Applications
Editor: Mike Potel
Visualizing Marine Environmental Changes to the Saemangeum Coast Jinah Kim Korea Institute of Ocean Science and Technology Jinah Park Korea Advanced Institute of Science and Technology
B
ecause the ocean plays such an important role in the Earth’s climate system, scientists have been striving to extract useful patterns or knowledge from marine environmental data to understand specific circumstances, recognize changes, and prevent adverse effects. A complete picture of the marine environment will involve five major research areas: marine meteorology, ocean physics and circulation, water quality, marine geology, and the marine ecosystem. The pieces of information gathered from these areas have a somewhat complicated correlation and sometimes involve cause-and-effect relationships with each other. Marine environmental datasets are obtained from observations and simulations and are diverse, complex, and heterogeneous. However, oceanographers often settle for mere statistics displayed in 2D time-series graphs or fragmentary figures. Such piecemeal analysis of oceanic processes and marine phenomena can’t capture the ocean’s complex dynamics and relationships. Instead, we need dimensionally advanced statistical and scientific visual analysis of long-term accumulated data. The first challenge for such analysis is handling the data, owing to their enormous size and varied formats. The next challenge is fusing the information for knowledge discovery. Finally, developing effective visualizations that tap into humans’ visual-reasoning ability is key to successful analysis. We’ve developed a visual analytics (VA) tool with an embedded GIS (geographic information system) for location-based oceanographic data. We find that it’s intuitive to employ geographic grids that integrate marine environmental data, 82
November/December 2012
geographic information, and multidimensional spatiotemporal distribution. Here, we show how we’ve applied our tool to analyze marine environmental changes due to land reclamation on South Korea’s Saemangeum Coast.
The Saemangeum Land Reclamation Project The Saemangeum Coast, located on the west side of the Korean Peninsula (see Figure 1), has undergone construction of a 33.9-km sea dyke with two sluice gates. The dyke actually includes four smaller dykes: the 2.7-km dyke 3, 4.7-km dyke 1, 11.4-km dyke 4, and 9.9-km dyke 2, which were completed in 1994, 1998, 2003, and 2006, respectively. The two sluice gates, Gareok and Shinsi, were completed in 2003 and 2006. The entire dyke was finished in 2009, and waterproofing construction for internal land development should end by 2020. The entire dyke yields approximately 22,800 hectares of artificial lakes and 28,300 hectares of land reclamation. The artificial lakes accept terrigenous effluent and exchange water with the ocean through the sluice gates. This exchange maintains the quality of the water enclosed by the dyke.1 Environmental management, focusing on controlling water quality and protecting the vulnerable environment, is the stakeholders’ major concern. Environmental changes that disturb the marine environment owing to this development project are inevitable. To minimize adverse impacts and to establish countermeasures in parallel with the development, the stakeholders must thoroughly understand how the changes have already affected the environment locally and globally. They must also continuously monitor the situation to accurately predict what will happen next.
Published by the IEEE Computer Society
0272-1716/12/$31.00 © 2012 IEEE
0 km
5 km
N36º N35º50’
Our study, supported by the Korean government, focuses on research for a marine environmental conservation project. Figure 2 shows the observation stations, consisting of observatories, buoys, moored equipment, and devices for water and geological sampling. Observation data are usually collected either every 10 minutes or every hour and are transmitted by wireless Internet, satellite, or a CDMA (code division multiple access) telecommunications network. The transmitted data are processed to produce advanced 72-hour forecast datasets, twice a day, using 3D meteorological and oceanographic numerical models. Depending on the resolution of a mesh-shaped grid consisting of a horizontal and vertical grid and on the region of interest, tens of gigabytes of datasets can be produced per day.2 Users must manage and explore potentially hundreds of parameters, variables, and functions; even a limited scope involves a variety of combinations in different circumstances for assessment and comparison. The main items are tides, currents, waves, water
Janghang
Gogunsan Archipelago
Gareok Gate (2006)
E126º20’
Gunsan (2003)
Mangeong River Gimje Dongjin River
2 (2006)
Shinsi Gate (2003)
E126º10’
4
3 (1994)
Sunyoodo
N35º40’
Ocean Environmental Data
Buan
(1998) 1 Byunsan
E126º30’
E126º40’
E126º50’
E127º
Figure 1. South Korea’s Saemangeum Coast has undergone construction of a 33.9-km sea dyke with two sluice gates, and the reclaimed land is being prepared for development. Each number indicates one of the four smaller dykes, with the completion date in parentheses.
