GIS-based spatial exploration and analysis strategy to support decision-making on the site selection for the national beach litter monitoring

Abstract

In South Korea, national beach litter monitoring is being conducted six times a year at intervals of about two months since 2008 to the present to identify the amount, types, and sources of marine debris nationwide. Twenty monitoring sites were surveyed until July 2014, and 20 more sites were added in September 2014. Most of the sites are located along the shoreline of the mainland and were selected in consideration of the physical environment such as slope and substrate of the shore, availability and accessibility of in-situ surveyors, and possibility of post-survey cleanups. Additional survey sites were necessary due to recent public interest in remote and island areas, foreign-born pollution, and the interest of municipal governments. In this study, a GIS-based spatial exploration and analysis strategy is proposed to support the decision-making of the Ministry of Oceans and Fisheries regarding the selection of additional sites for its national beach litter monitoring, and the goal is to derive a total of 20 additional prospective sites. As a selection constraint for additional monitoring sites, spatial uniform distribution was set as the top priority, and factors such as island coastlines, the degree of foreign-born pollution on existing monitoring sites, and the possibility of securing representativeness among municipal governments were considered. In order to locate an area that satisfies each condition and factor in a GIS-based analysis environment, related spatial data offered through domestic public data portals were collected and converted, or directly produced by the authors. Meanwhile, to increase spatial search efficiency, the national coastline area was remodeled into hexagonal grids spaced at 5 km intervals using QGIS software. Next, spatial information on factors to be considered was automatically inputted through the overlapping analysis method for each grid. In addition, information on whether or not existing monitoring sites were included in the attribute data of each individual grid was also inputted. If any existing monitoring sites were not included within a grid, the information on the distance to the center point of the nearest grid among those including existing monitoring sites was inputted into the grid's attribute so that it can be utilized for decision-making. Finally, it was possible to deduce a grid-based area that should be considered first when selecting additional monitoring sites through the implementation of spatial exploration and analysis strategies. By comprehensively considering detailed requirements such as budget, distance, and the accessibility of survey groups, it was possible to propose and aid in decision-making regarding 20 prospective monitoring sites. The GIS-based spatial exploration and analysis strategy proposed through this study is a method that can be referenced when selecting additional shoreline litter monitoring sites, and is expected to support scientific and efficient decision-making.