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RURAL ATTRACTIVENESS: TOWARDS AN INDEX FOR LESS FAVORED RURAL LESVOS,
GREECE
Kizos Thanasis,
Department of Geography,
University of the Aegean,
University Hill
Mytilini, GR – 81100
GREECE
Email: akizos@aegean.gr
Tel: 00302251036447
Spilanis Ioannis,
Department of Environmental Studies
University of the Aegean
Pralakidis Stergios
Department of Environmental Studies,
University of the Aegean.
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RURAL ATTRACTIVENESS: TOWARDS AN INDEX FOR LESS FAVORED RURAL LESVOS,
GREECE
Abstract
The attractiveness of an area can be defined as the image that different population groups have of
the area, concerning the desire for permanent residence in the area or/and economic activity. The
concept of attractiveness stems as particularly important in Less-Favored remote rural areas,
which are still considered of low attraction, despite current social transformations and movements
that regard ‘rural tradition’ as a vital part of the cultural richness of an area and seek residence or
vacation in rural destinations. The question is whether these transformations can reverse the trend
of agricultural abandonment, population decline and shrinking economic activity in LFA’s that
occurred over the past few decades. This paper will examine the attractiveness of Lesvos Island’s
rural areas, a LFA in Greece with the use of a spatial index, calculated with population, economic,
infrastructure and services data. Results indicate that attractiveness differences are expressed at
spatial level, with the remotest rural areas of the island classified lower with respect to
attractiveness, while higher attractiveness values are attached to rural areas lying closer to the
island’s main urban center, indicating that rural Lesvos is still unattractive.
Key words
Attractiveness, rural, Lesvos
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1. Introduction: The Attractiveness Concept
Some areas are more attractive to live in than others. The factors that influence such decisions are
not always clear and change with time. For industry and services, economic science has proposed
a series of factors that influence the attractiveness of areas. These factors are usually structured
around the location of the area in terms of raw materials availability and remoteness from markets;
population of the area; infrastructure availability; human resources availability and quality; tax
and/or investments motivation provided (Walker and Chapman, 1987; Labrianidis, 2000; Polyzos
and Petrakos, 2001; Mazzarol and Choo, 2003). The similar concept of attractiveness for
population groups is relatively neglected, receiving less academic attention1.
Existing definitions regard attractiveness as the image that population groups have for an area
(Maillet 1998). This definition is realized with the use of methodologies that measure and estimate
qualities and characteristics of the areas and their populations, such as accessibility, remoteness,
dynamism, competitiveness, research and development, human resources, infrastructures,
services available and more. For example, the EURISLES (1997, 2002) method measures
accessibility and remoteness of areas (European island Regions), as time-distance from a set
point in space. Similar is the approach of Cross and Nutley (1999) that measure remoteness and
services availability for the small islands of Western Ireland. Copus and Crabtree (1996) employ a
services availability and economy approach for remote rural Scotland. Portnov et al (2000) on the
other hand, use a method that estimates urban centers attractiveness and is based on a statistical
approach (correlating socioeconomic variables and developing an equation). OECD’s (1994)
approach is more abstract conceptually, as it aims at a variety of areas, countries and situations
and thus uses relatively simple population and economic indicators. European Union’s and
EUROSTAT method (CΕC 1987, 1991, 1994, 1999, 2002, 2004) is more elaborate with the use of
concepts such as dynamism, competitiveness, research and development, human resources and
infrastructures for European Regions (NUTS II), as part of a statistical approach that correlates
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Except in migration studies and population movements, for which different approaches are proposed, for example the United Nations
approach on migrants and refugees between countries (UNHCR, 1995), or theoretical approaches about population mobility (Tsaousi,
1997, Tapeinos, 1993) and internal migration (e.g. Stockdale, 2002; Portnov et al., 2000; Fischer et al., 2000; Wikhall, 2002 for more
references).
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existing empirical data with theoretical notions of attractiveness and development. The basic
assumptions of these methodologies are that: 1) the values of the indicators used are linked to the
attractiveness beliefs that societies hold and that people construct these beliefs and choose their
place of residence and/or occupation according to a model based on a series of factors, on a more
or less rational basis (Portnov et al., 2000), 2) the values of the indicators used reflect the
attractiveness ‘status’ of the areas they refer to. The two assumptions are linked, as attractiveness
is a ‘state’ of an area, but it is also a ‘state of mind’ for people. This approach is used in planning
procedures at national and international levels, as methodologies of organizations such as CEIES,
(CEIES, 1996), EUROSTAT, OECD, EURISLES; national planning procedures (eg. Gilg, 1996;
Portnov et al., 2000) and academic methodologies (Maillet, 1998; Cross and Nutley, 1999; Spilanis
et al., 2002; Engelen et al., 2002) prove.
A similar theoretical approach and scientific field of study, behavioral and environmental
geography, examines the reasons and the factors that influence the preference of environments
and landscapes. Different approaches include behavioral research (Walmsley and Lewis, 1993),
landscape aesthetics and preferences (Appleton, 1996; Lothian, 1999; Nash, 1999; Parsons and
Daniel, 2002) and environmental psychology (Nasar, 1988; Berleant, 1997) among others. Some
of these approaches are similar conceptually to attractiveness as developed here, although they
more often than not examine aesthetic and symbolic dimensions of preferences, attitudes and
decisions towards places and spaces for groups of people, while here we use less aesthetic and
symbolic and more economic and social dimensions. Nevertheless, we feel that a complete and
thorough examination of attractiveness should attempt to include such fields of analysis.
