# Multicriteria Spatial Decision Analysis for the Development of the Italian Minor Airport System

Authors: Maria Rosaria Guarini; Anthea Chiovitti; Francesco Rocca
Journal: Journal of Advanced Transportation
 (2018)

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## Abstract

The infrastructures supporting air transport throughout the world in the civil sector are classified as primary-level (large numbers of passengers and goods on both commercial and charter long- and medium-haul flights) and secondary-level (few passengers and goods on general aviation private, short-haul flights). In parallel with primary-level air traffic general growth all over the world and in Italy, the popularity of “individual” nonscheduled general aviation traffic increased in many countries since 1990s. The latter aviation has proved to be a valid alternative to rail and road transport for short-medium distance journeys (100-500 km) for classes of business and tourist passengers. In keeping with the national and international airport system development scenarios, the paper illustrates the results of in-depth analyses aiming to construct an integrated GIS-based Multicriteria Decision Analysis evaluation methodology. It gears towards formulating strategies for the development and streamlining of some existing (51) Italian minor airports and for the right locations for the new hubs required to construct an efficient second-level air transport network (the “highway in the sky”). Different levels of evaluation verify the suitability of airport services and infrastructure (status quo) and the attractiveness of airport hubs given the territorial facilities found in their catchment areas.


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## Body

## 1. Introduction

The infrastructure supporting air transport throughout the world is classified in the civil sector [1] depending on the quantity of passengers and/or goods handled and on the type of activities (aviation and/or nonaviation) carried out. Primary-level civil aviation infrastructure (airports belonging to the main network of each country) is earmarked for transporting large numbers of passengers and goods travelling on both commercial and charter long-haul and medium-haul flights. Secondary-level aviation infrastructure (general aviation, regional airports belonging to the secondary or minor network of each country) serves small numbers of passengers and goods travelling on private, short-haul flights.An analysis of air transport traffic in the main airports of the civil aviation sector reveals the following.(i)
Global air traffic, in the world [2], measured in Revenue Passenger Kilometres (RPK) from 1970 to 2015, doubles every 15 years. Furthermore, Revenue Passenger Kilometres (RPKs) grew 6.3% in 2016, as compared to 2015, and for the next 20 years Airbus forecasts a 4.4% annual growth in global air traffic.(ii)
Global air traffic, in Europe [3, 4], has seen continuous growth since November 2015; it particularly increased by 4.5% in September 2017 compared to September 2016. The number of air journeys expected in 2050 in Europe has also significantly increased (more than 25.000 Instruments Flight Rules Movements).(iii)
Global air traffic, in Italy, has grown in recent years; a 4.9% increase in the number of passengers was particularly recorded in the first quarter of 2017, compared to the same period the year before, as well as a 12.3% increase in the quantity of goods transported [5].Twelve countries have added over 50 daily flights to Spain, the UK, and Germany [3]. The increase in flights has caused congestion in European airports, which has led to considerable delays among both arrivals and departures. In fact, according to Air Traffic Flow Management (ATFM) indicators, total ATFM delays in European airports belonging to the main network of each state increased by 68.9% in September 2017 compared to September 2016. En-route ATFM delays increased by 83.3% mainly due to capacity and weather (excessive demand in relation to supply for take-off and landing spaces in airports and unfavorable weather conditions); airport ATFM delays increased by 49.6% [3].As regards the general growth of air traffic in airports belonging to the main network of each country, since the 1990s we have seen the growing popularity of “individual” [6], or private, noncommercial, nonscheduled “point to point” flights all over the world and in Italy, using general aviation aircraft [7, 8] operating out of the secondary airport network. They have proved to be a valid alternative to rail and road transport for short and medium distance journeys (100-500 km) for a number of different classes of users, including business and tourist passengers. This phenomenon is the result of the introduction of the latest aircraft, advanced ultralight aircraft and very light jets, on the general civil aviation market, particularly in Europe and in the United States. These differ from traditional general aviation aircraft due to their dynamic performance features (greater ease in take-off and landing, not requiring handling operations or even a control tower in many cases, ease in movement due to their smaller size, etc.) and extremely low fuel consumption vehicles. Their growing popularity has become particularly noticeable in the United States [9] since the early 2000s and in many European countries as well, such as the United Kingdom, France, and Germany [8, 10].In the air transport infrastructure sector, a survey on the current state of the Italian airport system highlights that only certain types of airports are important strategic hubs in the transport system, from the international to the national and local level, becoming an integral part of the general national intermodal system [11]. A territorial analysis of the infrastructural system of national transport networks, consisting of linear and spatial distributional components, as well as what is known as intermodal exchange hubs, allows us to identify various levels of development in transport systems (on rail, road, via sea and air), particularly in urban systems featuring a high level of infrastructural development. Such levels of development are defined by subsequent levels of integration with local and national production and distribution systems and are inextricably linked to demographic issues and problems to do with the competition for land use [12]. The degree of interaction between transport infrastructure networks and the socioeconomic and urban context can be affected by the strategic role of networks within multiregional and international competitive structures [13]. To this end, the Italian national airport plan (or PNA) [11] states that the main airport system network in Italy consists of 42 airports of national importance (grouped in two categories: main airports and service airports, depending on the characteristics indicated in the 2012 PNA) that can meet the demand for collective commercial traffic with differing levels of connectivity on a continental and international medium- and long-haul level. Some of these 42 airports (33) are included in the Trans-European Transport Network (TEN-T) and are classified according to various levels of importance based on what has been established by the European Union in its Single European Sky 2 initiative [14–16]. As mentioned earlier, airports of local importance (minor or general aviation airports) that mainly handle private as opposed to commercial flights are not included in the network defined by the 2012 PNA but they do constitute a secondary, or second-level network. Though it provides a service for the manufacturing areas and businesses in surrounding territories, Italy’s network of minor airports has not been particularly developed and is poorly connected to intermodal transport systems [10]. If it were to be properly enhanced and expanded, the network of minor airports could take on strategic importance in the national infrastructural system, playing a role that could support primary airport networks. The factors in favor of the development of the Italian minor airport system can be summarized into the following possibilities [11]: (a) absorbing air traffic, encouraging the streamlining of operations in main airports, and ensuring that the traffic demand from specific sectors is met; (b) meeting the demand for individual, business, and air-taxi service flights and redirecting part of that work to traditional transport networks, such as road and rail; (c) meeting the demand for activities that run parallel to passenger and goods transport, i.e., (i) nonaviation activities, which include a wide range of commercial and tourist services designed to satisfy different types of users, (ii) aviation activities, such as working flights, civil defence, reconnaissance activities, patrols, training, education and support provided to civil protection forces, air rescue, and the monitoring and protection of large swathes of territory and emergency services, not only when natural disasters occur but above all when fighting forest fires, which are increasingly frequent occurrences in this country, and more generally all forms of territorial surveillance that are designed to verify and avoid damaging alterations and attacks.It is in just such a framework of development that the possibility of turning minor (existing or planned) Italian airports into hubs that can attract economic development in marginalised areas where urban and territorial services are scarce or entirely absent has come to light.In keeping with the national and international airport system development scenarios mentioned earlier, this paper illustrates the results of in-depth analyses carried out as part of ongoing research, divided up into different steps. This research aims to construct an integrated GIS-based Multicriteria Decision Analysis evaluation methodology [12, 17–19]. This methodology is geared towards formulating strategies for the development and streamlining of the existing Italian minor airport network and the identification of the right locations for the new hubs that could be required to construct an efficient second-level air transport network (the “highway in the sky”). To this end, it is necessary to identify which of the 51 existing minor airport hubs should be used as the basis for the construction of a second-level network that could meet the growing demand for commercial and general aviation traffic and cover any “exposed” locations, i.e., places that are not currently served by minor airports. The second-level network will need to be formulated in a way that will ensure that its hubs are evenly distributed across the country and connected with other modes of transport, taking into account aspects regarding the following: (i) infrastructural characteristics that are intrinsic to airports (tracks, taxiways, and aprons); (ii) infrastructural characteristics that are extrinsic to airports: airport services (airport services, air traffic support services, services to passengers, and intermodal connectivity); (iii) facilities for manufacturing (companies and the labour market), tourism (the number of state museum institutes and related visitors), and the transport network infrastructure (boasting road, rail, and public transport infrastructure) in the areas served by each minor airport; (iv) current urban and territorial redevelopment processes (town planning forecasts and implementation from a vast level to a local scale) so as to ensure the integration and overlapping of systems (urban, environmental, and transport systems); (v) economic and financial aspects (cost/benefit ratio for the completion, upgrading, and/or conversion of airport hubs).As regards each of the existing 51 minor airports (alternatives,A.n) (Figure 1), the in-depth analysis involved in the implementation of the evaluation methodology proposed in this paper addresses points (i), (ii), and (iii) as listed above and is aimed at assessing the following:(1)
The suitability of airport services and infrastructure (status quo)(2)
The attractiveness of airport hubs given the territorial facilities found in their catchment areas.Figure 1
Italy’s minor airports.The aspects that should be taken into consideration when constructing and implementing an evaluation methodology are of a multicriterial and spatial nature; thus such a methodology attempts to build a data processing interface that will produce georeferenced analysis data for example to develop an assessment process that integrates multicriterial analysis (MCDA) and georeferencing and geoprocessing (GIS) tools. This allows us to solve decision-making problems that deal with a different number of aspects and objectives that sometimes clash with each other [20], including the spatial aspects of the data considered right from the start.If we examine the field literature concerning MCDA and GIS integration, what emerges is that it is possible to obtain three levels of integration [18, 21]: (i) loose coupling, where each of these two tools can use the information originating from the processing operations of the other as input data (their database and interface remain separate); (ii) tight coupling, where the MCDA uses a separate database despite being an integral part of the GIS (the two systems share the GIS data interface); (iii) full integration, where the MCDA and GIS share both the database and the interface (the set of commands found in GIS packages support particular data processing routines typical of both tools using the generic language of programming).The methodological approach proposed in the previous phases of research development, aimed at assessing the suitability of airport service and infrastructure facilities (status quo), was of the tight coupling kind [21, 22]. In this study, we illustrate an upgrade of the evaluation methodology to the full integration type (see Section 2) which permits assessment and further analysis for evaluating the status quo of Italian minor airports and evaluating (from scratch) the attractiveness of airport hubs in view of the territorial facilities of their catchment areas. The upgrade was carried out with the aim of implementing and managing a greater quantity of spatial/territorial data in an integrated and automated way, data that characterizes the decision-making problem analyzed here. Such an upgrade will also prove an essential basis for the development of further research phases.


## 2. Materials and Methods

### 2.1. Research Design

As mentioned earlier, this paper illustrates an integrated evaluation methodology designed to judge airport hubs both individually and taken together as far as the following aspects are concerned:(1)
The suitability (status quo) of their infrastructure and service facilities (paragraph 1, point 1):
(1.1)
The identification of characteristics: A: intrinsic infrastructural characteristics (criteria,Cj.A; subcriteria,Sj.A; indicators,Ij.A); B: extrinsic characteristics: airport services (criteria,Cj.B; subcriteria,Sj.B; indicators,Ij.B)(1.2)
Data collection (specific resource parametersiIj.A, Ij.B)(1.3)
The structuring and organization of the data collected (point 1.1 and 1.2) in order to obtain Judgment Levels (JL) for a “structural classification” (JLA) involvingA.n classification on the basis of the amount of intrinsic infrastructural resources, a “services classification” (JLB) involvingA.n classification on the basis of the extrinsic characteristics such as airport services available and an “overall classification of resources” (JLC) that combines the JLA and JLB levels(2)
Their attractiveness given the territorial facilities of their catchment areas (paragraph 1, point 2):
(2.1)
The identification of parameters designed to define the D profiles of manufacturing, tourism, and infrastructure in a particular territory (criteria,Cj.D; subcriteria,Sj.D; indicators,Ij.D)(2.2)
Data collection (specific resource parametersiIj.D)(2.3)
The structuring and organization of the data collected (points 2.1 and 2.2) in order to obtain Judgment Levels (JL) for a “territorial attractiveness classification” (JLD) involvingA.n classification on the basis of the presence of extrinsic infrastructural resources over a large area; “airport potential classification” (JLE) that classifies the 51 airports based on a combination of the previous evaluation phases.A synthetic overview (Figure2) shows the ensemble of Judgment Levels in relation to the considered aspects and with the type of obtainable classification (a complete list of abbreviations is shown in Abbreviations).Figure 2
Judgment Levels overview.The results obtained by implementing these Judgment Levels will be useful for singling out the parts of the country that are “covered” by minor airports and those that are “exposed” in order to identify new areas (among those judged to be exposed) where the system of minor airports could achieve national coverage.This paper is structured as follows: definition of the framework of the evaluation methodology (Section2.2), evaluation elements and selection, organization, and spatial data processing for JLA, JLB, JLC, JLD, and JLE (Section 2.3), interrogation of spatial data based on a multicriteria analysis (MCSDA) (Section 2.4), evaluation and discussion of results for JLA, JLB, JLC, JLD, and JLE levels according to the MCSDA approach (Section 3), and conclusions (Section 4).


### 2.2. The Framework of Evaluation Methodology

As mentioned earlier, the structure of the evaluation methodology we propose is based on a full integration between Multicriteria Decision Analysis (MCDA), which allows us to consider a multicriterial data set, and Spatial Decision Support Systems (SDSS) that implement the spatial/territorial analysis [12, 17–19, 21, 23–25]. The evaluation methodology proposed can be defined as a type of Multicriteria Spatial Decision Analysis (MCSDA) due to its ability to (i) contribute to the construction of databases with a suitable amount of information, exploiting the potential of Geographical Information Sciences (GISciences) [26], databases that can be consulted and easily updated, linked to the national infrastructure system (these systems have been developed since the 1990s [27–31] and have expanded enormously in the past 20 years [12, 32–34] thanks also to their integration with World Wide Web (WWW) technologies [35]); (ii) participate in the formation of TIS territorial information systems regarding the network of minor airports, such as those for the organization and consultation of data supporting decisions; (iii) combine geographic data (map criteria) with assessment indicators (decision-makers’ preference and uncertainties) in order to collect information that will prove suitable and useful when making decisions [36].Given the enormous range of data of all kinds that requires inclusion in the evaluation and the spatial nature and/or the strong spatial aspect of most of this data, the multicriterial spatial evaluation model (MCSDA) proposed is based on the creation of a Gis-based database [37]. Thanks to the use of computer software, it is possible to create, manage, and search data that helps us in making an assessment by constructing a database arranged in fields and columns. Using the database as a basis, the evaluation model is implemented using the management and processing of the data (of a multicriterial nature) using tools (computer software) for georeferencing and spatial analysis. Such instruments process and search the database using topographic overlay functions, spatial queries, network analysis, isochrones, and heatmaps. A number of different software packages, including open-source software (the most popular one is currently ESRI ArcGIS proprietary software and open-source QuantumGIS), allow us to create MCSDA models thanks also to the graphic interface that supports the input and management of data increasingly intuitively in recent releases. In order to complete this paper, we chose to use open-source QuantumGIS software to make implementation possible without having to resort to purchasing a licence. The MCSDA on each airport (evaluation alternatives) have been implemented in subsequent methodological steps as follows (Figure 3):Figure 3
Framework of the model.(1)
Consider (from the data used in previous research development phases) or define (ex novo) a set of evaluation elements made up of alternatives (An), criteria (Cj.n), subcriteria (Sj.n), indicators (Ij.n), specific resource parametersi(Ij.n) for JLA “structural classification”, JLB “services classification”, JLC “overall classification of resources”, JLD “territorial attractiveness classification”, and JLE “airport potential classification” Judgment Levels. (2)
Organize and spatially process the specific resource parameters, related to each alternative, through the database for JLA, JLB, JLC, JLD, and JLE Judgment Levels. (3)
Interrogate spatial data based on a multicriteria type evaluation procedure (MCSDA). (4)
Provide a specific classification for each JLA, JLB, JLC, JLD, and JLE Judgment Level. (5)
Extract the evaluation results for each JLA, JLB, JLC, JLD, and JLE Judgment Level.


### 2.3. Evaluation Elements, Organization, and Spatial Elaboration of Data through the DB for Judgment Levels JLA, JLB, JLC, JLD, and JLE

