Thoughts on “Spatial Scale Problems and Geostatistical Solutions: A Review”

This article talks about scale, which is a key information in spatial variation. Scale as a concept has a lot of definitions. Outside the field of geography, it can be used to describe the size or extent of certain event or process. However, scale often referred to as cartographic scale in geography. In physical geography, scale is often represented using ratio value (e.g. 1:1000); while in human geography, scale can often be represented using units such as division, city, and province. In either study, defining a suitable scale is really important. If the original dataset is not appropriately scaled, it would be very useful if the dataset and easily be rescaled. In my past project, I had working on two different census dataset with different unit. When using them separately, they all worked very well. However, it took me a lot of efforts to co-register this two datasets in order to use them simultaneously. This makes me wonder if we were trying to build a dataset, what would be the criteria of a suitable spatial scale. In other words, how do we characterize suitable in this case.

This article then brought up the concept of spatial variation. It was first introduced to me as a part of point pattern analysis. Kriging is also introduced as a method to estimate and then interpolate missing data values. However, it has a ‘smoothing’ problem. Is there any better way to solve this problem since I noticed that this article was published in 2000, which is very early.  And with the extensive development of computer science algorithm, is there any new interpolation technology that can minimize this problem?

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