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Spatial Analysis of Driving Factors on Land Cover Change’s Clusters in West Java Province
Ajie I.S.a, Deliar A.a, Virtriana R.a
a Faculty of Earth Sciences and Technology, Geodesy and Geomatics Engineering, Bandung Institute of Technology (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.The study of land cover change as a phenomenon is necessary to do in order to understand global environmental change. With a cluster, the pattern and concentration of land cover change can be indicated, so that decisions can be made on target. The understanding of land cover change can be improved by identifying the factors affecting it. Land cover change depends on the physical and socio-economic characteristics of a region. Thus, the driving factor of land cover change in each region is different. This research is conducted to identify the driving factors on West Java Province’s land cover change based on it is land cover change clusters. The relation between each factor and land cover change can be evaluated using the binary logistic regression, which is a data analysis method used to find a relationship between a binary response variable (y) and predictor variable(s) (x). In this case, the land cover change, as a dichotomous phenomenon, acts as the response variable, while the driving factors act as the predictor variables. The result of this study indicates that in general, the distance to the nearest central business district, as a factor, is the dominant driving factor of land cover change on West Java Province.[/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,Binary response variables,Central business districts,Data analysis methods,Global environmental change,Land-cover change,Predictor variables,Spatial analysis[/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/10.1088/1755-1315/448/1/012094[/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]