Italy’s 51 minor airports (A.nalternatives) were georeferenced in the 1984 World Geodetic System (WGS84) of geographic coordinates (georeferenced spatial data is data that refers to a physical location on the Earth’s surface using coordinates in a geographic reference system. Geodesy, based on satellite positioning, uses geodetic geocentric data, where the origin of the Cartesian triad coincides with the centre of the Earth’s mass, i.e., of a global type, unlike traditional geodesy [38]), in accordance with the information provided by the Ente Nazionale Aviazione Civile (ENAC) (Figure 1). The WGS84 is a global geocentric system created using spatial observations and consisting of a right-handed Cartesian triad whose point of origin coincides with the centre of the Earth’s mass [39].A shape-file was created for each minor airport, which allows us to archive vector data that can contain a range of information (e.g., location, form, attributes). In the case study proposed here, each minor airport is represented with a georeferenced point on the maps.Sets of elements for evaluation were considered or defined for each Judgment Level (Tables1 and 2) [22] (Table 3), consisting of multipleCj.n, Sj.n, andIj.n associated with data and the abovementionedi(Ij.n) parameters of specific facilities, linked to intrinsic and extrinsic characteristics and territorial profiles that should be considered for each alternativeA.n that has been geographically specified.Table 1
Evaluation Elements for JLA “structural classification”.
Criteria  (CA.n) Sub-criteria  (SA.n) Indicators  (IA.n) Unit of measure Specific resource parameteri(IA.nfor alternative (A.n) Specific classification indicator(pcsp.n) Structural requisites  (JLA) SA.1 Tracks IA.1 Length (meter) i(IA.1;A.n) pcsp.a(IA.1) pcsp.b(IA.1) pcsp.c(IA.1) >900 800≤IA.1≤900 <800 IA.2 Width (meter) i(IA.2;A.n) pcsp.a(IA.2) pcsp.b(IA.2) pcsp.c(IA.2) >18 15≤IA2≤18 <18 IA.3 Accessibility (corner obstacle) i(IA.3;A.n) pcsp.a(IA.3) pcsp.b(IA.3) pcsp.c(IA.3) α β γ IA.4 Paving (material type) i(IA.4;A.n) pcsp.a(IA.4) pcsp.b(IA.4) asphalt/tarmac grass  Structural requisites  (JLA) SA.2 Taxiways IA.5 Width (meter) i(IA.5;A.n) pcsp.a(IA.5) pcsp.b(IA.5) >10.5 ≤10.5 IA.6 Paving (material type) i(IA.6;A.n) pcsp.a(IA.6) pcsp.b(IA.6) asphalt/tarmac grass  Structural requisites  (JLA) SA.3 Aprons IA.7 Paving (material type) i(IA.7;A.n) pcsp.a(IA.7)  pcsp.a(IA.7) asphalt/tarmac  grass Source: Guarini M.R., Battisti F., Buccarini C., Chiovitti A., 2015: A Model of Multicriteria Analysis to Develop Italy’s Minor Airport System.Table 2
Evaluation elements for JLB “services classification”.
Criteria  (CB.n) Sub-criteria  (SB.n) Indicators  (IB.n)  Specific resource parameteri(IB.n) for alternative (A.n) Potential resource parameter(ppd.n) Services requisites  (JLB) SB.1 Airport services IB.1 VDS “advanced” (access) i(IB.1;An) ppd.a(IB.1;An) ppd.b(IB.1;An) open closed IB.2 Fire services (ICAO category) i(IB.2;An) ppd.a(IB.2;An) ppd.b(IB.2;An) ppd.c(IB.2;An) ppd.d(IB.2;An) 1 2 3 4 IB.3 ATS (time slot activities) i(IB.3;An) ppd.a(IB.3;An) ppd.b(IB.3;An) ppd.c(IB.3;An) ppd.d(IB.3;An) 0 8 12 24 IB.4 Refuelling (time slot activities) i(IB.4;An) ppd.a(IB.4;An) ppd.b(IB.4;An) ppd.c(IB.4;An) ppd.d(IB.4;An) 0 8 12 24 IB.5 Handling (time slots activities) i(IB.5;An) ppd.a(IB.5;An) ppd.b(IB.5;An) ppd.c(IB.5;An) ppd.d(IB.5;An) 0 8 12 24 IB.6 Lighting signs for RWY and TWY (presence) i(IB.6;An) ppd.a(IB.6;An) ppd.b(IB.6;An) yes no  Services requisites  (JLB) SB.2 Air traffic support services IB.7 Hangar for aircraft in transit (availability) i(IB.7;An) ppd.a(IB.7;An) ppd.b(IB.7;An) yes no IB.8 Service repairs (availability) i(IB.8;An) ppd.a(IB.8;An) ppd.b(IB.8;An) yes no  Services requisites  (JLB) SB.3 Services to passenger IB.9 Dining (facilities presence) i(IB.9;An) ppd.a(IB.9;An) ppd.b(IB.9;An) yes no IB.10 Hotel (km from airport) i(IB.10;An) ppd.a(IB.10;An) ppd.b(IB.10;An) ppd.c(IB.10;An) <1 1≤IA.10≤5 >5  Services requisites  (JLB) SB.4 Intermodal connectivity IB.11 City (minutes from airport by car) i(IB.11;An) ppd.a(IB.11;An) ppd.b(IB.11;An) ppd.c(IB.11;An) <10 10≤IA.11≤20 <20 IB.12 Chief town (minutes from airport by car) i(IB.12;An) ppd.a(IB.12;An) ppd.b(IB.12;An) ppd.c(IB.12;An) <20 20≤IA.11≤40 <40 IB.14 Taxy (facilities presence) i(IB.14;An) ppd.a(IB.14;An) ppd.b(IB.14;An) yes no IB.15 Car rental (facilities presence) i(IB.15;An) ppd.a(IB.15;An) ppd.b(IB.15;An) yes no IB.16 H u b   i n   buffer   50   k m   from   m i n o r   a i r p o r t _ i(IB.16;An) ppd.a(IB.16;An) ppd.b(IB.16;An) yes no IB.17 Harbour  in  buffer  50  km  from  minor  airport _ i(IB.17;An) ppd.a(IB.17;An) ppd.b(IB.17;An) yes no IB.18 Inter-port  in  buffer  50  km  from  minor  airport _ i(IB.18;An) ppd.a(IB.18;An) ppd.b(IB.18;An) yes no IB.19 Railway  stations  (Km from airport) _ i(IB.19;An) ppd.a(IB.19;An) ppd.b(IB.19;An) <6 >6 Source: elaboration from Guarini M.R., Battisti F., Buccarini C., Chiovitti A., 2015: A Model of Multicriteria Analysis to Develop Italy’s Minor Airport System.Table 3
Evaluation Elements for JLD “territorial attractiveness classification”.
Criteria  (CD.n) Sub-criteria  (SD.n) Indicators  (ID.n) Specific resource parameter i(ID.n) for alternative (A.n) Potential resource parameter (ppd.n) Sources Territorial Services  (JLD) SD.1 Tourist Vocation ID.1.1 Number of visitors (Demand) in state antiquity and art museum institutes (by province) i(ID.1.1;An) ppd.n(ID.1.1;A.n) ISTAT, Territorial Indicators for Development Policies, Cultural Heritage, indicators n. 076_P, census 2015: http://www.istat.it/it/archivio/16777 ID.1.2 Number of institutes (Supply) belonging to state antiquity and art museum institutes (by province) i(ID.1.2;An) ppd.n(ID.1.2;A.n) ISTAT, Territorial Indicators for Development Policies, Cultural Heritage, indicator n. 077_P, census 2015: http://www.istat.it/it/archivio/16777 ID.1.3 Amount of touristic accommodation (Supply) (hotels and similar establishments holiday accommodation and other short-stay accommodation, camping areas and equipped areas for motorhomes and caravans) in Isochrone area 15 minutes i(ID.1.3;An) ppd.n(ID.1.3;A.n) Elaboration in Structure Query Language from data ISTAT, Tourism Ateco 2007, Indicator: total collective accommodation establishments, 2015: http://dati.istat.it/?lang=it&SubSessionId  =39dcb0ba-24c5-4a0e-b637-2b85f16a9f52  &themetreeid=-200 ID.1.4 Number of touristic accommodations (Supply) (hotels and similar establishments, holiday accommodation and other short-stay accommodation, camping areas and equipped areas for motorhomes and caravans) in Isochrone area 30 minutes i(ID.1.4;An) ppd.n(ID.1.4;A.n) Elaboration in Structure Query Language from data ISTAT, Tourism Ateco 2007, Indicator: total collective accommodation establishments, 2015: http://dati.istat.it/?lang=it&SubSessionId  =39dcb0ba-24c5-4a0e-b637-2b85f16a9f52  &themetreeid=-200  Territorial Services  (JLD) SD.2 Infrastructural endowment for transport ID.2.1 Area served by road infrastructures: Space Interrogation in Structure Query Language - Isochrone area 15 minutes i(ID.2.1;An) ppd.n(ID.4;A.n) Elaboration in Structure Query Language from data ISTAT, Territorial bases and censorship variables, publication 2011: http://www.istat.it/it/archivio/104317 ID.2.2 Area served by road infrastructures: Space Interrogation in Structure Query Language - Isochrone area 30 minutes i(ID.2.2;An) ppd.n(ID.5;A.n) Elaboration in Structure Query Language from data ISTAT, Territorial bases and censorship variables, publication 2011: http://www.istat.it/it/archivio/104317 ID.2.3 Number of passengers (Demand) using public transport in the provincial capitals i(ID.2.3;An) ppd.n(ID.6;A.n) ISTAT, Territorial Indicators for Development Policies, City, data n. 650_C, census 2015: http://www.istat.it/it/archivio/16777 ID.2.4 Urban public transport networks in the provincial capitals i(ID.2.4;An) ppd.n(ID.2.4;A.n) ISTAT, Territorial Indicators for Development Policies, City, indicator n.138_C, census 2013: http://www.istat.it/it/archivio/16777  Territorial Services  (JLD) SD.3 Productive Vocation ID.3.1 Number of companies (in municipal areas) i(ID.3.1;An) ppd.n(ID.3.1;A.n) ISTAT and Infocamere Movimprese, Territorial Indicators for Development Policies, Demografia d'impresa data n. 138_P, census 2016: http://www.istat.it/it/archivio/16777 ID.3.2 Participation of population in the labour market (in municipal areas) i(ID.3.2;An) ppd.n(ID.3.2;A.n) ISTAT and Infocamere Movimprese, Territorial Indicators for Development Policies, Work indicator n. 108_P, census 2015: http://www.istat.it/it/archivio/16777The specific resource parameter i(Ij.n) identified for each A.n was arranged in a Microsoft Excel spreadsheet (which included the one created during previous phases of the research), sorted into fields and columns. The spreadsheet was laid out with A.n placed in rows and Cj.n and Sj.n in columns, while the i(Ij.n) was placed at the point where they cross. Saving the Excel file in a comma separated value format (.csv) allowed us to link the Excel spreadsheet to the Quantum GIS (QGIS) software chosen from among the open-source software packages available due to its intuitive graphic interface and the ease with which it can be combined with other IT tools. The tables that follow describe the ways and methods used to construct the set of evaluation elements for each level (through the database). The evaluation elements for the JLC and JLE summarizing levels consist of the results of previous Judgment Levels.The JLA “structural classification” (see paragraph 2.4) was implemented using the set of Cj.A, Sj.A, Ij.A, and i(Ij.A) designed to describe the intrinsic infrastructural characteristics of A.n (Table1) [22].The JLB “services classification” was implemented (see paragraph 2.4) using the set of Cj.B, Sj.B, Ij.B, and i(Ij.B) (Table2) [22] and the potential resource parameters (ppd.n) designed to describe the airport services available for each alternative A.n (Table 2). The JLB level refers to data used in previous phases of research development [22], combined with the addition of new Cj.B, Sj.B, Ij.B, and i(Ij.B) (Table 2) concerning the proximity of (i) hubs (or medium- or long-haul main airports), (ii) interports, (iii) ports, and (iv) railway stations (underline part in Table 2). The elements for evaluation used at this Judgment Level were built up on the basis of the following:(I)
The geographic area of analysis:The geographic areas taken into account for assessments (JLB) (Figure 4) carried out on alternative A.ns were identified by considering the territories closest to minor airports found in the 50 km buffer area (a spatial search in SQL Buffer: the process of creating a polygon that covers the area within a particular distance from objects that could comprise one or more points).(II)
Consultation and inclusion of the data retrieved (2017) from the Aeronautical Information Publication (AIP) [36] helps convey the level of airport services at each analyzed alternative.(III)
Consultation and inclusion of Italian Istituto Nazionale di Statistica (ISTAT) statistical data helps convey the level of services linked to intermodal connectivity (indicators from IB.16 to IB.19).Figure 4
The geographic area of analysis for the JLB: “services classification”.The JLC “overall classification of resources” was implemented (see paragraph 2.4) by considering the results obtained from the JLA and JLB Judgment Levels using the methods described at paragraphs 3.1 and 4.The JLD “territorial attractiveness classification” was implemented using the set of Cj.D, Sj.D, Ij.D, i(Ij.D), and potential resource parameters (ppd.n) for the alternative A.n (Table3) used to describe the facilities found in the territory surrounding each airport. The assessment elements used at this level were built up on the basis of the following:(I)
The definition of geographical areas of analysis:The geographical areas used for JLD assessments carried out on alternativeA.n (Figure 5) were identified by taking into account the territories closest to minor airports and the average time it takes to cross them by road, measured at two levels: (i) 15 minutes; (ii) 30 minutes. These geographical areas consist of a territory where towns are usually found for each minor airport hub, as well as the main council and provincial services.(II)
The consultation and inclusion of ISTAT statistical data helps build up an area’s profile as regards (i) manufacturing, (ii) tourism, and (iii) infrastructure in the territories surrounding minor airports.Figure 5
The geographical areas of analysis for the JLD: “territorial attractiveness classification” example for Arezzo airport “E. Bartolini”.The JLE “airport potential classification” was implemented (see paragraph 2.4) by considering the results obtained from the JLC and JLD Judgment Levels using the methods described at paragraphs 3.3 and 4.


### 2.4. The Definition of Spatial Queries for Each Judgment Level

For each Judgment Level, the procedure was implemented by defining spatial queries on the database as described in paragraph 2.3. Queries in Structure query language (Sql) have been defined for each Judgment Level according to the methodology outlined below.For the JLA “structural classification”, specific classification functionf(csp.n) was constructed to obtain a specific classificationcsp.n(A.n) for each indicatorI.A.n and for each alternativeA.n. The set ofcsp.n (A.n) has been aggregated into two sublevels (JLA1 and JLA2) through the concise classification functionsf(csnt.n) in order to obtain a concise classificationcsnt.n (An) for each alternativeA.n (Table 4) [22].Table 4
MCSDA-JLA “structural classification” procedure.
Criteria  (CA.n) Sub-criteria  (SA.n) Indicators  (IA.n) Specific resource parameteri(IA.n) for alternative (A.n) Specific classification function[f(csp.n)] Specific classification[csp.n(A.n)] Sub-Level Concise classification function[f(csnt.n)] Concise classification(csnt.n) Value classification and related score(cdm.n) Structural requisites  (JLA) SA.1 Tracks IA.1 Length i(IA.1;A.n) if i(IA.1;A.n)>pcsp.a(IA.1) csp.a(IA.1;A.n) = S JLA1 if pcsp.a(IA.1)≤i(IA.1;A.n)≤pcsp.b(IA.1) csp.b(IA.1;A.n) = A if i(IA.1;A.n)<pcsp.c(IA.1) csp.c(IA.1;A.n) = NS if cspc.a(IA.1; IA.2; IA.3; IA.4) = csnt.a(JLA1) (S) cdm.a(IA.1;A.n) S=1  IA.2 Width i(IA.2;A.n) if i(IA.2;A.n)>pcsp.a(IA.2) csp.a(IA.2;A.n) = S if pcsp.a(IA.2)≤i(IA.2;A.n)≤pcsp.b(IA.2) csp.b(IA.2;A.n) = A if cspc.b(IA.1); cspc.b(IA.2); cspc.b(IA.3); cspc.b(IA.4) = csnt.b(JLA1) (T) cdm.a(IA.1;A.n) T=0.5 if i(IA.2;A.n)<pcsp.c(IA.2) csp.c(IA.2;A.n) = NS  IA.3 Accessibility i(IA.3;A.n) if i(IA.3;A.n)>pcsp.a(IA.3) csp.a(IA.3;A.n) = S if pcsp.a(IA.3)≤i(IA.3;A.n)≤pcsp.b(IA.3) csp.b(IA.3;A.n) = A if i(IA.3;A.n)<pcsp.c(IA.3) csp.c(IA.3;A.n) = NS if cspc.c(IA.1); cspc.c(IA.2);cspc.c(IA.3)=NS = csnt.c(JLA1) (NS) cdm.a(IA.1;A.n) NS=0  IA.4 Paving i(IA.4;A.n) if i(IA.4;A.n)=pcsp.a(IA.4) csp.a(IA.4;A.n) = S if i(IA.4;A.n)=pcsp.b(IA.4) csp.b(IA.4;A.n) = A  SA.2 Taxiways IA.5 Width i(IA.5;A.n) if i(IA.5;A.n)>pcsp.a(IA.5) csp.a(IA.5;A.n) = S JLA2 if i(IA.5;A.n)<pcsp.b(IA.5) csp.b(IA.5;A.n) = A if cspc.a(IA.5; IA.6; IA.7) = csnt.a(JLA2) (S) cdm.a(IA.1;A.n) S=1  IA.6 Paving i(IA.6;A.n) if i(IA.6;A.n)=pcsp.a(IA.6) csp.a(IA.6;A.n) = S if i(IA.6;A.n)=pcsp.b(IA.6) csp.b(IA.6;A.n) = A if cspc.b(IA.5); cspc.b(IA.6); cspc.b(IA.7) = csnt.b(JLA2) (T) cdm.a(IA.1;A.n) T=0.5  SA.3 Aprons IA.7 Paving i(IA.7;A.n) if i(IA.7;A.n)=pcsp.a(IA.7) csp.a(IA.7;A.n) = S if i(IA.7;A.n)=pcsp.b(IA.7) csp.b(IA.7;A.n) = A  JLA structural requisites classification S = suitable; A= adaptable; NS = not suitable.  if (JLA1) and (JLA2)=S→ JLA=S if (JLA1) = T and (JLA2)=S→ JLA=T if (JLA1) = NS and (JLA2)=S or (JLA2)=T→ JLA=NS Source: elaboration from Guarini M.R., Battisti F., Buccarini C., Chiovitti A., 2015: A Model of Multicriteria Analysis to Develop Italy’s Minor Airport System.For the JLB “services classification”, threshold satisfaction (ssd.n) has been defined and considered in the construction of the value classification function f(cdm.n) in order to obtain a value classification and related score cdm.n(A.n) referred to each indicator I.B.n for each alternative A.n. Each cdm.n(A.n) has been implemented by the weighting and standardization of the value classification function f(cdm.n) in order to obtain a service requisites classification (weighted and standardized) cdm.n(A.n) for each alternative A.n (Table5) [22].Table 5
MCSDA-JLB “services classification” procedure.
Criteria (CB.n) Sub-criteria (SB.n) Indicators (IB.n) Specific resource parameteri(IB.n) for alternative (A.n) Potential resource parameter(ppd.n) Thresholds satisfaction(ssd.n) Value classification function[f(cdm.n)] Value classification and related score(cdm.n) Weighting and standardisation of value classification function[fnl(cdm.n)] JLB Services requisites (Weighted and standardised value) classification(cdmn.n) Service requisites (JLB) SB.1 IB.1 i(IB.1; An) ppd.a(IB.1;A.n) ssd(IB.1;St.n)VH ifppd.a(IB.1;A.n)>ssd(IB.1;St.n)VH = cdm.a(IB.1;A.n)VH=1 fnl[cdm.a(IB.1;A.n)VH ∗ p(IB.1;St.n)] cdmn.a(IB.1;A.n) or ssd(IB.1;St.n)H ifssd(IB.1;St.n)VH>ppd.a(IB.1;A.n)>ssd(IB.1;St.n)H = cdm.a(IB.1;A.n)H=0.75 fnl[cdm.a(IB.1;A.n)H ∗ p(IB.1;St.n)] ssd(IB.1;St.n)A ifssd(IB.1;St.n)H>ppd.a(IB.1;A.n)>ssd(IB.1;St.n)A = cdm.a(IB.1;A.n)A=0.5 fnl[cdm.a(IB.1;A.n)A ∗ p(IB.1;St.n)] ssd(IB.1;St.n)L ifssd(IB.1;St.n)A>ppd.a(IB.1;A.n)>ssd(IB.1;St.n)L = cdm.a(IB.1;A.n)L=0.25 fnl[cdm.a(IB.1;A.n)L ∗ p(IB.1;St.n)] ssd(IB.1;St.n)VL ifppd.a(IB.1;A.n)<ssd(IB.1;St.n)VL = cdm.a(IB.1;A.n)VL=0 fnl[cdm.a(IB.1;A.n)VL ∗ p(IB.1;St.n)]  ppd.b(IB.1;A.n) ssd(IB.1;St.n)VH ifppd.n(IB.1;A.n)>ssd(IB.1;St.n)VH = cdm.n(IB.1;A.n)VH=1 fnl[cdm.n(IB.1;A.n)VH ∗ p(IB.1;St.n)] cdmn.n(IB.1;A.n) or ssd(IB.1;St.n)H ifssd(IB.1;St.n)VH>ppd.n(IB.1;A.n)>ssd(IB.1;St.n)H = cdm.n(IB.1;A.n)H=0.75 fnl[cdm.n(IB.1;A.n)H ∗ p(IB.1;St.n)] ssd(IB.1;St.n)A ifssd(IB.1;St.n)H>ppd.n(IB.1;A.n)>ssd(IB.1;St.n)A = cdm.n(IB.1;A.n)A=0.5 fnl[cdm.n(IB.1;A.n)A ∗ p(IB.1;St.n)] ssd(IB.1;St.n)L ifssd(IB.1;St.n)A>ppd.n(IB.1;A.n)>ssd(IB.1;St.n)L = cdm.n(IB.1;A.n)L=0.25 fnl[cdm.n(IB.1;A.n)L ∗ p(IB.1;St.n)] ssd(IB.1;St.n)VL ifppd.n(IB.1;A.n)<ssd(IB.1;St.n)VL = cdm.n(IB.1;A.n)VL=0 fnl[cdm.n(IB.1;A.n)VL ∗ p(IB.1;St.n)]  Service requisites (JLB) … … … … … …   … … … …  Service requisites (JLB) SB.n IB.n  ppd.n(IB.n;A.n) ssd(IB.n;St.n)VH ifppd.n(IB.n;A.n)>ssd(IB.n;St.n)VH = cdm.n(IB.n;A.n)VH=1 fnl[cdm.n(IB.n;A.n)VH ∗ p(IB.1;St.n)] cdmn.n(IB.n;A.n) ssd(IB.n;St.n)H ifssd(IB.n;St.n)VH>ppd.n(IB.n;A.n)>ssd(IB.n;St.n)H = cdm.n(IB.n;A.n)H=0.75 fnl[cdm.n(IB.n;A.n)H ∗ p(IB.1;St.n)] ssd(IB.n;St.n)A ifssd(IB.n;St.n)H>ppd.n(IB.n;A.n)>ssd(IB.n;St.n)A = cdm.n(IB.n;A.n)A=0.5 fnl[cdm.n(IB.n;A.n)A ∗ p(IB.1;St.n)] ssd(IB.n;St.n)L ifssd(IB.n;St.n)A>ppd.n(IB.1;A.n)>ssd(IB.n;St.n)L = cdm.n(IB.n;A.n)L=0.25 fnl[cdm.n(IB.n;A.n)L ∗ p(IB.1;St.n)] ssd(IB.n;St.n)VL ifppd.n(IB.n;A.n)<ssd(IB.n;St.n)VL = cdm.n(IB.n;A.n)VL=0 fnl[cdm.n(IB.n;A.n)VL ∗ p(IB.1;St.n)]   VH= very high; H= high; A=average; L=low; VL=very low. Source: elaboration from Guarini M.R., Battisti F., Buccarini C., Chiovitti A., 2015: A model of Multicriteria Analysis to Develop Italy’s Minor Airport System.For the JLC “overall classification of resources”, the procedure was defined as follows. Assigning a weight of 2 to the Value Classification Score of the JLA level (multiplying each Value Classification Score by 2), representing the importance of the structural requisites (JLA) with respect to the service requisites (JLB), which is assigned a weight of 1, a Weighted Structural Classification JLAw and a Weighted Services Classification JLBw are obtained. By adding the values obtained in JLAw and in JLBw for each alternativeA.n a Weighted General Equipment Classification JLCw can be obtained.For subsequent procedural steps (JLCw, JLD, and JLE), univariate data analysis (UDA) [37] has been used; it allows defining, for a given range of numerical variables, the percentiles (Stn) included between 0 and 1, in order to define categorical variables (break values) which are significant compared to the range of numerical variables analyzed considering that “categorical variables can come from numeric variables by aggregating values” [37]. Starting from the qualification of the JLCw, JLD, and JLE outputs, a number of categorical variables considered as attractiveness thresholds (ssd.n) for the qualification of the Italian minor airports (alternative An) can be turned into different levels of evaluation.In detail for each level of evaluation mentioned above the procedure is defined as follows: for the classification of outputs obtained in JLCw, through the UDA 4 percentiles can be defined (St.1 = 1; St.2 = 0.6; St.3 = 0.3; St.4 = 0.0), in order to define 3 intervals of attraction in the JLCw for the evaluation of alternatives (A.n). These percentile values were assumed as Thresholds of Attractiveness(ssd.n) and these were considered in the construction of the value classification functionf (cdm.n) in order to obtain the ClassesCl.n(A.n) for the JLCw level.For the JLD “territorial attractiveness classification”, Thresholds of Attractiveness(ssd.n) were defined through the UDA considering 6 percentiles (St.1 = 1; St.2 = 0.8; St.3 = 0.6; St.4 = 0.4; St.5 = 0.2; St.6 = 0.0) for each range of specific resource parametersi(ID.n) for each indicatorID.n in order to define 5 intervals of attraction for the evaluation of specific resource parameteri(ID.n) for alternatives (A.n). Thesessd.n for each indicatorID.n. were considered in the construction of the value classification functionf(cdm.n) in order to obtain the value classification and related scorecdm.n(A.n) for eachA.n. Eachcdm.n(A.n) was analyzed by concise classification functionf(csnt.n) in order to obtain a territorial services classificationcsnt.n(A.n) for each alternativeA.n (Table 6).Table 6
MCSDA-JLD “territorial attractiveness classification” procedure.
Criteria  (CD.n) Sub-criteria  (SD.n) Indicators  (ID.n) Specific resource parameter i(ID.n) for alternative (A.n) Potential resource parameter (ppd.n) Attractiveness Thresholds(ssd.n) Value classification function[f(cdm.n)] Value classification and related score(cdm.n) Concise classification function[f(csnt.n)] JLD Territorial Services classification(csnt.n) Territorial Services (JLD) SD.1 ID.1.1 i(ID.1.1;An) ppd.n(ID.1.1;A.n) ssd St.1(ID.1.1) ifssd(ID.1.1;St.6)<ppd.n(ID.1.1;A.n)≤ssd(ID.1.1;St.5) = cdm.n(ID.1.1;A.n)=0.2 ssd St.2(ID.1.1) ifssd(ID.1.1;St.5)<ppd.n(ID.1.1;A.n)≤ssd(ID.1.1;St.4) = cdm.n(ID.1.1;A.n)=0.4 ssd St.3(ID.1.1) ifssd(ID.1.1;St.4)<ppd.n(ID.1.1;A.n)≤ssd(ID.1.1;St.3) = cdm.n(ID.1.1;A.n)=0.6 ssd St.4(ID.1.1) ifssd(ID.1.1;St.3)<ppd.n(ID.1.1;A.n)≤ssd(ID.1.1;St.2) = cdm.n(ID.1.1;A.n)=0.8 ssd St.5(ID.1.1) ifssd(ID.1.1;St.2)<ppd.n(ID.1.1;A.n)≤ssd(ID.1.1;St.1) = cdm.n(ID.1;A.n)=1 … … … … …   … … If 0.00 < M[fnl(cdm.n)]≤ 0.20; csnt.n=VL SD.2 ID.2.1 i(ID.2.1;An) ppd.n(ID.2.1;A.n) ssd St.1(ID.2.1) ifssd(ID.2.1;St.6)<ppd.n(ID.2.1;A.n)≤ssd(ID.2.1;St.5) = cdm.n(ID.2.1;A.n)=0.2 If 0.20 < M[fnl(cdm.n)]≤ 0.40; csnt.n=L ssd St.2(ID.2.1) ifssd(ID.2.1;St.5)<ppd.n(ID.2.1;A.n)≤ssd(ID.2.1;St.4) = cdm.n(ID.2.1;A.n)=0.4 If 0.40 < M[fnl(cdm.n)]≤ 0.60; csnt.n=A ssd St.3(ID.2.1) ifssd(ID.2.1;St.4)<ppd.n(ID.2.1;A.n)≤ssd(ID.2.1;St.3) = cdm.n(ID.2.1;A.n)=0.6 If 0.60 < M[fnl(cdm.n)]≤ 0.80; csnt.n=H ssd St.4(ID.2.1) ifssd(ID.2.1;St.3)<ppd.n(ID.2.1;A.n)≤ssd(ID.2.1;St.2) = cdm.n(ID.2.1;A.n)=0.8 If 0.80 < M[fnl(cdm.n)]≤ 1.00; csnt.n=VH ssd St.5(ID.2.1) ifssd(ID.2.1;St.2)<ppd.n(ID.2.1;A.n)≤ssd(ID.2.1;St.1) = cdm.n(ID.2.1;A.n)=1 … …  … …   … … SD.3 ID.3.1 i(ID.3.1;An) ppd.n(ID.3.1;A.n) ssd St.1(ID.3.1) ifssd(ID.3.1;St.6)<ppd.n(ID.3.1;A.n)≤ssd(ID.3.1;St.5) = cdm.n(ID.3.1;A.n)=0.2 ssd St.2(ID.3.1) ifssd(ID.3.1;St.5)<ppd.n(ID.3.1;A.n)≤ssd(ID.3.1;St.4) = cdm.n(ID.3.1;A.n)=0.4 ssd St.3(ID.3.1) ifssd(ID.3.1;St.4)<ppd.n(ID.3.1;A.n)≤ssd(ID.3.1;St.3) = cdm.n(ID.3.1;A.n)=0.6 ssd St.4(ID.3.1) ifssd(ID.3.1;St.3)<ppd.n(ID.3.1;A.n)≤ssd(ID.3.1;St.2) = cdm.n(ID.3.1;A.n)=0.8 ssd St.5(ID.3.1) ifssd(ID.3.1;St.2)<ppd.n(ID.3.1;A.n)≤ssd(ID.3.1;St.1) = cdm.n(ID.3.1;A.n)=1 … …  … …   … … VL=very low; L=low; A=average; H= high; VH= very high. M= arithmetic mean.By adding the Weighted General Equipment classification JLCw values and the territorial attractiveness classificationcsnt.n(A.n) the range of numerical variables for the JLE “airport potential classification” is obtained. From the numerical variables (scores) obtained for JLE, Thresholds of Attractiveness(ssd.n) were defined through the UDA [40]. For this purpose, the 4 percentile values(St.1 = 1; St.2 = 0.6; St.3 = 0.3; St.4 = 0.0)in order to define 3 intervals of attraction (ClassesCl.n) for the evaluation of the alternatives (A.n)in JLE are defined. These percentile values were assumed as Thresholds of Attractiveness(ssd.n) and these were considered in building the value classification functionf(cdm.n) in order to obtain ClassesCl.n(A.n) for the JLE level.


