Spatial data quality: Concepts

This chapter in the book “Fundamentals of Spatial Data Quality” gives a shot on basic concepts in spatial data quality by pointing out that the divergences between the reality and the representation are what the spatial data quality issues often deal with. And there are several aspects of where the errors would happen during the data production process such as the data manipulation process and the human involved data creation process. Moreover, spatial data quality is summarized to be assessed from internal and external aspects. This chapter explains well what the data quality is and what errors could be and is very easy to understand.

It is interesting that the introduction starts with a quote, “All models are wrong, but some are useful”. However, does it mean all spatial data or data created could be interpolated as the product of model or filter? Authors argue that the representation of reality may not be fully detailed and accurate but partially useful. But how to determine whether the data with those uncertainty or errors should be accepted is a much more urgent problem. Also, as the topic is “spatial data uncertainty” and spatial data quality issues discussed in the chapter, does the uncertainty exactly mean different sources of error assessed in spatial data quality?

The chapter defines the internal quality as level of similarity between data produced and perfect data while external quality means level of concordance between data product and user needs. My thought is if user participate in the data producing process (which is about internal quality), will the external quality be efficiently and effectively improved? Can we just replace “as requested by the manager” with “what user wanted” in Figure 2.4 and there should be no external quality worries?

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