Document Type : Research
Author
Assistant Professor, Department of Geography and Urban Planning, Bojnord University, Kausar University, Bojnord, Iran
Abstract
Today, planners have noticed regional planning to reduce regional inequalities and imbalances as an efficient strategy. Therefore, this research aims to design a regional development planning model for Bojnord city with a structure analysis approach. In terms of practical purpose, the research has been carried out in terms of descriptive-survey type with an exploratory-mixed approach.
The qualitative statistical population includes 20 of the experts and the officials of Bojnord City, been selected through the purposeful sampling method. In the quantitative part, all citizens over 25 years of age have been selected by the available sampling method to the number of 384 people by determining the sample size in unlimited communities.
The data collection tool is an interview and a researcher-made questionnaire by the researcher. It has been coded in the qualitative section and the interviews through the Grounded Theory method and MAXQDA. The finalization of the index is done by the Delphi technique. In the quantitative part, validation and relationships between variables are done by analyzing the structural equation modeling with the help of Lisrel software. The findings of this research shows that the most important planning factors for the regional development of Bojnord include 6 main factors and 40 sub-factors like economic; social; normal; political; managerial; infrastructure. Therefore, in the optimal model suitable for the sustainable development of the region, factors (social (with an impact factor of 0.54), natural factors (0.71)) as causal factors and factors (infrastructure with an impact factor of (0.64)) as background conditions have a positive impact and a direct influence on strategies (political factors). In other words, the higher and more suitable these factors are, the higher the regional development planning will be. Also, the (managerial) factor improves the relationship between causal factors and strategies with an impact factor (0.64) and plays a mediating role.
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