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Utilization of google maps for depicting landslide pattern in Indonesia
Sukristiyanti S.a,b, Wikantika K.a, Sadisun I.A.a, Yayusman L.F.a, Pamela P.c
a Remote Sensing and GIS Research Group, Faculty of Earth Sciences and Technology, ITB, Bandung, Indonesia
b Research Centre for Geotechnology, Indonesian Institute of Sciences (LIPI), Bandung, Indonesia
c Geological Agency of Indonesia, Bandung, 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 historical landslide data in GIS (Geographic Information System) environment is valuable to estimate pattern in landslides distribution and frequency, which are useful for landslide hazard analysis and mitigation. By using the landslide reports released officially by The National Agency for Disaster Management (BNPB), those non-spatial data need to be converted into the spatial ones. The reports primarily contain location, date of event, impact and triggering factor. This study is exploring Google Maps which is a web mapping service to process the historical landslide data of Indonesia. By preparing historical landslide data in the form of spreadsheet, Google Maps directly can change the whole data into a custom map ‘landslide distribution map’. The attribute can be edited, the map can change interactively, and vice versa. The appearance of the custom map can be styled by a certain column so its statistical information comes up. A landslide distribution map produced in Google Maps can be shared to others and can be exported as a GIS layer for further analysis. This article shows the utilization of facilities provided by Google Maps to prepare and analyse the landslide inventory map.[/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]Disaster management,GIS (geographic information system),Landslide distributions,Landslide inventories,Landslide pattern,Statistical information,Triggering factors,Web mapping services[/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]This study is supported by Saintek Kemenristekdikti 2018 Scholarship Program. The author would like to thank Research Centre for Geotechnology LIPI which facilitates this research. The author also would like to thank BNPB which provides historical landslide catalogue as a main data in this research.[/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/10.1088/1755-1315/500/1/012042[/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]