The approach followed here acknowledges that attractiveness can indeed be estimated with the
use of indicators. Yet, the notions, attitudes and beliefs of social groups that are connected with
the areas should first be addressed. As many different social approaches have demonstrated,
notions, attitudes and beliefs of social groups form attractiveness images (Halfacree, 1995;
Hoggart et al., 1995; Jones, 1995; Copus and Crabtree, 1996; Harrington and O’ Donogue, 1998;
Van Dam et al., 2002; Haartsen et al., 2003). These images influence the decisions that group
members make, which involve residence and/or employment. The first issue that this approach
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brings forward is that attractiveness is a relative term and can only be defined when compared to
‘unattractiveness’: when an area is attractive, another has to be unattractive and vice versa.
Therefore, attractiveness can be used to understand differences between areas as they are
expressed through attitudes and beliefs of social groups and measured through indicators that are
based on these beliefs.
The second issue of this approach proposes a slightly different definition, which defines
attractiveness as the image of a specific place or space that a group of individuals, linked in some
way to this space or place, holds at a specific spatio-temporal context. This definition provides a
methodology that is structured around three questions (van Dam et al., 2002): the first question is
“attractiveness for whom?” and refers to the social construction of attractiveness and thus to the
need to define the social group for which attractiveness is estimated, as different groups hold very
different views on attractiveness and how it is constructed. The groups can be distinguished on a
wide variety of criteria that refer to age (van Dam et al., 2002), sex (Cloke and Little, 1996), class
(Halfacree, 1995), race (Cloke and Little, 1996) etc. The second question is “attractiveness
when?” Attractiveness is a time varying concept and the period of estimation has to be defined
clearly, even in seasonal terms. The third question is “attractiveness how?” and refers to methods
of estimating attractiveness after the first two questions have been answered. The methodological
framework for attractiveness’ estimation process with this definition is: definition of the area, the
group, the time period, the issues and the indicators.
Despite the advantages of ‘lending an ear’ to what people have to say and defining clearly the
issues and the methods that this approach presents, it is also laden with some disadvantages. The
social construction and relativity of attractiveness ‘for population groups’ and the fact that people
should be asked about their opinions and beliefs, brings forward mobility issues and the question
of how to include all or at least many different groups and many different opinions and beliefs into
the estimation of a series of attractiveness indexes. This is important especially when policy issues
are raised, and many different attractiveness images should be considered in order to satisfy most
of the unattractive points. A typical example refers to the people who have already moved from an
area due to its low attractiveness. Their opinions and beliefs are important when policy issues of
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keeping the population are raised, as the unattractive points that have driven them away are
strong and are exactly what policies want to address. Such issues call for cautious and
complicated research strategies when using attractiveness for policy formulation (an example of
the diverse research strategies required is offered by Stockdale, 2002).
Another issue raised here, is that when discussing attractiveness both driving forces and results
should be considered. Driving forces are the causes of changing attractiveness opinions and
beliefs. The results of the driving forces are socioeconomic changes in the area examined (e.g.
population and economic changes over time).
The present paper examines the attractiveness driving forces and results for rural areas in LFAs
(Less- Favored Areas) with the formulation of an attractiveness index for its current inhabitants.
Rural areas are chosen due to the fact that some of them and especially remote and/or marginal
ones are considered unattractive. The attractiveness index is calculated in order to categorize
more conveniently the issues involved and the indicators used. The case study is presented after a
short discussion of some implications that rural attractiveness raises.
2. Rural Attractiveness
The subject of rural attractiveness has not received much attention in rural studies. The reason
appears to be the general conceptualization of rural areas as one-dimension agricultural spaces
until recently, with little differentiations in economic, social, gender and racial terms (Cloke and
Little, 1997).
Although regional and national cases differ considerably, rural attractiveness has been reduced in
general, after the industrial revolution and urbanization. Rural areas were considered insecure and
remote for residence, offered little opportunity apart from agriculture for economic activity and
were connected with ‘conservation’ and ‘backwardness’ in contrast with urban ‘progress’ and
‘modernization’ (Williams, 1985/ 1996; Hoggart et al., 1995; Nitsiakos, 1995; Woods, 2005).
Unattractiveness is expressed through population and economic decline, through the migration of
people (especially young) from rural areas and through the fact that “predominantly rural areas”
(OECD, 1994, CEC, 2004) are classified among the poorest regions in Europe. In European
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Union, the notion of rural ‘unattractiveness’ is expressed through the adaptation of the LFAs
scheme, a scheme designed and intended for rural areas that are lagging behind in terms of
agricultural development (Fennell, 1997; Richardson, 2000; Hadjimichalis 2003).
Recently, such attitudes in the Western world seem to be partly reversing in geographical and
social terms, giving to some rural spaces an air of increased attractiveness (Woods 2005).
Geographically, some rural areas are nowadays considered as more attractive than urban centers.
These may be rural areas that are located on the urban fringe, or are easily accessible from large
urban centers or have a unique quality (real or symbolic). The reasons behind this partial reversal
are the deterioration of the quality of urban life, technological developments that can reduce
distances and the signification of rural spaces with a high cultural potential as far as certain
symbolic social representations are concerned (landscapes, national, regional or local identities)
(Jackson, 1984; Bunce, 1994; Pratt, 1996; Lawrence, 1996; Marsden, 1999; Lewis and Sherwood,
2000; Skuras et al., 2005). This does not imply that all rural areas’ attractiveness has suddenly
increased. On the contrary, many rural areas, especially marginal and isolated ones, are still
considered highly unattractive and persisting demographic and economic decline underline the
fact (Baldock et al., 2001; Ruben et al., 2004; Ferrao et al., 2004; Bengs and Schmidt-Thomé,
2004).
