Geographical Cellular Automata (GCA) approach is based on complexity theory and is widely used in geospatial
modeling. A reason for the increasing attention given to GCA models is that they can easily be integrated with rasterbased
GIS environment. However, the behavior of the GCA models is affected by uncertainties arising from the
interaction between model elements, structures, and the quality of data sources used as model input. The objective of
this study is to examine the impacts of model elements on the generated outputs of a GIS-based GCA land-use growth
model using sensitivity analysis (SA) approach. The proposed SA method consists of KAPPA index with different
spatial metrics. A stochastic GCA model was built to model land use change in the changsha region (Hunan,China). The
transition rules were empirically derived from four Landsat-TM (30m resolution) images taken in 1996,1999, 2002 and
2005 that have been resampled to four resolutions (30, 60, 90, 120m). Five different neighbourhood configurations were
considered (Moore, Von Neumann, and circular approximations of 2, 3 and 4 cell radii). Simulations were performed
for each of the twenty spatial scale scenarios. Results show that spatial scale has a considerable impact on simulation
dynamics in terms of both land use area and spatial structure. The spatial scale domains present in the results reveal the
nonlinear relationships that link the spatial scale components to the simulation results.
KEYWORDS: Agriculture, Factor analysis, Data modeling, Analytical research, Probability theory, Roads, Applied research, Fuzzy logic, Mathematical modeling, Geographic information systems
Agricultural land gradation links land classification and land appraisal. It indicates the difference in agricultural
productivity resulted from differences in land's natural characteristics and/or the effectiveness and efficiency of
agricultural production at present and in the future. Technically, agricultural land is graded based on the sum of
weighted indices and further classified by equal-distance, or axis, or sum frequency curve. It is critical to define the
system of weights in this process. In practice, a single or mixed weight system has been widely applied in agricultural
land gradation. However, few studies put efforts in comparing outcomes in applying different systems of weights for a
specific area. This research applied several popular systems of weights, such as AHP, factor analysis, grey relation
analysis, entropy method, and etc., in gradating agricultural land in Jintan, Jiangsu province. Outcomes resulted from
different systems of weights were compared. The result did illustrate the obvious differences among these outcomes,
which in turn stood for differences among systems of weights. Considering biases inherent in different systems of
weights, a system of combined weights is highly recommended for the general practice in agricultural land gradation.
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