## 2.1. Research Design

As mentioned earlier, this paper illustrates an integrated evaluation methodology designed to judge airport hubs both individually and taken together as far as the following aspects are concerned:(1)
The suitability (status quo) of their infrastructure and service facilities (paragraph 1, point 1):
(1.1)
The identification of characteristics: A: intrinsic infrastructural characteristics (criteria,Cj.A; subcriteria,Sj.A; indicators,Ij.A); B: extrinsic characteristics: airport services (criteria,Cj.B; subcriteria,Sj.B; indicators,Ij.B)(1.2)
Data collection (specific resource parametersiIj.A, Ij.B)(1.3)
The structuring and organization of the data collected (point 1.1 and 1.2) in order to obtain Judgment Levels (JL) for a “structural classification” (JLA) involvingA.n classification on the basis of the amount of intrinsic infrastructural resources, a “services classification” (JLB) involvingA.n classification on the basis of the extrinsic characteristics such as airport services available and an “overall classification of resources” (JLC) that combines the JLA and JLB levels(2)
Their attractiveness given the territorial facilities of their catchment areas (paragraph 1, point 2):
(2.1)
The identification of parameters designed to define the D profiles of manufacturing, tourism, and infrastructure in a particular territory (criteria,Cj.D; subcriteria,Sj.D; indicators,Ij.D)(2.2)
Data collection (specific resource parametersiIj.D)(2.3)
The structuring and organization of the data collected (points 2.1 and 2.2) in order to obtain Judgment Levels (JL) for a “territorial attractiveness classification” (JLD) involvingA.n classification on the basis of the presence of extrinsic infrastructural resources over a large area; “airport potential classification” (JLE) that classifies the 51 airports based on a combination of the previous evaluation phases.A synthetic overview (Figure2) shows the ensemble of Judgment Levels in relation to the considered aspects and with the type of obtainable classification (a complete list of abbreviations is shown in Abbreviations).Figure 2
Judgment Levels overview.The results obtained by implementing these Judgment Levels will be useful for singling out the parts of the country that are “covered” by minor airports and those that are “exposed” in order to identify new areas (among those judged to be exposed) where the system of minor airports could achieve national coverage.This paper is structured as follows: definition of the framework of the evaluation methodology (Section2.2), evaluation elements and selection, organization, and spatial data processing for JLA, JLB, JLC, JLD, and JLE (Section 2.3), interrogation of spatial data based on a multicriteria analysis (MCSDA) (Section 2.4), evaluation and discussion of results for JLA, JLB, JLC, JLD, and JLE levels according to the MCSDA approach (Section 3), and conclusions (Section 4).


## 2.2. The Framework of Evaluation Methodology

As mentioned earlier, the structure of the evaluation methodology we propose is based on a full integration between Multicriteria Decision Analysis (MCDA), which allows us to consider a multicriterial data set, and Spatial Decision Support Systems (SDSS) that implement the spatial/territorial analysis [12, 17–19, 21, 23–25]. The evaluation methodology proposed can be defined as a type of Multicriteria Spatial Decision Analysis (MCSDA) due to its ability to (i) contribute to the construction of databases with a suitable amount of information, exploiting the potential of Geographical Information Sciences (GISciences) [26], databases that can be consulted and easily updated, linked to the national infrastructure system (these systems have been developed since the 1990s [27–31] and have expanded enormously in the past 20 years [12, 32–34] thanks also to their integration with World Wide Web (WWW) technologies [35]); (ii) participate in the formation of TIS territorial information systems regarding the network of minor airports, such as those for the organization and consultation of data supporting decisions; (iii) combine geographic data (map criteria) with assessment indicators (decision-makers’ preference and uncertainties) in order to collect information that will prove suitable and useful when making decisions [36].Given the enormous range of data of all kinds that requires inclusion in the evaluation and the spatial nature and/or the strong spatial aspect of most of this data, the multicriterial spatial evaluation model (MCSDA) proposed is based on the creation of a Gis-based database [37]. Thanks to the use of computer software, it is possible to create, manage, and search data that helps us in making an assessment by constructing a database arranged in fields and columns. Using the database as a basis, the evaluation model is implemented using the management and processing of the data (of a multicriterial nature) using tools (computer software) for georeferencing and spatial analysis. Such instruments process and search the database using topographic overlay functions, spatial queries, network analysis, isochrones, and heatmaps. A number of different software packages, including open-source software (the most popular one is currently ESRI ArcGIS proprietary software and open-source QuantumGIS), allow us to create MCSDA models thanks also to the graphic interface that supports the input and management of data increasingly intuitively in recent releases. In order to complete this paper, we chose to use open-source QuantumGIS software to make implementation possible without having to resort to purchasing a licence. The MCSDA on each airport (evaluation alternatives) have been implemented in subsequent methodological steps as follows (Figure 3):Figure 3
Framework of the model.(1)
Consider (from the data used in previous research development phases) or define (ex novo) a set of evaluation elements made up of alternatives (An), criteria (Cj.n), subcriteria (Sj.n), indicators (Ij.n), specific resource parametersi(Ij.n) for JLA “structural classification”, JLB “services classification”, JLC “overall classification of resources”, JLD “territorial attractiveness classification”, and JLE “airport potential classification” Judgment Levels. (2)
Organize and spatially process the specific resource parameters, related to each alternative, through the database for JLA, JLB, JLC, JLD, and JLE Judgment Levels. (3)
Interrogate spatial data based on a multicriteria type evaluation procedure (MCSDA). (4)
Provide a specific classification for each JLA, JLB, JLC, JLD, and JLE Judgment Level. (5)
Extract the evaluation results for each JLA, JLB, JLC, JLD, and JLE Judgment Level.


## 2.3. Evaluation Elements, Organization, and Spatial Elaboration of Data through the DB for Judgment Levels JLA, JLB, JLC, JLD, and JLE

Italy’s 51 minor airports (A.nalternatives) were georeferenced in the 1984 World Geodetic System (WGS84) of geographic coordinates (georeferenced spatial data is data that refers to a physical location on the Earth’s surface using coordinates in a geographic reference system. Geodesy, based on satellite positioning, uses geodetic geocentric data, where the origin of the Cartesian triad coincides with the centre of the Earth’s mass, i.e., of a global type, unlike traditional geodesy [38]), in accordance with the information provided by the Ente Nazionale Aviazione Civile (ENAC) (Figure 1). The WGS84 is a global geocentric system created using spatial observations and consisting of a right-handed Cartesian triad whose point of origin coincides with the centre of the Earth’s mass [39].A shape-file was created for each minor airport, which allows us to archive vector data that can contain a range of information (e.g., location, form, attributes). In the case study proposed here, each minor airport is represented with a georeferenced point on the maps.Sets of elements for evaluation were considered or defined for each Judgment Level (Tables1 and 2) [22] (Table 3), consisting of multipleCj.n, Sj.n, andIj.n associated with data and the abovementionedi(Ij.n) parameters of specific facilities, linked to intrinsic and extrinsic characteristics and territorial profiles that should be considered for each alternativeA.n that has been geographically specified.Table 1
Evaluation Elements for JLA “structural classification”.
Criteria  (CA.n) Sub-criteria  (SA.n) Indicators  (IA.n) Unit of measure Specific resource parameteri(IA.nfor alternative (A.n) Specific classification indicator(pcsp.n) Structural requisites  (JLA) SA.1 Tracks IA.1 Length (meter) i(IA.1;A.n) pcsp.a(IA.1) pcsp.b(IA.1) pcsp.c(IA.1) >900 800≤IA.1≤900 <800 IA.2 Width (meter) i(IA.2;A.n) pcsp.a(IA.2) pcsp.b(IA.2) pcsp.c(IA.2) >18 15≤IA2≤18 <18 IA.3 Accessibility (corner obstacle) i(IA.3;A.n) pcsp.a(IA.3) pcsp.b(IA.3) pcsp.c(IA.3) α β γ IA.4 Paving (material type) i(IA.4;A.n) pcsp.a(IA.4) pcsp.b(IA.4) asphalt/tarmac grass  Structural requisites  (JLA) SA.2 Taxiways IA.5 Width (meter) i(IA.5;A.n) pcsp.a(IA.5) pcsp.b(IA.5) >10.5 ≤10.5 IA.6 Paving (material type) i(IA.6;A.n) pcsp.a(IA.6) pcsp.b(IA.6) asphalt/tarmac grass  Structural requisites  (JLA) SA.3 Aprons IA.7 Paving (material type) i(IA.7;A.n) pcsp.a(IA.7)  pcsp.a(IA.7) asphalt/tarmac  grass Source: Guarini M.R., Battisti F., Buccarini C., Chiovitti A., 2015: A Model of Multicriteria Analysis to Develop Italy’s Minor Airport System.Table 2
Evaluation elements for JLB “services classification”.
Criteria  (CB.n) Sub-criteria  (SB.n) Indicators  (IB.n)  Specific resource parameteri(IB.n) for alternative (A.n) Potential resource parameter(ppd.n) Services requisites  (JLB) SB.1 Airport services IB.1 VDS “advanced” (access) i(IB.1;An) ppd.a(IB.1;An) ppd.b(IB.1;An) open closed IB.2 Fire services (ICAO category) i(IB.2;An) ppd.a(IB.2;An) ppd.b(IB.2;An) ppd.c(IB.2;An) ppd.d(IB.2;An) 1 2 3 4 IB.3 ATS (time slot activities) i(IB.3;An) ppd.a(IB.3;An) ppd.b(IB.3;An) ppd.c(IB.3;An) ppd.d(IB.3;An) 0 8 12 24 IB.4 Refuelling (time slot activities) i(IB.4;An) ppd.a(IB.4;An) ppd.b(IB.4;An) ppd.c(IB.4;An) ppd.d(IB.4;An) 0 8 12 24 IB.5 Handling (time slots activities) i(IB.5;An) ppd.a(IB.5;An) ppd.b(IB.5;An) ppd.c(IB.5;An) ppd.d(IB.5;An) 0 8 12 24 IB.6 Lighting signs for RWY and TWY (presence) i(IB.6;An) ppd.a(IB.6;An) ppd.b(IB.6;An) yes no  Services requisites  (JLB) SB.2 Air traffic support services IB.7 Hangar for aircraft in transit (availability) i(IB.7;An) ppd.a(IB.7;An) ppd.b(IB.7;An) yes no IB.8 Service repairs (availability) i(IB.8;An) ppd.a(IB.8;An) ppd.b(IB.8;An) yes no  Services requisites  (JLB) SB.3 Services to passenger IB.9 Dining (facilities presence) i(IB.9;An) ppd.a(IB.9;An) ppd.b(IB.9;An) yes no IB.10 Hotel (km from airport) i(IB.10;An) ppd.a(IB.10;An) ppd.b(IB.10;An) ppd.c(IB.10;An) <1 1≤IA.10≤5 >5  Services requisites  (JLB) SB.4 Intermodal connectivity IB.11 City (minutes from airport by car) i(IB.11;An) ppd.a(IB.11;An) ppd.b(IB.11;An) ppd.c(IB.11;An) <10 10≤IA.11≤20 <20 IB.12 Chief town (minutes from airport by car) i(IB.12;An) ppd.a(IB.12;An) ppd.b(IB.12;An) ppd.c(IB.12;An) <20 20≤IA.11≤40 <40 IB.14 Taxy (facilities presence) i(IB.14;An) ppd.a(IB.14;An) ppd.b(IB.14;An) yes no IB.15 Car rental (facilities presence) i(IB.15;An) ppd.a(IB.15;An) ppd.b(IB.15;An) yes no IB.16 H u b   i n   buffer   50   k m   from   m i n o r   a i r p o r t _ i(IB.16;An) ppd.a(IB.16;An) ppd.b(IB.16;An) yes no IB.17 Harbour  in  buffer  50  km  from  minor  airport _ i(IB.17;An) ppd.a(IB.17;An) ppd.b(IB.17;An) yes no IB.18 Inter-port  in  buffer  50  km  from  minor  airport _ i(IB.18;An) ppd.a(IB.18;An) ppd.b(IB.18;An) yes no IB.19 Railway  stations  (Km from airport) _ i(IB.19;An) ppd.a(IB.19;An) ppd.b(IB.19;An) <6 >6 Source: elaboration from Guarini M.R., Battisti F., Buccarini C., Chiovitti A., 2015: A Model of Multicriteria Analysis to Develop Italy’s Minor Airport System.Table 3
Evaluation Elements for JLD “territorial attractiveness classification”.
Criteria  (CD.n) Sub-criteria  (SD.n) Indicators  (ID.n) Specific resource parameter i(ID.n) for alternative (A.n) Potential resource parameter (ppd.n) Sources Territorial Services  (JLD) SD.1 Tourist Vocation ID.1.1 Number of visitors (Demand) in state antiquity and art museum institutes (by province) i(ID.1.1;An) ppd.n(ID.1.1;A.n) ISTAT, Territorial Indicators for Development Policies, Cultural Heritage, indicators n. 076_P, census 2015: http://www.istat.it/it/archivio/16777 ID.1.2 Number of institutes (Supply) belonging to state antiquity and art museum institutes (by province) i(ID.1.2;An) ppd.n(ID.1.2;A.n) ISTAT, Territorial Indicators for Development Policies, Cultural Heritage, indicator n. 077_P, census 2015: http://www.istat.it/it/archivio/16777 ID.1.3 Amount of touristic accommodation (Supply) (hotels and similar establishments holiday accommodation and other short-stay accommodation, camping areas and equipped areas for motorhomes and caravans) in Isochrone area 15 minutes i(ID.1.3;An) ppd.n(ID.1.3;A.n) Elaboration in Structure Query Language from data ISTAT, Tourism Ateco 2007, Indicator: total collective accommodation establishments, 2015: http://dati.istat.it/?lang=it&SubSessionId  =39dcb0ba-24c5-4a0e-b637-2b85f16a9f52  &themetreeid=-200 ID.1.4 Number of touristic accommodations (Supply) (hotels and similar establishments, holiday accommodation and other short-stay accommodation, camping areas and equipped areas for motorhomes and caravans) in Isochrone area 30 minutes i(ID.1.4;An) ppd.n(ID.1.4;A.n) Elaboration in Structure Query Language from data ISTAT, Tourism Ateco 2007, Indicator: total collective accommodation establishments, 2015: http://dati.istat.it/?lang=it&SubSessionId  =39dcb0ba-24c5-4a0e-b637-2b85f16a9f52  &themetreeid=-200  Territorial Services  (JLD) SD.2 Infrastructural endowment for transport ID.2.1 Area served by road infrastructures: Space Interrogation in Structure Query Language - Isochrone area 15 minutes i(ID.2.1;An) ppd.n(ID.4;A.n) Elaboration in Structure Query Language from data ISTAT, Territorial bases and censorship variables, publication 2011: http://www.istat.it/it/archivio/104317 ID.2.2 Area served by road infrastructures: Space Interrogation in Structure Query Language - Isochrone area 30 minutes i(ID.2.2;An) ppd.n(ID.5;A.n) Elaboration in Structure Query Language from data ISTAT, Territorial bases and censorship variables, publication 2011: http://www.istat.it/it/archivio/104317 ID.2.3 Number of passengers (Demand) using public transport in the provincial capitals i(ID.2.3;An) ppd.n(ID.6;A.n) ISTAT, Territorial Indicators for Development Policies, City, data n. 650_C, census 2015: http://www.istat.it/it/archivio/16777 ID.2.4 Urban public transport networks in the provincial capitals i(ID.2.4;An) ppd.n(ID.2.4;A.n) ISTAT, Territorial Indicators for Development Policies, City, indicator n.138_C, census 2013: http://www.istat.it/it/archivio/16777  Territorial Services  (JLD) SD.3 Productive Vocation ID.3.1 Number of companies (in municipal areas) i(ID.3.1;An) ppd.n(ID.3.1;A.n) ISTAT and Infocamere Movimprese, Territorial Indicators for Development Policies, Demografia d'impresa data n. 138_P, census 2016: http://www.istat.it/it/archivio/16777 ID.3.2 Participation of population in the labour market (in municipal areas) i(ID.3.2;An) ppd.n(ID.3.2;A.n) ISTAT and Infocamere Movimprese, Territorial Indicators for Development Policies, Work indicator n. 108_P, census 2015: http://www.istat.it/it/archivio/16777The specific resource parameter i(Ij.n) identified for each A.n was arranged in a Microsoft Excel spreadsheet (which included the one created during previous phases of the research), sorted into fields and columns. The spreadsheet was laid out with A.n placed in rows and Cj.n and Sj.n in columns, while the i(Ij.n) was placed at the point where they cross. Saving the Excel file in a comma separated value format (.csv) allowed us to link the Excel spreadsheet to the Quantum GIS (QGIS) software chosen from among the open-source software packages available due to its intuitive graphic interface and the ease with which it can be combined with other IT tools. The tables that follow describe the ways and methods used to construct the set of evaluation elements for each level (through the database). The evaluation elements for the JLC and JLE summarizing levels consist of the results of previous Judgment Levels.The JLA “structural classification” (see paragraph 2.4) was implemented using the set of Cj.A, Sj.A, Ij.A, and i(Ij.A) designed to describe the intrinsic infrastructural characteristics of A.n (Table1) [22].The JLB “services classification” was implemented (see paragraph 2.4) using the set of Cj.B, Sj.B, Ij.B, and i(Ij.B) (Table2) [22] and the potential resource parameters (ppd.n) designed to describe the airport services available for each alternative A.n (Table 2). The JLB level refers to data used in previous phases of research development [22], combined with the addition of new Cj.B, Sj.B, Ij.B, and i(Ij.B) (Table 2) concerning the proximity of (i) hubs (or medium- or long-haul main airports), (ii) interports, (iii) ports, and (iv) railway stations (underline part in Table 2). The elements for evaluation used at this Judgment Level were built up on the basis of the following:(I)
The geographic area of analysis:The geographic areas taken into account for assessments (JLB) (Figure 4) carried out on alternative A.ns were identified by considering the territories closest to minor airports found in the 50 km buffer area (a spatial search in SQL Buffer: the process of creating a polygon that covers the area within a particular distance from objects that could comprise one or more points).(II)
Consultation and inclusion of the data retrieved (2017) from the Aeronautical Information Publication (AIP) [36] helps convey the level of airport services at each analyzed alternative.(III)
Consultation and inclusion of Italian Istituto Nazionale di Statistica (ISTAT) statistical data helps convey the level of services linked to intermodal connectivity (indicators from IB.16 to IB.19).Figure 4
The geographic area of analysis for the JLB: “services classification”.The JLC “overall classification of resources” was implemented (see paragraph 2.4) by considering the results obtained from the JLA and JLB Judgment Levels using the methods described at paragraphs 3.1 and 4.The JLD “territorial attractiveness classification” was implemented using the set of Cj.D, Sj.D, Ij.D, i(Ij.D), and potential resource parameters (ppd.n) for the alternative A.n (Table3) used to describe the facilities found in the territory surrounding each airport. The assessment elements used at this level were built up on the basis of the following:(I)
The definition of geographical areas of analysis:The geographical areas used for JLD assessments carried out on alternativeA.n (Figure 5) were identified by taking into account the territories closest to minor airports and the average time it takes to cross them by road, measured at two levels: (i) 15 minutes; (ii) 30 minutes. These geographical areas consist of a territory where towns are usually found for each minor airport hub, as well as the main council and provincial services.(II)
The consultation and inclusion of ISTAT statistical data helps build up an area’s profile as regards (i) manufacturing, (ii) tourism, and (iii) infrastructure in the territories surrounding minor airports.Figure 5
The geographical areas of analysis for the JLD: “territorial attractiveness classification” example for Arezzo airport “E. Bartolini”.The JLE “airport potential classification” was implemented (see paragraph 2.4) by considering the results obtained from the JLC and JLD Judgment Levels using the methods described at paragraphs 3.3 and 4.


