The Impact of Data Time Span on Forecast Accuracy through Calibrating the SLEUTH Urban Growth Model

Abstract

Does the spacing of time intervals used for model input data have an impact on the model's subsequent calibration and so projections of land use change and urban growth? This study evaluated the performance of the SLEUTH urban growth and land use change model through two independent model calibrations with different temporal extents (1972 to 2006 vs. 2000 to 2006) for the historical Italian cities of Pisa Province and their surroundings. The goal in performing two calibrations was to investigate the sensitivity of SLEUTH forecasts to longer or shorter calibration timelines, that is does calibrating the model over a longer time period produce better model fits and therefore forecasts? The best fit parameters from each calibration were then used in forecasting urban growth in the area up to the year 2027. The authors findings show that the spatial growth estimated by the model was strongly influenced by the physical landscape and road networks. The forecast outputs over 100 Monte Carlo trials reflect the start of newly formed detached settlements towards and along existing roads, i.e., classic urban sprawl. The authors conclude that the short term calibration was a better model fit compared to the long term calibration. Nevertheless, the absolute preference for the short-term calibration over long-term implies that time-sensitivity in calibration remains a challenge for SLEUTH applications.

Calibrating SLEUTH with big data: Projecting California's land use to 2100

“…Several studies explored the use of parallel and high-performance computing to decrease the calibration time (Chaudhuri & Foley, 2019;Guan & Clarke, 2010). Others investigated the sensitivity of the model to the number of Monte Carlo iterations used (Goldstein, Dietzel, & Clarke, 2005); the duration used for calibration and forecasting (Peiman & Clarke, 2014); the changes made by the self-modification rules (Saxena & Jat, 2018); the means of including past and future exclusions (Akin, Clarke, & Berberoglu, 2014;Onsted & Clarke, 2012); and the use of alternative goodness of fit measures, such as landscape metrics (Herold, Couclelis, & Clarke, 2005). Sakieh, Salmanmahiny, and Mirkarimi (2016) tested alternative models against SLEUTH, such as logistic regression and a multi-layer perceptron.…”

Urban modelling and forecasting of landuse using SLEUTH model

“…The SLEUTH model was initially developed for the San Francisco Bay area (Şevik 2006). Over the time, it was repeatedly used for North America and Europe (Peiman and Clarke 2014;Berberoğlu et al 2016;Liu et al 2019). Some Indian subcontinent studies were carried out using the SLEUTH model (Kantakumar et al 2011;Chaudhuri and Clarke 2019;Saxena and Jat 2020a, b;Vani and Prasad 2021), but they are mainly focused on the metropolitan cities.…”

A Gis-Based Model for Post-Earthquake Personalized Route Planning Using the Integration of Evolutionary Algorithm and Owa

“…For example, a tourist may have a tendency to visit more landscapes, even if it takes longer hours (Nadi & Delavar, 2011). In after earthquake route planning, road segments which are wider and have lower number of high rises are more preferred (Peiman & Clarke, 2014;Pourrahmani et al, 2015). Nadi and Delavar (2011) proposed a spatial decision support system for personalized route planning.…”