Spatial MCDA in Marine Planning: Experiences from the Mediterranean and Baltic Seas

mpMarine Policy, Volume 48, September 2014

by Ilpo Tammi and Risto Kalliola


  • Joint use of GIS and multi-criteria decision analysis in marine planning is tested.
  • Two different methodological approaches are applied in two different European seas.
  • Spatial MCDA aids in structuring and evaluating marine planning decision problems.
  • The methods enable participation, communication and iterativity in MSP.
  • Proper sensitivity analysis is required due to diversity in data and model quality.

“This paper examines ways to guide spatial decision making processes by the combined use of geographical information systems (GIS), multi-criteria decision analysis (MCDA) and related decision support systems – also called spatial MCDA. Two local-scale case studies were conducted in multi-objective marine and coastal spatial planning problems, one focusing on experimental artificial reef siting in the Mediterranean Sea and one on spatial rearrangement of aquaculture production in the Baltic Sea.

Example datasetsfortheAegeancase,waterdepth(m)andprimaryproduction(mg/m3 chl-a, basedonMERISdata©ESA2012).

Example data sets for the Aegean case, water depth (m) and primary production (mg/m3 chl-a, based on MERIS data ©ESA 2012).

“In both cases similar analytical frameworks were utilized, yet the applied spatial MCDA techniques were tailored case-specifically according to local characteristics and the nature of the decision problems at hand. In both cases the joint use of GIS and MCDA was able to generate concrete, easily interpretable inputs for decision support via quantification and visualization of decision criteria, trade-offs, alternatives and uncertainty, thus supporting further use of GIS-based spatial decision support tools. Albeit such tools may provide valuable insight to MSP and ICZM, they also come with certain limitations which are commonly related to the quality of the input data and the used valuation criteria in situations that may contain a mixture of subjective and objective information. Based on the empirical findings, the applicability of spatial MCDA in these settings and in marine spatial planning in general is discussed.”

Identification of Optimum Scopes of Environmental Factors for Snails using Spatial Analysis Techniques in Dongting Lake Region, China

pnvParasites & Vectors 7:216, Published Online 09 May 2014

By Jin-Yi Wu, Yi-Biao Zhou, Lin-Han Li, Sheng-Bang Zheng, Song Liang, Ashley Coatsworth, Guang-Hui Ren, Xiu-Xia Song, Zhong He, Bin Cai, Jia-Bian You, and Qing-Wu Jiang

Owing to the harmfulness and seriousness of Schistosomiasis japonica in China, the control and prevention of S. japonica transmission are imperative. As the unique intermediate host of this disease, Oncomelania hupensis plays an important role in the transmission. It has been reported that the snail population in Qiangliang Lake district, Dongting Lake Region has been naturally declining and is slowly becoming extinct. Considering the changes of environmental factors that may cause this phenomenon, we try to explore the relationship between circumstance elements and snails, and then search for the possible optimum scopes of environmental factors for snails.

Moisture content of soil, pH, temperature of soil and elevation were collected by corresponding apparatus in the study sites. The LISA statistic and GWR model were used to analyze the association between factors and mean snail density, and the values in high-high clustered areas and low-low clustered areas were extracted to find out the possible optimum ranges of these elements for snails.


A total of 8,589 snail specimens were collected from 397 sampling sites in the study field. Besides the mean snail density, three environmental factors including water content, pH and temperature had high spatial autocorrelation. The spatial clustering suggested that the possible optimum scopes of moisture content, pH, temperature of the soil and elevation were 58.70 to 68.93%, 6.80 to 7.80, 22.73 to 24.23[degree sign]C and 23.50 to 25.97 m, respectively. Moreover, the GWR model showed that the possible optimum ranges of these four factors were 36.58 to 61.08%, 6.541 to 6.89, 24.30 to 25.70[degree sign]C and 23.50 to 29.44 m, respectively.

The results indicated the association between snails and environmental factors was not linear but U-shaped. Considering the results of two analysis methods, the possible optimum scopes of moisture content, pH, temperature of the soil and elevation were 58.70% to 68.93%, 6.6 to 7.0, 22.73[degree sign]C to 24.23[degree sign]C, and 23.5 m to 26.0 m, respectively. The findings in this research will help in making an effective strategy to control snails and provide a method to analyze other factors.”