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© 2018 by the authors; licensee Growing Science, Canada.
doi: 10.5267/j.dsl.2018.1.005
Decision Science Letters 7 (2018) 417–426
Contents lists available at GrowingScience
Decision Science Letters
homepage: www.GrowingScience.com/dsl
Ranking the cities with potential of tourism investment in Fars province using numerical
taxonomy
Ardeshir Bagheria,b*, Masoomeh Moharrerc, Moslem Bagheric and Maryam Nekooee Zadehc
aMasters Student, Department of Industrial Management, University of Shiraz, Iran
bExpert in the Monitoring and Evaluation at the Provincial Governorate of Fars, Iran
cAssistant Professor, Department of Tourism Management, University of Shiraz, Shiraz, Iran
C H R O N I C L E A B S T R A C T
Article history:
Received September 1, 2017
Received in revised format:
January 8, 2017
Accepted January 15, 2018
Available online
January 15, 2018
Tourism industry is one of the most important parts of economic development. It can create
job either directly or through stimulating other economic sectors. Therefore, it is important for
policy makers to determine the best places with good potentials for tourism development.Fars
province has been the center of civilization and the origin of the Iranian literature and
mysticism celebrities and could be a potential investment of tourism. However, this province
does not have the infrastructure required to accommodate tourists, especially during holiday
seasons. This study ranks different cities located in the Fras province having the necessary
potential for investment in the tourism sector by using the numerical taxonomy in terms of 12
indicators. The research population includes 29 cities in this province based on the devision of
2016. The necessary data are collected from Directorate General of Cultural Heritage,
Handicrafts, and Tourism in Fars province. It is found that the cities of Shiraz, Marvdasht,
Firouzabad, Sepidan, and Kazerun were the best cities for tourism investment, respectively.
.he authors; licensee Growing Science, Canada2018 by t©
Keywords:
Tourism
City
Fars Province
Numerical taxonomy
1. Introduction
Iran has special importance for foreign tourists due to its soil breadth, geographical location,
communication routes, and tourist attractions. Its charming nature, climate diversity, and its rich culture
and civilization have made this ancient country full of diverse attractions (Zangiabadi et al. 2012: 53).
Thus, there is a need to use all facilities and capabilities for creating a comprehensive and sustainable
development and replacement of new sources of income with oil resources. In this regard, development
of the tourism industry, which economists consider it as the third most dynamic and growing economic
phenomenon after the oil and auto industry is regarded as the essential need for economic development
(Madhushi & Naserpour, 2003: 27). The necessity of paying serious attention for tourism issue, as a
new phenomenon in the 21st century machine life, is being felt increasingly in the world and its
importance is increasing. Its importance at the current age depends more on its economic cycle, which
has high potential in the area of local and international economic dynamics (Zangiabadi et al., 2012,
418
52). In addition, tourism includes major part of the global economy and it is one of the largest industries
in the world. It is predicted that the number of international tourists to reach 1.6 billion people and its
turnover to increase to more than 2 trillion dollars by 2020 in the world (Zangiabadi et al., 2012).
Tourism also affects economic growth indirectly, since it shows the dynamic effect in the whole
economy in the form of spillovers or other externalities (Tayebi et al., 2007). Accordingly, when
tourism booms due to its high interaction with other economic activities, other economic activities
provided for that good and service will also boom along with it. It means that tourism can act as a
driving force for economic growth, which will expand other activities. Moreover, economic growth is
effective in tourism development. Economic development leads to tourism development through
development of tourism facilities and infrastructure, including development of transportation and roads,
expanding the electronic money, development of information and communication technology,
expanding the residencies, restaurants and hotels, the development of public health and development
of recreational facilities and welfare affairs (Tayebi et al., 2007). Tourism, as a service industry, plays
an important role in the development of many countries and has been expanding significantly in recent
years. This expansion subsequently yields several benefits for related businesses, such as hotels and
resorts, travel agencies, restaurants, and souvenir shops (Chaisumpunsakul & Pholphirul, 2017).
Travelling and tourism industry is considered as the greatest and the most diverse industry in the world.
Most countries consider this dynamic industry as the main source of revenue, employment and growth
of their private sector and infrastructural development (Makui & Nikkhah, 2011: 29).
