Urban Polycentricity and Architectural Heritage: A PROMETHEE-Based Multicriteria Approach

Abstract

Polycentricity is a multiscalar concept referring to the interconnections of neighboring spatial areas in search of synergies. More specifically, urban polycentricity has lately gained increasing momentum in academic research and strategic planning as urban polycentric structures could stimulate urban and regional performance in a sustainable way. Architectural heritage, with its connotative meanings, is among the indicators of incorporation in polycentric networking. The present paper investigates the challenges of implementing urban polycentricity methodological approaches to highlight new polycentric networks based on built architectural heritage. In this study, appointed architectural assets are considered as nodes of the potential networks. As a new research strand, the MCDA PROMETHEE method is applied to compile and assemble all indicators (namely, rank size and node hierarchy, accessibility, transportation, and digital promotion). The ultimate research goal is to build a new polycentricity index as an innovative methodological tool to highlight polycentric networking synergies on interurban and intraregional scales. The region of Thrace in Northern Greece is chosen to apply the proposed methodology. Research on ways to couple polycentricity and PROMETHEE methods is limited, and thus, the proposed methodological approach is expected to contribute to the field of urban and regional strategies for researchers and practitioners.

1. Introduction

The idea of polycentric urban networks and the policies developed to support them provide significant opportunities for developing in a sustainable way. The present preliminary research aims to formulate a new methodological framework relating three concepts: “polycentricity” focusing on the intraurban scale, “architectural heritage”, and MCDA methods. More specifically, the built heritage of the 19th–20th centuries Ottoman architecture was chosen as a field of reference. This study’s main challenge is to assemble all qualitative and quantitative data on the notions of “polycentricity” and “architectural heritage”. PROMETHEE is selected as a reliable method to compose all data available.

The review of the quite limited literature on polycentric development, architectural heritage, and MCDA methods highlighted the need for better understanding and enhancing the connections among these notions. This paper is the authors’ attempt to fill the methodological gap created by this fact and create a universal methodological tool to assemble all relevant qualitative and quantitative indicators. The Polycentricity Architectural Heritage Index (PAHI) is introduced as an innovative methodological tool for the assemblage of polycentricity and built heritage notions, based on the robust methodology of PROMETHEE in the context of small and medium-sized urban centers. Three Regional Units in the Region of Eastern Macedonia–Thrace (R.E.M.Th.) in Northern Greece, the Regional Units of Evros, Rodopi, and Xanthi (NUTS III), which form the geopolitical entity of Thrace, are chosen to apply the methodology.

This paper is structured around six main sections. After the Introduction, the second section, Literature Review, explores the use and adaptation of the notion of polycentricity, raising the issue of morphological polycentricity on an intraurban scale. Next, the Ottoman architecture of the 19th and 20th centuries within a historical and typological context is overviewed, followed by a chapter where PROMETHEE as an MCDA technique is presented. The third section, Materials and Methods, introduces this paper’s proposed methodology: a brief current situation analysis of the study area is attempted; the application of morphological polycentricity methodology is analyzed based on a sample of 25 selected architectural assets identified in urban and peri-urban areas; and the application of the PROMETHEE methodology to assemble all quantitative and qualitative criteria is discussed. The fourth section covers the results of the applied methodology. The results of the combined methodologies of polycentricity and PROMETHEE, as well as the formation of the proposed PAHI, are presented. In the fifth section, Discussion, the results and future applications of PAHI are provided, followed by the sixth session, Conclusions.

2. Literature Review

2.1. Polycentricity with a Focus on the Intraurban Scale

In a world where half the population lives in cities and generates approximately 80% of global output [1], academic research and planning policies turn to more polycentric configurations [2,3,4,5]. Inequalities on interurban and intraurban scales have been a trigger point for local and national authorities and policymakers, while deficiencies are traced in urban systems of medium-sized cities, especially in southeastern Europe [6]. Larger urban centers are higher in the hierarchy in terms of population densities, economic performance, labor, productivity, services, income, etc. [7] and thus attract more investments and become more competitive to medium and small-sized cities. Simultaneously, large cities and metropolises are called to face the challenges of congestion due to high population concentrations and urban sprawl, pollution, high land prices for living and labor, and built and natural environment decay, especially in the historical centers and CBDs.

The concept or phenomenon of polycentricity (or polycentrism or polycentric development) has attracted the interest of academia and spatial planning practitioners, especially during the last two decades. Polycentricity is not a new notion, but its complex nature makes it elusive [3,8], multidimensional, and difficult to analyze and implement relevant policies. In general, it reflects the transition from point to connections, as “The Atom is the past and the symbol for the next century is the dynamical Net, a brunch of dots connected to other dots” [9]. Analysis of complex networks refers to the research around the dynamic interactions among centers as connected microelements of a system, determining a macrolevel of functions of that system, which is not simply the sum of the microelements [10].

Polycentricity is a catchphrase within a reorientation of strategic spatial planning since the 1990s [11] and to date, it has been adopted by many spatial planning policies departing from the European Spatial Development Perspective (ESDP) [12]. Polycentricity was widely recognized in 2007 as a lever for spatial development policies by the European Commission and National Ministers in the ‘Territorial Agenda of the European Union’ [13,14]. Contemporary spatial development objectives embrace the idea that certain forms of spatial organization are preferable, namely that polycentric distribution of people, activities, and infrastructure is better than monocentric distribution [3,15]. Polycentricity contributes to economic, social, and territorial cohesion, as well as competitiveness, social justice, and sustainable development [16]. Policymaking initiatives suggest that spatial entities share their strong characteristics and complementarities in a synergetic way rather than acting “alone” [17] but after profound and interdisciplinary planning. Polycentricity is closely linked to the equitable distribution and development of urban centers within a region, not only the number of them as a sum [18].

Within the spatial strategic planning context, polycentricity still remains a quite vague notion with varying connotations on different spatial scales [8,11,19]. It is considered that polycentricity can be a tool to eliminate regional disparities [20] and thus occupies a critical place in European territorial policies as a priority for spatial development [17] on national, regional, and local scales. Based on the academic literature, there are many scales to explore polycentricity: the local or mesoscale, which is the focus of the current study, alludes to the intraurban level which is more fragmented [21]; the regional scale, the national, interurban, or “macro”-scale; and the continental or inter-regional scale [8,12], whereas the “mega”-scale is related to the intra-European level [8]. Gløersen (2005) introduces a quite different classification of polycentricity (based on ESPD), according to which, the microlevel refers to the intraregional scale, where synergies result in improved economic performance, the mesolevel to regional/inter-regional level with integrated city-regions through functional complementarity, and the macrolevel, referring to European level with the global integration zones apart from the classic Pentagon [22].

Based on research on urban studies, polycentric development is seen as the result of cities’ “regionalization” and transition from monocentric to polycentric patterns, where cities, metropolitan areas, and regions cooperate favoring balanced management and structured spatial development [23]. Investigating the topic of “co-existence” and collaboration of multiple centers, a polycentric network encourages cities to evolve in a more balanced and sustainable way and also develop the mechanisms to face challenges caused by agglomeration disadvantages [15,24]. On the “meso”-level, polycentricity anticipates the collaboration of cities with surrounding territories [12,13] and the investigation of common strengths for potential synergies in various fields.

In the last decades, a turn from cities as individual spatial entities to polycentric urban regions has been noted in polycentricity literature and academic research [25]. As Anas et al. (1998) indicate, the process of city sprawling has emerged into more polycentric patterns, mostly as former CBDs become the subcenters of an expanded urban area [26]. Focusing on the urban scale, cities or “co-evolving cities” [27] within the polycentricity context, have more advantages than within a monocentric spatial scheme. A more even distribution of urban agglomerations within a polycentric network benefits from the advantages and positive externalities of synergies and confronts more efficiently the challenges of agglomeration negative externalities [15,18,24].

