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SSS10 Proceedings of the 10th International Space Syntax Symposium

068

Up to where a city ends. Α spatiotemporal imprint of commuting patterns for the ten largest Greek cities

Abstract This paper deals with the composition and implementation of an analytical methodology for the redefinition of urban areas limits, which utilising both spatiotemporal socioeconomic and geographical data is applied for the ten largest cities in Greece in a Geographical Information Systems (GIS) environment. Critical points throughout the research are the selection of input data and the segmentation of space. As input data, we use parameters that can reflect the urban sprawl dynamics, such as housing, workplace and workplace distance. We examine urban centers as transmitters or receivers of manpower based on their ability to attract and repel people in strong connection with the workplace factor. Based on the central question, "Up to where the city ends?", we attempt to outline the "real" and not institutional boundaries of an urban center, which vary based on the parameter of transportation for work. The principal objective is to leverage methods and techniques in order to identify a methodological framework approach, analysis and evaluation of the spatial position, influence radius and relations arising from the variation of the dynamics of urban centers. The study of these spatial development components will be implemented on the basis of their evolution in time, using the same methodology in the range of the last decade (2001-2011). Thus, through this process, we attempt to deconstruct the existing reality, to record its individual components of the causes of its generation and ultimately to recompose them in an attempted understanding of this reality.

Keywords

Commuting, movement flows, Greece, city limits, cartographic mapping.

1. Introduction

The informal and yet profound influence of modern urban centers has recently led to the intensification of the intra-urban and exurban disparities, mainly due to the unequal distribution of economic resources. This question has been the subject of research by different disciplines, such as economics, sociology, statistics, demography and geography. In this context, methods and techniques of quantitative spatial analysis are being implied for the study of urban sprawl through the determination of the degree of influence and interdependence and the assessment of the dynamics of different urban concentrations.

Daphne Dimopoulou National Technical University of Athens [email protected] Maria Aggelidou National Technical University of Athens [email protected] Yorgos N. Photis National Technical University of Athens [email protected]

D Dimopoulou, M Aggelidou & Y N Photis Up to where a city ends. Α spatiotemporal imprint of commuting patterns for the ten largest Greek cities

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mailto:[email protected]





We need to leverage methods and techniques to identify a methodological framework approach for the analysis and evaluation of the position, influence radius and relationships that emanate from the variation of dynamics of urban centers. The study of these spatial development components will be implemented on the basis of their evolution in time, using the same methodology in two, distinct time periods. The proposed methodology is composed by official data and definitions but also reflects a personal approach, which relates to the anatomy of the two largest, Greek urban centers, Athens and Thessaloniki. Finally, it is deemed necessary to couple different scales between smaller and larger urban centers in order to export safer conclusions, achieving to cover an adequate range of cases of urban dynamics.

2. Background

The urban sprawl effect

In the reality of contemporary urban centers, the administrative map of the city "is being parceled" by the continuous and rapid traversal of people and goods. Thus, people perceive the borders and boundaries of urban areas, as permanently changing the shape and density. So new areas are emerging in the broader map of the city, with different characteristics (qualitative and quantitative), that affect the perception for the city. The consequences of this phenomenon are made visible both in social and spatial relations, which are being developed inside the fluid urban outline.

Urban sprawl is a multifaceted phenomenon that describes the sprawl of urban fabric, its functions and suburbs to the exurban area, resulting in the generation of low density urban concentrations. "The city limits become more porous, causing confusion in our ability to separate them with clear lines, regular lines between the city and the countryside, the natural and the artificial "(Chambers 1990 in Soja 2000: 150). The urbanization of suburban space takes extreme forms in the contemporary societies of globalized economy (Tsoukala, 2009). This effect causes the disruption of organizational structures previously creating a referential framework for the urban set.

Continuing their expansion, these urban concentrations were unified with other adjoining areas, hence generating a broader set: Metapolis of Francois Ascher, Generic City of Rem Koolhass, Ville Emergente, Citta Diffusa, Edge City, enumerate different descriptions of the significance of the diffuse city (Aisopos, 2001).