Social variations, as already discussed in the introduction, refer to different opinions and attitudes
towards the attractiveness of rural areas that different population groups hold. Such differences
can be very important, even for the same area (Halfacree, 1995; Labrianidis and Bella, 2004).
In these contemporary rural transformations and social imagery, farmers are ‘in the middle’.
Although by definition they ‘do’ the rural (as opposed to those who ‘consume’ the rural), there are
growing numbers of part time and ‘hobby’ farmers (Van der Ploeg et al., 2000; Gray 2000) that
can be regarded as both users and consumers of the rural. An attempt to investigate some of the
implications of the above transformations and differences with regard to the attractiveness of rural
areas is presented for the island of Lesvos, in North Aegean Region, Greece with the construction
of an attractiveness index for its rural settlements. This construction is not based on actual
opinions of the social group defined, the inhabitants of rural areas, although previous local
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research is utilized in order to adapt existing methodologies. The results are correlated with
existing farmers’ and farm typologies and dependence on farming incomes and with broader
changes of the socioeconomic structure of the settlements.
3. The Case of Rural Lesvos
Lesvos is one of the largest Greek islands (1.632,8 km2). It is a mountainous island, as 41 of its 73
settlements are characterized as mountainous, the rest as less favored according to Directive
85/148/EEC that defines LFAs in Greece. Its size and relief create isolation clusters for rural
settlements far from the capital, Mytilini (where almost 45% of the total population dwells). The
island has suffered intense depopulation and out-migration from the 1940s to the 1980s when the
population stabilized. The current population is ageing, due to the fact that it was the economically
active young inhabitants that migrated (Table 1). Lesvos is an island where agriculture still plays a
major role in production, incomes and employment, although the number of agricultural holdings is
decreasing (Table 1) along with a slight abandonment of utilized agricultural land.
The island can be divided into three zones according to land use (Kizos and Spilanis 2004, Map
1): The first is the grazing lands zone, located in the West – Northwestern part of the island and
comprised of barren grazing lands for sheep and goats that lie in steep slopes and shallow soils.
The second is the olive groves zone, located in the East-Northeastern part of the island (including
the capital Mytilini), which is a mountainous monoculture of terraced olive groves, with richer soils
than the grazing lands zone. The third is the intermediate zone that separates the first two zones.
It is a transitional zone, where olive groves and livestock coexist, along with arable land in the
relatively larger plains of the island. These land use zones correspond roughly to the geological
and climatic classification of the island (Higgins and Higgins 1996).
3.1. Research Method: Constructing an Attractiveness Index for Rural Lesvos
Previous research on farmers’ opinions about their place of residence (Kizos and Spilanis 2002)
has provided some general issues on rural attractiveness, which include jobs (as most important
and especially off-agriculture), services and infrastructure (with specific mention of health and
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public services), along with isolation (which refers to the islands’ remoteness from Greek
continental land and to inter-Lesvos isolation of remote settlements). Although not all rural
inhabitants are farmers, many farmers are inhabitants of rural Lesvos and their opinions can serve
as guidelines. The formulation of indicators is based on existing methodologies (especially
EURISLES, 1997; Cross and Nutley, 1999; and Portnov et al., 2000) and these indicators measure
the driving forces of the settlement’s attractiveness: available jobs except agriculture,
infrastructure, public services, health services, isolation–remoteness and cultural opportunities the
area has to offer.
Overall, seven indicators are used (Table 2). Indicators I1 and I2 (number of enterprises and
number of people who are not employed in agriculture in the area) represent the ‘available jobs
except agriculture’ driving force. Indicators I5 and I6 (degree of serving for Banks, Tax service,
Agriculture Bureau, Building Bureau and degree of serving for health hospitals), represent the
‘infrastructure – services’ driving force. These indicators measure the ‘degree of serving’ of the
services included, as the time that is required to access them is used for their calculation. Indicator
I3 (time-distance from Mytilini, which apart from the main urban center is also the only gate for
entering and leaving the island) represents the ‘isolation – remoteness’ driving force, but also
represents the ‘services availability inter-island’ driving force. Indicator I7 (degree of serving for
cinemas and cultural centers) represents the ‘cultural opportunities’ theme. Indicator I4
(population) refers to almost all driving forces, as population size is important for building the
isolation – remoteness feeling of permanent residents, it is important for economy, as greater
sizes develop more complex local economies and for the same reason it is also of great
importance for services and cultural opportunities. All the values of the services’ indicators are
calculated with the use of time-distance, or virtual distance, which stands for the time required
reaching the settlement where the specific service is located with an average speed, different
according to the type and quality of the shortest road connecting the two settlements. All indicators
are calculated with data from official statistics at settlement spatial level (73 administrative units
before the 1997 formation of 13 greater Municipalities) and most refer to 2001 (year of the
population census).
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The indicators used do not include symbolic and/or aesthetic indicators. The main reason behind
this is the bias of the approach in favor of permanent residents of rural areas. Although their actual
opinions are not recorded, other studies (e.g. Halfacree, 1995; van Dam at al., 2002; Labrianidis
and Bella, 2004) demonstrate that permanent residents of rural areas tend to ‘take for granted’ the
rural environment and setting and thus symbolic and/or aesthetic criteria are of lower significance
for them compared with socioeconomic and personal issues2.
The construction of the attractiveness index takes three issues into account:
The first issue deals with the calculation of sub-indexes for each driving force mentioned above.
