The impact of historical exclusion on the calibration of the SLEUTH urban growth model

•We assess the calibration effects within a cellular automaton urban growth model (UGM).•The calibration is one of the most critical steps of a modeling process.•Three different excluded layers were introduced to the UGM from the beginning of the calibration to increase its sensitivity to the local conditions.•Different exclusion layers actually multiply the number of different patterns of growth.•Every alternative scenario should be evaluated separately in terms of the quantity and distribution of urban development.

This paper aims to emphasize the importance of the calibration process in urban growth modeling studies. The application of cellular automata (CA) in urban modeling can give insights into a wide variety of urban phenomena. The SLEUTH model, being as a well-tested CA, was utilized. Calibration data for the model were acquired from different sources of remotely sensed data recorded in 1967, 1977, 1987, 1998 and 2007. In this context three different excluded maps representing different scenarios were utilized during the calibration process in order to analyze the effects of different policies on urban growth. Each calibration scenario yielded its own parameter values. Thirteen calibration metrics for each scenario were derived. Integrating different exclusion layers to the beginning of the calibration process has reduced the number of possible growth patterns. The overall growth characteristics of Adana were similar for all calibration results and defined as organic growth except for the fact that the spatial allocation and the amount of potential urban pixels were different.