The newly formed areas are becoming receptors of uses of residence and services, so that the process of diffusion is gradually developing. The characteristics of a diffuse city are:

• Long distance transportation for work • High car usage dependence • Inadequate facilities: e.g schools, health infrastructure, culture etc • Higher cost of infrastructure usage per person

The city does not grow in the form of ever-expanding circles (the Burgess Urban Land use model) around the historic center, but spreads in a more complex canvas of fluctuating densities, formed in basis of transport, telecommunications and information networks. Our approach seeks to uncover this complex mechanism of development and impact, using the workplace factor as a driving force behind this evolution.

Dynamic delimitation of urban areas

Attempts for delimiting urban areas arise, from the beginning of the century, in most developed countries, have been done mainly for statistical purposes. Today the interest for such demarcations is principally related to the need of creating or restructuring the levels of governance in large urban areas in order to support and manage the development perspective in the context of a growing spatial competition (Hardy &Al 1995, Cheshire&Gordon 1995, Camagni 2002). Although references and definitions for urban areas exist from an early period, the concept of urban center itself does not seem to be clearly defined. The statutory limits of Greek major cities include: a) the region eg. the


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entire city residential, impact network b) the main conurbation eg. the main body of continuous urban fabric, that has been developed around the residential cores of the historic city (Avdelidis, 2010). For this reason, we had to understand the diversity and the gradations of urban areas, which are concentrated around the center, since a horizontal approach would prevent analysis at different scales.

Identifying dynamic tendency indicators

Large urban centers have a particularly crucial role in the contemporary globalized economy while for them, external economies and the related division of labor are key to their prosperity. The concept that can be recognized behind this approach is that, "space and economy in their interweaving produce conditions that determine the competitiveness of various regions" (Foutakis 2004).

Based on the above, as a first indicator for the analysis that follows is selected the spatial distribution of employment as it relates to the production system of the area. As a second factor to be taken into account is the population. A predominant feature of urban areas is the increased concentrations of population (Aravantinos 1997). This feature can be described better as population density. As an additional criterion is the distance from the main urban core of the city, which in many cases in conjunction with the above strongly characterizes the urbanized areas. In the case of large urban centers of Greece, population growth reflects, to some extent, urban economic accumulation. Moreover, density as an indicator underscores the importance of space proximity in economic development. Some even argue that the density of urban areas is indeed an indication of economic dynamics and the quest for a “population density logic” in the cities of the 21st century can be objective about the "developed economies of knowledge" (Glaeser, 2000), while others connect it with the concept of social cohesion (Power 2001).

After selecting the indicators, we had to choose how the division of space would be processed. Dividing an area into individual parts forms an issue in the field of economic geography and is known as the problem of regionalization: involves the physical continuity of space and the element of dispersion of values of a characteristic around a mean value. Therefore, delimitation should take into consideration the continuity of characteristics and values and the analysis should be directed to the search of an area, with homogeneous features, that stem from indicators such as spatial distribution of employment, population, population density and distance from the main urban core.

The Greek city paradigm

Today, the “exurban” Greek area is being subject to intense reconstruction and exploitation tendencies concerning various categories of land use: industrial uses, tourism, residential uses, etc. The division of space into "inside" and "outside" of the city exists now only at the institutional level, providing that the limits between urban and “exurban” areas aren’t truly defined. The transformation of the city and its regional expansion, constitute a contemporary, development product by which is formed and the "intra-urban” and “extra-urban” area. Landscaping expresses the level of development of the productive forces as well as their correlation and reflects to the ground the total of social and urban development" (Melissas, 2007:3).

"The low density exurban expansion" (Morrill, 2006) is done without planning or control, because of the lack of appropriate regulations, or due to non-implementation of the existing ones. Illustrative examples of such practices are: the illegal partition and allotment of land, illegal construction, non-activated mechanisms of allocating activities in the suburban area. "Both production and regulation of space is based on non-planning mechanisms" (Economou, 2000: 53).