Six sub-indexes are calculated (Table 2): the first with indicators I1 and I2 (jobs except
agriculture), where I1 is considered as more important than I2 and is given 0,6 weight; the second
with indicator I3 (remoteness – isolation); the third with indicator I5 for banks (30% of value),
Agriculture Bureau (30% of value), Tax Service (20% of value) and Building Bureau (20% of
value3); the fourth with indicator I6 for hospitals; the fifth with indicator I4 for population size; and
the sixth with indicator I7 for cinemas (50% of value) and cultural centers (50% of value).
The second issue refers to the relevant importance of the themes. If all sub-indexes were to have
equal relevant importance, each would form a little more than 15% of the index value. The
approach taken here, attains different weightings to different indicators (Table 2 and equation (1)),
with the assumption that the economy driving forces (S-I1) are the most important and are
therefore given double weighting (30%); the population (SI-5) driving forces follow with almost
equal weighting (25%); then the services driving forces (SI-3) and remoteness - isolation (SI-2)
that are given the equal weighting (15%); followed by health services (SI-4), with weighting 10%
due to the fact that it represents only one service compared with the 4 that SI-3 represents; and
finally the cultural opportunities driving force (SI-6) that is considered of reduced importance and is
given small weighting (5%). The weightings used are based on our expert opinion. The absence of
work in the same vein in Greece or elsewhere to our knowledge does not offer solid grounds for
other choices. A similar work in Greece, that of Labrianidis and Bella (2004), compares views of
2
Lesvos farmers when asked why they do stay in Lesvos or why they do not leave (Kizos and Spilanis 2002), did not mention any
aesthetic criteria, although social problems like the difficulty of obtaining spouses in some rural areas were mentioned, especially for
livestock holders.
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different groups of locals on the attractiveness of a rural area of mountain Greece, but only
qualitative data are offered. Similar findings elsewhere include statistical approaches such as the
ones Portnov et al. (2000) use, or (micro) simulation techniques such as the ones Engelen et al.
(2000) for example use. Lack of data however, does not allow employing regression or simulation
techniques here.
The third issue deals with the matter of measurement units for the attractiveness index. The
equation that calculates the values of the index (AI) is:
AI = 0,3 * S-I1 + 0,25 * SI-5 + 0,15 * SI-3 + 0,1 * SI-4 + 0,15 * SI-2 + 0,05 * SI-6
(1)
All the indicators used in equation (1) are numbers (no measurement units) and therefore the
values of the attractiveness index are also numbers. All sub-indexes take positive values, with the
exception of the remoteness – isolation index that takes negative values for all settlements except
the city of Mytilini (where the value is 0).
Finally, a second set of indicators is used, indicators that depict the results of the driving forces
that the index has considered. Such indicators measure the changes in population size and quality
and the economic changes in the areas concerned, in accordance to the attractiveness definition
used. These indicators are (Table 3): the population change in the settlements from 1961 to 2001
and the population change from 1981 to 2001, the change in the number of the working force from
1991 to 2001 and the change in people employed in agriculture from 1991 to 2001.
3.2. Results and Discussion
3.2.1. Lesvos Typology for the Sub-Indexes of Attractiveness
The values of the two indicators that are used to calculate SI-1 are strongly correlated (Pearson’s
r= 0,991**, s= 0,00). Nevertheless, they are both kept in the calculation of SI-1, as they represent
different aspects of the employment status of an area: the first refers to the existence of
3
Banks and Agriculture Bureau are services needed by rural residents more often than the tax service and the Building Bureau and
therefore they are granted greater weightings in the calculation of the sub-index.
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enterprises in an area and the other to the number of people each enterprise employs. The strong
correlation in the Lesvos case indicates that all enterprises in the island are of the same type,
namely small, personal or family enterprises. The values of the indicators are also strongly
correlated with the population (Pearson’s r= 0,988**, s= 0,000 for both indicators) and weakly with
isolation (Pearson’s r= 0,255*, s= 0,031 for Ι1 values and Pearson’s r= 0,251*, s= 0,033 for Ι2
values), while no correlations are important with time-distance from Mytilini or with the values of
the services’ indicators.
The values of the isolation indicator (SI-2), apart from the correlation with the values of indicators
Ι1 and Ι2, are weakly correlated with the population of the settlements (Pearson’s r= 0,253*, s=
0,03), but are not correlated with the values of the rest indicators. These relations seem to indicate
that remote settlements can develop economic activities and keep their population.
The values of the services’ indicator (SI-3) are negatively and strongly correlated with real and
virtual distance from Mytilini (Pearson’s r= -0,856**, s= 0,00 for time-distance values). That seems
to indicate towards a negative image of remote settlements when availability and serviceability of
some basic services (public and private) are concerned. The same negative and strong correlation
stands for the values of the health services’ indicator (SI-4, Pearson’s r= -0,932**, s= 0,00 for
time-distance values), and negatively but not so strong with the cultural opportunities indicator
(Pearson’s r= -0,534**, s= 0,00), verifying this image. The fact that none of the three indicators’
values are correlated with population values appears to provide further verification.
This image is depicted in Map 2, where the spatial polarization of the island is obvious. The one
pole is the Mytilini area, which stands for 59% of the total population of the island along with Gera,
Euergetoula, Agiassos and Thermi Municipalities, while another important pole is the Kalloni area.
Smaller poles are Plomari, Molivos – Petra, Polihnitos, Antissa and Mantamados, of smaller
importance, as the attractiveness index values also prove.
3.2.2. The Attractiveness Index
The attractiveness’ index values range from 0,216 (Neohori) to 1,95 (Mytilini), with average 0,902.
