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Tabela de conteúdos
Introduction
The human being has always changed its environment. Changes result from complex social and economic iterations among its actors, which can vary across time and space, and exhibit “nonlinear dynamics with thresholds, reciprocal feedback loops, time lags, resilience, heterogeneity, and surprises” (LIU et al., 2007). Modelling these complex phenomena helps understanding its causes, how the complexity of the system emerges endogenously, and (hopefully) predicting future consequences of exogenous decisions such as external demands or even public policies.
With the increase of technologies for acquiring and manipulating geospatial data, there is an increasing availability of spatial data at the individual level allows creating models with a high level of disaggregation. As the data is disaggregated, It can eliminate the modifiable areal unit problem by creating objects that operate in the lowest level of analysis. [Sengupta & Sieber]. Methodologies for acquisition of data, see [Robinson et al] for a survey in the area of LUC, which can be extended for other areas. spatial data and Heterogeneity.
agent-based modelling for supporting this heterogeneity. the other approaches cannot fully manipulate this kind of data. ABM has been used in different areas of knowledge.
modelling dynamic processes such as ABM cannot be implemented within a GIS environment. GIS lacks this flexibility. lots of toolkits originally implemented for supporting ABM have been extended for supporting work with geospatial data, but it basically loads spatial data.
this work goes deeply in the representation of the space and the agents, and the requisites for agent-based modelling with geospatial data.
Newest software frameworks have made ABM easy enough to be attractive to areas such as economics, sociology, anthropology, physics and biology. The use of simulation frameworks relieves the modeller of programming the parts of the simulation that are not content-specific, such as simulation control and data structures (TOBIAS; HOFMANN, 2004). It also increases the reliability and efficiency of the model, as the most complex parts have been created and optimised by professional developers.
The Four Relationships
B&T shows that there are four relationships.
The next sections show how each relationship can enhance the modelling.
Neighbourhoods and localizations.