The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences · 2016 · 30 citations · 6 references
Social SciencesEnvironmental SciencesGeographyEnvironmental ChangeSpatial AnalysisSpatial Data AnalysisLand UseLand-use ChangeBinary Logistic RegressionCorrelation AnalysisLand Cover AnalysisLand Use Change AnalysisLand Use Change AssessmentLand-cover ChangeLand-use Change ModelingLogistic Regression
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.
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Applied Linear Regression Models.
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