I am working on a research- trying to model vegetation types based on factors affecting their distribution. In that case I have observed a strong relation between vegetation and soil types, from just overlaying the vegetation type polygon shapefile with the soil type polygon shapefile, now the problem is i want to quantify this relationship between the vegetation and soil types i.e.the vegetation-soil type correlation analysis so as to scientifically present this relationship. Please i want to know how to do that using ArcGIS 10.1, I have tried to search for solutions to this problem but with no success. I do not have the primary data which was used to create those polygon shapefiles so I am hoping there is way of just working with just the polygons.
[GIS] Correlation analysis of two polygon shapefiles with nominal data
arcgis-10.1arcgis-desktopcorrelationpolygonshapefile
Best Answer
A Pearson, Kendall or Spearman correlation (R) between two nominal variables is nonsensical. You want to explore techniques such as contingency analysis (Phi coefficient, Cramér's V, Persons C, Kappa, ...), factor analysis, canonical correspondence analysis or perhaps something in the family of Jaccard's similarity statistics. If you put one variable (y) in the context of a Binominal [0,1] process you could specify a logistic OLS regression and look at the odds ratio.
Your best bet, and simplest approach, would be a chi square testing the hypothesis of independence and homogeneity in a contingency table. In this case, a contingency table represents the frequencies of agreement between the two nominal variables.
Sorry, but without stepping out to Python, there is no tractable way to do this type of analysis in ArcGIS, which is not a statistical software. Although, you can set up the analysis in ArcGIS by performing an intersect of the polygon datasets to associate the attributes. I should note that there are additional concerns that you should explore regarding spatial uncertainty, aggregation errors, and change of support (MAUP).