Dynamic Indicators
Due to the recommendation initially defined by the Bruntland Report regarding the Sustainable Development (SD) perspective (Bruntland, 1987), increasing interest has been shown over the last few decades in the research of methodologies for analysing the temporal evolution of SD indicators (hereafter indicators). Indicators are pertinent data from the real-field or from a model of a given application, selected by analysts in agreement with stakeholders (Lanini & al., 2004), the evolution of which they wish to follow. Indicators may be spatial, such as the position of players (Jaques & al., 2006), the position of resources (Deschamps & al., 2006), the surface area of resources (Leistel, 2005), or non-spatial, such as water pollution levels, jobs, incomes or health (Chamaret & al., 2007).
One of these methodologies is the simulation of what we call a spatial-integrated socioeconomic model (hereafter SISM) in which the indicators are conceptualized (Daniell & al., 2005; Haberl & al., 2009). SISM are models in which socioeconomic players, situated in a geographical space measured at a territorial scale, exploit natural resources to satisfy the needs of the present with the purpose of not compromising the ability of future generations to meet their own needs. Regarding SISM design, two approaches are often used, even though non-exhaustive. On the one hand, when a SISM is designed at a micro (local) territorial scale, a multi-agent system (MAS) approach (Wooldridge, 2002) is appropriate (Hugues & Drogoul, 2002; Andriamasinoro & al., 2005). In this object-based approach, the indicators are selected among the properties of objects representing agents, environment, etc. On the other hand, when a SISM is designed at a larger scale, approaches such as system dynamics (Aburdene, 1988) are often used (Andersen & Ford, 2000; Adl & Parvisian, 2009) even if a MAS is still valid. In this non-object based approach, indicators are selected from the interconnected numerical variables forming the system dynamics model (as may be noted, properties and variables are selected at the model point of view while indicators are selected at the user point of view). At a simulation level, the common method often used to follow the temporal evolution of indicators during a simulation is by curves (Daniell & al., 2005, Orru & al., 2007; Andriamasinoro & Angel, 2007).
Since we are in a SISM context, we believe that following such dynamic evolution by means of the spatial environment should also be possible. In that case, the indicators must first be spatialised. Spatialisation can be defined as the mapping of a non-spatial indicator to the spatial environment by means of a shaped object, i.e. spatial objects having line, circle and polygon forms. As for spatial indicators, they are not concerned with a spatialisation process since they are implicitly spatialised. After a spatialisation is performed, the user may then, during an ongoing simulation, (a) visualise the evolution of the selected spatialised indicators inside the simulation platform itself (if such a displayer exists) and/or (b) export the current spatial environment state, at any time during the simulation, as (new shape) map data. These data are next imported into a GIS tool and visualised there as a superposition of files showing the spatial state of one system at different times.