While polycentricity as a concept alludes to the presence of more than one neighboring and interconnected urban area within a region, a wide range of factors should be investigated in order to explore all prospects of polycentric networking. Cities have multiple interactions, visible or not, with each other, for instance, infrastructure, trade, population, or information flows [27], forming a polycentric network where synergies take place. The word “synergy” derives from the Greek (syn + ergos), meaning a state in which the results of two or more cooperating entities are of additional value than the sum of the results for each entity acting alone (1 + 1 > 2) [28]. “Synergy” is interlinked with the concept of complementarity towards territorial cohesion, especially for remote areas [29].

Under this perspective, centers go beyond their traditional administrative boundaries and form different spatial structure typologies. Building blocks of polycentric spatial entities are the Functional Urban Areas (FUAs) [22], which are necessary units for measuring polycentricity [16]. In this study, the focus is on the Polycentric Urban Region (PUR) within which cities making up a polycentric urban network are hubs in an arrangement determined by infrastructure, connections, and flows [28]. According to Peter Hall (1966) (as mentioned in van Meeteren, 2020 [30]), “PURs epitomized the world-city model of the future”. The notion of PUR is still quite vague due to its complexity. The definition by CEC (1999) (as mentioned in Davoudi, 2003 [8]) indicates that a PUR is an area consisting of two or more formerly historically and politically distinct urban centers without a clear hierarchy, at a reasonable distance, and with functional connectivity [8]. A more straightforward definition implies that PURs are regions characterized by the presence of multiple, more or less proximate centers developed fairly [31,32]. Spatial development strategies have turned PURs into their focus, aiming to boost their competitiveness by developing agglomeration economies on the intraurban level. In terms of general urbanization economies [13,14], as well as specialized cultural, recreational, and sports amenities [3], PURs may provide, under certain circumstances, agglomeration benefits.

Based on the academic literature, polycentricity has two dimensions: the morphological aspect, which refers to the characteristics of the network nodes (size, location, and interconnection within the network) [33,34], and the functional aspect, which refers to the type and distribution of relationships among the nodes (commuting, commercial exchanges, education, leisure, etc.) [2,15,35,36]. Both components are unequivocally connected: interactions between cities are pivotal for polycentricity and hubs with no interactions could not compose a polycentric network [16]. An urban system is considered more polycentric when all areas constituting the network are more similarly measured, disseminated, distributed more evenly throughout a territory, and evenly accessible [3]. In this study, the focus is on the morphological polycentricity dimension.

Addressing the indicators of integration in a polycentric network, the most commonly used indicator is commuting regarding journey-to-work flows [8]. Cultural heritage is considered an indicator for investigating “interdependencies” within a spatial network; still, academic research on this topic is rather limited. Architectural heritage is considered a vital part and a “living organism” of cultural evidence and local identity. According to the United Nations definition (1972), Article 1 [37], architectural structures, i.e., buildings, monuments, and groups of buildings (groups of individual or connected buildings) are seen as part of cultural heritage due to their architectural morphology, typology, and unique features. Furthermore, architectural heritage is a lever to improve life quality and polycentric and economically sustainable development [38]. Polycentricity is related to built heritage, as polycentric urban forms are regarded as concentrations of cultural resources and important nodes for their promotion [39]. And vice versa, architectural assets could boost interactions among urban areas, for example, mobility for tourism or educational purposes and consequently synergies, i.e., the establishment of enterprises and financing mechanisms for cultural and spatial projects [38,39].

In Greece, inequalities in spatial development especially due to unfettered development, and the major role of the capital city of Athens since the 1960s, have been a challenge for spatial planning policies. Also, Greece is characterized by fragmented space and economic distribution, mostly due to its geomorphology with the numerous islands and mountainous areas [40], and polycentric development has been embodied in national and regional planning strategies in line with European planning goals for sustainable development. Polycentricity in Greece appears in the form of FUAs (i.e., the two metropolitan areas, Athens and Thessaloniki FUAs), as well as urban bipoles or tripoles (i.e., Xanthi–Kavala–Drama tripole) [41,42]. In any case, polycentric development in Greece should be more broadly implemented.

2.2. Ottoman Architecture Heritage of the 19th–20th Centuries

Greece is home to many monuments from the Ottoman Empire’s nearly 400-year occupancy between the 15th and 19th centuries. The exact number of Ottoman monuments in Greece is still unclear, and according to the President of ICOMOS (International Council on Monuments and Sites), Hellenic Division, the inventory includes 2300 sites and 8500 examples of vernacular architecture dating to the Ottoman era [43]. In Thrace, the Ottoman era formally began in the late 14th century, when the established population created settlements and spread new types of cultivation, contributing to the consolidation of the Ottomans in the wider area [44]. New commercial, economic, and administrative centers emerged, with the most significant agglomerations flourishing due to their location on the ancient Via Egnatia, the main road network system connecting the east and the west.

Cities and institutions had been rebuilt by the mid-17th century and more specifically since its second half (Evliyâ Çelebi era) [45]. Ottomans (re)built cities organized according to the Ottoman way of life and spatial patterns. Ottoman architecture refers to all constructions within the Ottoman Empire’s former boundaries by Ottoman architects and master builders in line with the morphological and typological standards of the Ottoman Empire. The style developed into one of the great arts of the Islamic world during the “Classical” Ottoman period, especially during the 18th century throughout Western Europe. In Thrace, many buildings or their remnants have been found [46], indicative examples of Ottoman architectural heritage and “mystical Islam” [47]. Characteristic examples of Ottoman architecture in the study area are religious buildings and sites (i.e., câmiis—mosques), accommodation facilities (khans—the inns of that period—or caravan serais—more urban and luxurious inns), production and manufacturing buildings and complexes, etc.

2.3. PROMETHEE as a Multicriteria Decision Analysis Method

Decision Support Systems (DSSs) have demonstrated remarkable efficacy, especially during the last decades in many rapidly expanding operational research fields. DSSs are a risk mitigation tool that include hazard models and forecast future disaster risks by using demographic, environmental, infrastructure data [48]. Thus, decision- and policymakers evaluate risks in planning and apply risk mitigation strategies. Additionally, DSSs can be elaborated in conjunction with computerized systems facilitating interactive data and modeling use to help decision-makers during problem-solving [49]. DSS techniques are applied to such matters as business, finance, industry, engineering, infrastructure, healthcare, environmental protection, energy planning, and long-term planning strategic planning of decisions [50,51,52]. Urban transformation processes can be analyzed using MultiCriteria Decision Analysis (MCDA) [49]. Within this framework, decisions incorporate weighing alternatives with advantages or disadvantages in relation to the set objectives. Competing multiple criteria for ranking and choosing alternatives is the basis of many decision-making challenges. Research on MCDA methods has provided many various techniques, namely AHP (Analytic Hierarchy Process), MAUT (MultiAttribute Utility Theory), ELECTRE, etc. Among them, PROMETHEE (Preference Ranking Optimization METHod for Enrichment Evaluation) methods are seen as effective techniques for ranking decisions [53].

Visual PROMETHEE is a multicriteria decision aid application. It enables the assessment of several possible decisions or items according to multiple often conflicting criteria, the identification of the best possible decision, the ranking of possible decisions from the best to the worst one, and the sorting of items into predefined classes [54]. Furthermore, visual PROMETHEE enables visualization of decisions to better understand the difficulties in making good decisions [55]. The approach selected for the current paper is PROMETHEE (Preference Ranking Organization METHod for the Enrichment of Evaluations), from Brans and Marechal (2005) [55]. According to this methodology and considering weights, each decision-maker is capable of assigning priorities depending on the criteria [56]. PROMETHEE method includes six types of preference functions (equations) to express the significance of the alternatives for a certain criterion/factor, and weights to reveal the relative importance of the criterion. These six types of preference functions are described as follows: Type I (usual criterion), Type II (quasi criterion), Type III (V-shape criterion), Type IV (level criterion), Type V (V-shape criterion), and Type VI (Gaussian criterion) [57].