If the values are divided in four categories (less than 0,5, from 0,5 to 1,0, from 1,0 to 1,5 and
greater than 1,5), most of the values are below 1,0 (43 out of 73) and only 4 are greater than 1,5
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(Kalloni, Skopelos, Agiassos and Mytilini with increasing order). What should be noted are the low
attractiveness values for some local centers with important population (for example Antissa,
Eressos, Mantamados have values lower than 1,0 and Petra and Molivos values close to 1,0). On
the other hand, smaller settlements have higher attractiveness values, mainly due to their small
time-distance from Mytilini and thus the quick and easy access to services. In fact, correlation
between attractiveness values and time-distance values increases when population decreases (,616** for all settlements, -0,658** for settlements under 2.000 inhabitants, -0,714** for
settlements under 1.000 inhabitants and -0,79** for settlements under 500 inhabitants, s=0,00 for
all correlations), which indicates that the attractiveness index is indeed ‘spatially oriented’. The
values are presented in Map 3.
The evaluation of this attractiveness index is performed, as already mentioned, with the use of
independent indicators that measure results of the driving forces of the attractiveness index (Table
3). The values of population change from 1961 to 2001 are correlated with the values of the
attractiveness index (Pearson’s r= 0,336**, s= 0,004), but are not correlated with the values of the
population change from 1981 to 2001 indicator (Table 3). This weak correlation is slightly
increasing for settlements with time-distance greater than 30 min from Mytilini (Pearson’s r=
0,371*, s= 0,013), and is becoming negative and increases for settlements with more than 15 min
time-distance from Mytilini that are classified in the third attractiveness category (values from 1,0
to 1,5), reaching medium correlation standards (Pearson’s r= -0,502**, s= 0,039, while the value
for settlements with more than 30 min time-distance from Mytilini is 0,612, but s= 0,06).
These values indicate that for remote and relatively small settlements the attractiveness index can
indeed describe in some extend the population changes. The data show that all remote
settlements (with time-distance greater than 30 min from Mytilini) have suffered population loss in
1961-2001, and the correlation between the values of time-distance with the values of
attractiveness index for all settlements with negative population change in 1961-2001 is medium
(Pearson’s r= 0,593**, s= 0,00 for all settlements with negative population and Pearson’s r=
0,640**, s= 0,00 for settlements with negative population and population less than 2.000 people).
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The fact that no correlations are important for population change from 1981 to 2001 indicate that
the attractiveness index can not describe so well recent changes and some alterations are
probably needed, especially for remote and relatively small settlements that appear to have gained
population in the last 20 years, fact that contradicts their low attractiveness values, although this
population is probably aged. Although data for age cohorts are not available at settlement level in
the 2001 census, the results for Municipality level suggest that rural Lesvos is ageing and this
ageing is responsible for the rise in population as old people either stay in rural areas or return as
pensioners. The municipality of capital Mitilini is the only one in the island where people over 65
are less than 20% of the total population (17,1% with average for the rest municipalities at 24,9%).
Another possible explanation can be that the recorded population of the census is higher than the
actual population, as during the day the census takes place many migrants from rural settlements
to Mytilini return to their villages to be recorded there, as many of them still feel strongly tied with
their ‘homelands’. Nitsiakos (1995) records this phenomenon in mountain continental Greece and
asserts that up to 20% of the residents of rural settlements can be such migrants.
The correlations between the values of the attractiveness index and the variables ‘working force
change 1991 - 2001’ and ‘people employed in agriculture change 1991 - 2001’ are also weak
(Pearson’s r= 0,341**, s= 0,00 for ‘working force change 1991 2001’ and Pearson’s r= 0,345**, s=
0,00 for ‘people employed in agriculture change 1991 2001’, Table 3) and remain weak for all the
attractiveness categories. The correlation is stronger when only the 64 settlements that lost
population from 1961 to 1991 are considered (Pearson’s r= 0,350**, s= 0,005 for ‘working force
change 1991 2001’ and Pearson’s r= 0,432**, s= 0,00 for ‘people employed in agriculture change
1991 2001’) and increase more for the 26 of the 64 settlements that are classified in the second
attractiveness category (values 0,5 to 1, Pearson’s r= 0,501**, s= 0,009 and Pearson’s r= 0,508**,
s= 0,008 respectively).
The weak correlations results for the change of people employed in agriculture can be partly
explained with the use of a typology of Lesvos’ farmers with criteria the ratio of agriculture to outof- agriculture income of the household that runs the farm, the source of the household’s out-ofagriculture income and the age of the owner (Kizos and Spilanis, 2002). Three distinct farmer
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groups result: “professionals”, “hobby/retired” and “semi-professionals”, results that are
comparable with those of similar typologies (Damianos and Skuras, 1996; Davis et al., 1997).
The results of the typology indicate that the professionals’ households are quite limited (31%
depends entirely on agriculture, Kizos and Spilanis, 2002), due to the fact that agricultural incomes
are small compared to the out-of-agriculture ones. The majority is located in remote settlements
with low attractiveness (only 1% of the total live in Mytilini, Kizos and Spilanis, 2002) where
services are poor and jobs out of agriculture difficult to find. On the other hand, the percentage of
older owners and “hobbyists” is significant (mainly olive farmers with out-of-agriculture family
incomes of a considerable size). In between these two extremes, intermediate cases of
households (‘semi-professionals) live all over the island but mainly in intermediate settlements with
average attractiveness values.
