Received: September 2012 Accepted: December 2012 Abstract SLEUTH is a cellular automata (CA) model that has been successfully applied worldwide over the last 15 years to simulate land use change. The objective of this paper is to review the current status of the SLEUTH land use change model. Clarke et al. (2007) and Clarke (2008a) provided detailed reviews of the model and its application from ...

This paper presents a fine-scale (30 meter resolution) regional land cover modeling system, based on the SLEUTH cellular automata model, that was developed for a 257000 km area comprising the Chesapeake Bay drainage basin in the eastern United States. As part of this effort, we developed a new version of the SLEUTH model (SLEUTH-3r), which introduces new functionality and fit metrics that subst...

Performance evaluation is crucial for the development and improvement of an urban cellular automata model, such as SLEUTH. In this paper, we employed multiple methods for map comparison andmodel validation to evaluate the simulation performance of the SLEUTH urban growth model in the Shenyang metropolitan area of China. These multiple methods included the relative operating characteristic (ROC)...

Domain Models [8, 9, 25] have long been used as a basis for software development and reuse. We present a specialized, simplified domain model that has been used for system testing in industry as the framework for a system testing approach we call Application Domain Based Testing. Sleuth, a test suite generation tool, is based on this concept. We report on the domain analysis, the domain model c...

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 cit...

SLEUTH is a well-known simulation model for forecasting urban growth using cellular automata techniques. Although recent work has been done to produce parallel implementations of SLEUTH, most of these efforts have been directed towards clustered MPI solutions. Recent advances in both the computing power of graphics cards and the development of general purpose programming interfaces for these de...

One of the powerful tools geospatial modeling uses is the Monte Carlo Method. However, little work has been done on measuring the optimal number of Monte Carlo iterations to be performed. In this work, we present the new utility of two metrics for deriving the number of Monte Carlo iterations needed to calibrate the CA-based SLEUTH Urban Growth Model. SLEUTH calibration is the process of choosi...

SLEUTH is a computational simulation model that uses adaptive cellular automata to simulate the way cities grow and change their surrounding land uses. It has long been known that models are of most value when calibrated, and that using backcasting (testing against known prior data) is an effective calibration method. SLEUTH’s calibration uses the brute force method: every possible combination ...

SLEUTH is a mature cellular automaton urban model, now applied to over 100 different cities and regions. In this paper, the model’s context and history is introduced, the applications reviewed and some model improvements and sensitivity tests summarized. A section discusses how the model works and then the model’s limitations and assumptions are critically examined with the intent of revealing ...

land use change is driven by interaction in space and time between humans and the environment that can be captured by computer simulation models (veldkamp and verburg, 2004). in the last few decades, land use change models have played an important role in understanding the causes, mechanisms and consequences of land use dynamics. sleuth is an open source cellular automata based land use change ...