3. Materials and Methods

3.1. Methodological Approach

This research focuses on the morphological polycentricity dimension and more specifically on interurban and intraurban polycentric networking prospects. Urban areas are considered all cities with a population of over 10,000 inhabitants; semiurban areas are those with a population of 2000 to 10,000 inhabitants, departing from the classification of urban and rural areas (based on population) by the National Statistical Service of Greece (as mentioned in Lamprianidis, 2011 [58]). Assets of the Ottoman period (19th–20th centuries) identified in urban areas are recorded for the following methodological approach. It should be noted that due to research limitations, the focus is on the architectural heritage of the mainland; thus, Samothrace Island in Evros, R.U., is not included.

To better explore built heritage and sustainable urban networking prospects in Thrace, this research analysis is organized in the following sections:

i.

First, the profile of the study area is examined (population, demographic, economic, spatial structure data, etc.).

ii.

An overview of architectural assets linked to Ottoman architecture is attempted based on their morphology, typology, and uses.

iii.

In the following step morphological polycentric development degree is estimated in terms of Ottoman asset density, as a novel approach to polycentricity methodology.

iv.

Transportation, accessibility, and spatial continuity data are elaborated in order to better investigate polycentricity factors.

v.

In order to apply PROMETHEE methodology a set of alternatives/actions and criteria are introduced, based on polycentricity and architectural heritage aspects.

vi.

A weight matrix is constructed regarding the defined criteria. Also, the performance of each alternative against each criterion is applied to all alternatives using the Likert scale.

vii.

An integrated database is created, including all selected architectural assets, polycentricity and virtual promotion criteria, weights, and the performance of each alternative.

viii.

Based on the above analysis (steps i to iv) and PROMETHEE analysis, the final PAH Index is proposed.

ix.

The final step includes remarks and recommendations for potential polycentric networking of architectural assets on urban and regional scales.

In Figure 1, a flowchart of the methodological approach is presented.

3.2. Research Area

The Region of Eastern Macedonia and Thrace (R.E.M.Th.—NUTS II level) is one out of the thirteen Administrative Regions of Greece. The Region consists of five Regional Units (R.U.—NUTS III level), namely Evros, Rodopi, Xanthi, Kavala, and Drama, organized in twenty-two (22) municipalities (LAU I level). In this paper, the focus is on the historical and geographical region of Thrace, the eastern part of R.E.M.Th., bordering with Turkey and Bulgaria, formed by Evros, Rodopi, and the Xanthi Regional Units. Thrace combines mountainous areas, plains, and coastal areas. The rugged terrain has been an obstacle to balanced development, especially of mountainous settlements. Thrace’s advantageous geographic location has historically allowed several towns to grow as significant commercial and trade hubs.

Thrace (Figure 2) has a total population of 172,648 residents. It includes twenty (20) urban centers of 2000 or more inhabitants, three (3) main urban centers, the Seats of the Regional Units, Alexandroupoli (59,723 inhabitants), Komotini (54,165 inhabitants), and Xanthi (58,760 inhabitants), and almost 195 rural centers of fewer than 2000 residents, according to the 2021 Population Census [59]. Evros, R.U., also belongs to the island of Samothrace, with 2596 inhabitants [59]. Thrace depended mainly on agriculture and livestock farming during the past decades. The share of agriculture in the regional GDP has declined over the past decade, but it still contributes to the national production of crops (tobacco, cotton, wheat, asparagus, etc.) [60]. Today, Thrace’s economy is based on manufacturing and on industrial sectors, commerce, education, and public services. In 2020, Thrace accounted for EUR 31,287 of GDP per capita, with Evros, R.U., accounting for EUR 11,678 GDP per capita (above the regional GDP per capita), Rodopi, R.U., EUR 10,665, and Xanthi, R.U., EUR 8944 [61]. Evros, R.U., had the highest Gross Value Added (GVA) by industry in 2020, 1504 million euros, followed by Rodopi, R.U., with 1027 mil. euros and Xanthi, R.U., with 872 mil. euros [62]. The services sector is mainly based on trade and tourism [63].

The area’s transportation infrastructure includes the international airport, “Democritus”, and the international port of Alexandroupoli, as well as the Egnatia Motorway (on the traces of ancient Via Egnatia) connecting Northern Greece from each side to the other. There are also three vertical brunches of Egnatia Motorway connecting Thrace with the Bulgarian borders. The national and local road infrastructure is quite satisfactory but needs an upgrade, especially in mountainous areas. The railway was a major transportation means during the past decades; however, today it is inactive since a severe upgrade is needed.

There are five operating Industrial Areas in Thrace (Orestiada town, Alexandroupoli, Komotini, Sapes town, and Xanthi) [65]. Since 1973, Democritus University of Thrace (DUTH) has been operating with eight faculties and nineteen schools in Komotini, Xanthi, Alexandroupoli, and Orestiada [66], as well as research institutes: ATHENA Research Centre—Xanthi Branch, Institute of Geology and Mineral Exploration (IGME)—Xanthi Branch, Fisheries Research Institute. Thrace is also located in the International Exhibition Center of Eastern Macedonia and Thrace Region (in the Komotini Industrial area). Regarding the healthcare sector, there are General Hospitals in Alexandroupoli (academic hospital), Didymoteicho, Komotini, and Xanthi, as well as health centers and diagnostic medical private clinics. There are strong inequalities in tourism development as coastal areas (mainly Alexandroupoli and Samothrace Island) have flourished during the last decades, and there is a need to face the seasonality of tourism activity. The selected area may be considered peripheral because of its remoteness from the metropolitan areas and major economic centers of Greece, Thessaloniki and Athens. Furthermore, there are strong inequalities among urban areas, especially mountainous areas and the hinterland. Therefore, this research aims to provide new prospects for economic and social development in Thrace.

3.3. Polycentricity Methodology

3.3.1. Subindex A’—Morphological Polycentricity

The level of polycentric spatial development is a state referring to the size, location, and characteristics of selected spatial entities (morphological polycentricity), with the cities being the nodes of the potential networks and/or the relationships among them (functional polycentricity). In this research, we address morphological polycentricity but with the architectural assets being considered as the nodes of the proposed networks.

Morphological polycentricity is calculated by the hierarchy of nodes in terms of size, rank, and location within a specific spatial area. Various methods can be used to measure morphological polycentricity, including the density of interest urban actors (for instance population and workforces) [67]. Forasmuch as the availability of data on NUTS III and LAU I level, this study takes as the main networking criterion the spatial concentration of architectural assets linked to the Ottoman footprint. Spatial networks can be more or less dense and differ in size and number of settlements they contain. This ratio is derived from the Degree of Polycentricity, which reflects the degree of interdependence of centers [68]. Part of the ESPON Project methodological approach (as described in ESPON Reports 1.1.1 and 1.3.3) [39] is followed for evaluating polycentricity degree in the study area.

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Location or distribution indicator

The present methodology extends beyond administrative boundaries, introducing new limits for measuring morphological polycentricity. The proposed boundaries enclose cyclical zones, with each asset (network node) being the center of each zone. In transportation planning, isochrones are usually built using the two variables time and speed to define the buffer zones for accessible locations [69]. For the calculation of this indicator, the method of beneficiary population based on 45 min isochrones is used, according to which each urban center has a service area based on a time distance of 45 min [16,70].

