There is no doubt that scale plays a large role in the way in which data is interpreted. The article by Atkinson and Tate provide a good overview of scale of measurement, scales of spatial variation, and the issues inherent to spatial data. However, if we draw from Kathryn’s seminar about spatial statistics, and realize that large scale spatial processes impact smaller scale processes and their patterns, I’m still unclear as to how rescaling of data top down, bottom up or applying geostatistical techniques can quantify the effects of large scale processes on smaller spatial processes.
An interesting suggestion that the authors cited from Milne (1991) was that to understand heterogeneity, conduct analysis across a wide range of measurement scales and extract the parameters that remained consistent to changes in scale. Though an expensive and time consuming task, if this could be done then it seems like great way to extract these features and focus on parameters that are scale sensitive to determine the appropriate scale necessary for analysis. Also, can those parameters that are robust to change tell the researching something about the study at hand? With the intensification of geospatial data and larger datasets, developing the necessary tools to better integrate multi-scale datasets for a more comprehensive evaluation is a mountainous task. A tough GIScience topic with no easy answer, but it is crucial that we recognize that the scale of measurement we choose and the changes to data variability once we rescale data can greatly affect the final results of the analysis.
-tranv