Based on what was stated, it can be realized that the development of the tourism industry would leave
positive impacts on other aspects and dimensions of the host country. The following figure summarizes
the most important impacts of the tourism industry development on the host country using the
information collected:
Fig. 1. Tourism industry impacts on the host country
Tourism
industry
impactsonthe
hostcountry
reducedrateof
unemployment
andcreating
job peaceand
altruism
economic
development
reverse
immigration
erodication
ofpoverty
fairly
distribution
ofweealth
development
of
recreational
infrastructure
development
ofcuktureof
thelocal
communities
developmentof
informationand
communication
technology
Development
of
transportaion
infrastructure
development
ofesidential
infrastructure
publichealth
development
boomof
local
businesses
A. Bagheri et al. / Decision Science Letters 7 (2018)
419
Tourism infrastructure development in different regions of the world does not follow symmetrically
equal pattern (Bagheri et al., 2018). Optimal exploitation of infrastructure and finding the inequalities
in tourist areas are essential for tourism development. By recognizing the infrastructure and ranking
them at the level of areas, tourists can be managed well. Therefore, coordination between the number
of tourists and the capacity of tourism spaces, especially infrastructure, is necessary. Determining the
superior centers and ranking them in tourism development for providing better service, social and
economic justice at the level of areas are essential. One of the methods used to rank the tourism areas
is investigating the urban facilities and services such as hotels and accommodations, access and
transportation networks, social communication facilities, cultural and art attractions, and providing
tourist services in all areas (Shamaee & Mousavand 2011, 25). Potential for supporting tourism is
defined as factors that enhance historical sites to be important and appropriate for tourism management.
Some historical sites may have high tourism attractiveness, but there are limitations to basic facilities
like toilets and car parking. Some of them may have a limitation from external factors such as
environmental conditions surrounding the tourist attractions. Communities around tourist attractions
are one of the environmental conditions that can affect the area. The cleanness, tidiness, safety, and
ancient buildings of these communities are considered (Sonchaem et al; 2017: 2-3)
Hence, investigating the development indicators and infrastructures of this industry in different regions
of Iran seems to be an essential. Fars province with potentials such as climate diversity and abundant
natural and historical attractions is one of the rich provinces in the tourism area. In the light of having
great historical and cultural-historical (four recorded World Heritage sites),ecotourism, rural and
nomadic (more than 12% of Iran’s nomadic people are living in this province), religious (more than
1400 holy tombs), and health resources and capacities since old days, this province tries to develop this
industry using its unique potentials and capacities to provide superior tourism services to attract foreign
tourism and currency and accordingly achieve the high goals of the resistive economy and development
outlook document of province, horizon 2025.
The position of this province as the center of international communications in the geopolitical region
of the south of Iran, social and economic communication channel of Iran with Persian Gulf countries,
the position of Shiraz as the center of the aerial communication and the most important socio-economic
center at south of Iran, and international reputation of this city along with Pasargadae and Persepolis as
the first tourism destinations in light of having 1404 holy tombs, including the holy shrines of Ahmadi
and Mohammadi, Hazrat Seyed Alaeddin Hussein, and Imam Ali ibn Hamzah have made this city
known as the third shrine of Ahl al-Bayt (family of the Islamic prophet Muhammad). It requires
considering this God-given gift as undeniable necessity.
Despite these potentials, infrastructures of tourism have been distributed in unbalanced form in cities
of this province, and each city has different facilities and infrastructures, and even different climates.
Thus, evaluating the cities and ranking them for investment seems to be essential and this can lead to
the development of this province. On the other hand, establishment of an international tourism city,
town, or village has always been considered in this province. Thus, paying attention to different needs
and interests of tourists makes it necessary to pay special attention to this issue. In addition, one of the
topics, having many critics in cultural communities, is the topic of promoting the abnormalities and
harms caused through cultural interactions with international tourisms. This topic can bring special
consequences for the host community. However, what can cause the lowest harm and damage to the
host community is establishment of the tourism villages and organizing the tourists, and location is one
of its important requirements, since tourisms have different needs and demands.