In this study, distances and travel time between neighboring urban centers are reduced, due to the linear distribution of the main urban centers and the transport infrastructure (i.e., Egnatia Motorway), favoring their connectivity. The distance among the main urban nodes is less than 45 min of time travel. Furthermore, the majority of the settlements are within each core’s periphery, so there is quite adequate connectivity in most cases.

During the last years, the 15 min city concept has been introduced in urban studies and planning policies as it encourages the development of polycentric areas in which points of interest can be reached by any travel mode [71], within a specified time threshold of 15 min. After thorough field research and secondary research of the relevant literature, and in line with the above methodology, the proposed zoning is based on 15 min isochrones (Figure 3). The 15 min isochrones define the zones within which assets are identified. This is a time travel threshold during which one would be willing to travel in order to visit neighboring assets but is also a proper threshold to cover, as many parts of the study area and avoid zone overlapping.

Table 1. The proposed zones in each Regional Unit and municipality.

Zone Number	R.U. Included	Municipalities Included
Ζ1	Evros, Rodopi	Didymoteicho (Evros R.U.)
Z2		Didymoteicho (Evros R.U.), Soufli (Evros R.U.)
Z3		Soufli (Evros R.U.)
Z4		Didymoteicho (Evros R.U.), Arriana (Rodopi R.U.)
Z5		Alexandroupoli (Evros R.U.)
Z6	Rodopi, Xanthi	Komotini, Arriana, Maroneia-Sapes, Iasmos (all based in Rodopi R.U.)
Z7		Komotini, Arriana, Iasmos (all based in Rodopi R.U.)
Z8		Iasmos (Rodopi R.U.), Komotini (Rodopi R.U.), Miki (Xanthi R.U.), Abdera (Xanthi R.U.)
Z9	Xanthi, Rodopi	Xanthi (Xanthi R.U.), Abdera (Xanthi R.U.), Iasmos (Rodopi R.U.)
Z10		Miki (Xanthi R.U.), Xanthi (Xanthi R.U.), Iasmos (Rodopi R.U.)
Z11		Miki, Xanthi (both based in Xanthi R.U.)
Z12		Miki, Xanthi (both based in Xanthi R.U.)

presents the proposed zones for applying the methodology of location/distribution polycentricity subindex. As can be observed, the proposed zones go beyond the administration boundaries (Regional Units), introducing a new spatial arrangement based on Ottoman architecture assets.

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Primacy subindex of zones including architectural assets

As indicated in the ESPON Project (2005) [16], morphological polycentricity can be measured in a polycentric urban system by the urban Primacy index and the rank-size rule [16,68,72]. To investigate whether a city dominates within a network [19], the Primacy Index is used to express the dominance of a prime city [73] over the whole area, by the ratio of permanent population in the larger city and the area’s total population [3,34,74,75]. The hierarchy level calculates the ‘superiority’ of the largest urban centre of an area over other centres by the ratio of the population of that main city n = 1 (pop(1) to the total population of the N urban centres (pop(n)) of the reference area. In line with this rule but with a new perspective, hierarchy here is calculated in each proposed zone by the ratio of the number of architectural assets within each zone to the total number of assets identified in the study area. This formula for calculating the distribution of architectural assets attempts to comply with the polycentricity rank-size methodology. Zones are ranked according to their size based on the number of assets (Equation (1)). The slope of the equation determines the level of dominance of some nodes within the network and therefore also the level of polycentricity in the study area [3,13,16].

Primacy index for population, as in

primacy = (pop(1))/(Σ(n = 1)^pop(n)]

is adapted for the number (density) of architectural assets in each zone:

primacy = (asset(1))/(Σ(n = 1)^N[assets(n)]

(1)

▪

Rank-size subindex of zones including architectural assets

Morphological polycentricity has been calculated using concentration measurements like Herfindahl indices or benchmarks based on rank-size rules [14,76]. The rank-size rule is calculated for architectural assets instead of population and GDP by Equation (2):

ln(size)= α + βln (rank)

(2)

The slope of Equation (2) based on Burger and Meijers (2012) [33], is measured by the squares log rank-size regression methodology, based on the ranking of asset density per zone and using β parameter (Pareto exponent β) [3,33,73]. The higher β parameter, the more polycentric the region [11]. This methodological step is also based on asset density instead of population.

3.3.2. Ottoman Architecture Assets Defining the Polycentric Zones

In this paper, Ottoman architecture assets are used for the calculations for polycentricity methodology and for delineating the proposed zones. Therefore, a unique architectural composition was created, many examples of which are found in the study area. These exquisite Ottoman architecture assets as mnemonic places enhance significant prospects for polycentric and cultural development within the framework of a medium-sized polycentric system of urban centres.

The challenge in this paper is to designate connections to and between the nodes related to visits to Ottoman assets in a way that preserves local cultural identity. An extensive analysis on the built heritage linked to the Ottoman footprint in urban centres was conducted revealing an abundance of assets. An indicative sample of twenty-five (25) architectural assets is chosen to apply the proposed methodology. The selected Ottoman architecture assets are constructions or architectural complexes built during the 19th and the 20th century and characteristic examples of local architecture. The following are some characteristic examples of Ottoman architecture assets throughout the study area.

▪

Tobacco warehouses

The remnants of factories, machineries and spaces of production (buildings or complexes) hold significance in portraying the history, technology, society and built environment of the past [77]. Tobacco cultivation flourished in Thrace since the 1620s [78] where the finest and most expensive tobacco was produced [79]. Tobacco warehouses mainly housed the collection, processing and storage of tobacco leaves and most constructions appeared during the late 1860s [79]. Wealthy merchants built two-storey structures with tobacco processing activities taking place on the ground floor (attic) and residences on the upper floor. Morphologically, these constructions had influences from the Western European industrial architecture, especially after the industrial revolution. Most of them were rectangular buildings with clear lines and plain surfaces interrupted by narrow openings. By the end of the 19th century, tobacco warehouses stood out by the former ones due to their size and great height. A representative example of local architecture is the Tobacco Warehouse of the Ottoman Monopoly “Rezi” (circa 1890), an industrial building located near the central square of Xanthi city center (Figure 4).

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Caravan serais

The main trade means within the Ottoman Empire were the caravans bringing products and goods through a network of roads linking the main towns. To serve the caravans, khans and caravan serais were constructed [80]. In the Balkans are identified three typologies: the large Turkish khans or caravan serais, the urban khans and the rural khans. The caravan serai (which means “caravan palace”) was a large, luxurious dormitory for travellers. The caravan serai typology was a large structure of two or three storeys of limestone, with rich decoration ornaments and motifs and a monumental character. The urban caravan serais were the evolution of this type with the addition of commercial uses [80]. On the ground floor there were stables for the horses and auxiliary spaces. Wooden stairs connected the ground with the upper floor where alleys led to the bedrooms. Next to the main entrance was the khan owner’s room [81]. After the 1550s, the “caravan” type developed, and gradually became a reference point for travellers and traders as a stock market where prices of products were fixed, and a place of social contact [80]. Often the areas close to caravans developed into organised settlements which evolved to villages and towns. Within the study area few caravan serais were constructed and even less survive until today, for example the caravan serai in Genissea (Figure 5).

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Khans

Khans were simpler constructions than caravan serais built on the caravan road networks, as spaces organized around an atrium with few openings on the exterior and a little mosque on the inside [82]. Urban khans were bigger than rural khans and were mainly of the type “bakery, shop and khan”. Khans were usually single or two-story constructions with an internal yard, rooms, and a wooden loggia (chagiati) on the upper floor, while on the ground floor were the auxiliary spaces. Early constructions had strict lines and during the second half of the 19th century neoclassical architecture features appeared. By the 20th century, many khans began to collapse due to construction weaknesses [43], while some traditional urban khans were converted into commercial and business spaces. The most characteristic surviving khan is the one located in Xanthi city center (Figure 6), which belonged to two merchants from Epirus [82].

