Document Type : علمی - پژوهشی
Authors
1 PhD Candidate in Geography and Rural Planning, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
2 Associate Professor, Department of Geography and Urban Planning, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
3 Professor, Department of Geography and Urban Planning, Shahid Chamran University of Ahvaz, Ahvaz, Iran.
Abstract
Abstract:
Population changes are considered as the most important transformational phenomenon of the contemporary era, because population changes have affected almost all aspects of life and the world around us and have had many effects on social, economic, political and environmental sub-systems. Is. In this regard; The aim of the current research is to predict and explain the structural components explaining demographic changes in Ahvaz metropolis.
It is practical in terms of targeting and descriptive-analytical in terms of methodology. In the process of data preparation and production, firstly, the explanatory components of demographic changes have been identified using the opinions of 15 professors and experts from relevant organizations through the Delphi method. In order to analyze the information of 11 factors as strong influencing factors on the demographic changes of the metropolis of Ahvaz, ISM interpretive-structural modeling was used, and then with Mic Mac software, and finally, using Spectrum software, to forecast the population until the horizon of 2051.
The results of the research showed that the factors of sex structure, population density, fertility, unemployment, active population and literacy are among the most influential factors on demographic changes and 5 factors of age structure, population growth, mortality, migration and life expectancy are among the most influential factors. Coming. Also, the results obtained from MikMak show that all factors except the population density factor are linked variables. Finally, the best scenario for population forecasting until 1430 is the third scenario (fertility decline), which has more than 98% accuracy in population estimation.
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