Competing visions? Simulating alternative coastal futures using a GIS-ANN web application

• Autor(a) principal: Morgado Sousa, Paulo
• Data de Publicação: 2014
• Outros Autores: Gomes, Eduardo, Marques Da Costa, Nuno
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10451/27547
• topic: Scenario building; Public participation; Coastal urban areas management; GIS; Artificial neural network; Multilayer perceptron; Web application

In this paper, we demonstrate the use of scenario building in the context of contested land use visions. We examine a small coastal community located 20 kms south of Lisbon. In Almada e Trafaria/Costa da Caparica, competing stakeholders such as central government, local government, environmental NGO's and private companies each have competing development visions for the area. These include the development of recreation and leisure facilities, a container terminal and the re-naturalization of unused land. We illustrate the added value of the GIS-ANN tool in steering negotiations between these different visions and the potential of a scenario building web application as a tool for problem solving. The emergence of user-created GIS-based web content in Planning has transformed passive users and consumers of geospatial information into active contributors to the development of spatial visions of the future. It allows stakeholders to gauge alternative future land uses thus making planning and decisionmaking processes potentially more transparent and democratic. In this paper, we detail a new method that enhances GIS-web-based public participation. We build on a combination of GIS basic capabilities and the data mining methods of Artificial Neural Networks (ANN), namely Multilayer Perceptron (MLP) packaged in a friendly (GUI) user interface that runs on the Google Earth platform. Users will be able to articulate different spatial development scenarios for a specific area, to conduct sensitivity analyses for various competing scenarios and to explore causal connections between them.