Spatial-temporal dynamics of Sichuan industrial structure with Markov chains approach

The status of things always changes with the process of time. Markov chains approach considers that as long as the current status is known, the future state of things can be forecasted without understanding the past state. Considering the spatial autocorrelation of spatial things, Markov chains is combined with spatial autocorrelation to develop the spatial Markov chains to study the influence of regional background on regional transition. The industrial structure of regions alters in different periods of time, and its development process and tendency can be approached approximately by using Markov chains methods. Based on the coefficient dataset of industrial structure at the county level in Sichuan province from 2000 to 2007, this paper attempts to apply Markov chains and Spatial Markov chain to investigate the spatial and temporal characteristics of industrial structure level in Sichuan. Firstly, all the coefficient data of industrial structure in Sichuan are classified into 4 different classes (low, middle-low, middle-high and high) and Markov transition probability matrix is estimated to explore whether the convergence of industrial structure level exists in Sichuan. Secondly, conditioning on each region's spatial lag at the beginning of each year, spatial Markov matrices are constructed to investigate the relationship between transition probability of different regions and their neighbors, and maps are accordingly made in order to visualize spatial patterns of class transitions. Thirdly, the evolutionary trends of industrial structure level in the next twenty years, fifty years and one hundred years are forecasted respectively by computing the twentieth, fiftieth and hundredth power of Markov transition probability matrix. Finally, the measures to improve the industrial structure level in Sichuan province are given.