Thoughts on “Spatial Data Quality: Concepts”

This chapter begins with the quote “All models are wrong but some are useful”, which I believe sums up the article fairly succinctly, as it addresses the constant imprecise, inaccurate, incomplete, and outdated nature of GIS data. This reminds me of when we discussed non-ideal data from last week’s Openshaw (1992) paper; however, this piece explains it in much more detail than Openshaw, relating it back to external and internal data quality differences and data representation challenges. 

Since the rise in popularity of the internet and user-generated content, there is a lot more concern towards accessing data quality and accuracy. I have been conducting a bit of research on VGI, as that is my research topic, and data accountability and accuracy are huge concerns in that field. Much like differing definitions of quality given here, there is no one correct way to access accuracy. It is all reliant on the type of data being extracted and researched, and the motives for collecting such data. For instance, if a project was collecting user-generated data concerning users’ perceptions of a place, then accuracy does not matter, whereas in OpenStreetMap, for example, there is a team of moderators carefully watching and reviewing users’ inputs, as accuracy is a top priority. Thus, I think the motives for the research, specifically whether the researcher is looking for more accurate data, more precise data, or both, is a very important component to address when examining spatial data quality. 

This topic also reminds me of when we discussed open government data and how there is often not consistent data throughout each department, i.e. the formatting of the data, the original scale of the data, etc. does not usually match across departments, thus challenging the quality of the end result. I worked on a GIS project last semester analyzing water quality levels and ran into quite a few hiccups when I realized there were many months and years missing from the data sets I was trying to analyze. In hindsight, I should have examined the spatial data quality of the data I was planning to use more before starting my research.

Overall, I think this chapter does a good job of explaining the complexity of spatial data quality and the errors inherent to geospatial research.

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