## 2.4. The Definition of Spatial Queries for Each Judgment Level

For each Judgment Level, the procedure was implemented by defining spatial queries on the database as described in paragraph 2.3. Queries in Structure query language (Sql) have been defined for each Judgment Level according to the methodology outlined below.For the JLA “structural classification”, specific classification functionf(csp.n) was constructed to obtain a specific classificationcsp.n(A.n) for each indicatorI.A.n and for each alternativeA.n. The set ofcsp.n (A.n) has been aggregated into two sublevels (JLA1 and JLA2) through the concise classification functionsf(csnt.n) in order to obtain a concise classificationcsnt.n (An) for each alternativeA.n (Table 4) [22].Table 4
MCSDA-JLA “structural classification” procedure.
Criteria  (CA.n) Sub-criteria  (SA.n) Indicators  (IA.n) Specific resource parameteri(IA.n) for alternative (A.n) Specific classification function[f(csp.n)] Specific classification[csp.n(A.n)] Sub-Level Concise classification function[f(csnt.n)] Concise classification(csnt.n) Value classification and related score(cdm.n) Structural requisites  (JLA) SA.1 Tracks IA.1 Length i(IA.1;A.n) if i(IA.1;A.n)>pcsp.a(IA.1) csp.a(IA.1;A.n) = S JLA1 if pcsp.a(IA.1)≤i(IA.1;A.n)≤pcsp.b(IA.1) csp.b(IA.1;A.n) = A if i(IA.1;A.n)<pcsp.c(IA.1) csp.c(IA.1;A.n) = NS if cspc.a(IA.1; IA.2; IA.3; IA.4) = csnt.a(JLA1) (S) cdm.a(IA.1;A.n) S=1  IA.2 Width i(IA.2;A.n) if i(IA.2;A.n)>pcsp.a(IA.2) csp.a(IA.2;A.n) = S if pcsp.a(IA.2)≤i(IA.2;A.n)≤pcsp.b(IA.2) csp.b(IA.2;A.n) = A if cspc.b(IA.1); cspc.b(IA.2); cspc.b(IA.3); cspc.b(IA.4) = csnt.b(JLA1) (T) cdm.a(IA.1;A.n) T=0.5 if i(IA.2;A.n)<pcsp.c(IA.2) csp.c(IA.2;A.n) = NS  IA.3 Accessibility i(IA.3;A.n) if i(IA.3;A.n)>pcsp.a(IA.3) csp.a(IA.3;A.n) = S if pcsp.a(IA.3)≤i(IA.3;A.n)≤pcsp.b(IA.3) csp.b(IA.3;A.n) = A if i(IA.3;A.n)<pcsp.c(IA.3) csp.c(IA.3;A.n) = NS if cspc.c(IA.1); cspc.c(IA.2);cspc.c(IA.3)=NS = csnt.c(JLA1) (NS) cdm.a(IA.1;A.n) NS=0  IA.4 Paving i(IA.4;A.n) if i(IA.4;A.n)=pcsp.a(IA.4) csp.a(IA.4;A.n) = S if i(IA.4;A.n)=pcsp.b(IA.4) csp.b(IA.4;A.n) = A  SA.2 Taxiways IA.5 Width i(IA.5;A.n) if i(IA.5;A.n)>pcsp.a(IA.5) csp.a(IA.5;A.n) = S JLA2 if i(IA.5;A.n)<pcsp.b(IA.5) csp.b(IA.5;A.n) = A if cspc.a(IA.5; IA.6; IA.7) = csnt.a(JLA2) (S) cdm.a(IA.1;A.n) S=1  IA.6 Paving i(IA.6;A.n) if i(IA.6;A.n)=pcsp.a(IA.6) csp.a(IA.6;A.n) = S if i(IA.6;A.n)=pcsp.b(IA.6) csp.b(IA.6;A.n) = A if cspc.b(IA.5); cspc.b(IA.6); cspc.b(IA.7) = csnt.b(JLA2) (T) cdm.a(IA.1;A.n) T=0.5  SA.3 Aprons IA.7 Paving i(IA.7;A.n) if i(IA.7;A.n)=pcsp.a(IA.7) csp.a(IA.7;A.n) = S if i(IA.7;A.n)=pcsp.b(IA.7) csp.b(IA.7;A.n) = A  JLA structural requisites classification S = suitable; A= adaptable; NS = not suitable.  if (JLA1) and (JLA2)=S→ JLA=S if (JLA1) = T and (JLA2)=S→ JLA=T if (JLA1) = NS and (JLA2)=S or (JLA2)=T→ JLA=NS Source: elaboration from Guarini M.R., Battisti F., Buccarini C., Chiovitti A., 2015: A Model of Multicriteria Analysis to Develop Italy’s Minor Airport System.For the JLB “services classification”, threshold satisfaction (ssd.n) has been defined and considered in the construction of the value classification function f(cdm.n) in order to obtain a value classification and related score cdm.n(A.n) referred to each indicator I.B.n for each alternative A.n. Each cdm.n(A.n) has been implemented by the weighting and standardization of the value classification function f(cdm.n) in order to obtain a service requisites classification (weighted and standardized) cdm.n(A.n) for each alternative A.n (Table5) [22].Table 5
MCSDA-JLB “services classification” procedure.
Criteria (CB.n) Sub-criteria (SB.n) Indicators (IB.n) Specific resource parameteri(IB.n) for alternative (A.n) Potential resource parameter(ppd.n) Thresholds satisfaction(ssd.n) Value classification function[f(cdm.n)] Value classification and related score(cdm.n) Weighting and standardisation of value classification function[fnl(cdm.n)] JLB Services requisites (Weighted and standardised value) classification(cdmn.n) Service requisites (JLB) SB.1 IB.1 i(IB.1; An) ppd.a(IB.1;A.n) ssd(IB.1;St.n)VH ifppd.a(IB.1;A.n)>ssd(IB.1;St.n)VH = cdm.a(IB.1;A.n)VH=1 fnl[cdm.a(IB.1;A.n)VH ∗ p(IB.1;St.n)] cdmn.a(IB.1;A.n) or ssd(IB.1;St.n)H ifssd(IB.1;St.n)VH>ppd.a(IB.1;A.n)>ssd(IB.1;St.n)H = cdm.a(IB.1;A.n)H=0.75 fnl[cdm.a(IB.1;A.n)H ∗ p(IB.1;St.n)] ssd(IB.1;St.n)A ifssd(IB.1;St.n)H>ppd.a(IB.1;A.n)>ssd(IB.1;St.n)A = cdm.a(IB.1;A.n)A=0.5 fnl[cdm.a(IB.1;A.n)A ∗ p(IB.1;St.n)] ssd(IB.1;St.n)L ifssd(IB.1;St.n)A>ppd.a(IB.1;A.n)>ssd(IB.1;St.n)L = cdm.a(IB.1;A.n)L=0.25 fnl[cdm.a(IB.1;A.n)L ∗ p(IB.1;St.n)] ssd(IB.1;St.n)VL ifppd.a(IB.1;A.n)<ssd(IB.1;St.n)VL = cdm.a(IB.1;A.n)VL=0 fnl[cdm.a(IB.1;A.n)VL ∗ p(IB.1;St.n)]  ppd.b(IB.1;A.n) ssd(IB.1;St.n)VH ifppd.n(IB.1;A.n)>ssd(IB.1;St.n)VH = cdm.n(IB.1;A.n)VH=1 fnl[cdm.n(IB.1;A.n)VH ∗ p(IB.1;St.n)] cdmn.n(IB.1;A.n) or ssd(IB.1;St.n)H ifssd(IB.1;St.n)VH>ppd.n(IB.1;A.n)>ssd(IB.1;St.n)H = cdm.n(IB.1;A.n)H=0.75 fnl[cdm.n(IB.1;A.n)H ∗ p(IB.1;St.n)] ssd(IB.1;St.n)A ifssd(IB.1;St.n)H>ppd.n(IB.1;A.n)>ssd(IB.1;St.n)A = cdm.n(IB.1;A.n)A=0.5 fnl[cdm.n(IB.1;A.n)A ∗ p(IB.1;St.n)] ssd(IB.1;St.n)L ifssd(IB.1;St.n)A>ppd.n(IB.1;A.n)>ssd(IB.1;St.n)L = cdm.n(IB.1;A.n)L=0.25 fnl[cdm.n(IB.1;A.n)L ∗ p(IB.1;St.n)] ssd(IB.1;St.n)VL ifppd.n(IB.1;A.n)<ssd(IB.1;St.n)VL = cdm.n(IB.1;A.n)VL=0 fnl[cdm.n(IB.1;A.n)VL ∗ p(IB.1;St.n)]  Service requisites (JLB) … … … … … …   … … … …  Service requisites (JLB) SB.n IB.n  ppd.n(IB.n;A.n) ssd(IB.n;St.n)VH ifppd.n(IB.n;A.n)>ssd(IB.n;St.n)VH = cdm.n(IB.n;A.n)VH=1 fnl[cdm.n(IB.n;A.n)VH ∗ p(IB.1;St.n)] cdmn.n(IB.n;A.n) ssd(IB.n;St.n)H ifssd(IB.n;St.n)VH>ppd.n(IB.n;A.n)>ssd(IB.n;St.n)H = cdm.n(IB.n;A.n)H=0.75 fnl[cdm.n(IB.n;A.n)H ∗ p(IB.1;St.n)] ssd(IB.n;St.n)A ifssd(IB.n;St.n)H>ppd.n(IB.n;A.n)>ssd(IB.n;St.n)A = cdm.n(IB.n;A.n)A=0.5 fnl[cdm.n(IB.n;A.n)A ∗ p(IB.1;St.n)] ssd(IB.n;St.n)L ifssd(IB.n;St.n)A>ppd.n(IB.1;A.n)>ssd(IB.n;St.n)L = cdm.n(IB.n;A.n)L=0.25 fnl[cdm.n(IB.n;A.n)L ∗ p(IB.1;St.n)] ssd(IB.n;St.n)VL ifppd.n(IB.n;A.n)<ssd(IB.n;St.n)VL = cdm.n(IB.n;A.n)VL=0 fnl[cdm.n(IB.n;A.n)VL ∗ p(IB.1;St.n)]   VH= very high; H= high; A=average; L=low; VL=very low. Source: elaboration from Guarini M.R., Battisti F., Buccarini C., Chiovitti A., 2015: A model of Multicriteria Analysis to Develop Italy’s Minor Airport System.For the JLC “overall classification of resources”, the procedure was defined as follows. Assigning a weight of 2 to the Value Classification Score of the JLA level (multiplying each Value Classification Score by 2), representing the importance of the structural requisites (JLA) with respect to the service requisites (JLB), which is assigned a weight of 1, a Weighted Structural Classification JLAw and a Weighted Services Classification JLBw are obtained. By adding the values obtained in JLAw and in JLBw for each alternativeA.n a Weighted General Equipment Classification JLCw can be obtained.For subsequent procedural steps (JLCw, JLD, and JLE), univariate data analysis (UDA) [37] has been used; it allows defining, for a given range of numerical variables, the percentiles (Stn) included between 0 and 1, in order to define categorical variables (break values) which are significant compared to the range of numerical variables analyzed considering that “categorical variables can come from numeric variables by aggregating values” [37]. Starting from the qualification of the JLCw, JLD, and JLE outputs, a number of categorical variables considered as attractiveness thresholds (ssd.n) for the qualification of the Italian minor airports (alternative An) can be turned into different levels of evaluation.In detail for each level of evaluation mentioned above the procedure is defined as follows: for the classification of outputs obtained in JLCw, through the UDA 4 percentiles can be defined (St.1 = 1; St.2 = 0.6; St.3 = 0.3; St.4 = 0.0), in order to define 3 intervals of attraction in the JLCw for the evaluation of alternatives (A.n). These percentile values were assumed as Thresholds of Attractiveness(ssd.n) and these were considered in the construction of the value classification functionf (cdm.n) in order to obtain the ClassesCl.n(A.n) for the JLCw level.For the JLD “territorial attractiveness classification”, Thresholds of Attractiveness(ssd.n) were defined through the UDA considering 6 percentiles (St.1 = 1; St.2 = 0.8; St.3 = 0.6; St.4 = 0.4; St.5 = 0.2; St.6 = 0.0) for each range of specific resource parametersi(ID.n) for each indicatorID.n in order to define 5 intervals of attraction for the evaluation of specific resource parameteri(ID.n) for alternatives (A.n). Thesessd.n for each indicatorID.n. were considered in the construction of the value classification functionf(cdm.n) in order to obtain the value classification and related scorecdm.n(A.n) for eachA.n. Eachcdm.n(A.n) was analyzed by concise classification functionf(csnt.n) in order to obtain a territorial services classificationcsnt.n(A.n) for each alternativeA.n (Table 6).Table 6
MCSDA-JLD “territorial attractiveness classification” procedure.
Criteria  (CD.n) Sub-criteria  (SD.n) Indicators  (ID.n) Specific resource parameter i(ID.n) for alternative (A.n) Potential resource parameter (ppd.n) Attractiveness Thresholds(ssd.n) Value classification function[f(cdm.n)] Value classification and related score(cdm.n) Concise classification function[f(csnt.n)] JLD Territorial Services classification(csnt.n) Territorial Services (JLD) SD.1 ID.1.1 i(ID.1.1;An) ppd.n(ID.1.1;A.n) ssd St.1(ID.1.1) ifssd(ID.1.1;St.6)<ppd.n(ID.1.1;A.n)≤ssd(ID.1.1;St.5) = cdm.n(ID.1.1;A.n)=0.2 ssd St.2(ID.1.1) ifssd(ID.1.1;St.5)<ppd.n(ID.1.1;A.n)≤ssd(ID.1.1;St.4) = cdm.n(ID.1.1;A.n)=0.4 ssd St.3(ID.1.1) ifssd(ID.1.1;St.4)<ppd.n(ID.1.1;A.n)≤ssd(ID.1.1;St.3) = cdm.n(ID.1.1;A.n)=0.6 ssd St.4(ID.1.1) ifssd(ID.1.1;St.3)<ppd.n(ID.1.1;A.n)≤ssd(ID.1.1;St.2) = cdm.n(ID.1.1;A.n)=0.8 ssd St.5(ID.1.1) ifssd(ID.1.1;St.2)<ppd.n(ID.1.1;A.n)≤ssd(ID.1.1;St.1) = cdm.n(ID.1;A.n)=1 … … … … …   … … If 0.00 < M[fnl(cdm.n)]≤ 0.20; csnt.n=VL SD.2 ID.2.1 i(ID.2.1;An) ppd.n(ID.2.1;A.n) ssd St.1(ID.2.1) ifssd(ID.2.1;St.6)<ppd.n(ID.2.1;A.n)≤ssd(ID.2.1;St.5) = cdm.n(ID.2.1;A.n)=0.2 If 0.20 < M[fnl(cdm.n)]≤ 0.40; csnt.n=L ssd St.2(ID.2.1) ifssd(ID.2.1;St.5)<ppd.n(ID.2.1;A.n)≤ssd(ID.2.1;St.4) = cdm.n(ID.2.1;A.n)=0.4 If 0.40 < M[fnl(cdm.n)]≤ 0.60; csnt.n=A ssd St.3(ID.2.1) ifssd(ID.2.1;St.4)<ppd.n(ID.2.1;A.n)≤ssd(ID.2.1;St.3) = cdm.n(ID.2.1;A.n)=0.6 If 0.60 < M[fnl(cdm.n)]≤ 0.80; csnt.n=H ssd St.4(ID.2.1) ifssd(ID.2.1;St.3)<ppd.n(ID.2.1;A.n)≤ssd(ID.2.1;St.2) = cdm.n(ID.2.1;A.n)=0.8 If 0.80 < M[fnl(cdm.n)]≤ 1.00; csnt.n=VH ssd St.5(ID.2.1) ifssd(ID.2.1;St.2)<ppd.n(ID.2.1;A.n)≤ssd(ID.2.1;St.1) = cdm.n(ID.2.1;A.n)=1 … …  … …   … … SD.3 ID.3.1 i(ID.3.1;An) ppd.n(ID.3.1;A.n) ssd St.1(ID.3.1) ifssd(ID.3.1;St.6)<ppd.n(ID.3.1;A.n)≤ssd(ID.3.1;St.5) = cdm.n(ID.3.1;A.n)=0.2 ssd St.2(ID.3.1) ifssd(ID.3.1;St.5)<ppd.n(ID.3.1;A.n)≤ssd(ID.3.1;St.4) = cdm.n(ID.3.1;A.n)=0.4 ssd St.3(ID.3.1) ifssd(ID.3.1;St.4)<ppd.n(ID.3.1;A.n)≤ssd(ID.3.1;St.3) = cdm.n(ID.3.1;A.n)=0.6 ssd St.4(ID.3.1) ifssd(ID.3.1;St.3)<ppd.n(ID.3.1;A.n)≤ssd(ID.3.1;St.2) = cdm.n(ID.3.1;A.n)=0.8 ssd St.5(ID.3.1) ifssd(ID.3.1;St.2)<ppd.n(ID.3.1;A.n)≤ssd(ID.3.1;St.1) = cdm.n(ID.3.1;A.n)=1 … …  … …   … … VL=very low; L=low; A=average; H= high; VH= very high. M= arithmetic mean.By adding the Weighted General Equipment classification JLCw values and the territorial attractiveness classificationcsnt.n(A.n) the range of numerical variables for the JLE “airport potential classification” is obtained. From the numerical variables (scores) obtained for JLE, Thresholds of Attractiveness(ssd.n) were defined through the UDA [40]. For this purpose, the 4 percentile values(St.1 = 1; St.2 = 0.6; St.3 = 0.3; St.4 = 0.0)in order to define 3 intervals of attraction (ClassesCl.n) for the evaluation of the alternatives (A.n)in JLE are defined. These percentile values were assumed as Thresholds of Attractiveness(ssd.n) and these were considered in building the value classification functionf(cdm.n) in order to obtain ClassesCl.n(A.n) for the JLE level.


