[vc_empty_space][vc_empty_space]
Spatial modeling of infrastructure resilience to the natural disasters using baseline resilience indicators for communities (BRIC) – Case study: 5 districts/cities of Bandung Basin Area
Nafishoh Q.a, Riqqi A.a, Meilano I.a
a Geodesy and Geomatics Engineering, Faculty of Earth Sciences and Technology, Institut Teknologi Bandung, Bandung, West Java, 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]The Bandung Basin area has highly susceptible to the natural disasters. Therefore, resilience measurement is useful to find out the capacity of an area in the facing of a natural disaster. Natural disaster resilience can be measured using BRIC (Baseline Resilience Indicators for Communities) model. This model comprises several indicators; includes social, economic, community, institution, infrastructure, and the environment. This research tries to measure resilience to the natural disasters with still focusing on infrastructure resilience measurement by spatial modeling and analyzed the dominant driving factor that contributes to this resilience trend. We generated a spatial modeling by applying a spatial analysis to the infrastructure objects. The infrastructure objects consist of the road, school, and health facilities. Those objects will be given some radius levels that indicate the resilience level by using buffer processing. An area closest to those objects will have high resilience and contrarily. Our result showed that almost all city areas (Bandung and Cimahi City) have high resilience because they have many infrastructure objects. But contrarily with the district areas which are still contained many patterns of low and moderate resilience level. The dominant driving factor of infrastructure resilience in this research area is a road. The areas which are closest to the road have high resilience and farther away from the road will have low resilience.[/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][/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/10.1063/1.4987116[/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]