It appears therefore that although the general pattern of farmers follows the attractiveness results,
there are some important issues raised. The first issue refers to what the indicator ‘people
employed in agriculture’ measures. If in a settlement the available employment options besides
agriculture are limited, then the changes of the values of the indicator are expected not to be
important in regard with attractiveness, as people that will not migrate will have to continue to work
in agriculture. In settlements with more options, this may not be so and indeed the percentage of
‘semi-professionals’ and ‘hobbyists’ in more attractive settlements verifies the point. The second
issue refers to the percentage of farmers in the total population of rural Lesvos. As already noted,
agriculture is important in Lesvos, but the specific land uses and their distribution on the island
(map 1) indicates that this distribution corresponds with the farmers’ typology (olives can be
managed easily by non-professionals, while sheep are more demanding in everyday laborious
management). This distribution of land uses and professional farmers coincides in broad terms
with the attractiveness index’s spatial depiction of Lesvos (map 3), leading to the conclusion that
the disadvantaged status of farmers and farming is partly responsible for the attractiveness
differences, as the most non-attractive settlements are the ones with the most professional
farmers (in the case of Lesvos, sheep herders) who can’t find alternative employment and either
16
leave or remain disadvantaged. This justifies the selection of the indicator ‘employment
opportunities except agriculture’ in the construction of the index.
4. Conclusion: Attractiveness Index and Rural Settlements
In this paper a methodology for estimating the attractiveness of rural Lesvos, a LFA area is
developed and the index is correlated with results of attractiveness driving forces. The findings
indicate that attractiveness in rural Lesvos is shaped around isolation and remoteness (that affects
also services availability) and employment opportunities, especially except agriculture, as in small
and remote rural settlements agriculture is something like a ‘last resort’ for employment.
Although this analysis is biased towards permanent residents of rural areas and does not cover all
aspects of their daily life, the data are revealing for the relationship between economic activity and
spatial polarization. The different attractiveness’ images that result from the index economically
and spatially correspond to the general scheme: attractive center, unattractive remote (and
mountainous) periphery. In remote settlements agriculture is almost the only employment
possibility; services and infrastructures are of bad quality or unavailable; in short an unattractive
place, that people want to leave and find a different job (but paradoxically not intend to abandon
agriculture altogether, rather keep it as a second, part time occupation, as it is a part of the local
identity, Kizos and Spilanis, 2002). In the main center and some smaller ones, farmers are
hobbyists or retired, they have access to out-of-agriculture incomes and live in more attractive
places. For farmers, who are an important part of the rural population in Lesvos and generally in
most LFAs, this image represents an actual ongoing process: the shift from full time to part
time/hobby farming when and where other employment opportunities are available; and their
moving from the unattractive countryside to attractive spaces, preferably close to or in the center
but also to more attractive smaller semi-urban spaces.
With regard to the methodology and calculation of the attractiveness index, it is designed to depict
this spatial polarization as social and economic differentiations and developments in space. Its
values measure the population size and the ‘servicing degree’ for a series of services, a degree
calculated by the time-distance required to access these services, including existence of service
17
and partly its quality. Other quality dimensions of the services are not taken into account, but as
quality is a relative term and comparing for example the health services in a small and remote
Lesvos settlement to Mytilini results into lower values for the settlement, but at the same time even
the services offered in Mytilini are of lower quality when compared to those available in the capital
of Greece, Athens. Therefore, as this comparison is conducted for Lesvos scale and not for a
general ‘service quality level’, it can be accepted that the indicators used measure the quality to
some degree. The adding of less spatially dependent quality standards could help to estimate it
better in a compatible way with the one presented here.
The evaluation of the attractiveness index is performed with the use of independent indicators that
measure attractiveness’ results, namely economic and population changes in the areas
concerned. Overall, the results of the correlations are suggestive that the index used can indeed
depict actual attractiveness differences, but also indicate that further research and/or alterations in
the construction of the indicators and the index are required. Such changes should include
regression techniques for the factors and the weightings and a further ‘spatialization’ of the index,
which could provide better correspondence between driving forces and results.
In conclusion, two issues stem as important for the attractiveness approach taken here: the first
issue is conceptual and refers to what factors can be regarded as causes (driving forces) and what
as effects or results of attractiveness. Here economic changes are considered as the major driving
force behind attractiveness changes, especially for relatively short time periods, when most major
extra local and global social and political influences can be regarded as of small variance. In the
approach taken here, we have adopted a strategy of ‘snapshots’ of the attractiveness of an area.
These snapshots are the results of attractiveness changes that are estimated with the driving
forces that constitute the index. The distinction is a fine one and indeed the issue requires more
empirical analysis and the inclusion of the opinions of the residents of the area as the definitions
state.
The second issue deals with attractiveness itself. We believe that the previous discussion has
demonstrated its use as a tool for understanding changes in the use of space. The example of
LFA rural space presented here is revealing in the sense that it is important to understand
18
behaviors and opinions formation processes that result in socioeconomic changes in rural areas
where production is disadvantaged and living difficult. This understanding can bring forward new
policies that may at least compensate such disadvantages.
19
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Indicator
Population
Population % change
Ageing indicator
Percentage of economically
active
Agriculture as % of Prefecture’s
GDP
% Of work force employed in
agriculture
Farms
Farms % change
Table 1. Socio-economic Indicators for Lesvos
Values (year measured)
90.643 (2001 census)
+4% (1991 to 2001)
-30% (1951 to 1981)
Greece = +6,6% (1991 to 2001)
146 (2001 census)
119,9 (1991 census)
Greece = 110 (2001 census)
36,9% (2001 census)
30,6% (1991 census)
15% (2001 data)
22% (2001 census,
38% excluding Mytilini)
16.006 (2001 census)
-21% (1971 to 2001)
Greece = 42,2% (2001 census)
Greece = 7% (2001 data)
29% (1991 census,
40% excluding Mytilini)
15.696 (1991 census)
Greece = 12% (2001 census)
26
Table 2. Attractiveness index construction and measurement process for the indicators used
Influenc
e on
Indicator
Parameters used
Measurement process and units
attractiv
eness
I1.
