World Applied Sciences Journal 22 (10): 1395-1400, 2013 ISSN 1818-4952 © IDOSI Publications, 2013 DOI: 10.5829/idosi.wasj.2013.22.10.538
The Estimation of the Underdevelopment Degree of Different Regions A Case Study of the Cities of Guilan Province in Iran
Farshad Sameni Kievani, Mohsen, khodadadi and Mohmmad. Jouzbarkand
Department of Accounting, Islamic Azad University, Roudsar and Amlash Branch, Roudsar, Iran
Abstract: Economic development is the most important goal of any societies. Thus, recognition of present status and possibilities of different regions of any country is essential. This study explains numerical taxonomy method and its mathematical relationship and suggests the various suitable indexes of economic development. And then based on statistical documents and the use of 45 common indexes of economic, social, educational and so on determines Ranking each city of Guilan province in terms of Economic welfare in the years 2001 and 2011. The results of this study are as follows: underdevelopment degrees of the cities of Guilan Province have reduced in the years during 2001 and 2011 and in this decade, the economic situation has been improved and the cities underdevelopment coefficient has changed from 0.7484 to 0.6277. Of course the rate of improvement has not been uniform in all the cities during this decade. Duality of Economic of the province cities have reduced these ten years. And improvement percentages of the poor cities are more than rich cities. According to findings of the research, the research suggests in the reduction of regional inequalities be a priority policy in the province.
Key words: Degree of development Indicators of development Numerical taxonomy and Guilan province
INTRODUCTION scientific reference. In this study, the cities are ranked based on the rate of development. Rich and poor regions All of country try to achieve development. are determined in the province. The research use of 45 Development in its broad sense means to improve the associated cases with different population and economic, quality of life in all its aspects, is nothing more than educational, cultural, hygienic, dwelling and welfare the increase in revenue, a better education,poverty facilities, industrial and roads for compare in 2001 and reduction and a healthier environment [1]. The balanced 2011; that 7 cases related to population part and economic development in any society is the most important of its part, 7 cases related to education part, 6 cases related to economic goals. To achieve to the balanced development hygiene and therapy part, 5 cases related to industry part, should be improved the various dimensions of 6 cases related to culture part, 3 cases related to roads and development in all of the regions. To design a model for 4 cases related to substructure part and 7 cases related to ranking of each city is important in order to eliminate service part. obstacles to economic development [2, 3]. There are many researches about the economic development levels in MATERIALS AND METHODS the different regions. For example, Badri and his colleagues [4] have analyzed the development degrees of There are different methods and models for rural areas Kamyaran city. The results show that the rate measurement level of successful and development and of development between areas of Kamyaran is not grading regions. One of the most current methods in equal. In the Kamyaran five villages are in developing, grading regions with respect degree of developing is one is developed and one is undeveloped [4]. But not do numerical Taxonomy analysis [5-7]. For first time; this unfortunately research about this subject in Guilan method suggest by M. Anderson in 1763. Today the province and this research want to compensate to help by Numerical Taxonomy is employed for activities, economic
Corresponding Author: Farshad Sameni Keivani, Department of Accounting, Islamic Azad University, Roudsar and Amlash Branch, Roudsar Iran. P.O. Box 4481757396. Tel: +989116115380. 1395 World Appl. Sci. J., 22 (10): 1395-1400, 2013 sectors, towns, regions, countries ranking with respect to libraries of mental training club for the children and several different or sometimes contrasting economic and teenage for per 1000 persons, the number of general social factor indices. There are many evidences in courts to the number of the towns of province, the rate of implementation of this method in Iran like the entire world the number of structural permits for per 1000 family, the [8]. rate of the number of every doctor for per 1000 persons, By use of this method; we can define homogenous the number of subscribers in domestic electricity to the groups in provinces and specify cities under developing number of family, the number of subscribers in industrial degree. Performing steps of numerical Taxonomy method electricity for per 100 families, Per cent of villages that is as follows [9, 10]: have electricity in province, the number of subscribers in domestic water – piping to the number of family, the Step 1: We consider a matrix (n X m) which n shows the number of subscribers in industrial water – piping for per number of cities (here it is 16) and m is used as the number 10 families, the number of gasoline stations for per 10000 of indicators (here it is 45). persons, the total rate of asphalt roads for 10 squares kilometer form measurement of province, the total rate of asphalt roads for per 10000 persons, the rate of main roads IN11 IN 12 IN 1m for per 1000 persons in province, the rate of the number IN IN IN IN = 21 22 2m taxi for per 1000 persons in province, the number of rural post – box for the number of villages, the number of urban INn12 IN n IN nm post – box rate to the number of towns, the number of Thus, each row of the matrix shows indicators of one post agency rate to the number of towns, per cent of city. villages have telephone, Head – money capital of villager cooperative companies for every villager person, Rate of expense industrial electricity to total rate expense The indicators of this research are including: electricity of province, the number of hygienic and Per cent of burgess, Reverse rate of unemployment, therapy centers for every 