Predicting of Urban Growth Pattern Using Logistic Regression Model in Gorgan Area

Document Type : Research Paper

Authors

1 Ph.D. Student of Environmental Sciences, Gorgan University of Agriculture Sciences and Natural Resources, Iran

2 Assistant prof., Dept. of Environmental Sciences, Gorgan University of Agriculture Sciences and Natural Resources, Iran

3 Associate Prof., Dept. of Environmental Sciences, Gorgan University of Agriculture Sciences and Natural Resources, Iran

4 Associate Prof., Dept. of Water management Sciences, Gorgan University of Agriculture Sciences and Natural Resources, Iran

Abstract

Modeling urban development patterns is an important technique for understanding complex urban growth processes. In this study, Logistic Regression model was conducted to model urban growth pattern of Gorgan area in North Iran, during the period 1988-2025. To do this, remotely sensed imagery of years 1988, 1998 and 2007 were used to produce land use maps. Also, dependent and independents variables were created to perform urban growth pattern modeling. Then, urban changes were detected during 1988 – 2007 and urban change modelling was achieved using Logistic Regression. After that, future urban grow pattern was predicted. The results indicated that urban areas have increased during study time period. Validation of model results was performed using Pseudo-R2 and ROC values which were more than 0.27and 0.83 respectively. Furthermore, Logistic Regression was applied to predict urban growth patterns for the years of 2016 and 2025. According to the results, appropriate implementations are needed to control land use changes, particularly urban growth, in order to preserve environmental as well as ecological balances of the area. The result could be help the managers to monitor and prevent the unplanned urban development in future. Model’s extracted maps can be used for managing and controlling future urban development.

Keywords

References

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History

  • Receive Date: 16 November 2014
  • Revise Date: 02 September 2015
  • Accept Date: 21 February 2015

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