Scales, levels and processes: Studying spatial patterns of British census variables
This paper is based on the assumption that there may be scale effects at all levels of areal data and that they vary both within areal units and between areal units. Spatial distributions are based on processes taking place in geographical space. A mapped pattern may reflect several distinct processes, each of which may affect a different area and operate at a different scale. The challenge for the spatial analyst is to identify these processes and evaluate their importance from the spatial pattern observed. Here the well known modifiable areal unit problem is not really a problem but a resource. Data at different scales can help us identify processes operating at different scales. We build on models and methods described by [Tranmer, M., & Steel, D. G. (2001). Using local census data to investigate scale effects. In N. J. Tate, & P. M. Atkinson (Eds.), Modelling scale in geographical information science (pp. 105122). Chichester: John Wiley and Sons], which facilitate the identification of processes occurring within areal units. The method is extended using concepts from multi-level modelling and spatial autocorrelation, through the application of local statistics applied to what may be termed area effect estimates. It is illustrated with respect to two very different census variables and three different study areas.
scale effects modifiable areal unit problem census data multi-level modelling spatial autocorrelation