1000 persons in province, the Proportional Frequency population (Frequency person in number of towns have gas rate to total towns, the number square kilometer), Reverse size of family, Reverse amount of villages have gas rate to total villages. net Sponsorship, the number of service of automobile as compared with the number of urban regions, Rate of the Step 2: At this point, the matrix will convert to the number of kindergarten as compared with, the number of standard matrix which all indicators have zero mean and beginners, Rate of the number of primary schools as one variance [11-14]. compared with the number of population regions, Rate of the number of primary classes to the number of primary SIN11 SIN 12 SIN 1m students, the ratio of number of elementary classes to the SIN SIN SIN number of elementary students, Rate of the number of SIN = 21 22 2m elementary schools to the number of population regions, Rate of the number of classes to the number of students SINn12 SIN n SINnm nm× in general high school, Rate of the number of high school The members of this matrix are calculated based on to the number of urban regions, the number of laboratory the following formula: centers of the number of population regions, the number of radiography centers of the number of population INij− IN j regions, the number of pharmacy to the number of SIN = j= 1,2,3,... m ij S population regions, the number of hygiene houses of j hygiene to the number of villages, the number of cinema Step 3: We calculate distances between districts with to the number the towns of province, the number of using of the following matrix [15]: saloon theater to the number the towns of province, the number of general libraries to the number the towns of DD11 12 D 1n d1 province, the number of available books in general DD21 22 D 21n d2 libraries for per 1000 persons, the number of libraries of Dd= = mental training club for children and teenage to the DD D × d number of the towns of province, the number of books n12 n nn nm n
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n And the D is: m 2 , Σ = d RESULTS AND DISCUSSION Dab=Σ= j1(SIN aj − SIN bj ) d = ii1 n The results show that the seven cities of Guilan province were top of the underdevelopment line in 2001 L21=−=+ d2 Sdd Ld2 S (See Tab.1 & 2 & Fig.1). Bandar Anzali and Talesh city are Step 4: At this step, the highest number in each column the most development and underdeveloped cities in the is considered as ideal amount and then determined the Gilan province, respectively, in 2001 and 2011. On the gap of every city from ideal city by use the following other hand, if we consider the coefficient of formula [16]. underdevelopment in 2011 the same as 2001 we see only Talesh and Rezvanshahr cities are located below of the m 2 underdevelopment line and other cities are transferred to C =Σ−= ()SIN SIN io j1 ij oj the above of the underdevelopment line but if it is considered the coefficient of same year eight cities are The degree of underdevelopment of the cities can be above of the underdevelopment line. By comparing the
determined as follows (di shows the underdeveloped results for years 2001 and 2011 can be stated the following degree) is obtained from the following equation [13]: points: In this decade, the economic situation has been
Cio improved and the cities underdevelopment coefficient has d j = Co changed from 0.7484 to 0.6277. Of course the rate of improvement has not been uniform in all the cities during
The C0 is obtained from the following equation: this decade. For example, the rating of Masal, shaft and CCo== io + 2 S io Astara cities have improved in 2011 compared to 2001. Conversely, the ratings of Somesara, Rezvanshahrand and
nn2 Siahkal cities have reduced. This is while the rating of Σ−i=11()CC io io Σi= C io SCio = , io = Bandar Anzali, Rasht, Fooman, Langerud, Roudsar, nn Roudbar and Talesh cities no have changed.
Table 1: The underdevelopment degree in Gilan province cities separately in 2001 Names of cities Development Ranking in 2001 Degree of Underdevelopment in 2001 Bandar Anzali 1 0.4233 Rasht 2 0.6043 Lahidjan 3 0.6467 Astaneh Ashrafieh 4 0.678 Someeh Sara 5 0.7046 Fooman 6 0.7383 Astara 7 0.7472 Langerud 8 0.7593 Roudsar 9 0.7655 Roudbar 10 0.7698 Siahkal 11 0.8199 Rezvanshahr 12 0.8258 Amlash 13 0.8479 Shaft 14 0.8762 Masal 15 0.8827 Talash 16 0.8841 Underdevelopment line 0.74835 Intensity of inequality 0.161252969 The most developed Bandar Anzali The most underdeveloped Talash
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Table 2: The underdevelopment degree in Gilan province cities separately in 2011 Names of cities Development Ranking in 2011 Degree of Underdevelopment in 2011 Bandar Anzali 1 0.3634 Rasht 2 0.52252 Astaneh Ashrafieh 3 0.5481 Lahidjan 4 0.5698 Astara 5 0.5847 Fooman 6 0.6187 Someeh Sara 7 0.6233 Langerud 8 0.6245 Roudsar 9 0.6582 Roudbar 10 0.6578 Shaft 11 0.6721 Amlash 12 0.6937 Siahkal 13 0.70186 Masal 14 0.7143 Rezvanshahr 15 0.7208 Talash 16 0.7693 Underdevelopment line 0.6276925 Intensity of inequality 0.155250638 The most developed Bandar Anzali The most underdeveloped Talash
Table 3: The Changes of Underdevelopment of Guilan province cities Degree of Degree of Percentage of Development Development Improvement Name of city Underdevelopment in 2001 Underdevelopment in 2011 improvement Ranking in 2001 Ranking in 2011 in the ranking Amlash 0.8479 0.6937 0.18 13 12 1 Astaneh Ashrafieh 0.678 0.5481 0.19 4 3 1 Astara 0.7472 0.5847 0.22 7 5 2 Bandar Anzali 0.4233 0.3634 0.14 1 1 0 Fooman 0.7383 0.6187 0.16 6 6 0 Lahidjan 0.6467 0.5698 0.12 3 4 -1 Langerud 0.7593 0.6245 0.18 8 8 0 Masal 0.8827 0.7143 0.19 15 14 1 Rasht 0.6043 0.52252 0.13 2 2 0 Rezvanshahr 0.8258 0.7208 0.13 12 15 -3 Roudbar 0.7698 0.6578 0.14 10 10 0 Roudsar 0.7655 0.6582 0.14 9 9 0 Shaft 0.8762 0.6721 0.23 14 11 3 Siahkal 0.8199 0.70186 0.14 11 13 -2 Someeh Sara 0.7046 0.6233 0.11 5 7 -2 Talash 0.8841 0.7693 0.13 16 16 0
Fig. 1: levels of development, overall in 1375 and 1385 (Using the column charts)
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Fig. 2: levels of development, overall in 1375 and 1385 (Using the pie chart)
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