Thus, given the importance of tourism due to its role in the development of the province, exploitation
level of its cities has been studied based on the indicators of tourism development, so that useful steps
can be taken for future use of provincial planners and managers in determining the priority of
investment in the cities of this province. Accordingly, this research was conducted to investigate cities
having high potential for investment in the tourism area.
420
2. Literature review
Makui and Nikkhah (2011) presented a fuzzy decision making methodology to find the best scenario
planning of an Iranian tourist industry. They considered two primary criteria for designing a
methodology which were the number residential and the season of the year. The proposed model of
this paper has considered analytical hierarchy process (AHP) (Saaty, 2004) method to prioritize
different criteria and using fuzzy numbers analyzed the proposed approach for an empirical study of
Iranian case study. Momeni and Ghahari (2013) investigated the development status of Fars province
cities using numerical taxonomy. Their research findings revealed that the number of developed cities
was reduced and the number of deprived cities was increased during the statistical period. Ardakani
(2014) presented an empirical investigation to determine important factors influencing development of
tourism industry in city of Yazd, Iran. The proposed study determined 40 different factors and, using
three methods of TOPSIS (Hwang & Yoon, 1981), SAW and Taxonomy (Sneath, & Sokal, 1973), the
factors have been ranked, accordingly. Feizabadi and Maleki (2015) also examined and compared the
development level in rural regions of Iran's provinces using numerical taxonomy technique and factor
analysis method. Bagheri et al. (2018) examined the hard assets of the tourism infrastructure in different
provinces of Iran. Their results showed that the development of tourism infrastructure in different
regions of Iran was asymmetric and imbalanced.
3. The study area
3.1 Geographic location of Fars province and country divisions:
With an area of 123946 km
2
, Fars province covers about 7.5% of the total area of Iran. It is located at
geographical position of 27
◦
3’ to 31
◦
40’ north latitude and 50
◦
36’ to 55
◦
35’ east longitude. This
province is considered as one of the most important tourist attractions in light of having many natural,
historical, geological, geomorphological, cultural-rural, and nomadic attractions at the level 1 of region.
Fig. 2. The map of Iran and province of Fars
This province has shared border with provinces of Kohgiluyeh and Boyerahmad and Isfahan, and parts
of Yazd province in north, Kerman Yazd provinces in east, Hormozgan province in south, and Bushehr
province in south. In addition, based on the latest divisions, Fars province has 29 cities.
A. Bagheri et al. / Decision Science Letters 7 (2018)
421
4. Methodology
Taxonomies or concept hierarchies are crucial for any knowledge-based system, i.e. a system equipped
with declarative knowledge about the domain, which is capable of reasoning on the basis of knowledge.
Concept hierarchies are in fact important because they allow to structure information into categories,
thus fostering its search and reuse (Cimiano et al., 2005). The method of this research is descriptive-
analytical and quantitative. It was conducted using numerical taxonomy spatial measurement models.
The research population included whole area of Fars province, including 29 cities based on the division
of 2016. The data were collected from Directorate General of Cultural Heritage, Arts and Crafts and
Tourism of Fars province. As the Gerash city did not have required information, this city was not
examined and 28 other cities were analyzed. Using the view of experts, 12 indicators were determined
to evaluate the cities with potential of establishing international tourism city. Indicators used in this
research included 1. Tourism spots with potential of investment, 2. Number of tourism sample areas,
3. Number of historical, cultural, and religious attractions, 4. Having rural and nomadic attractions, 5.
Number of natural attractions, 6. Number of national works recorded, 7-number of the international
sites recorded, 8-Being located in important communications roads of the province, 9- having rail lines,
10-having airports, 11-having lakes, rivers, dams, 12-Having appropriate climatic conditions (Table 1).
To conduct the research calculations, 12 tourism indicators were analyzed first by using numerical
taxonomy model.