Number
of
3 categories: 1) smaller % than 90% of Lesvos average for
enterprises in area*
enterprises/settlement, 2) from 90% to 110% of average and 3)
+
greater than 110% of average Values: 0, 1 and 2 for three
categories correspondingly
3 categories: 1) smaller % than 90% of Lesvos average for
I2.
People
not
people not employed in agriculture /settlement, 2) from 90% to
employed in agriculture
+
110% of average and 3) greater than 110% of average. Values:
in area*
0, 1 and 2 for three categories correspondingly
I3. Time-distance from
Calculated by the least time required to reach the settlement
Mytilini (main urban
from Mytilini. Different average speed assumed per type of road.
center and gate in and
Formula: [1- (Time-distance from Mytilini/ real distance from
out of the island) *
Mytilini)] and values starting from 0 (city of Mytilini)
Different value per population class. 4 classes used:
1. < 100 people,
value: 0
I4.
Population
of
+
2. From 100 to 2.000 people
value: 1
settlement
3. From 2.000 to 10.000 people
value: 2
4. > 10.000 people
value: 4
Influence zones (km) for each settlement, 3 degrees of serving
(high, medium, low). Difference in zone length depending on
type of service. For Agriculture and Building Bureaus:
High serving: time-distance < 30 min,
value =2
Serving from
I5.
Existence
and
Medium serving: 30 <time-dis. < 60 min
value=1
Banks, Tax service,
degree of serving for
+
Low serving: time-distance > 60 min
value=0
Agriculture Bureau,
public services
Building Bureau
For Banks, Tax service:
High serving: time-distance < 15 min,
value =2
Medium serving: 15 <time-dis. < 30 min
value=1
Low serving: time-distance > 30 min
value=0
Influence zones (km) for each settlement, 3 degrees of serving
I6.
Existence
and
(high, medium, low). Values:
Serving from
degree of serving for
+
High serving: time-distance < 30 min,
value =2
Hospitals
health services *
Medium serving: 30 <time-dis. < 60 min
value=1
Low serving: time-distance > 60 min
value=0
Influence zones (km) for each settlement, 3 degrees of serving
I7.
Existence
and Serving from
(high, medium, low). Values:
degree of serving for cinemas, cultural
+
High serving: time-distance < 15 min,
value =2
cultural opportunities
centers
Medium serving: 15 <time-dis. < 30 min
value=1
Low serving: time-distance > 30 min
value=0
2 components: a) number of enterprises in area (60% of value),
S-I1. Jobs except
+
b) people employed in other sectors except agriculture in area
agriculture
(40% of value). Weight 0,3 in index calculation
[1- (Time-distance from Mytilini/ real distance from Mytilini)].
S-I2. Remoteness
Weight 0,15 in index calculation
4 components: a) Bank (30% of value), b) Agriculture Bureau
S-I3. Services
(30 % of value), c) Tax service (20 % of value), d) Building
+
I8. Attractiveness index (except health)
Bureau (20 % of value). Value according to serving zone of
for Lesvos’ farmers
each settlement. Weight 0,15 in index calculation
+
SI-4. Health
1 component: Zone of serving. Weight 0,15 in index calculation
SI-5. Population
S-I6. Cultural
opportunities
+: if value increases attractiveness increases
-: if value increases attractiveness decreases
*Source: adapted from Pralakidis 2000.
+
+
1 component: Population size (value as I4). Weight 0,25 in
index calculation
2 components: a) cinemas (50% of value) and b) cultural
centers (50% of value) Weight 0,05 in index calculation
27
Table 3. Correlations of values of attractiveness index with selected variables
Population Populatio
Populatio
change
n change
n 2001
1961-2001 1981-2001
Attracti
veness
index
Pearson
Sig.