## 3. Results and Discussion

### 3.1. JLA “Structural Classification”, JLB “Services Classification”, and JLC “Overall Classification of Resources”

Below, maps illustrate the results of the implementation for each level of data evaluation, in accordance with the MCSDA methodology described in previous paragraphs. Regarding the evaluation of the suitability of an airport’s infrastructure and services (status quo) (Tables1 and 2), the implementation of this evaluation methodology led to results that were the same as those obtained in previous phases of evaluation in regard to the JLA (Figure 6) but that differed when it came to JLB (Figure 7) and JLC (Figure 8) levels [21, 22]. Next, we describe the methods used to define (i) Weighted General Classification Equipment JLCw (Table 7); (ii) the Statistical Analysis for the definition of Thresholds of Attractiveness (ssd.n) (Table 8(a)); (iii) the way we formulated Thresholds of Attractiveness for each quantity obtained in JLCw (Table 8(b)).Table 7
MCSDA implementation for JLC: definition of the Weighted General Classification Equipment JLCw.
Minor airport (A.n) JLA JLB JLC JLD JLE Value Classification (cdm.n) Value Classification Scores Weight Weighted Structural Classification JLAw Value Classification (cdm.n) Value Classification Scores Weight Weighted Services Classification JLBw Weighted General Equipment Classification JLCw ClassesCl.n(A.n) Territorial Service Classification (csnt.n) Value Classification Scores Final Classification Scores ClassesCl.n(A.n) Alessandria NS 0.00 2 0 VL 0.00 1.00 0.00 0.00 1.00 H 0.75 0.75 0.00  Alzate Brianza NS 0.00 2 0 VL 0.00 1.00 0.00 0.00 1.00 H 0.75 0.75 0.00  Aquino T 0.50 2 1 VL 0.00 1.00 0.00 1.00 0.00 A 0.50 1.50 0.50  Arezzo NS 0.00 2 0 A 0.50 1.00 0.50 0.50 0.00 A 0.50 1.00 0.00  Asiago T 0.50 2 1 VL 0.00 1.00 0.00 1.00 0.00 L 0.25 1.25 0.00  Belluno T 0.50 2 1 VL 0.00 1.00 0.00 1.00 0.00 L 0.25 1.25 0.00  Biella Cerrione S 1.00 2 2 H 0.75 1.00 0.75 2.75 1.00 L 0.25 3.00 1.00  Calcinate del Pesce NS 0.00 2 0 L 0.25 1.00 0.25 0.25 0.00 H 0.75 1.00 0.00  Capua T 0.50 2 1 A 0.50 1.00 0.50 1.50 1.00 H 0.75 2.25 1.00  Carpi Budrione T 0.50 2 1 VL 0.00 1.00 0.00 1.00 0.00 A 0.50 1.50 0.50  Casale Monferrato T 0.50 2 1 VL 0.00 1.00 0.00 1.00 0.00 H 0.75 1.75 0.50  Como Idroscalo S 1.00 2 2 VL 0.00 1.00 0.00 2.00 1.00 H 0.75 2.75 1.00  Cremona Migliaro NS 0.00 2 0 VL 0.00 1.00 0.00 0.00 1.00 A 0.50 0.50 1.00  Fano T 0.50 2 1 A 0.50 1.00 0.50 1.50 1.00 A 0.50 2.00 0.50  Ferrara T 0.50 2 1 A 0.50 1.00 0.50 1.50 1.00 H 0.75 2.25 1.00  Ferrara Aguscello NS 0.00 2 0 L 0.25 1.00 0.25 0.25 0.00 H 0.75 1.00 0.00  Foligno T 0.50 2 1 L 0.25 1.00 0.25 1.25 0.50 H 0.75 2.00 0.50  Gorizia T 0.50 2 1 VL 0.00 1.00 0.00 1.00 0.00 L 0.25 1.25 0.00  Guidonia T 0.50 2 1 H 0.75 1.00 0.75 1.75 1.00 H 0.75 2.50 1.00  L'Aquila Preturo NS 0.00 2 0 VH 1.00 1.00 1.00 1.00 0.00 L 0.25 1.25 0.00  Latina S 1.00 2 2 H 0.75 1.00 0.75 2.75 1.00 A 0.50 3.25 1.00  Lecce Lepore NS 0.00 2 0 A 0.50 1.00 0.50 0.50 0.00 A 0.50 1.00 0.00  Legnago NS 0.00 2 0 VL 0.00 1.00 0.00 0.00 1.00 H 0.75 0.75 0.00  Lucca Tassignano S 1.00 2 2 L 0.25 1.00 0.25 2.25 1.00 A 0.50 2.75 1.00  Lugo di Romagna T 0.50 2 1 VL 0.00 1.00 0.00 1.00 0.00 A 0.50 1.50 0.50  Massa Cinquale NS 0.00 2 0 A 0.50 1.00 0.50 0.50 0.00 L 0.25 0.75 0.00  Milano Bresso T 0.50 2 1 VL 0.00 1.00 0.00 1.00 0.00 VH 1.00 2.00 0.50  Modena T 0.50 2 1 VL 0.00 1.00 0.00 1.00 0.00 H 0.75 1.75 0.50  Novi Ligure T 0.50 2 1 L 0.25 1.00 0.25 1.25 0.50 H 0.75 2.00 0.50  Oristano Fenosu S 1.00 2 2 H 0.75 1.00 0.75 2.75 1.00 A 0.50 3.25 1.00  Padova T 0.50 2 1 VH 1.00 1.00 1.00 2.00 1.00 VH 1.00 3.00 1.00  Palermo Boccadifalco S 1.00 2 2 A 0.50 1.00 0.50 2.50 1.00 H 0.75 3.25 1.00  Pavullo nel Frignano T 0.50 2 1 H 0.75 1.00 0.75 1.75 1.00 A 0.50 2.25 1.00  Ravenna T 0.50 2 1 L 0.25 1.00 0.25 1.25 0.50 H 0.75 2.00 0.50  Reggio Emilia T 0.50 2 1 A 0.50 1.00 0.50 1.50 1.00 H 0.75 2.25 1.00  Rieti T 0.50 2 1 L 0.25 1.00 0.25 1.25 0.50 L 0.25 1.50 0.50  Roma Urbe S 1.00 2 2 H 0.75 1.00 0.75 2.75 1.00 H 0.75 3.50 1.00  Salerno Pontecagnano NS 0.00 2 0 VH 1.00 1.00 1.00 1.00 0.00 H 0.75 1.75 0.50  Sarzana Luni NS 0.00 2 0 VH 1.00 1.00 1.00 1.00 0.00 A 0.50 1.50 0.50  Thiene T 0.50 2 1 L 0.25 1.00 0.25 1.25 0.50 A 0.50 1.75 0.50  Torino Aeritalia S 1.00 2 2 A 0.50 1.00 0.50 2.50 1.00 H 0.75 3.25 1.00  Trento Mattarello T 0.50 2 1 H 0.75 1.00 0.75 1.75 1.00 A 0.50 2.25 1.00  Udine Campoformido NS 0.00 2 0 L 0.25 1.00 0.25 0.25 0.00 H 0.75 1.00 0.00  Valbrembo NS 0.00 2 0 L 0.25 1.00 0.25 0.25 0.00 H 0.75 1.00 0.00  Varese Venegono T 0.50 2 1 VL 0.00 1.00 0.00 1.00 0.00 A 0.50 1.50 0.50  Venezia T 0.50 2 1 VH 1.00 1.00 1.00 2.00 1.00 H 0.75 2.75 1.00  Vercelli NS 0.00 2 0 L 0.25 1.00 0.25 0.25 0.00 A 0.50 0.75 0.00  Vergiate T 0.50 2 1 L 0.25 1.00 0.25 1.25 0.50 H 0.75 2.00 0.50  Verona Boscomantico T 0.50 2 1 A 0.50 1.00 0.50 1.50 1.00 H 0.75 2.25 1.00  Viterbo T 0.50 2 1 VL 0.00 1.00 0.00 1.00 0.00 A 0.50 1.50 0.50  Voghera Rivanazzano S 1.00 2 2 VH 1.00 1.00 1.00 3.00 1.00 H 0.75 3.75 1.00Table 8
MCSDA implementation for JLC: statistical analysis (a) and Thresholds of Attractiveness definition (b).
(a)
Statistical analysis
Percentile functions Values St.4 percentile (0.0) 0.00 St.3 percentile (0.3) 1.00 St.2 percentile (0.6) 1.25 St.1 percentile (1) 3.00  (b)
Attractiveness Thresholds (ssd.n)
Classes Cl.n(A.n) JLCw Thresholds (ssd.n) Cl.n(A.n) = 0 0.00>JLCw≥1.00  Cl.n(A.n) = 0.5 1.00>JLCw≥1.25  Cl.n(A.n) = 1 1.25>JLCw≥3.00Figure 6
MCSDA implementation for JLA: spatial mapping.Figure 7
MCSDA implementation for JLB: spatial mapping.Figure 8
MCSDA implementation for JLC: assigning of Classes Cl.n(A.n), spatial mapping.


