تحلیل الگوی فضایی-زمانی رقابت های منطقه ای در ایران و نقش آن بر مهاجرت های بین منطقه ای (مقاله علمی وزارت علوم)
درجه علمی: نشریه علمی (وزارت علوم)
آرشیو
چکیده
رقابت های بین منطقه ای با توجه به تشدید حرکت سرمایه های مالی و نیروی انسانی در بین مناطق شهری در عصر جامعه شبکه ای در حال افزایش است. پژوهش حاضر با توجه به اهمیت فزاینده این موضوع، الگوی فضایی-زمانی رقابت های بین منطقه ای را در ایران در فاصله زمانی 1395-1380 تحلیل و تأثیر این رقابت ها را بر تعیین الگوی مهاجرت های بین منطقه ای با استفاده از روش های آمار فضایی مانند روش موران افتراقی I، روش K-Medioid و روش رگرسیون جغرافیایی موزون در فاصله زمانی 1395-1385 تبیین می کند. تحلیل الگوی تغییرات فضایی-زمانی مناطق موردمطالعه بر اساس شاخص رقابت پذیری منطقه ای و روش موران افتراقی I در فاصله زمانی 1395-1380 نشان داد که مناطق مشابه از نظر شاخص رقابت پذیری، در مجاورت جغرافیایی یکدیگر قرار ندارند و تغییرات هر منطقه در این فاصله زمانی بدون همبستگی معنادار فضایی-زمانی با مناطق مجاور آن رخ داده است. هم چنین طبقه بندی مناطق با استفاده از روش K-Medioid و بر اساس شاخص رقابت بین منطقه ای در همین فاصله زمانی نشان داد که مناطق برخوردار از معادن نفتی و گازی عمده، در طبقات بالای رتبه بندی قرار دارند. هم چنین، شاخص رقابت پذیری منطقه ای بر اساس مدل رگرسیون جغرافیایی موزون قادر است از 0.01 تا 0.9 مقدار تغییر در تعداد مهاجران واردشده به هر منطقه را در سال 1385 و بین 0.22 تا 0.83 این تغییرات را در سال 1395 تبیین کند. بر اساس مدل رگرسیون ساده نیز شاخص رقابت بین منطقه ای 0.29 تغییر در تعداد مهاجران واردشده را در سال 1385 و 0.56 این تغییرات را در سال 1395 تبیین می کند.Spatio-Temporal analysis of Inter-regional competitiveness and Its influence on Inter-regional migration patterns in Iran
Improving regional-territorial competitiveness has become a critical issue in regional planning due to the increase in the spatial movements of financial resources and human capital in the contemporary networked society. Considering the increasing importance of the issue, the present article using different spatial statistics methods such as differential Moran I method I, k-medoids cluster analysis, and geographically weighted regression, focused on analyzing the spatial-temporal change in inter-regional competitiveness and its influence on inter-regional migration in Iran from 2011 to 2016. The results showed that the pattern of spatio-temporal change in the inter-regional competitiveness index for each region during 2001-2016 has a weak correlation with that in its neighboring regions. In other words, regions showed weak similarities according to their competitiveness scores. The same pattern can be seen for the pattern of inter-regional migration. Regions exhibited negative but weak spatio-temporal correlation with each other in terms of the total number of immigrants in the years 2006 and 2011. The role of inter-regional competitiveness in explaining inter-regional migration increased in 2016 compared with 2006, implying the increasing significance of inter-regional competitiveness in determining the spatio-temporal pattern of inter-regional migration in Iran. As a result, those regions that do not take strategies to improve their regional competitiveness should expect to encounter an increase in the number of emigrants. In this way, inter-regional competitiveness can reinforce uneven regional development in the future.IntroductionImproving regional-territorial competitiveness has become a critical issue in regional planning due to the increase in the spatial movements of financial resources and human capital in the contemporary networked society. Considering the increasing importance of the issue, the present article focused on analyzing the spatial-temporal change in inter-regional competitiveness and its influence on inter-regional migration in Iran from 2011-2016. MethodologyTo assess the relationship between the spatial-temporal patterns of inter-regional competitiveness and migrations, at first, the regional competitiveness index was measured according to Equation 1. Equation 1=In equation 1, represents the amount of GDP of region i in yeart, is the total GDP of all regions in year t, measures GDP per capita of region i in year t, is the inverse distance between Region i and j in year t and represents the total inverse distance between all regions. GDP is the added value created by producing goods and services in year t. The inverse distance between regions was calculated based on equation 2. Equation 2=In equation 2, measures the geographical distance between region i and j in year t, and ln represents the natural logarithm of the geographical distance