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Driving-factors identification of land-cover change in west java using binary logistic regression based on geospatial data
a Faculty of Mathematics and Natural Sciences, Mathematics Study Program, Institute of Technology Bandung (ITB), Bandung, 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. All rights reserved.The Land is a fundamental factor in production activity. Accordingly, it is closely related to economic growth – which supports the living needs of human beings. In many cases, human activities related to land use are often uncontrollable, impacting many negative effects on the environment, both locally and globally. More broadly, these activities will lead to some changes in land cover and some other physical features such as climate. In order to understand the phenomenon of land cover changes, we approach them through modelling. To detect any changes in land cover in a region, it is necessary to identify the driving factors causing 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, i.e., binary. Binary Logistic Regression (BLR) model is one of the regression analyses which can be used to describe the nature of dichotomy. From the results of this study, the driving factors causing land-cover change in West Java were found, those are: The distances to the central business districts in some certain areas such as Bandung City, Bekasi Regency, Bekasi City, Bogor Regency, Karawang Regency, and Sukabumi Regency; the distance to the the capital of the province; the distance to the main roads; the population numbers; and some physical features of the land such as slope, curvature, and height. This predictive model had an accuracy level of 49,79%, which equals to 1.827.217,44 ha area.[/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]Binary logistic regression,Central business districts,Geo-spatial data,Land-cover change,Physical features,Population numbers,Predictive modeling,Production activity[/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][/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/10.1088/1755-1315/500/1/012003[/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]