### 3.2. JLD “Territorial Attractiveness Classification”

As regards the assessment of the attractiveness of airport hubs given the facilities available in their catchment areas (level JLD), the following are listed: the specific resource parametersi(ID.n)for each alternativeA.n (Table 9); the Statistical Analysis for the definition of Thresholds of Attractiveness (Table 10(a)); the building up of Thresholds of Attractiveness for eachID.nindicator (Table 10(b)); the results of the implementation, i.e., the concise classification for the JLD level (Table 11) and their spatial representation in maps (Figure 9).Table 9
MCSDA implementation for JLD: specific resource parameter i(ID.n).
NUTS 1: Geographical areas NUTS 2: Regions N° Minor airport NUTS 3: Provinces Specific resource parameter i(ID.n) for alternative (A.n) SD.1 Tourist Vocation ID.1 SD.2 Infrastructural endowment for transport SD.3 Productive Vocation ID.1.1 ID.1.2 ID.1.3 ID.1.4 ID.2.1 ID.2.2 ID.2.3 ID.2.4 ID.3.1 ID.3.2 North-West (ITC) Piemonte 1 Alessandria AL 10,517 2 13 25 14.72 56.56 1.392 214.4 2,125.00 71.4 2 Biella Cerrione BL 0 0 8 17 9.24 47.31 1.028 90.4 803.00 71.3 3 Casale Monferrato AL 10,517 2 12 20 16.47 62.17 1.392 214.4 2,125.00 71.4 4 Novi Ligure AL 10,517 2 1 59 11.02 51.19 1.392 214.4 2,125.00 71.4 5 Torino Aeritalia TO 1,784,722 12 15 70 8.92 49.51 229.000 596.1 13,383.00 71.4 6 Vercelli VC 0 0 7 12 13.22 53.97 0.651 51.4 884.00 70.3  North-West (ITC) Lombardia 7 Alzate Brianza CO 0 0 21 36 7.29 54.55 10.404 221.7 2,680.00 70.2 8 Como Idroscalo CO 0 0 30 30 7.46 52.76 10.404 221.7 2,680.00 70.2 9 Calcinate del Pesce VA 16,821 2 31 32 10.22 53.31 5.254 157.2 4,103.00 70.1 10 Cremona Migliaro CR 2,416 1 6 6 7.91 52.30 1.328 173.2 1,549.00 70.0 11 Milano Bresso MI 790,141 3 72 246 28.78 75.89 613.210 461.0 23,583.00 73.4 12 Valbrembo BG 0 0 20 60 23.20 51.57 17.987 158.9 5,195.00 66.6 13 Varese Venegono VA 16,821 2 22 28 7.97 45.31 5.254 157.2 4,103.00 70.1 14 Vergiate VA 16,821 2 12 51 9.01 54.36 5.254 157.2 4,103.00 70.1 15 Voghera Rivanazzano PV 37,420 3 12 16 16.62 53.70 6.872 158.7 2,731.00 71.1  North-West (ITC) Liguria 16 Sarzana Luni SP 16,307 3 19 19 4.10 8.09 13.250 303.3 1,325.00 70.3  North-West (ITC) Val D’Aosta Region without minor airports  North-East (ITD) Trentino Alto-Adige 17 Trento Mattarello TN 0 0 24 72 6.70 33.60 21.179 113.5 2,488.00 71.0  North-East (ITD) Veneto 18 Asiago VI 0 0 4 14 2.10 8.70 6.630 211.0 3,972.00 67.8 19 Belluno BL 8,069 1 5 9 2.14 8.09 2.376 55.3 708.00 72.7 20 Legnago VR 1,216 1 1 22 33.20 57.37 38.550 103.3 4,968.00 68.7 21 Padova PD 21,497 1 38 73 17.67 60.83 27.824 215.9 5,220.00 68.2 22 Thiene VI 0 0 6 53 2.90 52.12 6.630 211.0 3,972.00 67.8 23 Venezia VE 923,115 9 0 12 15.57 62.47 185.109 95.9 4,369.00 67.4 24 Verona Boscomantico VR 1,216 1 28 56 14.78 42.17 38.550 103.3 4,968.00 68.7  North-East (ITD) Emilia Romagna 25 Carpi Budrione MO 83,100 3 4 7 10.70 37.60 15.318 122.7 4,044.00 71.4 26 Ferrara Aguscello FE 129,319 5 23 26 12.74 35.65 8.901 29.1 1,690.00 74.6 27 Ferrara FE 129,319 5 25 26 15.73 34.53 8.901 29.1 1,690.00 74.6 28 Modena MO 83,100 3 17 19 9.78 36.23 15.318 122.7 4,044.00 71.4 29 Pavullo nel Frignano MO 83,100 3 0 1 3.14 7.39 15.318 122.7 4,044.00 71.4 30 Lugo di Romagna RA 354,265 8 0 0 4.29 10.69 6.729 38.4 1,906.00 73.1 31 Ravenna RA 354,265 8 16 34 17.75 55.83 6.729 38.4 1,906.00 73.1 32 Reggio Emilia RE 13,792 1 17 21 14.67 58.73 11.562 71.7 3,105.00 70.2  North-East (ITD) Friuli V Giulia 33 Gorizia GO 0 0 13 24 8.70 29.65 0.836 119.2 505.00 68.4 34 Udine Campoformido UD 155,279 4 16 22 16.74 61.34 11.805 282.8 2,171.00 68.1  Centre (ITE) Toscana 35 Arezzo AR 145,031 8 18 19 5.70 34.60 3.861 79.3 1,870.00 71.4 36 Lucca Tassignano LU 19,294 2 37 81 7.70 31.60 1.659 78.0 2,421.00 69.4 37 Massa Cinquale MS 0 0 20 34 2.10 7.09 0.910 213.1 1,310.00 68.8  Centre (ITE) Umbria 38 Foligno PG 194,061 10 30 91 7.54 48.23 24.700 83.9 3,301.00 71.4  Centre (ITE) Marche 39 Fano PU 397,365 3 4 23 13.73 32.43 2.673 153.7 1,882.00 67.9  Centre (ITE) Lazio 40 Aquino FR 1,146,403 5 5 6 2.50 8.04 0.853 202.8 1,690.00 74.6 41 Guidonia RM 18,156,284 55 9 169 11.53 36.38 1199.402 190.1 30,238.00 68.9 42 Latina LT 124,023 5 3 9 14.50 42.38 0.887 76.7 3,493.00 61.3 43 Rieti RI 0 0 2 5 4.80 6.09 2.665 104.1 770.00 61.6 44 Roma Urbe RM 18,156,284 55 2 278 2.80 6.87 1199.402 190.1 30,238.00 68.9 45 Viterbo VT 502,457 19 6 11 10.54 32.68 2.022 46.5 1,703.00 65.3  South (ITF) Abruzzo 46 L’Aquila Preturo AQ 92,335 9 5 7 3.80 7.76 3.451 56.3 1,476.00 63.3 Molise Region without minor airports Campania 47 Capua CE 563,676 10 6 17 14.75 50.33 1.500 284.8 5,641.00 45.5 48 Salerno Pontecagnano SA 435,790 8 10 22 15.55 35.98 5.450 325.8 7,150.00 54.2 Puglia 49 Lecce Lepore LE 9,145 1 9 27 3.50 8.74 1.400 80.1 4,897.00 55.3 Basilicata Region without minor airports Calabria Region without minor airports  Islands (ITG) Sicilia 50 Palermo Boccadifalco PA 494,006 17 8 39 12.35 33.58 24.500 212.3 5,339.00 50.2 Sardegna 51 Oristano Fenosu OR 123,575 3 10 13 4.30 7.09 0.150 121.8 512.00 63.2Table 10
MCSDA implementation for JLD: statistical analysis for the definition of Thresholds of Attractiveness (a) and definition of the Thresholds of Attractiveness for each Indicator (b).
(a)
Statistical analysis for the definition of Thresholds of Attractiveness (ssd.n)
SD.1 Tourist Vocation ID.1 SD.2 Infrastructural endowment for transport SD.3 Productive Vocation ID.1.1 ID.1.2 ID.1.3 ID.1.4 ID.2.1 ID.2.2 ID.2.3 ID.2.4 ID.3.1 ID.3.2 St.6 percentile (0.0) 0.00 0.00 0.00 0.00 2.10 6.09 0.15 29.12 505.00 45.46 St.5 percentile (0.2) 0.00 0.00 4.00 12.00 4.29 8.74 1.39 78.04 1690.00 67.44 St.4 percentile (0.4) 16615.40 2.00 8.00 19.60 7.95 35.23 5.25 116.94 2152.60 68.86 St.3 percentile (0.6) 117327.00 3.00 15.80 27.80 11.43 49.25 10.10 158.85 3972.00 70.22 St.2 percentile (0.8) 420420.00 8.00 22.60 57.80 15.24 53.86 23.17 213.92 4968.00 71.41 St.1 percentile (1) 18156284.00 55.00 72.00 278.00 33.20 75.89 1199.40 596.08 30238.00 74.59  (b)
Definition of Thresholds of Attractivity (ssd.n) for Indicators ID.n
SD.1Tourist Vocation ID.1 ID.1.1 ID.1.2 ID.1.3 ID.1.4 cdm.n = 0.2 0.00>ID.1.1≥243.20 0.00>ID.1.2≥0.20 0.00>ID.1.3≥4.20 0.00>ID.1.4≥11.20 cdm.n = 0.4 243.20>ID.1.1≥16.821.00 0.20>ID.1.2≥2.00 4.20>ID.1.3≥8.00 11.20>ID.1.4≥19.00 cdm.n = 0.6 16.821.00>ID.1.1≥104.831.00 2.00>ID.1.2≥3.00 8.00>ID.1.3≥15.40 19.00>ID.1.4≥27.00 cdm.n = 0.8 104.831.00>ID.1.1≥ 412.735.00 3.00>ID.1.2≥8.00 15.40>ID.1.3≥22.40 27.00>ID.1.4≥57.20 cdm.n = 1 412.735.00>ID.1.1≥18.156.284.00 8.00>ID.1.2≥55.00 22.40>ID.1.3≥72 57.00>ID.1.4≥278.00  SD.2Infrastructural endowment for transport ID.2.1 ID.2.2 ID.2.3 ID.2.4  cdm.n = 0.2 2.1>ID.2.1≥4.29 6.09>ID.2.2≥9.13 0.15>ID.2.3≥1.39 22.71>ID.2.4≥77.01 cdm.n = 0.4 4.29>ID.2.1≥7.97 9.13>ID.2.2≥34.60 1.39>ID.2.3≥5.25 77.01>ID.2.4≥113.51 cdm.n = 0.6 7.97>ID.2.1≥11.22 34.60>ID.2.2≥48.74 5.25>ID.2.3≥9.50 113.51>ID.2.4≥158.80 cdm.n = 0.8 11.22>ID.2.1≥15.09 48.74>ID.2.2≥53.81 9.50>ID.2.3≥22.51 158.80>ID.2.4≥213.66 cdm.n = 1 15.09>ID.2.1≥33.00 53.81>ID.2.2≥76.00 22.51>ID.2.3≥1.199.00 213.66>ID.2.4≥596.00  SD.3Productive Vocation ID.3.1 ID.3.2  cdm.n = 0.2 505.00>ID.3.1≥1.690.00 45.46>ID.3.2≥66.77 cdm.n = 0.4 1690.00>ID.3.1≥2.125.00 66.77>ID.3.2≥68.76 cdm.n = 0.6 2.125.00>ID.3.1≥3.972.00 68.76>ID.3.2≥70.22 cdm.n = 0.8 3.972.00>ID.3.1≥4.968.00 70.22>ID.3.2≥71.41 cdm.n = 1 4.968.00>ID.3.1≥30.238.00 71.41>ID.3.2≥74.59Table 11
MCSD implementation for JLD: concise classification.
NUTS 1:  Geographical areas NUTS 2:  Regions N° Minor airport NUTS 3:  Provinces Value classification and related score (cdm.n) Mean  (M) Concise classification SD.1 Tourist Vocation ID.1 SD.2 Infrastructural endowment for transport SD.3 Productive Vocation ID.1.1 ID.1.2 ID.1.3 ID.1.4 ID.2.1 ID.2.2 ID.2.3 ID.2.4 ID.3.1 ID.3.2 JLD (csnt.n) North-West (ITC) Piemonte 1 Alessandria AL 0.40 0.40 0.60 0.60 0.80 1.00 0.40 1.00 0.40 1.00 0.66 H 2 Biella Cerrione BL 0.20 0.20 0.40 0.40 0.60 0.60 0.20 0.40 0.20 0.80 0.40 L 3 Casale Monferrato AL 0.40 0.40 0.60 0.60 1.00 1.00 0.40 1.00 0.40 1.00 0.68 H 4 Novi Ligure AL 0.40 0.40 0.20 1.00 0.60 0.80 0.40 1.00 0.40 1.00 0.62 H 5 Torino Aeritalia TO 1.00 1.00 0.60 1.00 0.60 0.80 1.00 0.20 1.00 0.80 0.80 H 6 Vercelli VC 0.20 0.20 0.40 0.40 0.80 1.00 0.20 0.20 0.20 0.80 0.44 A  North-West (ITC) Lombardia 7 Alzate Brianza CO 0.20 0.20 0.80 0.80 0.40 1.00 0.80 1.00 0.60 0.60 0.64 H 8 Como Idroscalo CO 0.20 0.20 1.00 0.80 0.40 0.80 0.80 1.00 0.60 0.60 0.64 H 9 Calcinate del Pesce VA 0.40 0.40 1.00 0.80 0.60 0.80 0.60 0.60 0.80 0.60 0.66 H 10 Cremona Migliaro CR 0.40 0.40 0.40 0.20 0.40 0.80 0.20 0.80 0.20 0.60 0.44 A 11 Milano Bresso MI 1.00 0.60 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.96 VH 12 Valbrembo BG 0.20 0.20 0.80 1.00 1.00 0.80 0.80 0.80 1.00 0.20 0.68 H 13 Varese Venegono VA 0.40 0.40 0.80 0.80 0.40 0.60 0.60 0.60 0.80 0.60 0.60 A 14 Vergiate VA 0.40 0.40 0.60 0.80 0.60 1.00 0.60 0.60 0.80 0.60 0.64 H 15 Voghera Rivanazzano PV 0.60 0.60 0.60 0.40 1.00 0.80 0.60 0.60 0.60 0.80 0.66 H  North-West (ITC) Liguria 16 Sarzana Luni SP 0.40 0.60 0.80 0.40 0.20 0.20 0.80 1.00 0.20 0.80 0.54 A  North-West (ITC) Val D’Aosta Region without minor airports  North-East (ITD) Trentino Alto-Adige 17 Trento Mattarello TN 0.20 0.20 1.00 1.00 0.40 0.40 0.80 0.40 0.60 0.80 0.58 A  North-East (ITD) Veneto 18 Asiago VI 0.20 0.20 0.20 0.40 0.20 0.20 0.60 0.80 0.60 0.40 0.38 L 19 Belluno BL 0.40 0.40 0.40 0.20 0.20 0.20 0.40 0.20 0.20 1.00 0.36 L 20 Legnago VR 0.40 0.40 0.20 0.60 1.00 1.00 1.00 0.40 0.80 0.40 0.62 H 21 Padova PD 0.60 0.40 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.40 0.84 VH 22 Thiene VI 0.20 0.20 0.40 0.80 0.20 0.80 0.60 0.80 0.60 0.40 0.50 A 23 Venezia VE 1.00 1.00 0.20 0.40 1.00 1.00 1.00 0.40 0.80 0.40 0.72 H 24 Verona Boscomantico VR 0.40 0.40 1.00 0.80 0.80 0.60 1.00 0.40 0.80 0.40 0.66 H  North-East (ITD) Emilia Romagna 25 Carpi Budrione MO 0.60 0.60 0.20 0.20 0.60 0.60 0.80 0.60 0.80 0.80 0.58 A 26 Ferrara Aguscello FE 0.80 0.80 1.00 0.60 0.80 0.60 0.60 0.20 0.20 1.00 0.66 H 27 Ferrara FE 0.80 0.80 1.00 0.60 1.00 0.40 0.60 0.20 0.20 1.00 0.66 H 28 Modena MO 0.60 0.60 0.80 0.40 0.60 0.60 0.80 0.60 0.80 0.80 0.66 H 29 Pavullo nel Frignano MO 0.60 0.60 0.20 0.20 0.20 0.20 0.80 0.60 0.80 0.80 0.50 A 30 Lugo di Romagna RA 0.80 0.80 0.20 0.20 0.20 0.40 0.60 0.20 0.40 1.00 0.48 A 31 Ravenna RA 0.80 0.80 0.80 0.80 1.00 1.00 0.60 0.20 0.40 1.00 0.74 H 32 Reggio Emilia RE 0.40 0.40 0.80 0.60 0.80 1.00 0.80 0.20 0.60 0.60 0.62 H  North-East (ITD) Friuli V Giulia 33 Gorizia GO 0.20 0.20 0.60 0.60 0.60 0.40 0.20 0.60 0.20 0.40 0.40 L 34 Udine Campoformido UD 0.80 0.80 0.80 0.60 1.00 1.00 0.80 1.00 0.60 0.40 0.78 H  Centre (ITE) Toscana 35 Arezzo AR 0.80 0.80 0.80 0.40 0.40 0.40 0.40 0.40 0.40 1.00 0.58 A 36 Lucca Tassignano LU 0.60 0.40 1.00 1.00 0.40 0.40 0.40 0.40 0.60 0.60 0.58 A 37 Massa Cinquale MS 0.20 0.20 0.80 0.80 0.20 0.20 0.20 0.80 0.20 0.40 0.40 L  Centre (ITE) Umbria 38 Foligno PG 0.80 1.00 1.00 1.00 0.40 0.60 1.00 0.40 0.60 1.00 0.78 H  Centre (ITE) Marche 39 Fano PU 0.80 0.60 0.20 0.60 0.80 0.40 0.40 0.60 0.40 0.40 0.52 A  Centre (ITE) Lazio 40 Aquino FR 1.00 0.80 0.40 0.20 0.20 0.20 0.20 0.80 0.20 1.00 0.50 A 41 Guidonia RM 1.00 1.00 0.60 1.00 0.80 0.60 0.20 0.80 1.00 0.60 0.76 H 42 Latina LT 0.80 0.80 0.20 0.20 0.80 0.60 0.20 0.20 0.60 0.20 0.46 A 43 Rieti RI 0.20 0.20 0.20 0.20 0.40 0.20 0.40 0.40 0.20 0.20 0.26 L 44 Roma Urbe RM 1.00 1.00 0.20 1.00 0.20 0.20 0.20 0.80 1.00 0.60 0.62 H 45 Viterbo VT 1.00 1.00 0.40 0.20 0.60 0.40 0.40 0.20 0.40 0.20 0.48 A  South (ITF) Abruzzo 46 L'Aquila Preturo AQ 0.60 1.00 0.40 0.20 0.20 0.20 0.40 0.20 0.20 0.20 0.36 L Molise Region without minor airports Campania 47 Capua CE 1.00 1.00 0.40 0.40 0.80 0.80 0.40 1.00 1.00 0.20 0.70 H 48 Salerno Pontecagnano SA 1.00 0.80 0.60 0.60 1.00 0.60 0.60 1.00 1.00 0.20 0.74 H Puglia 49 Lecce Lepore LE 0.40 0.40 0.60 0.60 0.20 0.20 0.40 0.40 0.80 0.20 0.42 A Basilicata Region without minor airports Calabria Region without minor airports  Islands (ITG) Sicilia 50 Palermo Boccadifalco PA 1.00 1.00 0.40 0.80 0.80 0.40 1.00 0.80 1.00 0.20 0.74 H Sardegna 51 Oristano Fenosu OR 0.80 0.60 0.60 0.40 0.40 0.20 0.20 0.60 0.20 0.20 0.42 A    M = Arithmetic Mean If 0 < M≤ 0.20; csnt.n= VL If 0.20 < M≤ 0.40; csnt.n= L If 0.40 < M≤ 0.60; csnt.n= A If 0.60 < M≤ 0.80; csnt.n= H If 0.80 < M≤ 1; csnt.n= VHFigure 9
MCSDA implementation for JLD: spatial mapping.


### 3.3. JLE “Airport Potential Classification”

The implementation of the MCSDA for JLE provides us with an airport potential Classification Score for each alternativeA.n (Table 12). Next, the following are listed: the Statistical Analysis for the definition of Thresholds of Attractiveness(ssd.n) (Table 13(a)); the building up of Thresholds of Attractiveness for each score obtained in the JLE level (Table 13(b)); the results of the implementation (the attribution ofCl.n(A.n) classes for the JLE level) represented using maps (Figure 10).Table 12
MCSDA implementation for JLE: definition of the airport potential classification scores.
NUTS 1:  Geographical areas NUTS 2:  Regions N° Minor airport NUTS 3:  Provinces JLC JLD JLE Weighted General Equipment Classification JLCw ClassesCl.n(A.n) Territorial Service Classification (csnt.n) Value Classification Scores Final Classification Scores ClassesCl.n(A.n) North-West (ITC) Piemonte 1 Alessandria AL 0.00 1.00 H 0.75 0.75 0.00 2 Biella Cerrione BL 2.75 1.00 L 0.25 3.00 1.00 3 Casale Monferrato AL 1.00 0.00 H 0.75 1.75 0.50 4 Novi Ligure AL 1.25 0.50 H 0.75 2.00 0.50 5 Torino Aeritalia TO 2.50 1.00 H 0.75 3.25 1.00 6 Vercelli VC 0.25 0.00 A 0.50 0.75 0.00  North-West (ITC) Lombardia 7 Alzate Brianza CO 0.00 1.00 H 0.75 0.75 0.00 8 Como Idroscalo CO 2.00 1.00 H 0.75 2.75 1.00 9 Calcinate del Pesce VA 0.25 0.00 H 0.75 1.00 0.00 10 Cremona Migliaro CR 0.00 1.00 A 0.50 0.50 1.00 11 Milano Bresso MI 1.00 0.00 VH 1.00 2.00 0.50 12 Valbrembo BG 0.25 0.00 H 0.75 1.00 0.00 13 Varese Venegono VA 1.00 0.00 A 0.50 1.50 0.50 14 Vergiate VA 1.25 0.50 H 0.75 2.00 0.50 15 Voghera Rivanazzano PV 3.00 1.00 H 0.75 3.75 1.00  North-West (ITC) Liguria 16 Sarzana Luni SP 1.00 0.00 A 0.50 1.50 0.50  North-West (ITC) Val D’Aosta Region without minor airports  North-East (ITD) Trentino Alto-Adige 17 Trento Mattarello TN 1.75 1.00 A 0.50 2.25 1.00  North-East (ITD) Veneto 18 Asiago VI 1.00 0.00 L 0.25 1.25 0.00 19 Belluno BL 1.00 0.00 L 0.25 1.25 0.00 20 Legnago VR 0.00 1.00 H 0.75 0.75 0.00 21 Padova PD 2.00 1.00 VH 1.00 3.00 1.00 22 Thiene VI 1.25 0.50 A 0.50 1.75 0.50 23 Venezia VE 2.00 1.00 H 0.75 2.75 1.00 24 Verona Boscomantico VR 1.50 1.00 H 0.75 2.25 1.00  North-East (ITD) Emilia Romagna 25 Carpi Budrione MO 1.00 0.00 A 0.50 1.50 0.50 26 Ferrara Aguscello FE 0.25 0.00 H 0.75 1.00 0.00 27 Ferrara FE 1.50 1.00 H 0.75 2.25 1.00 28 Modena MO 1.00 0.00 H 0.75 1.75 0.50 29 Pavullo nel Frignano MO 1.75 1.00 A 0.50 2.25 1.00 30 Lugo di Romagna RA 1.00 0.00 A 0.50 1.50 0.50 31 Ravenna RA 1.25 0.50 H 0.75 2.00 0.50 32 Reggio Emilia RE 1.50 1.00 H 0.75 2.25 1.00  North-East (ITD) Friuli V Giulia 33 Gorizia GO 1.00 0.00 L 0.25 1.25 0.00 34 Udine Campoformido UD 0.25 0.00 H 0.75 1.00 0.00  Centre (ITE) Toscana 35 Arezzo AR 0.50 0.00 A 0.50 1.00 0.00 36 Lucca Tassignano LU 2.25 1.00 A 0.50 2.75 1.00 37 Massa Cinquale MS 0.50 0.00 L 0.25 0.75 0.00  Centre (ITE) Umbria 38 Foligno PG 1.25 0.50 H 0.75 2.00 0.50  Centre (ITE) Marche 39 Fano PU 1.50 1.00 A 0.50 2.00 0.50  Centre (ITE) Lazio 40 Aquino FR 1.00 0.00 A 0.50 1.50 0.50 41 Guidonia RM 1.75 1.00 H 0.75 2.50 1.00 42 Latina LT 2.75 1.00 A 0.50 3.25 1.00 43 Rieti RI 1.25 0.50 L 0.25 1.50 0.50 44 Roma Urbe RM 2.75 1.00 H 0.75 3.50 1.00 45 Viterbo VT 1.00 0.00 A 0.50 1.50 0.50  South (ITF) Abruzzo 46 L’Aquila Preturo AQ 1.00 0.00 L 0.25 1.25 0.00 Molise Region without minor airports Campania 47 Capua CE 1.50 1.00 H 0.75 2.25 1.00 48 Salerno Pontecagnano SA 1.00 0.00 H 0.75 1.75 0.50 Puglia 49 Lecce Lepore LE 0.50 0.00 A 0.50 1.00 0.00 Basilicata Region without minor airports Calabria Region without minor airports  Islands (ITG) Sicilia 50 Palermo Boccadifalco PA 2.50 1.00 H 0.75 3.25 1.00 Sardegna 51 Oristano Fenosu OR 2.75 1.00 A 0.50 3.25 1.00Table 13
MCSDA implementation for JLE: statistical analysis (a) and Thresholds of Attractiveness construction (b).
(a)
Statistical Analysis
Percentile functions Values St.4 percentile (0.0) 0.50 St.3 percentile (0.3) 1.25 St.2 percentile (0.6) 2.00 St.1 percentile (1) 3.75  (b)
Attractiveness Thresholds (ssd.n)
Classes Cl.n(A.n) JLEw Thresholds (ssd.n) Cl.n(A.n) = 0 0.50 >JLE_Scores≥ 1.25  Cl.n(A.n) = 0.5 1.25>JLE_Scores≥2.00  Cl.n(A.n) = 1 2.00>JLE_Scores≥3.75Figure 10
MCSDA implementation for JLE: spatial mapping.


### 3.4. Summary and Discussion

The analysis of the output obtained (Figure11) allows us to identify the number and percentage of minor airports compared to the total that fall within each class of assessment (Cl.n) for each Judgment Level:(i)
In JLC “overall classification of resources”, the summarizing level for assessing the suitability (status quo) of an airport’s services and infrastructure (paragraph 1, point 1) pertains to classes the three ClassesCl.C.1, Cl.C.2,andCl.C.3, related to Judgment Level C, representing the level of facilities (from lowest to highest) of overall resources: 22 (43%), 6 (12%), and 23 (45%), respectively, of second-level hubs.(ii)
In JLE “airport potential classification”, the summarizing level for assessing the attractiveness of airport hubs compared to the facilities found in their catchment areas (paragraph 1, point 2) pertains to the ClassesCl.E.1, Cl.E.2,andCl.E.3, related to Judgment Level E, representing the levels (from lowest to highest) of scores obtained in previous Judgment Levels: 15 (30%), 17 (33%), and 19 (37%), respectively.Figure 11
Analysis of output of evaluation for alternatives A.n for JLC, “overall classification of resources”, and JLE, “airport potential classification”.Such data describes the status quo and the potential for developing the existing infrastructural assets of our minor airports through upgrading and/or expansion work for the choice of hubs worth including in the “highway in the sky” network. Further integrated analysis, such as Fuzzy Analysis, Strategic Planning Tools (SPT), Participation Techniques (PT), and Financial and Economic Analysis [41–47], could be useful for achieving the aim of the development of the Italian Network. At this stage of the research, such work can be defined as follows: (i) of greater scope forA.n falling within theCl.1 classes both for the JLC level and for the JLE level; (ii) of average scope forA.n falling within theCl.2 classes both for the JLC level and for the JLE level; (iii) of minor scope or nil forA.n that fall withinCl.3 classes both for the JLC and for JLE levels.The heatmap of minor airports on national territory (Figure12) was built up using the Qgis software (by the space query heatmap) considering each alternativeA.n according to JLE “airport potential classification” three classes obtained asCl.E.1, Cl.E.2,andCl.E.3related to Judgment Level E “airport potential classification”. The three classes (Cl.E.1, Cl.E.2,andCl.E.3) show the coverage of the areas on national territory by minor airports in three levels: (i) max coverage: (ii) medium coverage; (iv) low coverage or no coverage. It is remarkable that minor airports offer the maximum coverage of northern areas of Italy while they offer a low coverage (or no coverage) of areas in the Adriatic Coast, in the Southern areas, and in the island. In the northern areas the maximum coverage is characterized by the presence of minor airports pertaining to the ClassesCl.E.1, Cl.E.2,andCl.E.3.This is to be considered in successive assessments phases that could allow understanding whichA.n could be added in the network (called “highway in the sky”) and which ones have to be excluded. In the Adriatic Coast and in the southern areas in particular the low coverage of the areas is characterized by minor airports pertaining toCl.E.1andCl.E.2representing the lowest classes in JLE final classification. The map in Figure 13 shows the overlap degree for maximum covered northern areas highlighting the necessity to better explore the issue of the alternative choice among minor airports.Figure 12
Definition of coverage of the areas by minor airports throughout the country: heatmap.Figure 13
Definition of the coverage of the areas by minor airports through the country: buffering.


