美国论文查重-土地流失分析的自动校正措施，土地流失分析的另一项重要措施是采用特殊的自动校正措施。这个方法是由Patrick Alfred Pierce Moran开发的。空间自校正就是这样一种方法，它可以将信号与附近的一些位置进行相关。空间自校正方法具有多维、多向的特点。空间自动校正可以定义为近距离物体与其他近距离物体之间的比较。Moran’s I的分类可以分为正向自动校正、负向自动校正和无空间自动校正。空间自校正被认为是重要的，因为有许多统计数据依赖于观测，这些观测可能与彼此的独立性有关。如果存在自动修正，如果在地图中存在自动修正，就会违反观测结果通常是独立的这一事实。接下来有关美国论文查重-土地流失分析的自动校正措施分享给大家阅读。
Another important measure for the analysis of land loss is by the use of special auto correction measure. This method has been developed by Patrick Alfred Pierce Moran. Spatial auto correction is one such method which may be the correlation of the signal amongst some of the nearby locations. The method of spatial auto correction is much more characterized by multidimensional and multi directional. Spatial auto correction may be defined as the way in which the close objects are in comparison with other closer objects. The classification of the Moran’s I can be done as positive auto correction, negative auto correction and no spatial auto-correction.
Spatial auto correction is considered to be important because there are a number of statistics which relies on the observations which may be related to the independence with one another. In case there is an existence of auto correction, in case there is an existence of auto correction in the map, it leads to the violation of the fact that the observations are generally independent.
Another potential application which may be used is in the analyzing the clusters and the dispersion of ecology (Shaffer & Gary 79). Thus, this application can be easily used for the purpose of identification of the auto correction analysis.
Positive auto-correction may occur when the Moran’s value is close to +1. This indicates that these are clustered together. One of the examples of this is the elevation dataset because one needs to expect the similar evaluation values which may be closer to one another. One of the examples of the same is the elevation dataset because similar elevation values are required to be expected closer to one another. Thus the analysis can be done with the help of clustering in the land pattern (Kenney & Melissa, 3388).
Then, find the land loss of each research point in 2001. Logical regression analysis of SPSS will be used to find the relationship between the fragmentation and land loss, after calculating the fragmentation-degree of study point. The different methods of fragmentation analysis will lead to variance results of relationships. My purpose is to managing and summarizing the three processes.