Land intensified utilization is an important part of land utilization research. Using the artificial neural network model (ANN) to evaluate a city's level of land utilization intensity, it will eliminate the negative impact of subjective factor which is used to assess and complex calculate. As a result, the assessment results would be more scientific and objectively. Based on intensity of land investment, land utilization intensity and land utilization efficiency, an intensive land conservation evaluation system is established with application of status of Lanzhou City's urban land. After training by testing data, BP artificial neural network was applied to evaluate the level of intensified utilization form 1988-2008 in Lanzhou. The results shown that the urban land utilization of Lanzhou is on the upward trend and that can be divided into three categories: low, moderate and intensive.
Based on the sift research and changing data of land use from 1996 to 2008, the current situation and changes of land-use
spatial structure in Tianshui were analyzed by applying GIS technology and quantitative geographic model. The results
indicated that there were obvious internal differences in land-use spatial structure. Firstly, the diversity index of spatial
structure was comparatively high; Secondly, the garden plots and transportation land increased obviously, though the
index of land use degree fluctuates little since 1996, decreasing slightly as a whole; Thirdly, the information entropy of
land use increased generally, and the total land use tended to random, the disorder degree of lands begins to increase and
the types of land use are in a period of adjustment.
The existence of spatial dependence breaks the basic assumption that the samples are independent of each other in most
of the classical methods of statistical analysis. Supported by GIS, we used the method of spatial autocorrelation analysis
to make a preliminary analysis of the population spatial differences and distribution characteristics in various districts
and counties in Gansu Province in 2006. The results showed that the population distribution in Gansu have positive
spatial correlationship, and presented a significant spatial aggregating feature. But from local spatial autocorrelation
analysis, we can see that the relevance of regional distribution of population in majority of Gansu is not significant,
indicating that these regions within a certain range had not form regional growth poles with strong attraction. This
conclusion can serve better for the formulation of population policies and sustainable development strategy of the
economy and society.
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