The new, diffuse, urban space is expanding, fragmented, heterogeneous and the role of the tertiary sector with internationalized services associated with production is clearly an important, shaping factor. Greece, appears to have a solid, central governance structure, along with fragmented, local control, regarding the institutional framework of spatial policy. This results in the inability to coordinate the dynamic creation of new, urban, areas, thus creating "arrhythmias" in the city function (Lalenis, Eftychiadou, 2012).


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3. Methodology

Analysis and modeling tools

The development process is the result of multiplex political, social and economic processes, whose geographical aspect, in fact, forms an integral part of them. This geographic dimension both of every development procedure and particularly of this specific project is leading us to choose a Geographic Information System (GIS) as the main analytical tool. As the key feature of GIS can be considered the ability to combine the geometric-geographic data with descriptive information, also indices or general phenomena that correspond to specific geographic entities in a way that allows an overall handling (Koutsopoulos, 2002:69-90).

As modeling tool, during the final stages of the study, we used the computer design software Rhinoceros3D based on mathematical models "NURBS" [Non-Uniform Rational B-Splines]1. Designing in Rhino focuses on the production of mathematical representations of freeform curves and surfaces, accurately and in computer graphics.2 A mathematical model "NURBS" offers great flexibility and accuracy during the dynamic processing of curves and surfaces -designated using common mathematical types- both in the context of analysis procedures and standard shapes (P.Schneider, 2014). The "splines" in mathematics are numerical representations which are partially defined by polynomial functions and with a high degree of smoothness at the points where the polynomial pieces joined (knots) (K.L.Judd, 1998).

1 NURBS: non-uniform rational basis of arbitrary shape. "B-spline" [basic spline]: according to numerical analysis is a splines function which has the minimal support on the degree given, smoothness and processing sector. 2 http://www.rhino3d.com/


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Data

We use data from the National Statistical Service of Greece (ELSTAT), which personally gave us the information we needed meeting the theoretical requirements of our model. We use the classification of urban centers arising from the population census of 2011 and the data for residence and workplace of the censuses 2001 and 2011. The database indicates the number of people employed by place of residence and their workplace. More extensively, the data consist of:

The geographical area of research

The geographical area of research forms the basis of research for the comparison of digital backgrounds that had to be used subsequently. We select the ten largest urban centers in Greece to cover a range of cases. We study the eight largest urban centers in their municipality scope along with their suburban areas that are settlements with strong urbanization dynamics. The cases of Athens and Thessaloniki are examined separately with a different method.

The first step of the classification is based on a quantitative and an empirical record. Concerning Athens and Thessaloniki we chose to transcend the boundaries of their municipalities, as their urban setting, spreads to the administrative region level. Therefore, we study the region of Attica and the Regional Unit of Thessaloniki as urban footprints, while the other cities were studied up to their municipality limits.

It is evident that, apart from the urban complex of the city, we are also interested in the exurban areas, inextricably tied to the urban core.

Development centers and the dynamics of settlements

In the General Framework of Urban Planning and Sustainable Development, a composite index for the characterization of settlements and urban centers is being used based on their growth dynamics. This index is the composite result of population changes, employment and the ability of engaging social equipment, that indirectly connects us with the local productive system and the developmental dynamics of urban centers. So we attempt a re-classification based on the size of the population.

Thus, arises the need to develop a methodology which could involve different sizes and would evaluate them in an aggregate manner. The development within an urban concentration is accomplished by: the quantitative and qualitative development of the values of all variables (Tsouderos, 2002) which describes the urban particularities of each city, but simultaneously serves as an objective evaluation system. For this reason, in the case of Athens and Thessaloniki, we follow the “ring” method, as a means of separating the geographical boundaries and we classify three successive rings for each city, examining: 1) their radial distance from the administrative center 2) the number of their population 3) their density


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For example, we are interested that the municipality, which is classified in the second ring, can be far from the center, while its population density endorses its urban character. Each category was scored with an influence degree.

