Analysing data across geographic scales in Honduras: detecting levels of organisation within systems
2001
Nelson, A.
There is a growing disparity between the disciplines that aim to understand and explain the phenomena and relationships that characterise the environment. Social physics leads towards finding universal relationships in data on the basis of well-understood and statistically robust methodologies. Recent trends in quantitative geography have focused on empirical, visual, exploratory and local methods to reveal patterns that can subsequently form part of a model specification to design experiments and test hypotheses. The information technology data explosion during the last two decades has increased differences between the social physics and the quantitative geographical paradigms. The view expressed here is that problems in spatial analysis and modelling have been deliberately ignored to date or treated as a special case of aspatial modelling. To continue such a trend would imply that location is not relevant for spatial analysis. This paper reviews misconceptions surrounding the use of spatial data and describes a set of spatial analysis methodologies to permit scale-sensitive and location specific analyses of socio-economic and biophysical data from a range of sources using examples that demonstrate the need for more geographical approaches to inherent geographical problems. The examples illustrate that scale and aggregation artefacts can be observed, accounted for and even used to advantage, new relevant areal units can be designed within GIS environments, and that system boundaries of complex agro-ecosystems can be automatically derived from the combination of a spatial regression model and a neural classifier.
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