10 km
Water quality
Gunsan Harbor
Ocean physics Marine geology and ecosystem
Mangeong Bridge
Gogunsan Archipelago
Gareok Gate Gareok Tower Byunsan Gosapo Weedo
Gomsoman
Figure 2. These observation stations consist of observatories, buoys, moored equipment, and devices for water and geological sampling. Observation data are usually collected either every 10 minutes or every hour.
IEEE Computer Graphics and Applications
83
Applications
Thematic Cartography
T
hematic cartography communicates themes, patterns, trends, or distributions in a geographic area at a glance by visualizing quantitative values for geographic information. It includes these types of maps:
■■ A
choropleth map displays defined regions shaded or patterned according to the frequency of some statistical variable—for example, population or precipitation density. ■■ A proportional-symbol map uses different-sized symbols to express a phenomenon’s frequency at a location (see Figure 4 in the main article). ■■ An isarithmic map is a contour or isopleth map connecting the same values continuously, such as pressure, elevation, or depth. ■■ A dot map shows the density of a phenomenon’s occurrence at a location, using a single symbol repeatedly. ■■ A dasymetric map is the same as the choropleth map, except that the regions aren’t predefined (see Figures 3 and 5 in the main article).
Because almost all the data contains geographic-location information, visualization on a map is the most reasonable choice. temperature, salinity, sea surface wind, air pressure, air temperature, water velocity, sea surface height, water quality, geomorphologic geology, and the ocean ecosystem.
GIS-Based Visual Analytics Because almost all the data contain geographiclocation information, visualization on a map is the most reasonable choice. The map includes georeferenced satellite images, which geographically tie together the marine environmental data and the derived results. Our VA tool has three main components: ■■
■■
■■
The data collection module builds a geospatial database. The geoprocessing module performs GIS operations to manipulate spatial data. The data visualization module supports various analyses.
The data collection module collects different formats of observed and simulated data to meet a specified production period and cycle. First, it preprocesses the raw data according to the file for84
November/December 2012
mat and some written rules for managing exceptional cases such as data missing owing to device glitches. Then, it stores the data in an Oracle database or a geospatial database, depending on the data’s attributes. We classify ocean environmental data according to the ArcGIS Marine Data Model3—for example, using feature points, time series points, trajectory lines, and time duration areas. We classify the data generated by 3D ocean circulation and hydrodynamic numerical models as a mesh-shaped grid because they’re multidimensional and have a volumetric array structure. Data classified as a mesh-shaped grid are stored in the geospatial database, which provides an efficient data structure for storing huge geospatial datasets. The geoprocessing module processes the data in the database for eventual visualization on a map. It uses such common GIS operations as georeferencing, geographic feature overlay, feature selection and analysis, topology processing, raster processing, and data conversion. Providing visual criteria that are more finely detailed and more accurate involves additional considerations and geoprocessing, such as clipping, reclassification, extraction by masks, interpolation, and mosaicking. The data visualization module applies appropriate statistical graphs and thematic-cartography methods. For statistical graphs, we use scatterplots, time series graphs, histograms, or box plots. Examples of thematic cartography include choropleth, proportional-symbol, isarithmic, dot, and dasymetric maps. (For more on this, see the sidebar.) For cases in which time-duration-area datasets overlap with profile-line or trajectory-line datasets based on observations, we apply thematic cartography to produce a distributional or bathymetric chart.
Results Here, we demonstrate how we used our tool to monitor changes in water quality, zooplankton distribution, and marine geology. Using the Arc– GIS Marine Data Model, we classified the data according whether they involved feature points or trajectory lines. The selected feature points represented the surrounding area. To make a spatialdistribution chart (such as for water quality) on the map, we used georeferencing, Kriging interpolation, rasterization, and clipping. To identify patterns of morphological changes and quantitative volume, we performed spatial-arithmetic operations between the different spatial-distribution charts on the maps. The results can explain changes in
long-term trends, correlating the dyke construction’s progress with elements of topography and ocean physics and circulation.
