Takada's group developed a method for estimating the yearly transition matrix by calculating the mth power roots of a transition matrix with an interval of m years. However, the probability of obtaining a yearly transition matrix with real and positive elements is unknown. In this study, empirical verification based on transition matrices from previous land-use studies and Monte-Carlo simulations were conducted to estimate the probability of obtaining an appropriate yearly transition probability matrix. In 62 transition probability matrices of previous land-use studies, 54 (87%) could provide a positive or small-negative solution. For randomly generated matrices with differing sizes or power roots, the probability of obtaining a positive or...
The application of the Markov chain to modeling agricultural succession is well known. In most cases...
Linear programming (LP) is widely used to select the manner in which forest lands are managed. Becau...
Linear programming (LP) is widely used to select the manner in which forest lands are managed. Becau...
<p>Probability matrix of land use transfer from 1992 to 2003 (×10<sup>−2</sup>).</p
hine esea for ine Land change is often studied with Markov models to develop a probability transitio...
In the field of urban and regional planning, several Markov chain models for land use conversion hav...
The set of models available to predict land use change in urban regions has become increasingly comp...
One of the main objectives of land-use change models is to explore future land-use patterns. Therefo...
If a scientist overlays two perfectly accurate maps of land categories of the same place from two po...
<p>(A) shows the original categorization, (B) shows our coarser categorization (note that it is a di...
grantor: University of TorontoTwo matrix growth models of mixed uneven-aged hard maple fo...
grantor: University of TorontoTwo matrix growth models of mixed uneven-aged hard maple fo...
The application of the Markov chain to modeling agricultural succession is well known. In most cases...
The application of the Markov chain to modeling agricultural succession is well known. In most cases...
The application of the Markov chain to modeling agricultural succession is well known. In most cases...
The application of the Markov chain to modeling agricultural succession is well known. In most cases...
Linear programming (LP) is widely used to select the manner in which forest lands are managed. Becau...
Linear programming (LP) is widely used to select the manner in which forest lands are managed. Becau...
<p>Probability matrix of land use transfer from 1992 to 2003 (×10<sup>−2</sup>).</p
hine esea for ine Land change is often studied with Markov models to develop a probability transitio...
In the field of urban and regional planning, several Markov chain models for land use conversion hav...
The set of models available to predict land use change in urban regions has become increasingly comp...
One of the main objectives of land-use change models is to explore future land-use patterns. Therefo...
If a scientist overlays two perfectly accurate maps of land categories of the same place from two po...
<p>(A) shows the original categorization, (B) shows our coarser categorization (note that it is a di...
grantor: University of TorontoTwo matrix growth models of mixed uneven-aged hard maple fo...
grantor: University of TorontoTwo matrix growth models of mixed uneven-aged hard maple fo...
The application of the Markov chain to modeling agricultural succession is well known. In most cases...
The application of the Markov chain to modeling agricultural succession is well known. In most cases...
The application of the Markov chain to modeling agricultural succession is well known. In most cases...
The application of the Markov chain to modeling agricultural succession is well known. In most cases...
Linear programming (LP) is widely used to select the manner in which forest lands are managed. Becau...
Linear programming (LP) is widely used to select the manner in which forest lands are managed. Becau...