3.3.3. Application of Visual PROMETHEE—Net Flow Index Calculation

The approach implemented for the multicriteria analysis is PROMETHEE and more specifically the Visual PROMETHEE Academic Edition software which is the most comprehensive application. The PROMETHEE method is chosen in this study as a reliable and robust MCDA technique. Among PROMETHEE’s advantages is that it is possible to activate as many alternatives and/or criteria as needed. It also provides multiple options for the preference functions [52]. The steps for the proposed decision-making approach to rank the various zones include the following:

• Definition of alternative actions/zones
• Definition of decision criteria
• Weight assignment to each criterion
• Evaluation of each zone’s performance against each criterion—Definition of a qualitative scale

The aim of this part of the methodology is the ranking of actions/zones by Visual PROMETHEE software and creation of PAHI.

▪

Definition of alternatives/actions (polycentric zones)

The twelve proposed zones are considered alternatives for PROMETHEE methodology.

▪

Definition of decision criteria

In the second step, the criteria on which the decision is to be based are defined and at the same time, it is highlighted that it is desirable to maximize the performance of the action on each criterion. The criteria selected in a multicriteria analysis should be multifaceted and sufficient in number, aiming to express conflicting goals and aspirations. The highlighted criteria should also reveal the advantages and disadvantages of each alternative. The criteria on which the decision is based are Morphological Polycentricity and Virtual promotion, and they are nine in number. They were defined on the basis of international literature, international practice, and experience and are directly associated with the study’s goals as a first attempt to cover a range of dimensions connected to the polycentricity concept.

Table 2. Main structure of PROMETHEE: actions and criteria.

Actions	Hypercriteria	Subcriteria
Z1, Z2, Z3, Z4, Z5, Z6, Z7, Z8, Z9, Z10, Z11, Z12	Polycentricity	Asset density Spatial continuity Accessibility
	Asset virtual promotion	Number of Google citations Number of Sites/Social media

summarizes the main components of the PROMETHEE methodology.

Criteria are divided into two groups or hypercriteria. The first hypercriterion includes subcriteria connected to the polycentricity notion. The newly introduced asset density criterion is based on previously examined primacy and rank-size analysis in Section 3.3.1. More specifically, C_1. Number of assets refers to the number of assets per zone. This is a new and major indicator for the proposed polycentricity methodology. A zone with high asset density is higher in hierarchy, and thus, the considered zone tends to monocentricity.

According to Gordon et al. (1989) (as mentioned in Morris et al. (2018)) [83] it is argued that travel distances in polycentric areas are shorter due to the more balanced dispersion of population and functions, while private car travel is expected to grow in monocentric configurations leading to congestion and environmental decay [84]. Thus, public transit transports a large number of passenger flows [85] and it is important to explore accessibility and interconnectivity to and among the proposed zones. In particular, C_2. Schedules/Regional buses refers to the number of regional bus schedules per zone, and C_3. Schedules/Local buses refers to the number of local bus schedules per zone. Data are retrieved from the Intercity Bus websites for each R.U. and/or Municipality. C_4. Railway schedules criterion refers to the number of train schedules per zone. At the time of this research, the passenger train schedules are temporarily out of operation; however, transferability of the results implies taking into account all forms of public transportation.

Based on the academic literature, the classification of road networks to form the accessibility criteria is as follows: Motorways and other major roads with at least two lanes for each direction; National roads, meaning roads of major importance for the area; Local roads, meaning local roads of high or lower importance connecting urban and rural areas [86]. Criteria C_5., C_6., C_7. are selected to explore accessibility to each and among zones. C_5. Access to Egnatia Motorway refers to the number of highway overpasses to Egnatia Motorway per zone. C_6. Access to National road network refers to the number of major roads trespassing through each zone. C_7. Access to Local road network refers to the number of less important roads trespassing through each zone connecting urban and rural areas. Data is retrieved from Egnatia Motorway Observatory [87] where available and field research.

The second group of hypercriteria includes subcriteria connected to architectural heritage promotion. In this study, virtual promotion is selected as a field of investigation. The rise of digital media as data sources provides with access to information for a vast range of research fields [88]. Digital tools are universally accessible and easy to manage, can promote cultural assets, and thus “put a place on the map” by enhancing alternative cultural heritage destinations and creating the potential for tourist flows and socioeconomic development. Digital platforms like Google and social media are a “meeting point” for locals, visitors, businesses, investors, administrative and managing bodies, culture and tourism service providers, etc. [89]. Based on Pan and Fesenmaier (2006) (as mentioned in [90,91]), Google as a search engine intermediates between web authors and search engine users, making an important triad for marketers. It is argued that Google is one of the most used search engines, with internal algorithms for evaluating the ability of Google to return the most pertinent data to the user’s query [92]. Arguments on the use of Google as a quality data source are mainly based on the result ranking and the correlation between web traffic and site popularity [90,92]; however, these go beyond our research aim.

Assets within the study area are not widely promoted via the Internet or may not all have a website/social media organized by their managing bodies. However, many of them are promoted through other sites, digital publications, articles, etc. So, other types of official virtual references to each asset are possible. In this study, Google is chosen among other search engines as a widespread and easy-to-use tool to answer the simple query on how many sources refer to each selected asset in the aspect of counting those sources and not evaluating the information provided by Google at this point. C_8. Number of Google citations (number of sources with reference to each asset on Google) and C_9. Number of sites/social media (Facebook, Instagram, Twitter) are selected as subcriteria.

▪

Weight assignment to each criterion

The critical point of the proposed approach is the weighting of each criterion. According to PROMETHEE methodology, each criterion is given a weight along with the given preference function. Using this strategy, one can select among six forms of the preference function (Level, Linear, Gaussian, V-shaped, Usual, and U-shaped), each of which can be represented by two thresholds (Q and P). On one hand, the biggest deviation that the decision-maker considers as not important is represented by the indifference threshold (Q). On the other hand, the smallest deviation considered crucial for decision making is represented by the preference threshold (P) [93].

Based on the literature, the most appropriate weighting method in this case is the objective preferences of the decision makers and stakeholders [83], the experts. Five independent experts provided their opinion on the structure of the weight matrix with the weighting of each criterion, and the initial value of weights was normalized for the decision matrix. These experts were selected from three different groups, academia, business sector, and local authorities, so as to provide different perspectives on the case. The first expert is from academia, architect and Professor Emeritus, former Director of the School of Civil Engineering Regional Development Laboratory, and supervisor in numerous theses on regional and urban development and publications on this research field. The second expert is also from academia, Full Professor with specialization in the fields of project and construction management, decision, and risk analysis, with a focus on MCDM methods. Director of the Laboratory of Urban and Regional Planning and Development in the Department of Civil Engineering with participation in projects and funded programs, member of the editorial board of scientific journals, and supervisor of master and doctoral theses on Project Management and MCDM methods. The third expert is an architect and researcher with expertise in regional development and polycentricity. The expertise is documented by participation in projects, studies, and funded programs on polycentric development and cultural heritage research fields. The fourth expert is a freelancer architect with specialization in projects of restoration and regeneration of built heritage, as well as the revival of urban districts. The fifth expert is from the business sector but also from the local authorities’ sector and is currently a freelancer architect and urban planner, former deputy mayor of the Municipality Technical Works and Projects Directorate during the past decade, with participation in regional spatial development plans.

PROMETHEE requires defining preference function of each criterion, the choice between preference functions depends on the decision maker, who has to set the preference or the indifference thresholds [94]. Based on Bagherikahvarin and De Smet (2016) [95], the framework of PROMETHEE methods is based on the definition of preference functions to aggregate the related information on each criterion.