The role of the city as a transmitter and as a receiver

We argue that the essence of contemporary urban centers is twofold: the city behaves simultaneously as a transmitter and as a receiver. Its ability to accumulate human resources and activities and also disperse them depends mainly on its development prospective. A city that is developing rapidly seems to mainly act as a "magnet" for residents who move away from the cities affected by desurbanization and unemployment.

This does not necessarily mean that all employees, who reside inside an urban center, also work within its limits. Studying this phenomenon in conditions of economic crisis and intense labor mobility, we observe high percentage of transportation for work both inside the municipality boundaries and away from them. The ability of the city to attract people depends on the specific social, economic, geographic characteristics of the area. Also, when we refer to the transportation for work, we consider workplace factor as an everyday commuting or as a temporary accommodation in the workplace.

4. Tendency polygons identification

The criterion, and consequently the analysis parameter, for determining the “real” city limits of each city is the population's movement for work, without the time parameter. Precisely because we are not looking to define the Functional Urban Areas of Greece (G. Doris, 2008), but we are interested in a more qualitative approach to urban boundaries which may indicate even temporary stay. More thoroughly, we are interested in the permanent municipality of residence and work municipality in order to include a number of cases of this bidirectional movement.

The ultimate objective of the calculation procedure of polygons indicating each city dynamics through its actual limits, is to create 10 digital backgrounds, one for each city, which will be imprinted with two polygons that show the change of city boundaries in one decade range (2001-2011). This process is completed through a computational model which is developed in three different stages:

1. Algorithm Selection for Finding Tendency Peak Points

2. Quantification and Limits Calculation

3. Composition, Modeling and Visualization of Results


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The initial step for organizing, processing and analyzing data for the transaction of the intended results, is to define two flows for each time (4 flows creation). According to the first flow, the 10, under study, major Greek cities channel active population towards the rest of Greece (stay in interest city ---> work in other municipalities), both for 2001 and 2011. In the second flow, and also for the same times, these cities attract human workforce from other Greek cities (stay in other cities ---> work in interest city).

Working in GIS environment, we generate layers for each city, each flow and each of the two times, to implement the model in each individual case. During the census of 2001 the administrative division of Greek municipalities was under Kapodistrias Plan (910 municipalities), whereas during the 2011 census the municipalities were combined under Kallikratis Plan (325 municipalities). For this reason and to be able to compare the results in a common spatial framework, we edited all the data, to match the structure of “Kallikratis” division.

Because - in the later steps of the computational model- we will need the distance and the axes between cities and other municipalities, we seek a method of conversion of the so far existing polygons -which represent Greek municipalities- into useful points. Then, we find the centroid points for each polygon (CentroidPoint) and focus on them only (Figure 3).


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For each one of the ten largest cities, for each flow and each time, the following procedure for computing tendency peak-points is applied, always according to the bidirectional moving worker population: we calculate the Euclidian distance between the Centroid of under study urban centers and the centroids of the rest Greek municipalities in which movement for work is detected, but also the angle between them (to the horizontal axis).

Given the results of the procedure above, the centroid points of the rest municipalities were classified in octants of the Cartesian System, based on the angle between them and each centroid point of the under study city (Table_5). The distribution into octants is chosen to simplify the process of specifying the final tendency polygons of the ten cities and to organize the spatial imprinting of percentage results during the comparative and inference procedure for each movement flow (Figure 4).


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Subsequently, we seek for each one of the eight parts of the Cartesian System the city's dynamics based on both its distance from the rest municipalities and the number of employed people who move among them. For this reason, the spatial Mean Center in each octant (case field) is calculated and is weighted depending on the number of people moving in each flow between the city and the rest municipalities (weight field). Thereafter, the 8 found Mean Centers are correlated with the sum of moving people per octant (Summarize).

