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Evaluating the Capability of Geographically Weighted Regression in Improvement of Urban Growth Simulation Performance
Using Cellular Automata
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Babak Mirbagheri *   , Abbas Alimohammadi |
| K.N. Toosi University of Technology |
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Abstract: (4115 Views) |
| 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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| Keywords: Cellular Automata, Geographically Weighted Regression, Logistic Regression |
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Full-Text [PDF 1810 kb]
(1419 Downloads)
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Type of Study: Research |
Subject:
GIS
Received: 2016/06/14 | Accepted: 2017/02/1 | Published: 2018/09/22
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