between region i and j in year t. In the next step, the spatio-temporal changes in the regional competitiveness index between 2001 and 2016 were analyzed using differential Moran I method I. In the third step, using k-medoids cluster analysis, the regions were classified based on their inter-regional competitiveness index to explore the spatio-temporal changes in the index in 2001, 2006, 2011, and 2016. The k-medoids cluster analysis was also used to classify regions by integral number of immigrant volumes in 2006 and 2016. Finally, the geographically weighted regression was applied to measure the influence of inter-regional competitiveness on inter-regional migration in 2006 and 2016, respectively. Results and discussionThe results showed that the regions were weakly correlated according to the changes in the spatio-temporal pattern of the inter-regional competitiveness index during 2010-2016. In other words, the regions that were close to each other in terms of competitiveness index were not geographically contiguous, and the changes in the competitiveness of each region showed no significant correlation with the neighboring regions. While the direction of the correlations from 2001 to 2006 and from 2006 to 2011 was negative, it was positive between 2011 and 2016 and from 2001 to2016, indicating that the changes in the inter-regional competitiveness score of each region were geographically different from its neighboring regions in the first two periods. In contrast, each region showed changes similar to that of its neighboring regions in the second two periods. K-Medioid cluster analysis showed that regions that occupied top ranks according to the inter-regional competition index during 2006-2016 have rich resources of oil and gas mines. In addition, the analysis of the spatio-temporal variability in the number of immigrants between 2006 and 2016 showed a negative but weak correlation between regions. Accordingly, the two provinces of Kermanshah and Hamadan were identified as migration-cold regions surrounded by regions having lower numbers of immigrants than the average in the same period. As the K-Medioid-based classification of regions in terms of the number of immigrants in 2006 and 2016 showed, Tehran region was situated in the first rank, and Kerman, Mazandaran, East Azarbaijan, and West Azarbaijan regions occupied the second rank. The geographically weighted regression model was applied to explain the role of inter-regional competitiveness in the pattern of inter-regional migrations in Iran in 2006 and 2016, respectively. As the model showed, the variable of inter-regional competitiveness explained about 0.01 to 0.9 of the variation in the number of immigrants in 2011. This model explained between 0.22 and 0.83 of the change in the number of immigrants in 2016. In addition, the simple non-geographic regression showed that 0.29 changes in the number of immigrants in 2006 and 0.56 of these changes in 2016 are dependent on inter-regional competitiveness. ConclusionIn general, the pattern of spatio-temporal change in the inter-regional competitiveness index for each region during 2001-2016 had a weak correlation with that in its neighboring regions. In other words, regions showed weak similarities with each other according to their competitiveness scores. The same pattern can be seen for the pattern of inter-regional migration. Regions exhibited negative but weak spatio-temporal correlation with each other in terms of the total number of immigrants in the years 2006 and 2011. The role of the inter-regional competitiveness index in explaining inter-regional migration increased in 2016 compared with 2006, implying the increasing significance of inter-regional competitiveness in determining the spatio-temporal pattern of inter-regional migration in Iran. As a result, those regions that do not take strategies to improve their regional competitiveness, should expect to encounter an increase in the number of emigrants. In this way, inter-regional competitiveness can reinforce uneven regional development in the future. FundingThere is no funding support. Authors’ ContributionAuthor contributed equally to the conceptualization and writing of the article. All of the authors approved thecontent of the manuscript and agreed on all aspects of the work declaration of competing interest none. Conflict of InterestAuthor declared no conflict of interest. AcknowledgmentsWe are grateful to all the scientific consultants of this paper.
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