Table 1
Research indicators
Indicator Abbreviation
Tourism s
p
ots with
p
otential of investment C1
N
umber of tourism sample areas C2
N
umber of historical, cultural, and religious attractions C3
Having rural and nomadic attractions C4
N
umber of natural attractions C5
N
umber of national works recorde
d
C6
N
umber of the international works recorded C7
Being located in important communications roads of the province C8
Having rail lines C9
Having airports C10
Having lakes, rivers, dams C11
Having appropriate climatic conditions C12
5. Numerical taxonomy analysis for ranking the cities with potential of investment
Several methods have been used to determine the development level of the regions and one of the most
important of them is numerical taxonomy. The numerical taxonomy divides a set into more or less
homogeneous subsets and uses as a scale for recognizing the level of economic and social development
in planning (Bidabad, 1983: 27). This method was proposed for the first time by Adenson in 1763.
However, it was developed and expanded by a number of Polish mathematicians in the early 1950s.
This methodology was proposed as a tool to rank the development level of different nations in
UNESCO in 1968. This method is one of the most complex ranking techniques, determining the
development level by combining multiple indicators. The goal of regional planning might be equalizing
the development levels of the regions and the optimal spatial balance of development levels of
development and establishing the social justice through it (Qadiri & Habibi, 2004: 155). The numerical
taxonomy analysis technique is implemented in several steps as follows (Rohlf, 2013; Sneath & Sokal,
1973):
1- Forming the initial matrix Table 2. Forming the standard matrix 3. Calculating the distances between
the parts 4. Calculating the distance between each part and other parts 5. Drawing an optimum diagram
6. Determining the homogeneous parts 7. Calculating the compound distance of each region of the
422
optimal region 8. Calculating the development level of the parts, and 9- Ordering of the parts based on
the development level.
2- The initial information matrix table should be formed based on the indicators used, so that the name
of studied regions is placed in one column and the values of the used indicators are placed in the
opposite columns.
11 1
1
n
mmn
XX
XX
(1)
Here X, n and m represent the name of the different regions, places and indicators, respectively.
In this step, the data matrix was formed as described in the Table 4 in Appendix using the data collected
from the Directorate General of Cultural Heritage, Handicrafts, and Tourism of Fars province.
3-After preparing the data matrix, as different indicators might have different scales; it is needed that
the used indicators to be abandoned from the scale so that the lack of homogeneity of the indicators to
be resolved. For this purpose, a standardization method can be used, which by transforming the
indicators to the standardized indicator, the mean of all indicators would be zero and their SD would
be 1. We form the matched standard matrix as follows:
11 1
1
n
mmn
Z
Z
ZZ
(2)
To calculate the standard matrix, the following equation can be used:
, (3)
where
1
1n
ij
i
XX
n
and
2
ij
i
X
X
Sn
i=1,…,n.
4-Calculating distances between parts: After calculating the standard matrix, difference or distance of
each part relative to the other parts of each of the indicators can be obtained.
In this step, given the standardized numbers in the standard matrix Z, we obtain the compound distances
between different n activities for m indicators as follows: if we obtain the distance between the activities
in paired, the compound distance matrix would be obtained. As matrix of distances is a symmetric
matrix, it can be concluded that this matrix is symmetric and its diameter is equal to zero. Moreover,
the matrix is square with dimensions of n by n. Members of this matrix show the compound distance
of each activity from another activity, and in each row of this matrix, the lowest value represents the
shortest distance between that activity and other activities or the greatest proximity.
Step 4: Determining the shortest distances
Each element of the matrix C represents the distance between two activities in the considered indicator.
In this matrix, we determine the shortest distance in each row and write it in the separate column (for
example, column d). Then, we calculate the mean and standard deviation of the shortest distances of
each row, that is, column d. Then, we calculate the upper limit distances (d+) and the lower limit
distances (d-) according to the following equation to determine the homogeneous activities.
d(+) = d + 2Sd d(-) = d - 2Sd
A. Bagheri et al. / Decision Science Letters 7 (2018)
423
In this step, the activities, whose minimum distances are between upper limit and lower limit distances,
would be homogenous and they are placed in same group. However, if minimum distances of two
activities are more than upper limit and less than lower limit distance, these activities should be deleted,
due to non-homogeneity.Determining the homogeneous groups was formed as described in the Table
5 in Appendix.
Stage 5: Ranking of homogeneous activities in terms of criteria studied
If all activities are not placed in a homogeneous group in this step, then we would form the data matrix
for homogeneous activities:
Table 4 shows that, except for the city of Larestan, the rest of cities are in a homogeneous group. Thus,
the row of data related to the Larestan city is deleted and the matrix of new data is formed. Then, their
mean and standard deviation are calculated.