Between the centroid of the city and the spatial Mean Center in every octant we search the weighted distance in which the tendency peak-point is, depicting the city's dynamics for this specific octant, ultimately forming the boundary point of the requested tendency polygon. The coordinates of this point (w_xi, w_yi) are given from the formula below:


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Particularly in the cases of Athens and Thessaloniki we have implemented segmentation of the wider region in “ring” zones of influence which modulate the method of identification of tendency polygons as follows: the Mean Centers per octant above are calculated for each “ring” zone separately but always taking as a reference point the centroid of city, resulting in taking three Mean Centers per octant. Aiming to compose these 3 Mean Centers/octant we calculate a new Mean Center/octant by giving, through the described procedure, an influence degree depending on the “ring” zone from which the initial Mean Center eventuated (weight field).

From this point onwards the calculation method is common to all cities and continues with the algorithm previously recorded for finding tendency peak-points.

Given the new points for each city, we need a modeling tool for the support of this research. Based on these points, multiplied by the radius of each city, the polygons defining city's tendency to attract or to channel workforce are generated mapping the city's dynamics, resulting in four digital backgrounds, two for each flow and two for each time, for each one of the ten largest Greek cities.

In order to compose time and flow polygons into one, to have the final tendency polygons, we insert these two curves to the modeling software (polygons outlines) and we work to get their exact intermediate curve (TweenCurves3).

Both the final, general polygons (Figures 5 and 6) and those of the individual flows and times (Figures 7 and 8) now reflect the reality of these ten cities with absolute consistency of the initial data.

3 This command divides the two curves in an equal number of points, finds the average between the corresponding points on curves and interpolate the exactly intermediate curve between those points, based on the accuracy set by the user.


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5. Implementation of the computational model of city tendencies identification

The basic conclusion is that Greece appears more urbanized if we consider the limits of urban centers that identify urban concentrations, compared with the administrative boundaries of urban areas.

Athens

During the entire decade 2001-2011, we observe an accumulation of population flows for employment purposes, mainly in the northwest of the city, obviously because of its intense relationship with the industrial poles of Attica and Veiotia and on the axis of its interconnection with central Evia. Although in 2001 we note greater intensity at the peaks of polygon dynamics for the capital, the spatial distribution of city's tendency both for 2001 and 2011 takes similar form. It is noteworthy that the reduction of the polygon's surface for 2011 coincides with the decrease -in absolute numbers- of the working population linked, for both flows, with Athens. (Figures 5 and 6)

Considering the role of the city as a transmitter and receiver in 2001, we observe that although the areas of the two polygons are approximately equal, the distribution and the minimum/maximum intensity points vary between them. More thoroughly, Athens receives more workforce from Veiotia, but sends more to Evia, while its tendency, on both flows, is detected as larger το the west. Generally at this time Athens operates more as a powerful receiver than a employee transmitter. (Figure 7)

In contrast, in 2011, Athens clearly operates more as transmitter, mostly to the regions of central Evia. Its flow surface has slightly changed -in size and distribution, whereas the receiver's surface presents a totally different view: the distribution of the polygon appears quite consistent in all four quadrants and its area has shrunk considerably compared with 2001. (Figure 7)

Thessaloniki


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Thessaloniki, over the decade 2001-2011, shows expansion of its surface and tendency dynamics, while polygons' spatial distribution and peaks axis remain the same. This indicates desurbanization symptoms, probably due to reasons of owner-occupied housing, agricultural occupations, wider use of countryside, in both traditional and business ways (Anthopoulou Th., Kamperis Ν., Petrou Μ., 2013), or relocating industrial uses. (Figures 5 and 6)

In 2001, the ability of receiving appears stronger with intense tendency mainly in the 1st and 2nd quadrants. Also the polygon distribution that imprints the city's function as a workforce transmitter is almost symmetrical at west and east, while the whole surface is regularly distributed. Comparing, however, the polygons of the two flows we can notice that they follow a similar distribution in all directions. (Figure 8)

