In this article, the authors highlighted the importance of spatial scales in geography and the often need of rescaling data for multi-scale analysis. They raised the problem that due to the scale-dependent spatial variations, this rescaling process is very difficult. To solve this problem, they proposed some geostatiatical approaches for modelling the spatial dependence (variogram) and to predict the effects of rescaling (generalization). They suggested that this should be the first step for addressing scale-dependence problem.
The discussion of scale-independence processes reminds me of Anderson’s (2018) article “Biodiversity Monitoring, Earth Observations and the Ecology of Scale”, in which author discussed the need of muti-scale modelling and multi-scale mapping for biodiversity monitoring. The author explained that since the biodiversity pattern is driven by multiple ecological processes that act across different scale, there is no single best scale of measurement. However, translating patterns across different scales is challenging because of the scale mismatches of the data. I think this is a good example of how scale can play an important role in GIScience and it is also where the geostatistical approaches come into play.
One doubt that remains to me is related to one of the main critiques of geostatistcal approach(or modelling approach in general), which is the need of prior understanding of the spatial processes. Nowadays as more and more data-driven approaches is available (e.g. machine learning) and challenging the model-driven approach, does geostatistical approach still have a place in data analysis? I would like to learn more about the comparison of the two types of approaches.