Evaluating the Capability of Geographically Weighted Regression in Improvement of Urban Growth Simulation Performance Using Cellular Automata

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MIRBAGHERI BABAK; ALIMOHAMMADI ABBAS

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Journal: ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY Year:2018 | Volume:6 | Issue:2 Page(s): 43-64

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Title

Evaluating the Capability of Geographically Weighted Regression in Improvement of Urban Growth Simulation Performance Using Cellular Automata

Author(s)

MIRBAGHERI BABAK | ALIMOHAMMADI ABBAS | Issue Writer Certificate

Keywords

Cellular AutomataQ1

Geographically Weighted RegressionQ1

Logistic RegressionQ2

Abstract

Geographically Weighted Logistic Regression (GWLR) is a local version of Logistic Regression (LR) which estimates different relationships between independent and dependent variables at each location. In this research, local model (GWLR) is used for defining CA transition rules and evaluating GWLR capabilities in terms of enhancing urban development prediction accuracy. Also, a new parameter named “ Edge Expansion Coefficient” was defined for the determination of tradeoff between two important urban development processes: edge expansion and spontaneous growth. Moreover, in order to assess the prediction accuracy, fuzzy Kappa statistic was applied along with the traditional Kappa coefficiency. The developed CA model in this study was run for the prediction of urban development in south west of Tehran metropolitan area during 2004-2013 period. The results of the study showed that, using GWLR model for defining CA‟ s transition rules, one can significantly increase urban development prediction‟ s accuracy compared to that of predicted urban development by CA model based on Logistic Regression (Logistic-CA). The prediction accuracies of the proposed model in this research and the Logistic-CA were 0. 54 and 0. 30, respectively, as measured by Kappa coefficient. Also, the prediction accuracies of the proposed model were calculated to be 0. 68 and 0. 76 when measured in terms of fuzzy Kappa statistic with halving distances of 50 and 100 meters in exponential distance decay function, respectively.

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APA: Copy

MIRBAGHERI, BABAK, & ALIMOHAMMADI, ABBAS. (2018). Evaluating the Capability of Geographically Weighted Regression in Improvement of Urban Growth Simulation Performance Using Cellular Automata. ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY, 6(2 ), 43-64. SID. https://sid.ir/paper/230103/en

Vancouver: Copy

MIRBAGHERI BABAK, ALIMOHAMMADI ABBAS. Evaluating the Capability of Geographically Weighted Regression in Improvement of Urban Growth Simulation Performance Using Cellular Automata. ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY[Internet]. 2018;6(2 ):43-64. Available from: https://sid.ir/paper/230103/en

IEEE: Copy

BABAK MIRBAGHERI, and ABBAS ALIMOHAMMADI, “Evaluating the Capability of Geographically Weighted Regression in Improvement of Urban Growth Simulation Performance Using Cellular Automata,” ENGINEERING JOURNAL OF GEOSPATIAL INFORMATION TECHNOLOGY, vol. 6, no. 2 , pp. 43–64, 2018, [Online]. Available: https://sid.ir/paper/230103/en

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