摘要
Based on the entropy weight method and the gray relational analysis method, we first calculate the weight of each index and the correlation coefficient between the indicators, get the urban intelligent growth index, and then calculate the annual growth rate of the composite index. We find the following results. First, Suzhou to improve its environment, the success rate of 109.67%. Saint Louis to improve its economy, the growth rate of 57.4%. Second, the sensitivity of the data analysis, each of the indicators is increased by 10%, 20%, 30%, other indicators remain unchanged. Recalculate the city’s intelligent growth index, we find that the greater the volatility, the greater the potential. The total population of the city, built green area, the total length of the bus operating a greater potential, built-up area, the smallest regional GDP potential. Finally, we propose an improved model combining remote sensing with GIS to analyze urban expansion and farmland loss from time and space qualitatively and quantitatively.
Based on the entropy weight method and the gray relational analysis method, we first calculate the weight of each index and the correlation coefficient between the indicators, get the urban intelligent growth index, and then calculate the annual growth rate of the composite index. We find the following results. First, Suzhou to improve its environment, the success rate of 109.67%. Saint Louis to improve its economy, the growth rate of 57.4%. Second, the sensitivity of the data analysis, each of the indicators is increased by 10%, 20%, 30%, other indicators remain unchanged. Recalculate the city’s intelligent growth index, we find that the greater the volatility, the greater the potential. The total population of the city, built green area, the total length of the bus operating a greater potential, built-up area, the smallest regional GDP potential. Finally, we propose an improved model combining remote sensing with GIS to analyze urban expansion and farmland loss from time and space qualitatively and quantitatively.
