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11 th International Symposium for GIS and Computer Cartography for Coastal Zones Management(O’Sullivan and Unwin, 2002). Some examples of marine studies that have conducted spatial analysis using distance-basedspatial neighbourhoods include: development of a procedure for analysis of multi-image change in thespatial characteristics of the cover of a coral reef (LeDrew et al., 2004), exploration of geographically weightedregression (GWR) method as applied to Atlantic cod (Gadus morhua) fisheries data (Windle et al., 2010), and explorationof the relationship between seagrass, fish and nutrients in temperate seagrass systems (White et al., 2011).Spatial neighbourhood definitions in marine studies are also based on adjacency and k-nearest, which are neighbourscreated based on a constant number (k) of locations in each neighbourhood (Nelson and Robertson, 2012). An exampleof a marine spatial study that used a k-nearest neighbourhood definition is a study of indicators for assessmentof marine environmental conditions which used inverse distance weighted (IDW) (Chang et al., 2006). Whenspatial neighbourhoods are defined by adjacency or contiguity, the influence of geographic features only occurswhen they are surrounded or share a common boundary among them (O’Sullivan and Unwin, 2002). One exampleof a marine study that defined the spatial neighbourhood by adjacency is Chandrasekharan et al. (2008) which conducteda spatial-temporal analysis of sample data to understand the suitability of land for agriculture and the reclamationperiod of the Tsunami of 2004 affected coastal areas of Nagapattinam district of Tamilnadu state in India.The use of proximity polygons (Voronoi polygons and Constrained Delaunay triangulation), to define point patternsneighbourhoods based on contiguity or adjacency has been well established (Gold, 1992, 1994a; Nordvik andHarding, 2008). The approach proposed in this paper adds an extra step, and constrains neighbourhoods definedthrough Voronoi methods by removing neighbors that are separated by land barriers. Using a coastline polygon weconstrain neighborhoods defined through triangulation.Reasons to select a spatial neighbourhoodA spatial neighbourhood can be defined as the area where geographic features influence one another. The level ofinfluence or interaction of geographic features is normally defined by the user and often operates behind the scene asa roving window. For example, the spatial neighborhood defines the local window when conducting GWR and thespatial extent and data used for interpolation calculations at individual pixels. The choice of spatial neighborhood issomewhat subjective but should be carefully considered as it will influence results (Nelson and Robertson, 2012).For instance, if neighborhoods are based on distance it suggests that there is some known distance at which spatialinteraction becomes negligible. Figure 1a shows distance standard approach for spatial neighbourhood definition.When neighborhoods are defined arbitrarily, as is often the case since common definitions were developed for computationalsimplicity, spatial relationships may be improperly defined. A simple demonstration is to consider scale.Larger or smaller neighbours will pick up interaction at different scales, and if the wrong scale is defined, patternsand processes will be incorrectly represented. Current spatial neighbourhood definitions are problematic when appliedto marine environments due to limited accounting of physical barriers such as land (islands or promontories).Figure 1b illustrates spatial neighbourhoods that are impeded by land as shown when they are overlay over a coastalarea.Case studyIn this case study we present an approach to modifying spatial neighbourhoods based on barriers to interactioncaused by coastlines and islands. As marine mammals are constrained by the shoreline, land surface provides a barrierforcing the neighbourhood selection to occur only within the water. In order to delineate spatial neighbourhoodsfor the study of marine environments this research will integrate land (barriers) and observation of marine mammalsrepresented as points (observations) in the process of creating a weighting matrix, which in turns can be used indiverse spatial analysis models.DataData for this research is a sub-sample (seven) of marine mammal data acquired using surveys to estimate theabundance of marine mammal species in the inner waters of coastal British Columbia during the summer monthsfrom 2004 to 2006 and 2008 as well as spring and fall months of 2007. Marine mammal and shoreline data wereprovided by Raincoast Conservation Foundation (Best and Halpin, 2009). To generate spatial neighbourhood barriersbased on physical conditions, coastline data which show islands and inlets are also shown.218