Water Quality Water quality is the most critical criterion to measure the project’s adverse effects. In this study, chemical oxygen demand (COD) indicated water quality. To visualize the COD data, we applied a dasymetric map to irregularly observed point data (see Figure 3). In Figure 3, the deepest blue and darkest red indicate 0.2 and 10.0 milligrams of organic material per liter, respectively. The lower the value is, the better the water quality. Usually, values less than 3.0 (orange in the figure) are considered acceptable, and values greater than 6.0 are out of range, indicating problematically poor quality. We divide water quality into six grades, with grade VI being the worst. Figure 3a shows the water quality in 2003, when dyke 2 was in progress. It shows that the degree of COD around dyke 2 was worse than the surrounding area and was even out of range. Also, the offshore COD was also out of range because of the exchange of sea water through Shinsi from inside to outside the dyke, due to the east-to-west tidal current. In the area’s north, grades I and II were maintained, owing to the Gogunsan Archipelago (see Figure 1). Figure 3b shows the water quality in 2005, when dyke 2 was almost at the final stage. Owing to the ocean circulation and sea water exchange through the two sluice gates, the water quality was somewhat stable. But at that time, to prepare for dredging the land inside the dyke, the sluice gates were opened only to decrease the water level. So, the water quality inside the dyke didn’t appear good, but it stayed mostly at grades II and III. In May 2009, dredging started inside the completed dyke. Previously, if water quality was found to be deteriorating, water was periodically exchanged through the sluice gates. However, at this time, the sluice gates again were operated only to lower the water level as much as possible for dredging. So, the grade of the water inside the dyke was overly high, and the water quality outside the sea dyke was relatively poor, owing to the influence of water exchange (see Figure 3c).
(a)
(b)
(c)
Zooplankton Distribution
Figure 3. Water quality distribution charts depicting chemical oxygen demand (COD), using a dasymetric map, for (a) August 2003, (b) September 2005, and (c) May 2009. We divide water quality into six grades, with grade VI being the worst. The numbers in the legend refer to milligrams of organic material per liter.
In Figure 4, the pie charts illustrate the zooplankton populations’ variation and the species diversity from 2007 to 2009. Zooplankton populations appeared to increase after the dyke’s completion. However, species diversity significantly decreased;
only the copepod population increased. Copepods are one of the most important groups of crustaceans and can thrive in unstable conditions of water temperature, salinity, anoxia, and so on. Also, owing to
IEEE Computer Graphics and Applications
85
Applications
Marine Geology Our main objective in monitoring marine geology is to identify areas and quantities of erosion or deposition. Figures 5a and 5b show dasymetric maps of the seabed topography in 2006 and 2008, respectively, around the Gogunsan Archipelago. Figure 5c shows the distribution of geomorphologic changes by calculating the difference between these two raster datasets using a GIS arithmetic operation. Serious erosion is evident around the Gogunsan Archipelago and Sunyoodo island (see Figure 1). With the click of the mouse at any location on the map, we can perceive the progression of topographical changes and the quantities involved.
(a)
B
y using geospatial distribution maps to correlate changes in water quality, zooplankton, and seabed geology with dyke construction progress, we can easily and intuitively comprehend how development is changing the marine environment. Clearly, our approach can improve analysis by mobilizing humans’ visual-reasoning ability while integrating different visual and geospatial patterns. In particular, considering the data’s type and complexity, employing GIS operations to geoprocess the data for visualization using thematic cartography was effective, letting us intuitively resolve spatial dimensions of geographic locations. Data continues to accumulate, and ocean-related phenomena are becoming more complex and frequent. In addition, as climate change becomes an increasingly serious issue and ocean development continuously increases, interest in the ocean and the demand for marine information are increasing. So, it’s becoming more important to find significant patterns among a myriad of materials distributed spatially over a long period. This study shows that VA is powerful for understanding such complex data. We plan to employ advanced multivariate statistical visualization to probe spatiotemporal correlations and cause-and-effect relations to identify changes and extract interesting patterns and useful knowledge.
(b)
(c) Figure 4. Zooplankton distribution charts using a proportional-symbol map with pie charts, for (a) July 2007, (b) July 2008, and (c) September 2009. Zooplankton populations appeared to increase after the dyke’s completion. However, species diversity significantly decreased; only the copepod population increased.
the disappearance and weakness of tidal movement and current flow, copepods weren’t widely distributed, and the local volume grew noticeably. 86
November/December 2012
Acknowledgments This research was part of the Development of Korea Operational Oceanographic System project, funded by the Ministry of Land, Transport and Maritime Affairs, South Korea. The Korea Institute of Ocean Science and Technology’s Storm-Surge Inundation Prediction and Hazard Map project also partially supported this research and provided real-time sim-
ulation of ocean observation and numerical-model data.