## 3.1. JLA “Structural Classification”, JLB “Services Classification”, and JLC “Overall Classification of Resources”

Below, maps illustrate the results of the implementation for each level of data evaluation, in accordance with the MCSDA methodology described in previous paragraphs. Regarding the evaluation of the suitability of an airport’s infrastructure and services (status quo) (Tables1 and 2), the implementation of this evaluation methodology led to results that were the same as those obtained in previous phases of evaluation in regard to the JLA (Figure 6) but that differed when it came to JLB (Figure 7) and JLC (Figure 8) levels [21, 22]. Next, we describe the methods used to define (i) Weighted General Classification Equipment JLCw (Table 7); (ii) the Statistical Analysis for the definition of Thresholds of Attractiveness (ssd.n) (Table 8(a)); (iii) the way we formulated Thresholds of Attractiveness for each quantity obtained in JLCw (Table 8(b)).Table 7
MCSDA implementation for JLC: definition of the Weighted General Classification Equipment JLCw.
Minor airport (A.n) JLA JLB JLC JLD JLE Value Classification (cdm.n) Value Classification Scores Weight Weighted Structural Classification JLAw Value Classification (cdm.n) Value Classification Scores Weight Weighted Services Classification JLBw Weighted General Equipment Classification JLCw ClassesCl.n(A.n) Territorial Service Classification (csnt.n) Value Classification Scores Final Classification Scores ClassesCl.n(A.n) Alessandria NS 0.00 2 0 VL 0.00 1.00 0.00 0.00 1.00 H 0.75 0.75 0.00  Alzate Brianza NS 0.00 2 0 VL 0.00 1.00 0.00 0.00 1.00 H 0.75 0.75 0.00  Aquino T 0.50 2 1 VL 0.00 1.00 0.00 1.00 0.00 A 0.50 1.50 0.50  Arezzo NS 0.00 2 0 A 0.50 1.00 0.50 0.50 0.00 A 0.50 1.00 0.00  Asiago T 0.50 2 1 VL 0.00 1.00 0.00 1.00 0.00 L 0.25 1.25 0.00  Belluno T 0.50 2 1 VL 0.00 1.00 0.00 1.00 0.00 L 0.25 1.25 0.00  Biella Cerrione S 1.00 2 2 H 0.75 1.00 0.75 2.75 1.00 L 0.25 3.00 1.00  Calcinate del Pesce NS 0.00 2 0 L 0.25 1.00 0.25 0.25 0.00 H 0.75 1.00 0.00  Capua T 0.50 2 1 A 0.50 1.00 0.50 1.50 1.00 H 0.75 2.25 1.00  Carpi Budrione T 0.50 2 1 VL 0.00 1.00 0.00 1.00 0.00 A 0.50 1.50 0.50  Casale Monferrato T 0.50 2 1 VL 0.00 1.00 0.00 1.00 0.00 H 0.75 1.75 0.50  Como Idroscalo S 1.00 2 2 VL 0.00 1.00 0.00 2.00 1.00 H 0.75 2.75 1.00  Cremona Migliaro NS 0.00 2 0 VL 0.00 1.00 0.00 0.00 1.00 A 0.50 0.50 1.00  Fano T 0.50 2 1 A 0.50 1.00 0.50 1.50 1.00 A 0.50 2.00 0.50  Ferrara T 0.50 2 1 A 0.50 1.00 0.50 1.50 1.00 H 0.75 2.25 1.00  Ferrara Aguscello NS 0.00 2 0 L 0.25 1.00 0.25 0.25 0.00 H 0.75 1.00 0.00  Foligno T 0.50 2 1 L 0.25 1.00 0.25 1.25 0.50 H 0.75 2.00 0.50  Gorizia T 0.50 2 1 VL 0.00 1.00 0.00 1.00 0.00 L 0.25 1.25 0.00  Guidonia T 0.50 2 1 H 0.75 1.00 0.75 1.75 1.00 H 0.75 2.50 1.00  L'Aquila Preturo NS 0.00 2 0 VH 1.00 1.00 1.00 1.00 0.00 L 0.25 1.25 0.00  Latina S 1.00 2 2 H 0.75 1.00 0.75 2.75 1.00 A 0.50 3.25 1.00  Lecce Lepore NS 0.00 2 0 A 0.50 1.00 0.50 0.50 0.00 A 0.50 1.00 0.00  Legnago NS 0.00 2 0 VL 0.00 1.00 0.00 0.00 1.00 H 0.75 0.75 0.00  Lucca Tassignano S 1.00 2 2 L 0.25 1.00 0.25 2.25 1.00 A 0.50 2.75 1.00  Lugo di Romagna T 0.50 2 1 VL 0.00 1.00 0.00 1.00 0.00 A 0.50 1.50 0.50  Massa Cinquale NS 0.00 2 0 A 0.50 1.00 0.50 0.50 0.00 L 0.25 0.75 0.00  Milano Bresso T 0.50 2 1 VL 0.00 1.00 0.00 1.00 0.00 VH 1.00 2.00 0.50  Modena T 0.50 2 1 VL 0.00 1.00 0.00 1.00 0.00 H 0.75 1.75 0.50  Novi Ligure T 0.50 2 1 L 0.25 1.00 0.25 1.25 0.50 H 0.75 2.00 0.50  Oristano Fenosu S 1.00 2 2 H 0.75 1.00 0.75 2.75 1.00 A 0.50 3.25 1.00  Padova T 0.50 2 1 VH 1.00 1.00 1.00 2.00 1.00 VH 1.00 3.00 1.00  Palermo Boccadifalco S 1.00 2 2 A 0.50 1.00 0.50 2.50 1.00 H 0.75 3.25 1.00  Pavullo nel Frignano T 0.50 2 1 H 0.75 1.00 0.75 1.75 1.00 A 0.50 2.25 1.00  Ravenna T 0.50 2 1 L 0.25 1.00 0.25 1.25 0.50 H 0.75 2.00 0.50  Reggio Emilia T 0.50 2 1 A 0.50 1.00 0.50 1.50 1.00 H 0.75 2.25 1.00  Rieti T 0.50 2 1 L 0.25 1.00 0.25 1.25 0.50 L 0.25 1.50 0.50  Roma Urbe S 1.00 2 2 H 0.75 1.00 0.75 2.75 1.00 H 0.75 3.50 1.00  Salerno Pontecagnano NS 0.00 2 0 VH 1.00 1.00 1.00 1.00 0.00 H 0.75 1.75 0.50  Sarzana Luni NS 0.00 2 0 VH 1.00 1.00 1.00 1.00 0.00 A 0.50 1.50 0.50  Thiene T 0.50 2 1 L 0.25 1.00 0.25 1.25 0.50 A 0.50 1.75 0.50  Torino Aeritalia S 1.00 2 2 A 0.50 1.00 0.50 2.50 1.00 H 0.75 3.25 1.00  Trento Mattarello T 0.50 2 1 H 0.75 1.00 0.75 1.75 1.00 A 0.50 2.25 1.00  Udine Campoformido NS 0.00 2 0 L 0.25 1.00 0.25 0.25 0.00 H 0.75 1.00 0.00  Valbrembo NS 0.00 2 0 L 0.25 1.00 0.25 0.25 0.00 H 0.75 1.00 0.00  Varese Venegono T 0.50 2 1 VL 0.00 1.00 0.00 1.00 0.00 A 0.50 1.50 0.50  Venezia T 0.50 2 1 VH 1.00 1.00 1.00 2.00 1.00 H 0.75 2.75 1.00  Vercelli NS 0.00 2 0 L 0.25 1.00 0.25 0.25 0.00 A 0.50 0.75 0.00  Vergiate T 0.50 2 1 L 0.25 1.00 0.25 1.25 0.50 H 0.75 2.00 0.50  Verona Boscomantico T 0.50 2 1 A 0.50 1.00 0.50 1.50 1.00 H 0.75 2.25 1.00  Viterbo T 0.50 2 1 VL 0.00 1.00 0.00 1.00 0.00 A 0.50 1.50 0.50  Voghera Rivanazzano S 1.00 2 2 VH 1.00 1.00 1.00 3.00 1.00 H 0.75 3.75 1.00Table 8
MCSDA implementation for JLC: statistical analysis (a) and Thresholds of Attractiveness definition (b).
(a)
Statistical analysis
Percentile functions Values St.4 percentile (0.0) 0.00 St.3 percentile (0.3) 1.00 St.2 percentile (0.6) 1.25 St.1 percentile (1) 3.00  (b)
Attractiveness Thresholds (ssd.n)
Classes Cl.n(A.n) JLCw Thresholds (ssd.n) Cl.n(A.n) = 0 0.00>JLCw≥1.00  Cl.n(A.n) = 0.5 1.00>JLCw≥1.25  Cl.n(A.n) = 1 1.25>JLCw≥3.00Figure 6
MCSDA implementation for JLA: spatial mapping.Figure 7
MCSDA implementation for JLB: spatial mapping.Figure 8
MCSDA implementation for JLC: assigning of Classes Cl.n(A.n), spatial mapping.


## 3.2. JLD “Territorial Attractiveness Classification”

As regards the assessment of the attractiveness of airport hubs given the facilities available in their catchment areas (level JLD), the following are listed: the specific resource parametersi(ID.n)for each alternativeA.n (Table 9); the Statistical Analysis for the definition of Thresholds of Attractiveness (Table 10(a)); the building up of Thresholds of Attractiveness for eachID.nindicator (Table 10(b)); the results of the implementation, i.e., the concise classification for the JLD level (Table 11) and their spatial representation in maps (Figure 9).Table 9
MCSDA implementation for JLD: specific resource parameter i(ID.n).
NUTS 1: Geographical areas NUTS 2: Regions N° Minor airport NUTS 3: Provinces Specific resource parameter i(ID.n) for alternative (A.n) SD.1 Tourist Vocation ID.1 SD.2 Infrastructural endowment for transport SD.3 Productive Vocation ID.1.1 ID.1.2 ID.1.3 ID.1.4 ID.2.1 ID.2.2 ID.2.3 ID.2.4 ID.3.1 ID.3.2 North-West (ITC) Piemonte 1 Alessandria AL 10,517 2 13 25 14.72 56.56 1.392 214.4 2,125.00 71.4 2 Biella Cerrione BL 0 0 8 17 9.24 47.31 1.028 90.4 803.00 71.3 3 Casale Monferrato AL 10,517 2 12 20 16.47 62.17 1.392 214.4 2,125.00 71.4 4 Novi Ligure AL 10,517 2 1 59 11.02 51.19 1.392 214.4 2,125.00 71.4 5 Torino Aeritalia TO 1,784,722 12 15 70 8.92 49.51 229.000 596.1 13,383.00 71.4 6 Vercelli VC 0 0 7 12 13.22 53.97 0.651 51.4 884.00 70.3  North-West (ITC) Lombardia 7 Alzate Brianza CO 0 0 21 36 7.29 54.55 10.404 221.7 2,680.00 70.2 8 Como Idroscalo CO 0 0 30 30 7.46 52.76 10.404 221.7 2,680.00 70.2 9 Calcinate del Pesce VA 16,821 2 31 32 10.22 53.31 5.254 157.2 4,103.00 70.1 10 Cremona Migliaro CR 2,416 1 6 6 7.91 52.30 1.328 173.2 1,549.00 70.0 11 Milano Bresso MI 790,141 3 72 246 28.78 75.89 613.210 461.0 23,583.00 73.4 12 Valbrembo BG 0 0 20 60 23.20 51.57 17.987 158.9 5,195.00 66.6 13 Varese Venegono VA 16,821 2 22 28 7.97 45.31 5.254 157.2 4,103.00 70.1 14 Vergiate VA 16,821 2 12 51 9.01 54.36 5.254 157.2 4,103.00 70.1 15 Voghera Rivanazzano PV 37,420 3 12 16 16.62 53.70 6.872 158.7 2,731.00 71.1  North-West (ITC) Liguria 16 Sarzana Luni SP 16,307 3 19 19 4.10 8.09 13.250 303.3 1,325.00 70.3  North-West (ITC) Val D’Aosta Region without minor airports  North-East (ITD) Trentino Alto-Adige 17 Trento Mattarello TN 0 0 24 72 6.70 33.60 21.179 113.5 2,488.00 71.0  North-East (ITD) Veneto 18 Asiago VI 0 0 4 14 2.10 8.70 6.630 211.0 3,972.00 67.8 19 Belluno BL 8,069 1 5 9 2.14 8.09 2.376 55.3 708.00 72.7 20 Legnago VR 1,216 1 1 22 33.20 57.37 38.550 103.3 4,968.00 68.7 21 Padova PD 21,497 1 38 73 17.67 60.83 27.824 215.9 5,220.00 68.2 22 Thiene VI 0 0 6 53 2.90 52.12 6.630 211.0 3,972.00 67.8 23 Venezia VE 923,115 9 0 12 15.57 62.47 185.109 95.9 4,369.00 67.4 24 Verona Boscomantico VR 1,216 1 28 56 14.78 42.17 38.550 103.3 4,968.00 68.7  North-East (ITD) Emilia Romagna 25 Carpi Budrione MO 83,100 3 4 7 10.70 37.60 15.318 122.7 4,044.00 71.4 26 Ferrara Aguscello FE 129,319 5 23 26 12.74 35.65 8.901 29.1 1,690.00 74.6 27 Ferrara FE 129,319 5 25 26 15.73 34.53 8.901 29.1 1,690.00 74.6 28 Modena MO 83,100 3 17 19 9.78 36.23 15.318 122.7 4,044.00 71.4 29 Pavullo nel Frignano MO 83,100 3 0 1 3.14 7.39 15.318 122.7 4,044.00 71.4 30 Lugo di Romagna RA 354,265 8 0 0 4.29 10.69 6.729 38.4 1,906.00 73.1 31 Ravenna RA 354,265 8 16 34 17.75 55.83 6.729 38.4 1,906.00 73.1 32 Reggio Emilia RE 13,792 1 17 21 14.67 58.73 11.562 71.7 3,105.00 70.2  North-East (ITD) Friuli V Giulia 33 Gorizia GO 0 0 13 24 8.70 29.65 0.836 119.2 505.00 68.4 34 Udine Campoformido UD 155,279 4 16 22 16.74 61.34 11.805 282.8 2,171.00 68.1  Centre (ITE) Toscana 35 Arezzo AR 145,031 8 18 19 5.70 34.60 3.861 79.3 1,870.00 71.4 36 Lucca Tassignano LU 19,294 2 37 81 7.70 31.60 1.659 78.0 2,421.00 69.4 37 Massa Cinquale MS 0 0 20 34 2.10 7.09 0.910 213.1 1,310.00 68.8  Centre (ITE) Umbria 38 Foligno PG 194,061 10 30 91 7.54 48.23 24.700 83.9 3,301.00 71.4  Centre (ITE) Marche 39 Fano PU 397,365 3 4 23 13.73 32.43 2.673 153.7 1,882.00 67.9  Centre (ITE) Lazio 40 Aquino FR 1,146,403 5 5 6 2.50 8.04 0.853 202.8 1,690.00 74.6 41 Guidonia RM 18,156,284 55 9 169 11.53 36.38 1199.402 190.1 30,238.00 68.9 42 Latina LT 124,023 5 3 9 14.50 42.38 0.887 76.7 3,493.00 61.3 43 Rieti RI 0 0 2 5 4.80 6.09 2.665 104.1 770.00 61.6 44 Roma Urbe RM 18,156,284 55 2 278 2.80 6.87 1199.402 190.1 30,238.00 68.9 45 Viterbo VT 502,457 19 6 11 10.54 32.68 2.022 46.5 1,703.00 65.3  South (ITF) Abruzzo 46 L’Aquila Preturo AQ 92,335 9 5 7 3.80 7.76 3.451 56.3 1,476.00 63.3 Molise Region without minor airports Campania 47 Capua CE 563,676 10 6 17 14.75 50.33 1.500 284.8 5,641.00 45.5 48 Salerno Pontecagnano SA 435,790 8 10 22 15.55 35.98 5.450 325.8 7,150.00 54.2 Puglia 49 Lecce Lepore LE 9,145 1 9 27 3.50 8.74 1.400 80.1 4,897.00 55.3 Basilicata Region without minor airports Calabria Region without minor airports  Islands (ITG) Sicilia 50 Palermo Boccadifalco PA 494,006 17 8 39 12.35 33.58 24.500 212.3 5,339.00 50.2 Sardegna 51 Oristano Fenosu OR 123,575 3 10 13 4.30 7.09 0.150 121.8 512.00 63.2Table 10
MCSDA implementation for JLD: statistical analysis for the definition of Thresholds of Attractiveness (a) and definition of the Thresholds of Attractiveness for each Indicator (b).
(a)
Statistical analysis for the definition of Thresholds of Attractiveness (ssd.n)
SD.1 Tourist Vocation ID.1 SD.2 Infrastructural endowment for transport SD.3 Productive Vocation ID.1.1 ID.1.2 ID.1.3 ID.1.4 ID.2.1 ID.2.2 ID.2.3 ID.2.4 ID.3.1 ID.3.2 St.6 percentile (0.0) 0.00 0.00 0.00 0.00 2.10 6.09 0.15 29.12 505.00 45.46 St.5 percentile (0.2) 0.00 0.00 4.00 12.00 4.29 8.74 1.39 78.04 1690.00 67.44 St.4 percentile (0.4) 16615.40 2.00 8.00 19.60 7.95 35.23 5.25 116.94 2152.60 68.86 St.3 percentile (0.6) 117327.00 3.00 15.80 27.80 11.43 49.25 10.10 158.85 3972.00 70.22 St.2 percentile (0.8) 420420.00 8.00 22.60 57.80 15.24 53.86 23.17 213.92 4968.00 71.41 St.1 percentile (1) 18156284.00 55.00 72.00 278.00 33.20 75.89 1199.40 596.08 30238.00 74.59  (b)
Definition of Thresholds of Attractivity (ssd.n) for Indicators ID.n
SD.1Tourist Vocation ID.1 ID.1.1 ID.1.2 ID.1.3 ID.1.4 cdm.n = 0.2 0.00>ID.1.1≥243.20 0.00>ID.1.2≥0.20 0.00>ID.1.3≥4.20 0.00>ID.1.4≥11.20 cdm.n = 0.4 243.20>ID.1.1≥16.821.00 0.20>ID.1.2≥2.00 4.20>ID.1.3≥8.00 11.20>ID.1.4≥19.00 cdm.n = 0.6 16.821.00>ID.1.1≥104.831.00 2.00>ID.1.2≥3.00 8.00>ID.1.3≥15.40 19.00>ID.1.4≥27.00 cdm.n = 0.8 104.831.00>ID.1.1≥ 412.735.00 3.00>ID.1.2≥8.00 15.40>ID.1.3≥22.40 27.00>ID.1.4≥57.20 cdm.n = 1 412.735.00>ID.1.1≥18.156.284.00 8.00>ID.1.2≥55.00 22.40>ID.1.3≥72 57.00>ID.1.4≥278.00  SD.2Infrastructural endowment for transport ID.2.1 ID.2.2 ID.2.3 ID.2.4  cdm.n = 0.2 2.1>ID.2.1≥4.29 6.09>ID.2.2≥9.13 0.15>ID.2.3≥1.39 22.71>ID.2.4≥77.01 cdm.n = 0.4 4.29>ID.2.1≥7.97 9.13>ID.2.2≥34.60 1.39>ID.2.3≥5.25 77.01>ID.2.4≥113.51 cdm.n = 0.6 7.97>ID.2.1≥11.22 34.60>ID.2.2≥48.74 5.25>ID.2.3≥9.50 113.51>ID.2.4≥158.80 cdm.n = 0.8 11.22>ID.2.1≥15.09 48.74>ID.2.2≥53.81 9.50>ID.2.3≥22.51 158.80>ID.2.4≥213.66 cdm.n = 1 15.09>ID.2.1≥33.00 53.81>ID.2.2≥76.00 22.51>ID.2.3≥1.199.00 213.66>ID.2.4≥596.00  SD.3Productive Vocation ID.3.1 ID.3.2  cdm.n = 0.2 505.00>ID.3.1≥1.690.00 45.46>ID.3.2≥66.77 cdm.n = 0.4 1690.00>ID.3.1≥2.125.00 66.77>ID.3.2≥68.76 cdm.n = 0.6 2.125.00>ID.3.1≥3.972.00 68.76>ID.3.2≥70.22 cdm.n = 0.8 3.972.00>ID.3.1≥4.968.00 70.22>ID.3.2≥71.41 cdm.n = 1 4.968.00>ID.3.1≥30.238.00 71.41>ID.3.2≥74.59Table 11
MCSD implementation for JLD: concise classification.
NUTS 1:  Geographical areas NUTS 2:  Regions N° Minor airport NUTS 3:  Provinces Value classification and related score (cdm.n) Mean  (M) Concise classification SD.1 Tourist Vocation ID.1 SD.2 Infrastructural endowment for transport SD.3 Productive Vocation ID.1.1 ID.1.2 ID.1.3 ID.1.4 ID.2.1 ID.2.2 ID.2.3 ID.2.4 ID.3.1 ID.3.2 JLD (csnt.n) North-West (ITC) Piemonte 1 Alessandria AL 0.40 0.40 0.60 0.60 0.80 1.00 0.40 1.00 0.40 1.00 0.66 H 2 Biella Cerrione BL 0.20 0.20 0.40 0.40 0.60 0.60 0.20 0.40 0.20 0.80 0.40 L 3 Casale Monferrato AL 0.40 0.40 0.60 0.60 1.00 1.00 0.40 1.00 0.40 1.00 0.68 H 4 Novi Ligure AL 0.40 0.40 0.20 1.00 0.60 0.80 0.40 1.00 0.40 1.00 0.62 H 5 Torino Aeritalia TO 1.00 1.00 0.60 1.00 0.60 0.80 1.00 0.20 1.00 0.80 0.80 H 6 Vercelli VC 0.20 0.20 0.40 0.40 0.80 1.00 0.20 0.20 0.20 0.80 0.44 A  North-West (ITC) Lombardia 7 Alzate Brianza CO 0.20 0.20 0.80 0.80 0.40 1.00 0.80 1.00 0.60 0.60 0.64 H 8 Como Idroscalo CO 0.20 0.20 1.00 0.80 0.40 0.80 0.80 1.00 0.60 0.60 0.64 H 9 Calcinate del Pesce VA 0.40 0.40 1.00 0.80 0.60 0.80 0.60 0.60 0.80 0.60 0.66 H 10 Cremona Migliaro CR 0.40 0.40 0.40 0.20 0.40 0.80 0.20 0.80 0.20 0.60 0.44 A 11 Milano Bresso MI 1.00 0.60 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.96 VH 12 Valbrembo BG 0.20 0.20 0.80 1.00 1.00 0.80 0.80 0.80 1.00 0.20 0.68 H 13 Varese Venegono VA 0.40 0.40 0.80 0.80 0.40 0.60 0.60 0.60 0.80 0.60 0.60 A 14 Vergiate VA 0.40 0.40 0.60 0.80 0.60 1.00 0.60 0.60 0.80 0.60 0.64 H 15 Voghera Rivanazzano PV 0.60 0.60 0.60 0.40 1.00 0.80 0.60 0.60 0.60 0.80 0.66 H  North-West (ITC) Liguria 16 Sarzana Luni SP 0.40 0.60 0.80 0.40 0.20 0.20 0.80 1.00 0.20 0.80 0.54 A  North-West (ITC) Val D’Aosta Region without minor airports  North-East (ITD) Trentino Alto-Adige 17 Trento Mattarello TN 0.20 0.20 1.00 1.00 0.40 0.40 0.80 0.40 0.60 0.80 0.58 A  North-East (ITD) Veneto 18 Asiago VI 0.20 0.20 0.20 0.40 0.20 0.20 0.60 0.80 0.60 0.40 0.38 L 19 Belluno BL 0.40 0.40 0.40 0.20 0.20 0.20 0.40 0.20 0.20 1.00 0.36 L 20 Legnago VR 0.40 0.40 0.20 0.60 1.00 1.00 1.00 0.40 0.80 0.40 0.62 H 21 Padova PD 0.60 0.40 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.40 0.84 VH 22 Thiene VI 0.20 0.20 0.40 0.80 0.20 0.80 0.60 0.80 0.60 0.40 0.50 A 23 Venezia VE 1.00 1.00 0.20 0.40 1.00 1.00 1.00 0.40 0.80 0.40 0.72 H 24 Verona Boscomantico VR 0.40 0.40 1.00 0.80 0.80 0.60 1.00 0.40 0.80 0.40 0.66 H  North-East (ITD) Emilia Romagna 25 Carpi Budrione MO 0.60 0.60 0.20 0.20 0.60 0.60 0.80 0.60 0.80 0.80 0.58 A 26 Ferrara Aguscello FE 0.80 0.80 1.00 0.60 0.80 0.60 0.60 0.20 0.20 1.00 0.66 H 27 Ferrara FE 0.80 0.80 1.00 0.60 1.00 0.40 0.60 0.20 0.20 1.00 0.66 H 28 Modena MO 0.60 0.60 0.80 0.40 0.60 0.60 0.80 0.60 0.80 0.80 0.66 H 29 Pavullo nel Frignano MO 0.60 0.60 0.20 0.20 0.20 0.20 0.80 0.60 0.80 0.80 0.50 A 30 Lugo di Romagna RA 0.80 0.80 0.20 0.20 0.20 0.40 0.60 0.20 0.40 1.00 0.48 A 31 Ravenna RA 0.80 0.80 0.80 0.80 1.00 1.00 0.60 0.20 0.40 1.00 0.74 H 32 Reggio Emilia RE 0.40 0.40 0.80 0.60 0.80 1.00 0.80 0.20 0.60 0.60 0.62 H  North-East (ITD) Friuli V Giulia 33 Gorizia GO 0.20 0.20 0.60 0.60 0.60 0.40 0.20 0.60 0.20 0.40 0.40 L 34 Udine Campoformido UD 0.80 0.80 0.80 0.60 1.00 1.00 0.80 1.00 0.60 0.40 0.78 H  Centre (ITE) Toscana 35 Arezzo AR 0.80 0.80 0.80 0.40 0.40 0.40 0.40 0.40 0.40 1.00 0.58 A 36 Lucca Tassignano LU 0.60 0.40 1.00 1.00 0.40 0.40 0.40 0.40 0.60 0.60 0.58 A 37 Massa Cinquale MS 0.20 0.20 0.80 0.80 0.20 0.20 0.20 0.80 0.20 0.40 0.40 L  Centre (ITE) Umbria 38 Foligno PG 0.80 1.00 1.00 1.00 0.40 0.60 1.00 0.40 0.60 1.00 0.78 H  Centre (ITE) Marche 39 Fano PU 0.80 0.60 0.20 0.60 0.80 0.40 0.40 0.60 0.40 0.40 0.52 A  Centre (ITE) Lazio 40 Aquino FR 1.00 0.80 0.40 0.20 0.20 0.20 0.20 0.80 0.20 1.00 0.50 A 41 Guidonia RM 1.00 1.00 0.60 1.00 0.80 0.60 0.20 0.80 1.00 0.60 0.76 H 42 Latina LT 0.80 0.80 0.20 0.20 0.80 0.60 0.20 0.20 0.60 0.20 0.46 A 43 Rieti RI 0.20 0.20 0.20 0.20 0.40 0.20 0.40 0.40 0.20 0.20 0.26 L 44 Roma Urbe RM 1.00 1.00 0.20 1.00 0.20 0.20 0.20 0.80 1.00 0.60 0.62 H 45 Viterbo VT 1.00 1.00 0.40 0.20 0.60 0.40 0.40 0.20 0.40 0.20 0.48 A  South (ITF) Abruzzo 46 L'Aquila Preturo AQ 0.60 1.00 0.40 0.20 0.20 0.20 0.40 0.20 0.20 0.20 0.36 L Molise Region without minor airports Campania 47 Capua CE 1.00 1.00 0.40 0.40 0.80 0.80 0.40 1.00 1.00 0.20 0.70 H 48 Salerno Pontecagnano SA 1.00 0.80 0.60 0.60 1.00 0.60 0.60 1.00 1.00 0.20 0.74 H Puglia 49 Lecce Lepore LE 0.40 0.40 0.60 0.60 0.20 0.20 0.40 0.40 0.80 0.20 0.42 A Basilicata Region without minor airports Calabria Region without minor airports  Islands (ITG) Sicilia 50 Palermo Boccadifalco PA 1.00 1.00 0.40 0.80 0.80 0.40 1.00 0.80 1.00 0.20 0.74 H Sardegna 51 Oristano Fenosu OR 0.80 0.60 0.60 0.40 0.40 0.20 0.20 0.60 0.20 0.20 0.42 A    M = Arithmetic Mean If 0 < M≤ 0.20; csnt.n= VL If 0.20 < M≤ 0.40; csnt.n= L If 0.40 < M≤ 0.60; csnt.n= A If 0.60 < M≤ 0.80; csnt.n= H If 0.80 < M≤ 1; csnt.n= VHFigure 9
MCSDA implementation for JLD: spatial mapping.