N
,336**
,004
73
,128
,279
73
,437**
,000
73
** Correlation significant at 0,01 level.
Working
force
change
1991-2001
,341**
,003
73
People
% Employed
Working Working
Enterpri
Employed in
in
force
force
ses
agric. change
agriculture
2001
1991
(2002)
1991-2001
2001
,345**
,416**
,428**
-,618**
,412**
,003
,000
,000
,000
,000
73
73
73
73
72
28
Map 1. Land Uses in Lesvos
Land Use Zones in Lesvos
N
MITHYMNA
MITHYMNA
PETRA
MANTAMADOS
PETRA
MANTAMADOS
AGIA P ARASKEV I
ANTIS SA
KALLONI
KALLONI
AGIA PARASKEVI
ERESOS-ANTISSA
LOUTROPOLI THERMIS
LO UTRO PO LI THE RMIS
ERES OS
EVERGETOULA
LAMPO Y MYLI
MYTILINI
AGIASOS
POLICHNITOS
University of the Aegean-Dep. of Envir onmental Studies
Labor atory of Local and Island Development
Director: Assistant Professor I. Spilanis
Mapping: Stergios Pralakidis
Mu n ic ip a li ty
La n d U se
PAP PADOS
PLOMARI
GERA
G ra ze l a nd s
O li ve s
Fo rm e r a d mi n is tra tiv e u ni t
POLICHNITOS
Sc ale 1:270000
LEGEND
S ettl e m e nt
MYTILINI
AGIASOS
Inte r m e d ia te
PLOMARI
29
Map 2. Degree of Serving for Selected Services in Lesvos
Existence and degree of serving from services
N
HOSPITAL
BANKS
MITHYMNA
CINEMAS
MITHYMNA
MITHY MNA
MITHY MNA
PETRA
PETRA
PETRA
MANTAMADOS
PET RA
MANTAMADOS
MANTAMADOS
PET RA
AGIAPARASKEVI
ANTISSA
KALLONI
MITHYMNA
MITHY MNA
MANTAMADOS
MANTAMADOS
PET RA
AGIAPARASKEVI
ANTISSA
KAL LONI
KALLONI
AGI A PARASKE VI
ERES OS-ANTI SS A
AGIAPARASKEVI
ANTISSA
KAL LONI
KALLONI
AGI A PARASKE VI
ERES OS-ANTI SS A
AGI A PARASKE VI
LOUTRO POLI THE RM IS
LOUTROPOLI THERMIS
LOUTRO POLI THE RM IS
LOUTROPOLI THERMIS
ERESOS
EVE RGETOULA
LOUTROPOLI THERMIS
ERESOS
EVE RGETOULA
LAMPOY MYL I
EVE RGETOULA
LAMPOY MYL I
LAMPOY MYL I
MYTI LI NI
MYTI LI NI
MYTILINI
AGI AS OS
POLICHNITOS
KAL LONI
ERES OS-ANTI SS A
LOUTRO POLI THE RM IS
ERESOS
MANTAMADOS
AGIASOS
POLICHNITOS
POLI CHNIT OS
MYTI LI NI
MYTILINI
AGI AS OS
MYTILINI
AGI AS OS
AGIASOS
POLICHNITOS
POLI CHNIT OS
AGIASOS
POLI CHNIT OS
PAPPADOS
PAPPADOS
PLOM ARI
PAPPADOS
PLOM ARI
PLOM ARI
GERA
GERA
PLOMARI
GERA
PLOMARI
PLOMARI
Univ ers ity of the Aegea n-D ep. of Env ir onm ental Studies
Labor atory of Loca l a nd Is la nd De velopm ent
Dire ctor: Ass is tant Profes sor I. Spilanis
Ma pping: Ster gios Pra la kidis
Scale 1:800000
LEG EN D
Valu e o f se rvice ab ility
Sett lem ent
BUILDING BUREAU
AGRICULTURE BUREAU
Existen ce o f s ervices
Low
Mun icipality
Med ium
Fo rm e r a dm inistrat ive u nit
High
TAX SERVICE
MITHYMNA
MITHY MNA
PETRA
PETRA
MANTAMADOS
PET RA
CULTURAL
CENTERS
MITHYMNA
MITHY MNA
PETRA
MANTAMADOS
MANTAMADOS
PET RA
AGIAPARASKEVI
MANTAMADOS
MANTAMADOS
PET RA
AGIAPARASKEVI
ANTISSA
KAL LONI
ANTISSA
KALLONI
AGI A PARASKE VI
ERES OS-ANTI SS A
KAL LONI
KALLONI
AGI A PARASKE VI
ERES OS-ANTI SS A
LOUTROPOLI THERMIS
ERESOS
EVE RGETOULA
LOUTRO POLI THE RM IS
LOUTROPOLI THERMIS
ERESOS
EVE RGETOULA
LAMPOY MYL I
EVE RGETOULA
LAMPOY MYL I
LAMPOY MYL I
MYTI LI NI
MYTI LI NI
MYTILINI
AGI AS OS
AGIASOS
AGIASOS
Ì ÕÔÉËÇÍ ÇMYTILINI
AGI AS OS
POLICHNITOS
POLI CHNIT OS
AGIASOS
POLI CHNIT OS
PAPPADOS
PLOM ARI
MYTI LI NI
Ì ÕÔÉËÇÍ ÇMYTILINI
AGI AS OS
POLICHNITOS
POLI CHNIT OS
PAPPADOS
PLOM ARI
GERA
PLOMARI
AGI A PARASKE VI
LOUTRO POLI THE RM IS
LOUTROPOLI THERMIS
POLICHNITOS
KAL LONI
ERES OS-ANTI SS A
LOUTRO POLI THE RM IS
ERESOS
MANTAMADOS
AGIAPARASKEVI
ANTISSA
KALLONI
MITHYMNA
MITHY MNA
PAPPADOS
PLOM ARI
GERA
PLOMARI
GERA
PLOMARI
30
Map 3. Attractiveness Index Values for Lesvos’ Settlements
Attractiveness Index for Lesvos' farmers
N
MITHYMNA
MITHYMNA
PETRA
MANTAMADOS
PETRA
MANTAMADOS
AGIA P ARASKEV I
ANTIS SA
KALLONI
KALLONI
AGIA PARASKEVI
ERESOS-ANTISSA
LOUTROPOLI
THERMIS
LO UTRO PO LI
THE RMIS
ERES OS
EVERGETOULA
LAMPO Y MYLI
MYTILINI
MYTILINI
AGIASOS
POLICHNITOS
University of the Aegean-Dep. of Envir onmental Studies
Labor atory of Local and Island Development
Director: Assistant Professor I. Spilanis
Mapping: Stergios Pralakidis
POLICHNITOS
PAP PADOS
Sc ale 1:270000
LEGEND
S ettl e m e nt
AGIASOS
Attractiveness
PLOMARI
GERA
Lo w
Mu n ic ip a li ty
S ma l l
Fo rm e r a d mi n is tra tiv e u ni t
Me d iu m
Hi gh
PLOMARI