References 1. H.J. Lie et al., “Changes in Marine Environment by a Large Coastal Development of the Saemangeum Reclamation Project in Korea,” Ocean and Polar Research, vol. 40, no. 3, 2008, pp. 475–484. 2. C.S. Kim et al., “Saemangeum Coastal System Research for Marine Environmental Conservation,” white paper, Ministry of Land, Transport and Maritime Affairs, Korea, Feb. 2011. 3. J. Breman, D. Wright, and P.N. Halpin, “The Inception of the ArcGIS Marine Data Model,” Marine Geography: GIS for the Oceans and Seas, J. Breman, ed., Esri, 2002, pp. 3–9.
(a)
Jinah Kim is a research scientist at the Korea Institute of Ocean Science and Technology’s Coastal Disaster Research Center and a PhD student in the Korea Advanced Institute of Science and Technology’s Computer Science Department. Contact her at [email protected]. Jinah Park is an associate professor in the Korea Advanced Institute of Science and Technology’s Computer Science Department, and leads the Computer Graphics and Visualization Lab. Contact her at [email protected].
(b)
Contact department editor Mike Potel at potel@wildcrest. com. Selected CS articles and columns are also available for free at http://ComputingNow.computer.org.
Call rticles for A
(c) Figure 5. Seabed topographic charts around the Gogunsan Archipelago, using a dasymetric map, for (a) May 2006 and (b) June 2008, and (c) the geomorphologic changes. Serious erosion is evident around the Gogunsan Archipelago and Sunyoodo island (see Figure 1). For Figures 5a and 5b, the unit of measurement is centimeters. Negative numbers indicate erosion; positive numbers indicate deposition.
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Visit CG&A on the Web at www.computer.org/cga IEEE Computer Graphics and Applications
87
Editor: Mike Potel
Visualizing Marine Environmental Changes to the Saemangeum Coast Jinah Kim Korea Institute of Ocean Science and Technology Jinah Park Korea Advanced Institute of Science and Technology
B
ecause the ocean plays such an important role in the Earth’s climate system, scientists have been striving to extract useful patterns or knowledge from marine environmental data to understand specific circumstances, recognize changes, and prevent adverse effects. A complete picture of the marine environment will involve five major research areas: marine meteorology, ocean physics and circulation, water quality, marine geology, and the marine ecosystem. The pieces of information gathered from these areas have a somewhat complicated correlation and sometimes involve cause-and-effect relationships with each other. Marine environmental datasets are obtained from observations and simulations and are diverse, complex, and heterogeneous. However, oceanographers often settle for mere statistics displayed in 2D time-series graphs or fragmentary figures. Such piecemeal analysis of oceanic processes and marine phenomena can’t capture the ocean’s complex dynamics and relationships. Instead, we need dimensionally advanced statistical and scientific visual analysis of long-term accumulated data. The first challenge for such analysis is handling the data, owing to their enormous size and varied formats. The next challenge is fusing the information for knowledge discovery. Finally, developing effective visualizations that tap into humans’ visual-reasoning ability is key to successful analysis. We’ve developed a visual analytics (VA) tool with an embedded GIS (geographic information system) for location-based oceanographic data. We find that it’s intuitive to employ geographic grids that integrate marine environmental data, 82
November/December 2012
geographic information, and multidimensional spatiotemporal distribution. Here, we show how we’ve applied our tool to analyze marine environmental changes due to land reclamation on South Korea’s Saemangeum Coast.
