4.8 The Bay of Fundy and the tides of climate change

3.4 Description of the Community Adaptation Viewer

The Community Adaptation Viewer (CAV) is a web-based spatial decision support system. It is a thin-client, Javascript enabled web software (Lieske, 2015). It allows on-the-fly assessment of social and economic vulnerability. The use of Javascript API and the Dojo Toolkit eliminates the need for the user to install software In order to run the CAV (the “thin-client” approach).

The CAV architecture consists of three interlocking layers: a data layer (consisting of physical infrastructure as well as vulnerability data), a visualization layer (the GUI interface), and a processing layer. The underlying geospatial information, derived from high resolution LiDAR (1 m) and other sources, were housed in a in a geodatabase to be hosted as map service layers on ArcGIS Server 10.1. The data layer contains the infrastructure, which was in part obtained from the province’s geospatial data gateway and in part derived from orthophotography. Flood damages were assessed using damage depth curves and additional methods from the U.S. Army Corps of Engineers and Natural Resources Canada, among other. Social vulnerability was assessed by adapting literature indicators (see details in Lieske (2015)) to the situation of New Brunswick.

The visualization layer contains a map viewer pane with several menus, allowing users to navigate the map (including pan and zoom), to select layers, to create features ad tasks (figure 32).

Schema of the main user interface of the community adaptation viewer

Figure 32. Schema of the main user interface of the community adaptation viewer

Source: Lieske (2015)

Further developments could include the creation of hyperlinks to additional material such as pictures, videos, reports, sites, but a balance between richness of information and usability has to be struck. 3-D visualisations have also been developed, but were judged less pertinent when the participants were highly familiar with the area under consideration. They are however able to raise awareness at a local level, especially when specific features of the built environment can be included in a more realistic manner (video 1)

Video 1a, 1b. 3-D visualization of lower Sackville during a flood event. The clip on the right includes a more realistic representation of the buildings. It also displays an entire family to show the variable vulnerability of different people.

Source : L. Salisbury, Geospatial Modelling Lab (GML), Mount Allison University (2012). For inquiries, please contact Dr. David Lieske, Department of Geography and Environment, Mount Allison University (dlieske@mta.ca).