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Open data visual analytics to support decisions on physical investments
Wasesa M.a, Mashuri M.b, Handayani P.c, Putro U.S.a
a School of Business and Management, Bandung Institute of Technology, Bandung, Indonesia
b Indonesian Center for Logistics and Value Chains, Bandung, Indonesia
c Water Science and Management Masters Programme, Utrecht University, Utrecht, Netherlands
[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]© 2019 The Authors.Before making decisions about the location of physical investments (e.g. factories, warehouses, stores, etc.), investors have to conduct thorough profiling on the prospective locations of the physical facilities. For this profiling purpose, investors consider a number of important aspects such as the location’s local resources, market size, economic growth, regional minimum wage (salary) standard, land acquisition cost, human development index, and gross domestic product (GDP). While conducting manual research on those decision variables can cost extensive time and efforts, in this article, we present an open data visual analytics which will help investors in making decisions on the prospective location of physical investments. Investors can use the web-based application to easily gather information and do a quick comparison among prospective investment locations in terms of the selected decision variables.[/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]Decision variables,Descriptive analytics,Gross domestic products,Human development index,Physical investments,Spatio-temporal data,Visual analytics,Web-based applications[/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]Descriptive analytics,Open data,Physical investment,Spatiotemporal data,Visual analytics[/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/10.1016/j.procs.2019.11.185[/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]