The Saemangeum Land Reclamation Project The Saemangeum Coast, located on the west side of the Korean Peninsula (see Figure 1), has undergone construction of a 33.9-km sea dyke with two sluice gates. The dyke actually includes four smaller dykes: the 2.7-km dyke 3, 4.7-km dyke 1, 11.4-km dyke 4, and 9.9-km dyke 2, which were completed in 1994, 1998, 2003, and 2006, respectively. The two sluice gates, Gareok and Shinsi, were completed in 2003 and 2006. The entire dyke was finished in 2009, and waterproofing construction for internal land development should end by 2020. The entire dyke yields approximately 22,800 hectares of artificial lakes and 28,300 hectares of land reclamation. The artificial lakes accept terrigenous effluent and exchange water with the ocean through the sluice gates. This exchange maintains the quality of the water enclosed by the dyke.1 Environmental management, focusing on controlling water quality and protecting the vulnerable environment, is the stakeholders’ major concern. Environmental changes that disturb the marine environment owing to this development project are inevitable. To minimize adverse impacts and to establish countermeasures in parallel with the development, the stakeholders must thoroughly understand how the changes have already affected the environment locally and globally. They must also continuously monitor the situation to accurately predict what will happen next.
Published by the IEEE Computer Society
0272-1716/12/$31.00 © 2012 IEEE
0 km
5 km
N36º N35º50’
Our study, supported by the Korean government, focuses on research for a marine environmental conservation project. Figure 2 shows the observation stations, consisting of observatories, buoys, moored equipment, and devices for water and geological sampling. Observation data are usually collected either every 10 minutes or every hour and are transmitted by wireless Internet, satellite, or a CDMA (code division multiple access) telecommunications network. The transmitted data are processed to produce advanced 72-hour forecast datasets, twice a day, using 3D meteorological and oceanographic numerical models. Depending on the resolution of a mesh-shaped grid consisting of a horizontal and vertical grid and on the region of interest, tens of gigabytes of datasets can be produced per day.2 Users must manage and explore potentially hundreds of parameters, variables, and functions; even a limited scope involves a variety of combinations in different circumstances for assessment and comparison. The main items are tides, currents, waves, water
Janghang
Gogunsan Archipelago
Gareok Gate (2006)
E126º20’
Gunsan (2003)
Mangeong River Gimje Dongjin River
2 (2006)
Shinsi Gate (2003)
E126º10’
4
3 (1994)
Sunyoodo
N35º40’
Ocean Environmental Data
Buan
(1998) 1 Byunsan
E126º30’
E126º40’
E126º50’
E127º
Figure 1. South Korea’s Saemangeum Coast has undergone construction of a 33.9-km sea dyke with two sluice gates, and the reclaimed land is being prepared for development. Each number indicates one of the four smaller dykes, with the completion date in parentheses.
10 km
Water quality
Gunsan Harbor
Ocean physics Marine geology and ecosystem
Mangeong Bridge
Gogunsan Archipelago
Gareok Gate Gareok Tower Byunsan Gosapo Weedo
Gomsoman
Figure 2. These observation stations consist of observatories, buoys, moored equipment, and devices for water and geological sampling. Observation data are usually collected either every 10 minutes or every hour.
IEEE Computer Graphics and Applications
83
Applications
Thematic Cartography
T
hematic cartography communicates themes, patterns, trends, or distributions in a geographic area at a glance by visualizing quantitative values for geographic information. It includes these types of maps:
■■ A
choropleth map displays defined regions shaded or patterned according to the frequency of some statistical variable—for example, population or precipitation density. ■■ A proportional-symbol map uses different-sized symbols to express a phenomenon’s frequency at a location (see Figure 4 in the main article). ■■ An isarithmic map is a contour or isopleth map connecting the same values continuously, such as pressure, elevation, or depth. ■■ A dot map shows the density of a phenomenon’s occurrence at a location, using a single symbol repeatedly. ■■ A dasymetric map is the same as the choropleth map, except that the regions aren’t predefined (see Figures 3 and 5 in the main article).
Because almost all the data contains geographic-location information, visualization on a map is the most reasonable choice. temperature, salinity, sea surface wind, air pressure, air temperature, water velocity, sea surface height, water quality, geomorphologic geology, and the ocean ecosystem.
GIS-Based Visual Analytics Because almost all the data contain geographiclocation information, visualization on a map is the most reasonable choice. The map includes georeferenced satellite images, which geographically tie together the marine environmental data and the derived results. Our VA tool has three main components: ■■
■■
■■
The data collection module builds a geospatial database. The geoprocessing module performs GIS operations to manipulate spatial data. The data visualization module supports various analyses.