In 2011, Thessaloniki continues operating more as receiver due to the strong relations between Thessaloniki and eastern Macedonia and Katerini region.. The polygons of dynamics of both flows are distributed similarly, especially in the 2nd and 4th quadrant. In addition, the coastal areas of Halkidiki, affect the city equally in the role of transmitter and receiver, mainly due to strong tourism activity, enlarging the respective quadrants proportionally to the general polygons. Eventually, the dynamics of the city compared to this of 2001 is appreciably homogenized. (Figure 8)

It is worth noting the various similarities identified in the status of Athens and Thessaloniki as receivers for workforce and also in the distribution shapes for each flow. We argue, therefore, that this phenomenon is a natural consequence of the increasing number of common features presented between both megalopolis while their size increases too. Furthermore, these two cities' tendency polygons are smaller comparing to their actual population number, judging by the tendency surfaces of the rest eight cities. This happens because Athens, as a capital and Thessaloniki as the second largest urban center, tend more to retain their workforce than the other smaller cities.

Patra

Concerning Patra we don’t observe any special, quantitative change in the general tendency surfaces for 2001 and 2011. However, there is a slight shift on the polygons' direction, which shows that channels flows to Peloponnese more than the rest of the mainland. (Figure 5 and 6)

In 2001, the city acts in proportion as receiver and transmitter. The direction of its tendency dynamics turns to the west, due to the popular tourist areas in the western coast Peloponnese and the islands. It clearly understood that Patra supplies northeast Peloponnese and Sterea Ellada. (Figure 9)

Also in 2011 its role is twofold, while it shows noteworthy changes on the direction of tendency's quadrant to the interior of Peloponnese, demonstrating that during the crisis period the scope of workflows are geographically distributed within the limits of Peloponnese. (Figure 9)


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Heraklion

Heraklion of Crete, we note a significant reduction from 2001 to 2001 and a more uniform distribution of the general polygon. Despite this, Heraklion is strongly related for everyday bidirectional movement reasons -more than the other cities- with the western and southern part of Crete. Especially in 2001, the city's tendency to the south is extremely dynamic. (Figure 5 and 6)

In 2001 the role of receiver is stronger and clearly tends to accumulate working population mostly from the island's south and a bit less from northwest. (Figure 10) In 2011, the transmitter's polygon area remains the same, while the receiver's is quite smaller with similar peaks to those of 2001. (Figure 10)

Larisa

In Larisa, we observe, in both times, an even distribution of dynamics’s polygons. We detect a slightly increased intensity at south and a weaker to the northwest. The tendency surface has expanded from this of 2001, despite the decrease of working population related to this city concluding that in 2011 Larisa channels workforce further. (Figure 5 and 6)

In 2001 the city operates more as a transmitter to the southeast, and receives more evenly from the surrounding areas. (Figure 11) In 2011, as a transmitter tends to be stronger to the same southeast direction, while as a receiver shows a new tendency to its northwest. (Figure 11)


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Volos

The city of Volos seems to be a special case, after the findings highlighted by the implementation of this methodology's.

In 2011, the general tendency surface has extremely shrunk and the peak points have shifted, comparing to those of 2001, from the 3rd quadrant to the 2nd. In 2001, there is a really strong tendency to southwest of the city and significantly weaker to its southeast. In 2011 the general polygons’ distribution becomes relatively normal with a minimum intensity to the southwest and both roles of the city weakening, as now Volos “holds” much more active population within its boundaries.(Fig._5&6)

In 2001, Volos as a transmitter shapes crucially the overall picture of its dynamics, with its tendencies -mainly on the 3rd and much less on the 1st quadrant- identified as the strongest of all the other nine cities.(Fig._12) Contrariwise, 2011 transmitter’s polygon rotates towards the 2nd quadrant, whereas the distribution in the 1st, 3rd and 4th quadrant are also very normalized.(Fig._12)

Rhodes


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The city of Rhodes presents an almost even distribution for both times. The tendency polygons have nearly equal areas and only differ due to the tendency's direction. For example in 2011 it becomes clear that the city gathers and sends manpower more from/to Dodecanese.(Fig._5&6)

