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Using Geographically Weighted – Binary Logistic Regression to Analyze Land Cover Change Phenomenon (Case Study: Northern West Java Development Region)
Muzdalifah Q.R.a, Deliar A.a, Virtriana R.a, Naufal A.a, Ajie I.S.a
a Remote Sensing and Geographic Information Science Research Group, Bandung Institute of Technology (ITB), Ganesha, 40132, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2020 IOP Publishing Ltd.Land is one of the important resources that can be used to supply the needs of human life. Uncontrolled land utilization will cause land cover change phenomenon. Land cover change phenomenon can be analyzed by using a model. To get an accurate result, the selection of models in the analysis of land cover change must be based on the characteristics of land cover change phenomenon itself. Land cover change is a binary phenomenon and strongly related to the local characteristics of a region. A model that can be used in the analysis of binary phenomena is Binary Logistic Regression (BLR) model. However, the application of BLR model has a disadvantage. BLR model is one of the global models which assumes that the analyzed phenomenon has homogeneous characteristics for the entire study area. This does not correspond to the characteristic of land cover change phenomenon. Therefore, we need another local model that is able to show local characteristic variations of land cover change. Geographically Weighted Regression (GWR) model is one of the local spatial regression techniques that can be used to analyze phenomena that have spatially heterogeneous characteristics. The application of GWR model for binary phenomena (dependent variable) such as land cover change is called Geographically Weighted – Binary Logistic Regression (GW-BLR) model. This research aims to analyze land cover change phenomenon in the Northern West Java development region using GW-BLR and compares the result to BLR model. The results of this research indicate that the analysis of land cover change in the Northern West Java development region using GW-BLR model has a higher level of accuracy compared to BLR model. The modeling results of land cover change using GW-BLR model has an overall accuracy value of 91.10% and using BLR model has an overall accuracy value of 84.09%. Therefore, it can be concluded that land cover change phenomenon in Northern West Java development region can be analyzed more accurately by considering its local spatial characteristics through using the GW-BLR model.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Analysis of binaries,Binary logistic regression,Dependent variables,Geographically weighted regression models,Heterogeneous characteristic,Local characteristics,Overall accuracies,Spatial characteristics[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text]The authors would like to thank the Institute for Research and Community Service (LPPM), Bandung Institute of Technology (ITB), which has provided research funding through ITB Research Program 2019.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1088/1755-1315/448/1/012121[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]