## 3.3. JLE “Airport Potential Classification”

The implementation of the MCSDA for JLE provides us with an airport potential Classification Score for each alternativeA.n (Table 12). Next, the following are listed: the Statistical Analysis for the definition of Thresholds of Attractiveness(ssd.n) (Table 13(a)); the building up of Thresholds of Attractiveness for each score obtained in the JLE level (Table 13(b)); the results of the implementation (the attribution ofCl.n(A.n) classes for the JLE level) represented using maps (Figure 10).Table 12
MCSDA implementation for JLE: definition of the airport potential classification scores.
NUTS 1:  Geographical areas NUTS 2:  Regions N° Minor airport NUTS 3:  Provinces JLC JLD JLE Weighted General Equipment Classification JLCw ClassesCl.n(A.n) Territorial Service Classification (csnt.n) Value Classification Scores Final Classification Scores ClassesCl.n(A.n) North-West (ITC) Piemonte 1 Alessandria AL 0.00 1.00 H 0.75 0.75 0.00 2 Biella Cerrione BL 2.75 1.00 L 0.25 3.00 1.00 3 Casale Monferrato AL 1.00 0.00 H 0.75 1.75 0.50 4 Novi Ligure AL 1.25 0.50 H 0.75 2.00 0.50 5 Torino Aeritalia TO 2.50 1.00 H 0.75 3.25 1.00 6 Vercelli VC 0.25 0.00 A 0.50 0.75 0.00  North-West (ITC) Lombardia 7 Alzate Brianza CO 0.00 1.00 H 0.75 0.75 0.00 8 Como Idroscalo CO 2.00 1.00 H 0.75 2.75 1.00 9 Calcinate del Pesce VA 0.25 0.00 H 0.75 1.00 0.00 10 Cremona Migliaro CR 0.00 1.00 A 0.50 0.50 1.00 11 Milano Bresso MI 1.00 0.00 VH 1.00 2.00 0.50 12 Valbrembo BG 0.25 0.00 H 0.75 1.00 0.00 13 Varese Venegono VA 1.00 0.00 A 0.50 1.50 0.50 14 Vergiate VA 1.25 0.50 H 0.75 2.00 0.50 15 Voghera Rivanazzano PV 3.00 1.00 H 0.75 3.75 1.00  North-West (ITC) Liguria 16 Sarzana Luni SP 1.00 0.00 A 0.50 1.50 0.50  North-West (ITC) Val D’Aosta Region without minor airports  North-East (ITD) Trentino Alto-Adige 17 Trento Mattarello TN 1.75 1.00 A 0.50 2.25 1.00  North-East (ITD) Veneto 18 Asiago VI 1.00 0.00 L 0.25 1.25 0.00 19 Belluno BL 1.00 0.00 L 0.25 1.25 0.00 20 Legnago VR 0.00 1.00 H 0.75 0.75 0.00 21 Padova PD 2.00 1.00 VH 1.00 3.00 1.00 22 Thiene VI 1.25 0.50 A 0.50 1.75 0.50 23 Venezia VE 2.00 1.00 H 0.75 2.75 1.00 24 Verona Boscomantico VR 1.50 1.00 H 0.75 2.25 1.00  North-East (ITD) Emilia Romagna 25 Carpi Budrione MO 1.00 0.00 A 0.50 1.50 0.50 26 Ferrara Aguscello FE 0.25 0.00 H 0.75 1.00 0.00 27 Ferrara FE 1.50 1.00 H 0.75 2.25 1.00 28 Modena MO 1.00 0.00 H 0.75 1.75 0.50 29 Pavullo nel Frignano MO 1.75 1.00 A 0.50 2.25 1.00 30 Lugo di Romagna RA 1.00 0.00 A 0.50 1.50 0.50 31 Ravenna RA 1.25 0.50 H 0.75 2.00 0.50 32 Reggio Emilia RE 1.50 1.00 H 0.75 2.25 1.00  North-East (ITD) Friuli V Giulia 33 Gorizia GO 1.00 0.00 L 0.25 1.25 0.00 34 Udine Campoformido UD 0.25 0.00 H 0.75 1.00 0.00  Centre (ITE) Toscana 35 Arezzo AR 0.50 0.00 A 0.50 1.00 0.00 36 Lucca Tassignano LU 2.25 1.00 A 0.50 2.75 1.00 37 Massa Cinquale MS 0.50 0.00 L 0.25 0.75 0.00  Centre (ITE) Umbria 38 Foligno PG 1.25 0.50 H 0.75 2.00 0.50  Centre (ITE) Marche 39 Fano PU 1.50 1.00 A 0.50 2.00 0.50  Centre (ITE) Lazio 40 Aquino FR 1.00 0.00 A 0.50 1.50 0.50 41 Guidonia RM 1.75 1.00 H 0.75 2.50 1.00 42 Latina LT 2.75 1.00 A 0.50 3.25 1.00 43 Rieti RI 1.25 0.50 L 0.25 1.50 0.50 44 Roma Urbe RM 2.75 1.00 H 0.75 3.50 1.00 45 Viterbo VT 1.00 0.00 A 0.50 1.50 0.50  South (ITF) Abruzzo 46 L’Aquila Preturo AQ 1.00 0.00 L 0.25 1.25 0.00 Molise Region without minor airports Campania 47 Capua CE 1.50 1.00 H 0.75 2.25 1.00 48 Salerno Pontecagnano SA 1.00 0.00 H 0.75 1.75 0.50 Puglia 49 Lecce Lepore LE 0.50 0.00 A 0.50 1.00 0.00 Basilicata Region without minor airports Calabria Region without minor airports  Islands (ITG) Sicilia 50 Palermo Boccadifalco PA 2.50 1.00 H 0.75 3.25 1.00 Sardegna 51 Oristano Fenosu OR 2.75 1.00 A 0.50 3.25 1.00Table 13
MCSDA implementation for JLE: statistical analysis (a) and Thresholds of Attractiveness construction (b).
(a)
Statistical Analysis
Percentile functions Values St.4 percentile (0.0) 0.50 St.3 percentile (0.3) 1.25 St.2 percentile (0.6) 2.00 St.1 percentile (1) 3.75  (b)
Attractiveness Thresholds (ssd.n)
Classes Cl.n(A.n) JLEw Thresholds (ssd.n) Cl.n(A.n) = 0 0.50 >JLE_Scores≥ 1.25  Cl.n(A.n) = 0.5 1.25>JLE_Scores≥2.00  Cl.n(A.n) = 1 2.00>JLE_Scores≥3.75Figure 10
MCSDA implementation for JLE: spatial mapping.


## 3.4. Summary and Discussion

The analysis of the output obtained (Figure11) allows us to identify the number and percentage of minor airports compared to the total that fall within each class of assessment (Cl.n) for each Judgment Level:(i)
In JLC “overall classification of resources”, the summarizing level for assessing the suitability (status quo) of an airport’s services and infrastructure (paragraph 1, point 1) pertains to classes the three ClassesCl.C.1, Cl.C.2,andCl.C.3, related to Judgment Level C, representing the level of facilities (from lowest to highest) of overall resources: 22 (43%), 6 (12%), and 23 (45%), respectively, of second-level hubs.(ii)
In JLE “airport potential classification”, the summarizing level for assessing the attractiveness of airport hubs compared to the facilities found in their catchment areas (paragraph 1, point 2) pertains to the ClassesCl.E.1, Cl.E.2,andCl.E.3, related to Judgment Level E, representing the levels (from lowest to highest) of scores obtained in previous Judgment Levels: 15 (30%), 17 (33%), and 19 (37%), respectively.Figure 11
Analysis of output of evaluation for alternatives A.n for JLC, “overall classification of resources”, and JLE, “airport potential classification”.Such data describes the status quo and the potential for developing the existing infrastructural assets of our minor airports through upgrading and/or expansion work for the choice of hubs worth including in the “highway in the sky” network. Further integrated analysis, such as Fuzzy Analysis, Strategic Planning Tools (SPT), Participation Techniques (PT), and Financial and Economic Analysis [41–47], could be useful for achieving the aim of the development of the Italian Network. At this stage of the research, such work can be defined as follows: (i) of greater scope forA.n falling within theCl.1 classes both for the JLC level and for the JLE level; (ii) of average scope forA.n falling within theCl.2 classes both for the JLC level and for the JLE level; (iii) of minor scope or nil forA.n that fall withinCl.3 classes both for the JLC and for JLE levels.The heatmap of minor airports on national territory (Figure12) was built up using the Qgis software (by the space query heatmap) considering each alternativeA.n according to JLE “airport potential classification” three classes obtained asCl.E.1, Cl.E.2,andCl.E.3related to Judgment Level E “airport potential classification”. The three classes (Cl.E.1, Cl.E.2,andCl.E.3) show the coverage of the areas on national territory by minor airports in three levels: (i) max coverage: (ii) medium coverage; (iv) low coverage or no coverage. It is remarkable that minor airports offer the maximum coverage of northern areas of Italy while they offer a low coverage (or no coverage) of areas in the Adriatic Coast, in the Southern areas, and in the island. In the northern areas the maximum coverage is characterized by the presence of minor airports pertaining to the ClassesCl.E.1, Cl.E.2,andCl.E.3.This is to be considered in successive assessments phases that could allow understanding whichA.n could be added in the network (called “highway in the sky”) and which ones have to be excluded. In the Adriatic Coast and in the southern areas in particular the low coverage of the areas is characterized by minor airports pertaining toCl.E.1andCl.E.2representing the lowest classes in JLE final classification. The map in Figure 13 shows the overlap degree for maximum covered northern areas highlighting the necessity to better explore the issue of the alternative choice among minor airports.Figure 12
Definition of coverage of the areas by minor airports throughout the country: heatmap.Figure 13
Definition of the coverage of the areas by minor airports through the country: buffering.


## 4. Conclusions

The evaluation methodology proposed for subsequent levels of analysis helps define the overall performance of Italy’s infrastructural system of minor airports as regards the suitability of intrinsic infrastructural facilities, services (status quo), and the attractiveness of airport hubs given the territorial facilities present in their catchment areas.The figures and graphics obtained and illustrated here serve as the basis for future phases of analysis also through the use of Multicriteria Decision Analysis (MCDA) integrated with other evaluation tools or techniques (Fuzzy Analysis, Strategic Planning Tools, Participation Techniques, Financial and Economic Analysis) built up to evaluate (i) the value of money for constructing a second-level airport network “highway in the sky” following the quantification of the amount of investment necessary and the socioeconomic, financial, and environmental repercussions associated with identifying/constructing each airport hub in the geographic area concerned (following the redevelopment/upgrading of existing infrastructure and/or the planning of new infrastructure); (ii) the inclusion of second-level network hubs within the processes of territorial and urban transformation underway (planning forecasts and implementation programmes from the macro-area level to the local one) so as to guarantee the integration and overlapping of systems (urban, environmental, and transport systems); (iii) the sustainability of upgrading/expansion work and/or relocation (on the basis of the exposed areas identified) and therefore the structure of the network on the basis of the investment time necessary to guarantee that it becomes fully operational.Output data can thus be useful to qualify and quantify the parameters upon which the construction of the second-level network should be based, which will need to be adequately distributed in order to cover the national territory and connect with other modes of transport.The evaluation model proposed, based on sets of assessment elements that are used to obtain results for JLA “structural classification”, JLB “services classification”, JLC “overall classification of resources”, JLD “territorial attractiveness classification”, and JLE “airport potential classification” Judgment Levels, can be integrated with and/or replaced by other descriptive elements of additional and/or supplementary profiles for alternativeA.n. A sensitivity analysis can be conducted on the JLCw and JLD levels, analyzing the repercussions on the JLE level, in that they are a combination of previous Judgment Levels, shifting Thresholds of Attractiveness by a few percentage points(ssd.n). These can be defined by taking into account a variation of the St.n. percentage values obtained here on the basis of further statistical analyses. In this way, the robustness of the results can be measured.The work put forward here can provide regulatory bodies and air traffic managers with decision-making support when developing improvement programmes and plans in the short, medium, and long term, designed to create a minor airport network that takes into account ongoing territorial and infrastructural mechanisms and processes of socioeconomic development.



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*Source: 6847030-2018-07-12.xml*