In 2001, Rhodes behaves more as a transmitter, since its double tendency direction, northwest and east, strengthens its capacity to channel flows almost in all the islands around it. In 2011, Rhodes becomes more a receiver, as the transmitter’s polygon shrinks in direction intensity and area.(Fig._13)

Iοannina

Ioannina is characterized by greater dynamic in 2001. That polygon's area is clearly larger and the tendency's direction sharper. In 2001 the city is directed more towards the northwest Greece and in 2011 the distribution is even. Thus we conclude that, Ioannina don’t exceed the limits of Epirus region, which seems reasonable due to the particular geomorphology of the area.(Fig._5&6)

In 2001 the city behaves more as a receiver mainly from the northwest. It's interesting to observe that, in both polygons the city “sends” and attracts from the same areas.(Fig._14) In 2011, the city dynamics appears quite weak compared to those of 2001, but is notable that the tendency direction remains almost the same and the city behaves equally.(Fig._14)

Chania

Regarding Chania city, there is a noticeable decrease between 2001-2011. However the polygon of 2001 is really striking compared to this of 2011. The first has two equal, strong tendency spikes, to the northwest and southeast, which clearly indicates that the scope of that city surpasses the limits of Crete and absorbs flows from the mainland. This marks the importance of Chania as a center of development in the wider region. In contrast, in 2011, the flow towards southeast becomes more intense, while the flow to southwest weakens, which can be explained by the economic crisis and the reduction of distances for work.(Fig._5&6)

The polygons of 2001 are particularly interesting because of the difference of their areas. Chania operates mostly as human resource transmitter in two clear directions.(Fig._15) Similarly, in 2011 continues operating as a transmitter definitely directed towards southeast. This might be explained by a number of reasons: reduction of covered distances, expansion to the limits of the island, Heraklion acts as a strong development pole etc.


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Chalkida

Chalkida’s dynamics slightly changes with a decreasing progress in both quantitative and geospatial levels, from 2001 to 2011. What really happens is that the soft peaks on the 2nd and the 4th quadrant of 2001's polygon, in 2011 become smoother eventually forming a very similar polygon, with same size but with more even distribution.(Fig._5&6)

More thoroughly, in 2001 the role of the city in both streams is very clear. Chalkida receives much of its manpower from Veiotia and the rest of Sterea Ellada, while it channels a huge number of its active population in Attica.(Fig._16)

In 2011, these two interrelations are significantly feeble by shifting the peaks of both transmitter's and receiver's polygon closer to the core of Chalkida, which reflects a clearly weaker interface either with the surrounding industrial zones or with the capital. (Figure 16)


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6. Conclusions - Discussion

We attempted the identification of dynamic limits of urban centers. The study of transportations for work from and towards the ten largest cities in Greece may help us to draw conclusions regarding those parameters that determine the variation of these limits. The questions we try to answer deals with: the way dynamic tendency indicators are formed and the factors that produce them.

It’s doubtless that the geographical position of the urban centers and particularly their relationships with the surrounding settlements, compose two of the most important parameters of their evolution over time and their integration in development strategies.

The results of the implementation of the proposed methodological approach proves to be an integrated tool for the support the analysis procedure, as it appears more flexible in different cases of application. Its flexibility is based on the fact that it can be adapted to the requirements of any problem under investigation, while its opportunities stem from their capacity to manage databases with update, processing or redesign capabilities.


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https://brandingplaces.wordpress.com/2013/05/12/%CE%B1%CF%83%CF%84%CE%B9%CE%BA%CE%AE-%CE%B4%CE%B9%CE%AC%CF%87%CF%85%CF%83%CE%B7-%CE%BA%CE%B1%CE%B9-%CF%80%CE%B5%CF%81%CE%B9%CE%B1%CF%83%CF%84%CE%B9%CE%BA%CF%8C%CF%82-%CF%87%CF%8E%CF%81%CE%BF%CF%82/

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