The preference function is (Equation (3))

Pj (a,b) = Fj[dj(a,b)] ∀(a,b) ∈A

(3)

For each criterion, the preference function reflects the deviation between alternatives a and b by a preference degree, between 0 and 1 [96].

The value for each alternative reflects the preference for the given alternative (in this case zone), while preference is expressed with a positive and a negative flow. The result is a final value, the Net flow which is the subtraction of the negative flow from the positive flow.

The Net flow Phi for each alternative is calculated by Equation (4):

$${Phi\left(a\right)=Phi}^{+}\left(a\right)-{Phi}^{-}\left(a\right)$$

(4)

▪

Evaluation of each zone’s performance against each criterion—Definition of a qualitative scale

The next step was the evaluation/assessment of the performance of each alternative (zone) in relation to each criterion. Preference functions are used in PROMETHEE based on the outranking principle to assign preference values when two alternatives are compared with respect to a particular criterion [97,98]. The experts defined the preference function for each criterion based on the criterion’s nature and how it affects performance alternatives [97,98]. In this stage, their personal perspectives played a crucial role in determining the optimal solution [94] and the performances were obtained through careful consideration of each criterion. A five-point qualitative scale based on Likert scale was chosen for all of the criteria to evaluate the performance of each zone. The scale rating is as follows:

• Very bad (VB).
• Bad (B).
• Average (A).
• Good (G).
• Very Good (VG).

4. Results

4.1. Morphological Polycentricity Analysis Results

The ongoing research showcases a plethora of results. Morphological polycentricity is calculated by applying the proposed methodology and by using the number of Ottoman architectural assets as the crucial indicator to measure morphological polycentricity.

4.1.1. Primacy Subindex

Based on Equations (1) and (2),

Table 3. The results of Primacy index and rank-size rule applied to polycentricity index.

Thrace—Primacy and Rank-Size Rule Results
Zones (Alternatives)	Hierarchy (per Number of Assets)	ln(Size)	ln(Rank)	Primacy Rate	ln(Size) = α + β × ln(Rank)
Z1	6	0.69	1.79	0.080	α	10.96	0.69
Z2	8	0.00	2.08	0.040	β	−0.029	4.57
Z3	3	1.10	1.10	0.120
Z4	4	0.69	1.39	0.080
Z5	5	0.69	1.61	0.080
Z6	1	1.61	0.00	0.200
Z7	9	0.00	2.20	0.040
Z8	7	0.69	1.95	0.080
Z9	2	1.10	0.69	0.120
Z10	7	0.69	1.95	0.080
Z11	10	0.00	2.30	0.040
Z12	11	0.00	2.40	0.040

shows Primacy index and rank-size rule results for hierarchy based on the number of architectural assets per zone. The hierarchy classification of the most polycentric to the most monocentric zones indicates that zone Z6 (based on Komotini city assets) is the most polycentric zone concentrating 5 out of the 25 assets. Zones Z9 (based on Xanthi city assets) and Z4 (based on Soufli town assets) follow with 3 out of 25 asset concentrations each. Due to the proposed zoning, the distribution of the selected assets is quite balanced, and the area tends to polycentricity.

4.1.2. Rank-Size Rule Subindex

A similar trend is seen in rank-size applications. Based on Equation (2): rank-size rule, the slope of the regression line indicates the level of hierarchy and thus the level of polycentricity within the study area (Figure 7). The flatter slope of the line indicates a higher level of polycentricity. The slope of the regression line is quite smooth, so the study area tends to polycentricity.

4.2. PROMETHEE Methodology Results—Introduction of PAH Index

The significance of each criterion was evaluated, expressing the preference for visiting an architectural asset and consequently an urban area. The highest level of evaluation is the value of 1, reflecting the higher importance, whereas the value 0 is assigned to the lowest one. Intermediate values may be calculated by the normalization procedure [84]. The weight for each subcriterion is set depending on the weight of the respective hypecriterion. The weight of each criterion combined with a certain performance results in an integrated score for each alternative.

The mean weights as set by the experts are given in

Table 4. Weighting factors for the decision criteria.

Codes	Criteria	Weights
C1	Number of assets (zone density)	W1	0.383
C2	Schedules—regional buses	W2	0.095
C3	Schedules—local buses	W3	0.060
C4	Railway schedules	W4	0.055
C5	Access to Egnatia Motorway (highway overpass)	W5	0.055
C6	Access to national road network	W6	0.045
C7	Access to local road network	W7	0.035
C8	Number of Google citations	W8	0.136
C9	Number of sites/social media (Facebook, Instagram, Twitter)	W9	0.136

.

Also, preference for each alternative on each criterion was given by the experts, and the results are shown in

Table 5. Preference results based on Likert scale.

	Criteria	C1	C2	C3	C4	C5	C6	C7	C8	C9
Alternatives	Z1	G	VB	VB	VB	VB	VB	A	VB	VB
	Z2	B	VB	A	VB	VB	VB	A	VB	VB
	Z3	VG	VB	A	VB	VB	VB	G	VG	VB
	Z4	A	A	A	VB	VB	VB	G	VG	VB
	Z5	A	B	VG	VB	G	G	G	VG	B
	Z6	G	VG	G	VB	A	G	VG	VB	A
	Z7	VB	A	B	VB	VB	VB	VB	VB	VB
	Z8	B	G	B	VB	A	A	B	G	VB
	Z9	G	G	G	VB	A	G	B	VG	B
	Z10	B	B	B	VB	VB	VB	VB	VB	VB
	Z11	B	G	G	VB	B	G	B	VG	B
	Z12	VB	B	G	VB	B	G	B	VG	B

.

4.2.1. Ranking of the Zones

Figure 8 presents the PROMETHEE input data template. The operation PROMETHEE Table is selected. It should be noted that in order to comprehend the following diagrams, Visual PROMETHEE allows the user to choose the color of each alternative, which is useful for result display. These colors remain the same in all other analyses to make the changes in the diagrams more distinct [99].

4.2.2. PROMETHEE Complete Ranking—PAH Index

Basically, the methodological process includes the pairwise comparison of actions on each criterion, the generation of unicriterion flows, and their aggregation into global flows [85]. In this study, the Polycentricity Architectural Heritage Index (PAHI) as the Net flow Phi is introduced. Net Phi is an overall measure of the evaluation of alternatives against all others. For the calculation of Phi, the proposed methodology for the polycentricity hierarchy of architectural assets is included as a contribution to the current methodology and an attempt to go one step further.

The ranking of the actions is presented in the following

Table 6. PROMETHEE Table.

Rank	Alternative	Phi	Phi+	Phi−
1	Z9	0.5361	0.6217	0.0856
2	Z6	0.5339	0.6616	0.1277
3	Z5	0.3636	0.5667	0.2031
4	Z3	0.2568	0.4887	0.2319
5	Z11	0.141	0.4098	0.2687
6	Z4	0.0461	0.3707	0.3246
7	Z1	−0.1024	0.2882	0.3907
8	Z12	−0.1455	0.3002	0.4457
9	Z8	−0.1725	0.2692	0.4417
10	Z2	−0.42	0.1073	0.5273
11	Z10	−0.4386	0.098	0.5366
12	Z7	−0.5987	0.0559	0.6545

, which is based on the Net flow Phi. The results of PROMETHEE, including morphological polycentricity calculations on architectural heritage, are summarized in column Phi of Net flow, which is the proposed PAH Index. The comprehensive ranking of the alternatives, from best to worst, is provided by PROMETHEE complete ranking. In this case, the alternatives are ranked using the Net flow (Net Phi). It is considered that the option with the larger Net Phi is the best. Negative values for Net flow are generally seen as unfavorable data. Ranking the proposed zones aims at identifying the “hierarchy” of the best to the worst alternative. PROMETHEE compares the positive and negative flows for each pair of alternatives—if the positive flow of one alternative is higher than or equal to the positive flow of another alternative and if the negative flow of the first alternative is lower than or equal to the negative flow of the second alternative, then the first alternative is preferred over the second [99]. Preference in this case reflects the preferability to visit a zone and its assets.

