGN LGW
BOS BNE DTW LHR BHX TXL MFM BWI ONT MCO CIA MAD CLE IAD HKG LAS CPH LHR
BWI MEL FLL MAD BRU CGK NRT CLE SFO MSP IST STN CVG IAH ICN LAX DUS MUC
CLE SYD IAH CGN CNX SIN CVG AMS ORD CGK IND MEM MEL MCO GVA VIE
CVG LAS CPH DEL DCA ARN PHL CNX JAX MSP PER MIA KEF
HNL MCO DUS DXB DFW ATH PHX DEL MCI MSY SYD OAK LJU
IAD MDW FCO MNL DTW BRU SAN DXB MKE PDX TPE ORD MAD
IND MSP GVA SEL HNL CGN SEA HKT PBI PHL PHX MAN
JAX ORD HAM IND CPH YVR MNL PIT RDU SAN ORY
MCI PHL HEL JAX DUS YYC SEL RIC SLC SEA SOF
MEM PHX MAN MCI EDI AKL SHA RNO YUL SFO TLL
MKE SAN ORY MEM FCO BNE SAT YVR SJC WAW
MSY SEA OSL MKE GVA HKG SDF YWG SNA
ONT SJC WAW MSY HAM ICN SMF YYC BHX
PBI SLC ZRH PBI HEL MEL STL YYZ CIA
PDX FRA PDX LIS PEK TPA AMS EDI
PIT IST PIT LJU SYD YEG ARN FCO
RDU LIS RDU MAN TPE YHZ ATH IST
RIC STN RIC ORY WLG YOW BRU TXL
RNO AKL RNO OSL MXP BUD AKL
SAT BKK SAT PRG PRG HAM BKK
SDF CAN SDF RIX RIX HEL CAN
SMF PEK SJC TXL BOM LIS CGK
STL PVG SLC WAW CHC MLA CNX
TPA SHA SMF ZRH CNS OSL HKT
YEG SZX STL BKK DEL STN PEK
YHZ TPE TPA CAN HAK ZRH PVG
YOW YEG PVG HDY DXB SEL
YUL YHZ SIN MFM KUL SZX
YVR YOW SZX MNL SIN
YWG YUL PEN WLG
YYC YWG SHA
YYZ YYZ XMN
BUD BHX
BTS BUD
KEF BTS
LJU KEF
MLA MLA
MXP MXP
PRG SOF
RIX TLL
SOF ADL
TLL BOM
ADL CHC
BOM CNS
CHC HAK
CNS HDY
HAK KUL
HDY MFM
HKT PEN
KUL XMN
PEN
PER
WLG
XMN
20092006 2007
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