In the next step, we standardize the matrix of the new homogeneous data, and in the matrix of
standardized indicators, the ideal is considered for each of the indicators, and after finding the ideal
values for each of the activities, we calculate the “ideal” for each activity. Selecting the ideal value
depends on the type of indicators investigated, so that if the selected indicators are positive, we consider
the largest number of each column as the ideal. However, if the indicator is negative, the larger number
indicates non-ideal. Thus, the smallest value is selected as the ideal value. As in this problem, larger
values are better, so the maximum of each column is selected as the ideal point.Standard matrix of
homogeneous group was formed as described in the Table 6 in Appendix.
Step 6: Calculating the level of having homogeneous activities
In this step, a combined indicator called “level of having” is introduced, which has a limited range and
it is between zero and one. As the value of fi is closer to zero, the “level of having” would be lower.
According to this “level of having”, activities can be ranked and prioritized based on the indicators
investigated.
Table 2
Determining the “level of having” in each city in Fars province
cities Abadeh Arsenjan Estahban Eghlid Bawanat Pasargad Jahrom Kharah Khorrambid
CIO 11.7 11.50 10.6 9.97 9.5 10.02 10.48 11.18 10.83
Fi 0.853 0.843 0.809 0.761 0.725 0.765 0.8 0.853 0.827
city Khonj Darab Rostam Zarrin Dasht Sepidan Sarvestan shiraz Farashband Fasa
CIO 10.69 10.11 11.2 12.31 8.94 10.38 4.43 0.803 9.91
Fi 0.816 0.772 0.875 0.94 0.682 0.793 0.338 10.51 0.756
city Firoozabad Qir and Kazrin Kezeroun Kovar Lamerd Marvdasht Mamasani Mehr Neyriz
CIO 8.65 11.60 9.05 10.86 11.41 8.29 9.82 10.99 10.05
Fi 0.661 0.885 0.691 0.829 0.871 0.633 0.75 0.839 0.768
Mean = 10.15 Standard deviation = 1.47 CO = 13.10
6. Findings
This research tried to investigate the potential of Fars province cities in terms of investment in tourism
infrastructure using numerical taxonomy. It was found that the cities of Shiraz, Marvdasht, Firouzabad,
Sepidan, and Kazeroun, respectively, were the best cities in terms of the potentials for tourism
investment. Table 3 presents the results of the research and ranking of the cities of province in this
regard. Based on the indicators used in this research, cities were prioritized. Findings of research and
status and facilities of the cities with higher rank suggest the accuracy of the research. However, the
issue which should be considered is that Fras province is diverse in terms of climate and if the
conditions for investment in cities with high rank are not provided, investment can be performed in
other places. However, the results of this research can provide great help for macro-investment policies.
424
Table 3
Ranking the cities
Ran
k
Cit
y
CIO Fi Rank Cit
y
CIOFi
1 Shiraz 4.433 0.338 15 Farshaban
d
10.5110.803
2 Marvdasht 8.291 0.633 16 Estahban 10.6000.809
3 Firuzaba
d
8.654 0.661 17 Khen
j
10.6910.816
4 Se
p
idan 8.938 0.682 18 Khorrambid 10.8300.827
5 Kazerun 9.049 0.691 19 Kovar 10.8620.829
6 Bawanat 9.496 0.725 20 Mehr 10.9920.839
7 Mamasani 9.824 0.750 21 Arsan
j
an 11.0460.843
8 Fasa 9.905 0.756 22 Abadeh 11.1670.853
9 E
g
lid 9.966 0.761 23 Kharame 11.1780.853
10 Pasar
g
as 10.015 0.765 24 Rostam 11.1960.855
11 Ne
y
riz 10.054 0.768 25 Lamerd 11.4130.871
12 Darab 10.111 0.772 26
Q
ir and Kazrin 11.5960.885
13 Sarvestan 10.381 0.793 27 Zarrin dasht 12.3130.940
14 Jahrom 10.484 0.800
Fig. 3. The ranking of the cities with potential of tourism investment in the Fars province
7. Suggestions
However, the point that should be considered in this method is that numerical taxonomy divides the
indicators into homogeneous and non-homogeneous groups, deletes the non-homogeneous groups, and
does not prioritize them, which is considered as the shortcoming of taxonomy method. For example,
Larestan city was among the non-homogeneous groups in this research and it was not included in the
investigations, while this city has an appropriate environment in terms of having tourism infrastructure,
such as the airport and tourist attractions. However, it is poor in terms of some other indicators, which
a separate study needs to be conducted for this city and feasibility to be performed.