The data collection module collects different formats of observed and simulated data to meet a specified production period and cycle. First, it preprocesses the raw data according to the file for84
November/December 2012
mat and some written rules for managing exceptional cases such as data missing owing to device glitches. Then, it stores the data in an Oracle database or a geospatial database, depending on the data’s attributes. We classify ocean environmental data according to the ArcGIS Marine Data Model3—for example, using feature points, time series points, trajectory lines, and time duration areas. We classify the data generated by 3D ocean circulation and hydrodynamic numerical models as a mesh-shaped grid because they’re multidimensional and have a volumetric array structure. Data classified as a mesh-shaped grid are stored in the geospatial database, which provides an efficient data structure for storing huge geospatial datasets. The geoprocessing module processes the data in the database for eventual visualization on a map. It uses such common GIS operations as georeferencing, geographic feature overlay, feature selection and analysis, topology processing, raster processing, and data conversion. Providing visual criteria that are more finely detailed and more accurate involves additional considerations and geoprocessing, such as clipping, reclassification, extraction by masks, interpolation, and mosaicking. The data visualization module applies appropriate statistical graphs and thematic-cartography methods. For statistical graphs, we use scatterplots, time series graphs, histograms, or box plots. Examples of thematic cartography include choropleth, proportional-symbol, isarithmic, dot, and dasymetric maps. (For more on this, see the sidebar.) For cases in which time-duration-area datasets overlap with profile-line or trajectory-line datasets based on observations, we apply thematic cartography to produce a distributional or bathymetric chart.
Results Here, we demonstrate how we used our tool to monitor changes in water quality, zooplankton distribution, and marine geology. Using the Arc– GIS Marine Data Model, we classified the data according whether they involved feature points or trajectory lines. The selected feature points represented the surrounding area. To make a spatialdistribution chart (such as for water quality) on the map, we used georeferencing, Kriging interpolation, rasterization, and clipping. To identify patterns of morphological changes and quantitative volume, we performed spatial-arithmetic operations between the different spatial-distribution charts on the maps. The results can explain changes in
long-term trends, correlating the dyke construction’s progress with elements of topography and ocean physics and circulation.
Water Quality Water quality is the most critical criterion to measure the project’s adverse effects. In this study, chemical oxygen demand (COD) indicated water quality. To visualize the COD data, we applied a dasymetric map to irregularly observed point data (see Figure 3). In Figure 3, the deepest blue and darkest red indicate 0.2 and 10.0 milligrams of organic material per liter, respectively. The lower the value is, the better the water quality. Usually, values less than 3.0 (orange in the figure) are considered acceptable, and values greater than 6.0 are out of range, indicating problematically poor quality. We divide water quality into six grades, with grade VI being the worst. Figure 3a shows the water quality in 2003, when dyke 2 was in progress. It shows that the degree of COD around dyke 2 was worse than the surrounding area and was even out of range. Also, the offshore COD was also out of range because of the exchange of sea water through Shinsi from inside to outside the dyke, due to the east-to-west tidal current. In the area’s north, grades I and II were maintained, owing to the Gogunsan Archipelago (see Figure 1). Figure 3b shows the water quality in 2005, when dyke 2 was almost at the final stage. Owing to the ocean circulation and sea water exchange through the two sluice gates, the water quality was somewhat stable. But at that time, to prepare for dredging the land inside the dyke, the sluice gates were opened only to decrease the water level. So, the water quality inside the dyke didn’t appear good, but it stayed mostly at grades II and III. In May 2009, dredging started inside the completed dyke. Previously, if water quality was found to be deteriorating, water was periodically exchanged through the sluice gates. However, at this time, the sluice gates again were operated only to lower the water level as much as possible for dredging. So, the grade of the water inside the dyke was overly high, and the water quality outside the sea dyke was relatively poor, owing to the influence of water exchange (see Figure 3c).
(a)
(b)
(c)
Zooplankton Distribution
Figure 3. Water quality distribution charts depicting chemical oxygen demand (COD), using a dasymetric map, for (a) August 2003, (b) September 2005, and (c) May 2009. We divide water quality into six grades, with grade VI being the worst. The numbers in the legend refer to milligrams of organic material per liter.