As shown in

Table 7. Ranking results for morphological polycentricity hierarchy and PROMETHEE.

Morphological Polycentricity Density (Hierarchy)	PROMETHEE Ranking
Z6	Z9
Z3	Z6
Z9	Z5
Z1	Z3
Z4	Z11
Z5	Z4
Z8	Z1
Z10	Z12
Z2	Z8
Z7	Z2
Z11	Z10
Z12	Z7

and Figure 9—PROMETHEE Complete Ranking, the best alternative is zone Z9 (based in Xanthi Municipality) scoring 0.5361 (score ranges from +1 to −1). The second alternative ranked is Z6 (based in Komotini Municipality) with a score of 0.5339 (competitive to Z9 with a sigh ranking difference), followed by Z5 scoring 0.3636 and Z3 scoring 0.2568. The next alternative above 0 is Z11 with a score of 0.141, followed by Z4 with a score of 0.046. On the negative side of the axis is Z1, with a score of −0.1024, followed by Z12 scoring −0.145 and Z8 with −0.172. Z2 follows, scoring −0.42, Z10 scores −0.439, and the worst alternative is Z7, with a score of −0.599. The green color represents the positivity of the axis, while red represents all values below 0.

PROMETHEE rainbow diagram (Figure 10) illustrates the final ranking of the zones in measuring their performance. PROMETHEE rainbow is a synthesis of the Net flows. Alternatives (zones) are displayed from the left to the right side based on their rank. Each alternative is illustrated by a vertical bar consisting of colored blocks, the set criteria. Each block represents the contribution of each criterion to the total Net flow value for each alternative. The size of each block demonstrates the Net flow (Phi⁺-Phi⁻) multiplied by the corresponding weight of the given criterion [93]. Blocks with positive values of each alternative are displayed above the horizontal axis, while blocks with negative values are under the horizontal axis. Criteria contributing positively to each alternative’s Net flow are displayed in ascending order (based on the contribution degree) above the horizontal axis, while criteria contributing negatively to each alternative’s Net flow are accordingly below the axis.

Zone 9 is ranked first as the best alternative. Figure 11 illustrates the action profile of Z9, with the performance of all criteria that contributed to the final flow of this alternative. The criteria contributing the most to its performance are C1 (Asset density), C2 (Schedules of regional buses), C5 (Access to Egnatia Motorway), and C6 (access to National road network), followed by C3 (Schedules of local buses), C8 (Number of Google citations), and C9 (Number of sites/social media). C4 (Train schedules) contributes nothing to Z9 performance, while C7 (access to local road network) contributes negatively to Z9 performance. The same analysis was performed for each alternative in order to identify the weak criteria for each alternative and propose appropriate actions for enhancement.

4.3. Sensitivity Analysis

Within the polycentricity PROMETHEE methodological assemblance framework, the choice of spatial analysis to explore polycentric development prospects is attempted, providing significant outcomes. A sensitivity analysis was conducted regarding the criteria weights. For a decision maker, the decision of the exact weight values is a demanding task. There can be ambiguities; thus, it is important to figure out the influence of a modification in the weights on the ranking. Weight stability intervals (WSIs) evaluate the impact of a specific weight value alteration, while all other criteria remain the same, on actions’ rankings. Sensitivity analysis provided a range of weights for each criterion within which the ranking remains unchanged. For the first criterion, C_1. Number of assets (zone density), WSIs range from 35.27 to 40.05 (Figure 12). Within this variation range, the action ranking remains the same.

In the same aspect, sensitivity analysis was performed for each weight on each criterion. For C_2. Schedules/Regional buses criterion, WSIs are between 0.00 and 9.82, for C3. Schedules/Local buses, WSIs are between 3.65 and 8.40, for C4. Railway Schedule, WSIs are between 0 and 100. For C_5. Access to Egnatia Motorway, WSIs range from 0.00 to 10.65, for C_6. Access to National road network, WSIs range from 0.00 to 7.75, for C_7. Access to Local road network, WSIs range from 1.74 to 3.57, for C_8. Number of Google citations (presence on Google), WSIs range from 13.01 to 16.22, and finally, for C_9. Number of sites/social media (Facebook, Instagram, Twitter), WSIs are between 13.76 and 16.50. The wider range (100%) is noted for C₄ due to the particularity of the criterion. Since there are no active train schedules temporarily due to necessary and continuously demanding upgrade works, the preference is the same for each action, and thus, any change in the relative weights for this criterion has no impact on ranking.

5. Discussion

Research findings so far concern many different aspects. PROMETHEE is basically used to quantify the dispersion of assets throughout the study area, showing that there are perspectives of highlighting alternative destinations and ranking them. It should be stressed that the results of morphological polycentricity differ from the PROMETHEE results (Table 7). If taking into account morphological polycentricity alone, Z6 stands out in the hierarchy. When combining polycentricity PROMETHEE criteria and preference function, results are very different. After applying PROMETHEE, Zone Z9 ranks first as more attractive to visitors and therefore investments and income; thus, other zones show perspectives of enhancing their assets. However, Z9 ranks third in morphological polycentricity methodology. Another indicative example is Z11, which ranks fifth in PROMETHEE, while in morphological polycentricity it is penultimate. It should also be stressed that morphological polycentricity arranges more highly in hierarchy zones, with assets mainly identified in Municipality Seats (i.e., Z6, Z3, Z9). Thus, PROMETHEE ranking highlights other zones (i.e., Z5) than polycentricity methodology and Municipality Seats, promoting alternative spatial networks and underlying the crucial contribution of PROMETHEE to the proposed methodology.

Furthermore, all qualitative and quantitative data, as well as the proposed methodology on asset polycentricity, were combined by PROMETHEE and gave reasonable results, confirming the research hypotheses. Effective planning strategies and policymakers need to take into account the prospects of architectural asset networking for balanced and sustainable development. The proposed PAH Index based on Net Phi flow incorporating asset polycentric density subindex is designed to highlight a novel approach to polycentric development strategies and PROMETHEE methods for assembling attributes of various fields.

It should also be underlined that morphological polycentricity results would differ if the Primacy Index was calculated based on population data, as Alexandroupoli, Komotini, and Xanthi cities would stand out in the hierarchy due to high population concentrations. Thrace tends to monocentricity in terms of population with the three Regional Unit Seats concentrating not only on population but also economic activities, services, administration, etc., while smaller cities, especially in mountainous areas, remain underdeveloped.

Within the study area, a plethora of Ottoman-related assets have been identified and recorded. Unique sites, locations, and buildings that could be considered to continue the history and tradition of the Ottoman were identified. These assets feature the potential to enhance alternative destinations. The results clearly show the polycentric networking based upon the Ottoman architecture assets offers perspectives for entrepreneurial synergies within Thrace. Also, our study concentrates on 25 Ottoman architecture assets identified in urban centers in Thrace, as a sample to investigate the potential of creating a form of Polycentric Urban Region within the study area. The results indicate that the use of polycentricity data, such as density in terms of assets instead of population, connectivity, and accessibility to each proposed zone, as well as data related to Ottoman assets, i.e., virtual promotion, can be effectively combined with PROMETHEE methods.

Architectural heritage as a vital part of cultural heritage is selected to enhance polycentric development opportunities. Cultural heritage is associated with polycentric and sustainable territorial development generating interconnections between areas in terms of physical flows of visitors and financial flows from their economic exploitation [39]. According to the results, it is necessary to upgrade transportation connectivity, focusing on infrastructure and means, especially for mountainous areas, because of their distance from major cities and Egnatia Motorway. Also, multimodality among the network nodes is crucial, as no effective polycentric development application can be achieved without efficient and abundant road and railway connections.