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Appendix
Table 4
Data matrix
City C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12
Abadeh 4 2 5 1 17 42 0 1 1 0 1 0
Arsenjan 3 1 6 1 13 111 0 1 1 0 1 1
Estahban 4 3 10 1 13 110 0 1 0 0 3 1
Eglid 8 6 5 1 15 45 0 1 1 0 4 1
Bawanat 10 3 56 1 35 47 0 1 0 0 2 1
Pasargad 6 2 5 1 6 112 2 1 1 0 3 1
Jahrom 5 3 4 1 16 71 0 1 1 1 2 0
Kharame 8 0 7 1 10 30 0 1 1 0 3 0
Korrambidbid 4 2 10 1 8 92 0 1 1 0 2 1
Khenj 3 1 60 1 20 15 0 1 1 0 2 0
Darab 5 1 40 1 30 93 0 1 0 1 2 0
Rostam 3 2 7 1 10 60 0 1 0 0 2 1
Zarrin Dasht 3 2 10 1 12 26 0 0 0 0 5 0
Sepidan 12 6 10 1 32 95 0 1 0 0 11 1
Sarvestan 6 4 4 1 23 20 0 1 1 0 5 0
Shiraz 20 11 130 1 57 616 1 1 1 1 1 1
Farashband 10 1 20 1 9 98 0 1 0 0 5 0
Fasa 4 4 10 1 21 68 0 1 0 1 12 0
Firouzabad 15 3 43 1 90 107 0 1 0 0 2 1
Qir and Karzin 5 1 10 0 8 52 0 1 1 0 5 0
Kazerun 10 4 55 1 20 188 0 1 0 0 5 0
Kovar 15 0 17 1 20 42 0 1 0 0 1 0
Larestan 47 4 647 1 71 182 0 1 1 1 9 0
Lamerd 4 2 10 1 18 62 0 1 0 1 2 0
Marvdasht 8 7 13 1 7 299 2 1 1 0 5 1
Mamasani 5 4 27 1 15 82 0 1 0 0 9 1
Mehr 7 1 8 1 0 71 0 1 1 0 5 0
Neyriz 10 2 13 1 26 50 0 1 0 0 9 0
mean 8.71 2.93 44.39 0.93 22.21 103.07 0.18 0.96 0.5 0.21 4.21 0.43
SD 8.62 2.37 121.15 0.26 20.06 116.7 0.55 0.19 0.51 0.42 3.13 0.50
426
Table 5
Determining the homogeneous groups
City Clr: City Clr: City Clr: City Clr:
Abadeh 0.76 Kharame 0.99 Sarvestan 1.59 Kavar 1.64
Arsanjan 0.62 Khorrambid 0.62 Shiraz 5.48 Larestan 7.66
Estahban 0.71 Khonj 0.76 Farashband 1.61 Lamerd 3.31
Eghlid 1.94 Darab 2.28 Fasa 2.82 Marvdasht 2.73
Bawanat 1.49 Rostam 0.71 Firuzabad 2.85 Mamasani 1.59
Pasargad 2.73 Zarrin Dasht 5.41 Qir and
Ki
3.31 Mohr 0.99
Jahrom 2.28 Sepidan 1.59 Kazerun 1.61 Neyriz 1.64
UCL upper limit 5.52 Lcl lower limit -1.11 Mean 2.20 Standard deviation 1.66
Table 6
Standard matrix of homogeneous group
City
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12
Yes.no
number
Yes.no
Yes.no
Yes.no
number
number
number
Yes.no
number
number
number
Abadeh -0.76 -0.37 -0.62 0.28 -0.19 -0.49 -0.33 0.19 1.02 -0.47 -1.00 -0.88