In Figure 4, the pie charts illustrate the zooplankton populations’ variation and the species diversity from 2007 to 2009. Zooplankton populations appeared to increase after the dyke’s completion. However, species diversity significantly decreased;
only the copepod population increased. Copepods are one of the most important groups of crustaceans and can thrive in unstable conditions of water temperature, salinity, anoxia, and so on. Also, owing to
IEEE Computer Graphics and Applications
85
Applications
Marine Geology Our main objective in monitoring marine geology is to identify areas and quantities of erosion or deposition. Figures 5a and 5b show dasymetric maps of the seabed topography in 2006 and 2008, respectively, around the Gogunsan Archipelago. Figure 5c shows the distribution of geomorphologic changes by calculating the difference between these two raster datasets using a GIS arithmetic operation. Serious erosion is evident around the Gogunsan Archipelago and Sunyoodo island (see Figure 1). With the click of the mouse at any location on the map, we can perceive the progression of topographical changes and the quantities involved.
(a)
B
y using geospatial distribution maps to correlate changes in water quality, zooplankton, and seabed geology with dyke construction progress, we can easily and intuitively comprehend how development is changing the marine environment. Clearly, our approach can improve analysis by mobilizing humans’ visual-reasoning ability while integrating different visual and geospatial patterns. In particular, considering the data’s type and complexity, employing GIS operations to geoprocess the data for visualization using thematic cartography was effective, letting us intuitively resolve spatial dimensions of geographic locations. Data continues to accumulate, and ocean-related phenomena are becoming more complex and frequent. In addition, as climate change becomes an increasingly serious issue and ocean development continuously increases, interest in the ocean and the demand for marine information are increasing. So, it’s becoming more important to find significant patterns among a myriad of materials distributed spatially over a long period. This study shows that VA is powerful for understanding such complex data. We plan to employ advanced multivariate statistical visualization to probe spatiotemporal correlations and cause-and-effect relations to identify changes and extract interesting patterns and useful knowledge.
(b)
(c) Figure 4. Zooplankton distribution charts using a proportional-symbol map with pie charts, for (a) July 2007, (b) July 2008, and (c) September 2009. Zooplankton populations appeared to increase after the dyke’s completion. However, species diversity significantly decreased; only the copepod population increased.
the disappearance and weakness of tidal movement and current flow, copepods weren’t widely distributed, and the local volume grew noticeably. 86
November/December 2012
Acknowledgments This research was part of the Development of Korea Operational Oceanographic System project, funded by the Ministry of Land, Transport and Maritime Affairs, South Korea. The Korea Institute of Ocean Science and Technology’s Storm-Surge Inundation Prediction and Hazard Map project also partially supported this research and provided real-time sim-
ulation of ocean observation and numerical-model data.
References 1. H.J. Lie et al., “Changes in Marine Environment by a Large Coastal Development of the Saemangeum Reclamation Project in Korea,” Ocean and Polar Research, vol. 40, no. 3, 2008, pp. 475–484. 2. C.S. Kim et al., “Saemangeum Coastal System Research for Marine Environmental Conservation,” white paper, Ministry of Land, Transport and Maritime Affairs, Korea, Feb. 2011. 3. J. Breman, D. Wright, and P.N. Halpin, “The Inception of the ArcGIS Marine Data Model,” Marine Geography: GIS for the Oceans and Seas, J. Breman, ed., Esri, 2002, pp. 3–9.
(a)
Jinah Kim is a research scientist at the Korea Institute of Ocean Science and Technology’s Coastal Disaster Research Center and a PhD student in the Korea Advanced Institute of Science and Technology’s Computer Science Department. Contact her at [email protected]. Jinah Park is an associate professor in the Korea Advanced Institute of Science and Technology’s Computer Science Department, and leads the Computer Graphics and Visualization Lab. Contact her at [email protected].
(b)
Contact department editor Mike Potel at potel@wildcrest. com. Selected CS articles and columns are also available for free at http://ComputingNow.computer.org.
Call rticles for A
(c) Figure 5. Seabed topographic charts around the Gogunsan Archipelago, using a dasymetric map, for (a) May 2006 and (b) June 2008, and (c) the geomorphologic changes. Serious erosion is evident around the Gogunsan Archipelago and Sunyoodo island (see Figure 1). For Figures 5a and 5b, the unit of measurement is centimeters. Negative numbers indicate erosion; positive numbers indicate deposition.
ting ompu
eerive C test p ervas the la P E rs on E IE l pap e , an d e fu il se b o ,u seek s
access
review
velo ed de
ubiqu
itous
techn
er Furth ils:
det a perva comp
sive@
u
ter.org
. www ter. u p c om ive er vas org/p
ible
co
pmen
ng. mputi
, so ology
g sensin intera
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