Polycentric urban networks can be achieved by enhancing architectural assets in each zone with virtual tools, i.e., promoting them through the local authorities’ or managing bodies’ sites. During recent years, social media have gained increased attention, among other features, as low (or non)-cost promotion tools. Social media (Facebook/Twitter/Instagram/TikTok, etc.) can be easily created and maintained by managing authorities to provide useful information about assets, especially for urban centers lower in the hierarchy, to create opportunities to attract more visitors.

PAHI is introduced as an innovative tool combining two methodological approaches, morphological polycentricity, with the new asset density indicator and PROMETHEE methodology. Also, the formation of polycentric zones based on 15 min isochrones is endorsed in this study to introduce new polycentric spatial typologies. The interrelation of the two methodologies is the authors’ first attempt to provide a formal methodological technique for assembling quantitative and qualitative data. It is designed to enhance the best spatial zone (alternative) based on the criteria set and which zones are less preferred to visit. In this aspect, PAHI may provide an initial attempt at a set of indicators and an analytic approach to which spatial arrangement is more suitable to boost a “sustainable exploitation” of cultural resources [39].

Regarding the sustainability of this study’s outputs, PAHI aims to reach long-term objectives. PAHI is designed to promote the creation of synergetic networks among local authorities, cultural heritage managing bodies, businesses, and local societies. By ranking the proposed actions, PAHI based on Net Flow, could contribute to spatial planning policies and better implementation of strategies for a more sustainable and balanced territorial development. Furthermore, by identifying the less preferred actions PAHI methodology could suggest initiatives to improve the “less attractive areas” within the polycentric urban network and enhance alternative destinations and products. This may be a new base for promoting urban and regional initiatives in the cultural and tourism sectors, as well as entrepreneurial synergies and joint strategic spatial and marketing plans for further economic and social opportunities, especially in remote areas.

Moreover, a balanced polycentric network may thwart massive population and economic concentrations, favoring the development of other cities but also protecting the prime cities’ microclimate and the wider area’s cultural and natural environment from overcrowding and negative externalities. Polycentric forms are more environmentally friendly and may promote more balanced territorial development and cohesion and generate positive outcomes in terms of economic and social development [38]. In line with the Sustainable Development Goals, polycentric development is a prerequisite for sustainable and balanced territorial development [100], social cohesion, and economic competitiveness, as well as environmental protection [13,101].

In this aspect, the proposed polycentric forms may show further benefits, for instance, climate change actions. Yue et al., 2019 [102] argue that polycentric urban forms may affect surface temperature as an indicator of landscape shaping. Polycentric urban forms, which are more compact and less dispersed could benefit climate (for example restraining CO₂ emissions). More specifically, functional polycentricity flows within polycentric areas can alter commuting patterns and allocation of populations and functions, benefiting energy and heat reduction. Restraining climate change also has the aspect of synergies and commitment, meaning cooperation among governance and spatial entities to find and apply methods for climate change challenges [103,104]. Climate change strategies can be better implemented in small and medium-scale governance units linked through networks. Polycentricity favors the dissemination of good practices among smaller but interconnected urban systems [103].

Limitations

This research has faced limitations and further examination is necessary in future work. First, considering the lack of available socioeconomic data on NUTS III and LAU I levels, research is based mainly on primary research. Departing from the methodological approaches to measuring morphological development [73,105,106], this study focuses on morphological polycentricity in order to identify the potential of polycentric development in the study area. Future research could extend to other functions beyond cultural exchanges, namely economic interconnections, travel-to-work flows, and labor or product market flows. Second, the geographic area is delineated on the mainland due to the insufficiency of Ottoman architectural assets on Samothrace Island and connectivity, which could be included in future work.

PROMETHEE methodology is chosen in this study as a robust methodology showing no severe limitations. In the proposed methodology, an attempt is made to include as many and more relevant criteria to polycentricity and asset “attractiveness”; however, criteria and the list of experts could be enriched in future research. As a general limitation, the determination of qualitative criteria could be considered. A very careful evaluation of each qualitative criterion’s performance is necessary in order to assess it as objectively as possible.

6. Conclusions

Originating in Christaller’s central-place theory (1933) and Losch’s developed version (1954) [107,108], the concept of a balanced polycentric urban system was introduced into the discussion about the spatial future of Europe during the period from 1999 to 2007. The extent to which urbanization economies develop without inequities has often been associated with “size” or “density”, and many studies have shown that larger and denser cities perform better in terms of labor productivity and urban wage levels [109]. “Size” and “density” are taken into account for examining whether a study area tends to polycentricity or monocentricity, and in the next stage, what actions should be made.

The tested and confirmed research hypotheses provide useful information for urban and regional policymakers, and the conclusions may have broad implications. The focus on morphological polycentric development analyzed by PROMETHEE methods may lead to sustainable urban and regional development and, if examined thoroughly and more specifically, this combined method and the introduced PAH Index may provide a new analytical view on the issue of polycentricity.

Polycentricity and the PROMETHEE methodology have a main common approach: they both attempt to investigate the ranking among the alternatives/nodes of the network in order to identify whether there are and which are the “best performing” solutions. The primacy index and rank-size rule used to measure morphological polycentricity endeavor to classify and single out the “prime” node of the network, to recognize whether the area tends to monocentricity or to polycentricity. Also, PROMETHEE enhances the solution which stands out when ranking all alternatives in pairs. The next step for both methodologies could be the proposal of actions needed to upgrade the other nodes/alternatives.

PROMETHEE is a useful methodology to reach the paper’s goals and assemble qualitative and quantitative attributes by giving each alternative a specific weight and performance score based on various criteria. The proposed PAHI based on PROMETHEE Net Phi is designed to be applied in various ways, such as by enriching the used alternatives and criteria. PAHI could be a useful tool for combining various criteria in numerous cases and spatial levels. Multiple other indicators could be used to investigate diverse patterns of polycentric networking with the proper adjustments on a regional scale. For the PAH Index, the methodology could be expanded beyond the morphological to functional polycentricity aspects, investigating flows among the network nodes. Functional indicators could be synergies among authorities or managing bodies of the architectural heritage for further promotion actions, tourist flows, school excursions to assets, and common actions for environmental protection. This study focuses on Ottoman architecture assets (19th–20th century); however, research could investigate other cultural heritage categories, depending on the local identity of each area under examination. In addition, instead of polycentric zones, it could be applied mutatis mutandis on other spatial entities, i.e., LAU I or LAU II entities (depending on the availability of data), to rank them and enhance the best-performing area. Also, architectural assets could themselves be regarded as alternatives and ranked in order to locate the most “attractive” ones.

Research goals so far have been confirmed. Research could be further extended to various spatial scales (local, regional/interregional, national scale) based on the study area’s profile and strengths (i.e., number and characteristics of settlements, network density, accessibility, travel time). It should be noted though that any single indicator should be thoroughly examined before being added as a criterion in PROMETHEE, as it may have its own advantages and disadvantages. It would be of great interest to compare the PROMETHEE methodology with other MCDA methods such as ELECTRE, TOPSIS, AHP, etc.

The present methodology highlights a new aspect of polycentric development and MCDA methods, providing opportunities for smaller cities to attract investments in the cultural sector on a regional or interregional level. More specifically, the academic literature on polycentricity combined with PROMETHEE methodology is rather limited. The proposed PAH Index aims to take research one step further in the fields of polycentric development, cultural heritage, and MCDA methods, as well as to be a useful tool for policymakers and authorities toward sustainable spatial planning.