THE CLASSICAL ASSUMPTION TEST TO DRIVING FACTORS OF LAND COVER CHANGE IN THE DEVELOPMENT REGION OF NORTHERN PART OF WEST JAVA']JAVA

被引:9
作者
Ainiyah, Nur [1 ]
Deliar, Albertus [1 ]
Virtriana, Riantini [1 ]
机构
[1] Inst Teknol Bandung, Geodesy & Geomat, Remote Sensing & Geog Informat Syst Res Div, Fac Earth Sci & Technol, Bandung, Indonesia
来源
XXIII ISPRS CONGRESS, COMMISSION VI | 2016年 / 41卷 / B6期
关键词
Land Cover Change; Driving Factors; Classical Assumption Test; Binary Logistic Regression;
D O I
10.5194/isprsarchives-XLI-B6-205-2016
中图分类号
P9 [自然地理学];
学科分类号
0705 ; 070501 ;
摘要
Land cover changes continuously change by the time. Many kind of phenomena is a simple of important factors that affect the environment change, both locally and also globally. To determine the existence of the phenomenon of land cover change in a region, it is necessary to identify the driving factors that can cause land cover change. The relation between driving factors and response variables can be evaluated by using regression analysis techniques. In this case, land cover change is a dichotomous phenomenon (binary). The BLR's model (Binary Logistic Regression) is the one of kind regression analysis which can be used to describe the nature of dichotomy. Before performing regression analysis, correlation analysis is carried it the first. Both correlation test and regression tests are part of a statistical test or known classical assumption test. From result of classical assumption test, then can be seen that the data used to perform analysis from driving factors of the land cover changes is proper with used by BLR's method. Therefore, the objective of this research is to evaluate the effectiveness of methods in assessing the relation between driving factors of land cover change that assumed can affect to land cover change phenomena. This research will use the classical assumed test of multiple regression linear analysis, showing that BLR method is efficiency and effectiveness solution for researching or studying in phenomenon of land cover changes. So it will to provide certainty that the regression equation obtained has accuracy in estimation, unbiased and consistent.
引用
收藏
页码:205 / 210
页数:6
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