Measuring the level of Industrial Development in Ardabil Counties with the Approach of Spatial Justice

IntroductionIn most countries, especially in developing countries and Iran, is not spatial justice in the distribution of development indicators in the industrial sector. This issue, led to the imbalance in regional development, increased social inequality and imbalance in the development indicators distribution. Ardebil province with a lot of background on the subject of regional spatial planning, have been faced with the problem of imbalance in the development and distribution of unbalanced development indicators. Due to the necessity of spatial planning approach to regional development, it is necessary for identifying and analyzing the industrial development and spatial distribution patterns of industrial indicators in Ardabil province. The major aim of this study is to assess the spatial justice in the distribution of industrial development of the province of Ardabil. This article made from five main sections: introduction, theoretical principles, methods, results, discussion and conclusions.
Methodology The territory of this research with an equivalent of Ardabil 17800 km2 between 37 to 45 42' 39˝ North latitude of the Equator and 30 to 47 55' 48˝ East longitude from the Meridian in northwestern Iran with 1,248,488 population in the year 1390. The study type is descriptive – analytical and its method is exploratory and correlation. Research data were collected from the provincial Statistical Yearbook 1391, counties population data and from Iran’s Small Industries & Industrial Towns Organization web site in 1394. To evaluate and rank the cities, the 47 indicators were used. This consisted of two thematic territory is to describe and analyze the data. ELECTRE model, the coefficient of variation spatial autocorrelation (Moran Index), hot spots and the geographic weighted regression methods were used.
Results & DiscussionThe results of the application of Moran statistics show that the value of this statistic for the spatial distribution of industry is 0.75 and it is close to one. The hot spot analysis revealed that in this clustering trend, the county of Ardabil with the Z-value of 2.95 and a p-value of 0.003 in the significance level of 99 percent is the pole of the province's industrial accumulation. The other countries, due to the high amount of negative Z and high amount of p value, industry distribution are random. The hot spot analysis of the spatial distribution of active mines in Ardebil province shows that the county of Namin with z value of 2.3 and a P value of 0.01 of have been developed in the mining sector.
Then the counties of Germi and Khalkhal with Z value of 1.06 and 0.24 and p-value of 0.28 and 0.80 are developed in the spatial distribution of mining in the Ardabil province. The results of the C.V method showed that there is no spatial justice in indicators distribution.
According to this the value of C.V, 80% of the index is greater than one, this result will be achieved which is balanced and balanced distribution of the indicators are not entitled to. In addition, the results of the analysis of the data in the ELECTRE model indicate that Ardabil and Namin city respectively, with 8 ratings and 7 are entitled to part of the city. The city of Nier, Kosar, Khalkhal and Meshkin Shahr, somewhat developed and Sareyn, Parsabad, Germi and Bileh Savar although having the high geographical capabilities, but with the 4-, 5-, 6-, 9- are undeveloped counties in the province. Regression results showed that between the geography, industrial development of the city of Namin with value 1.87, Nier with value 0.7, Ardabil with the value 0.66 and Kosar with a value of 0.66, there is a significant relationship between industrial development and in proportion of them population. This relation about the city of Khalkhal and Meshkin Shahr, respectively, with a standard deviation of 0.17 and 0.06, is less.
Finally, in terms of the relationship between industrial development and population, the city of Bileh Savar, Germi, Sarain and Pars Abad is negative. The results of the regression analysis and analysis of the geographical weighted method show that the relationship between industrial development with urbanization, respectively, in the case of the county of Ardabil, Nier and Namin, with the amount of 1.88, 0.64 and 0.54, significantly high. As a result, the counties that have a high urbanization rates, also more developed. In the counties of Khalkhal, Meshkin Shahr and Kosar, with the values of 0.49, 0.30 and 0.03 this relationship is less.
The findings of this research are match with the results of the findings of Fathi and Nastaran about imbalances in the distribution of development indicators. The results of counties ranking showed that there is a deep gap between the areas that they are quite developed and completely stripped. Hence, the results of this study are conformed to with Ziari et al., and Ghanbari et al., and Rahmati research results about the predominance of the capital county in developmental level in the province. The findings of this research conform to with the results of Hatami et al., about the ignore of the economic view in the establishment of industries.
ConclusionThis study shows that the geographical distribution of industries is highly clustered and Ardabil County is the dominant center of industry accumulation in the province. The mining industries in the province of Ardabil between regions or districts are distributed unfairly. despite some counties such as Khalkhal and Germi have mines, they rank is lower in terms of industrial development. The spatial distribution of industrial development in counties is unequal and inconsistent with the equivalent development. Therefore, Ardabil counties with a score of 7, is far from city Bileh Savar with a score of 9. Finally, it was concluded that there is a significant correlation between the rate of urbanization, immigration and population with counties industrial development. This means that the more developed counties in terms of industry also have high values in population, internal migration and urbanization.