Arsanjan -0.99 -0.78 -0.58 0.28 -0.41 0.09 -0.33 0.19 1.02 -0.47 -1.00 1.10
Estahban -0.76 0.05 -0.44 0.28 -0.41 0.08 -0.33 0.19 -0.95 -0.47 -0.34 1.10
Eglid 0.16 1.29 -0.58 0.28 -0.30 -0.47 -0.33 0.19 1.02 -0.47 -0.01 1.10
Bawanat 0.63 0.05 1.23 0.28 0.81 -0.45 -0.33 0.19 -0.95 -0.47 -0.67 1.10
Pasargad -0.30 -0.37 -0.62 0.28 -0.80 0.10 3.26 0.19 1.02 -0.47 -0.34 1.10
Jahrom -0.53 0.05 -0.66 0.28 -0.25 -0.25 -0.33 0.19 1.02 2.06 -0.67 -0.88
Kharame 0.16 -1.20 -0.55 0.28 -0.58 -0.59 -0.33 0.19 1.02 -0.47 -0.34 -0.88
Khorrambid -0.76 -0.37 -0.44 0.28 -0.69 -0.07 -0.33 0.19 1.02 -0.47 -0.67 1.10
Khonj -0.99 -0.78 1.38 0.28 -0.02 -0.72 -0.33 0.19 1.02 -0.47 -0.67 -0.88
Darab -0.53 -0.78 0.65 0.28 0.53 -0.06 -0.33 0.19 -0.95 2.06 -0.67 -0.88
Rostam -0.99 -0.37 -0.55 0.28 -0.58 -0.34 -0.33 0.19 -0.95 -0.47 -0.67 1.10
Zarrin Dasht -0.99 -0.37 -0.44 0.28 -0.47 -0.63 -0.33 -5.00 -0.95 -0.47 0.32 -0.88
Sepidan 1.09 1.29 -0.44 0.28 0.65 -0.04 -0.33 0.19 -0.95 -0.47 2.29 1.10
Sarvestan -0.30 0.46 -0.66 0.28 0.14 -0.68 -0.33 0.19 1.02 -0.47 0.32 -0.88
Shiraz 2.94 3.37 3.92 0.28 2.04 4.37 1.46 0.19 1.02 2.06 -1.00 1.10
Farashband 0.63 -0.78 -0.08 0.28 -0.63 -0.02 -0.33 0.19 -0.95 -0.47 0.32 -0.88
Fasa -0.76 0.46 -0.44 0.28 0.03 -0.27 -0.33 0.19 -0.95 2.06 2.62 -0.88
Firuzabad 1.78 0.05 0.76 0.28 3.87 0.06 -0.33 0.19 -0.95 -0.47 -0.67 1.10
Qir and Karezin -0.53 -0.78 -0.44 -3.47 -0.69 -0.41 -0.33 0.19 1.02 -0.47 0.32 -0.88
Kazerun 0.63 0.46 1.20 0.28 -0.02 0.74 -0.33 0.19 -0.95 -0.47 0.32 -0.88
Kovar 1.78 -1.20 -0.18 0.28 -0.02 -0.49 -0.33 0.19 -0.95 -0.47 -1.00 -0.88
Lamerd -0.76 -0.37 -0.44 -3.47 -0.13 -0.32 -0.33 0.19 -0.95 2.06 -0.67 -0.88
Marvdasht 0.16 1.71 -0.33 0.28 -0.75 1.69 3.26 0.19 1.02 -0.47 0.32 1.10
Mamasani -0.53 0.46 0.18 0.28 -0.30 -0.15 -0.33 0.19 -0.95 -0.47 1.63 1.10
Mehr -0.07 -0.78 -0.51 0.28 -1.14 -0.25 -0.33 0.19 1.02 -0.47 0.32 -0.88
Neyriz 0.63 -0.37 -0.33 0.28 0.31 -0.43 -0.33 0.19 -0.95 -0.47 1.63 -0.88
Max 2.94 3.37 3.92 0.28 3.87 4.37 3.26 0.19 1.02 2.06 2.62 1.10
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