Spatial Data Mining (Shekhar et al)

I found this paper particularly tough to get into, as Spatial Data Mining veers more towards a tool used in G.I.S. than any of the topics we have covered thus far in my opinion. Although the tweaking of methods like SAR and MRF models to meet the issues regular data mining ran into (i.e. ignoring spatial auto-correlation, and inferring spatial heterogeneity) is a sign of tool building, I still find this topic in GIScience to be very technical and definitely in the tool realm of G.I.S. Furthermore, many of the clustering techniques mentioned (i.e. K-means) have been around for years now, and have been accepted as the standard in most regular G.I.S. projects, making me ask the question “what makes spatial data mining so special?”. Is it simply the size of the data being mined, and the unsupervised aspect of it? As this paper cites papers from 1999 & 2000 on spatial data mining’s ability to work with large amounts of data back then, I wonder how well spatial data mining works with big data, and how the validation process and statistical analysis of this would work today.

Although this paper focuses on the uses of spatial data mining and the raster dataset, I wondered that if this technique were used to go over vector data possibly including personal information (i.e. age or phone number) and tied this to space to look for ‘hidden patterns’, this would definitely be a violation of privacy.

All in all, although this field seems quite complex, it also seems very simple in that it embodies all of the basic algorithms used in traditional GIS projects, though on a larger scale.

-MercatorGator

 

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