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Hataminejad H, Shahi A, Imani R. Analysis of Spatial Factors Affecting Immigrants Locating Using Forecasting Method by Decision Tree (Case Study: Mashhad, Iran). JFCV 2020; 1 (2) :1-18
URL: http://jvfc.ir/article-1-40-en.html

Residential migration and displacement is one of the urban phenomena that has a cause and effect relationship with social, economic and spatial structures. This type of migration is influenced by factors such as proximity to work, length of residence, employment status, income level, age, gender and family circumstances. Therefore, the location of neighborhoods and the choice of neighborhood to live in is very important and influenced by many factors to the extent that locational situation affects site selection. The present study is applied and descriptive-analytical in terms of method and purpose respectively. The data required for the research have been extracted using the information of the statistical blocks of the 2011 census. By studying the research literature and determining the required indicators, they have been extracted and weighed by hierarchical analysis. The average migration index was selected as the target index and other indicators were selected as the input index in the decision tree. Decision tree calculations were performed in Clementine software. The results show that the level of education has the greatest impact on the location of immigrants in the neighborhoods of Mashhad. In total, 12 indicators with different impacts have affected the average migration. However, it is clear that the lower the number of predictors in this city, the higher the rate of migration. The results also show that the data tree mining system will be able to predict the rate of migration and retention of people in urban neighborhoods by measuring the predictors and the extent of their changes. According to the results obtained from, the efficiency of this system is acceptable.