I have two gridcell-based point layers that I am interested in investigating their spatial correlation in QGIS, or alternatively in R as well. The variables I am interested in are:
- Map 1: Point layer of percent change in price
- Map 2: Point layer of demand estimate
A few word on the context of the analysis. The maps are generated via a simple supply-demand pricing framework, which is spatially explicit at the gridcell level. For each gridcell, we have a supply curve that characterizes the availability of a resource k based on data on availability and procurement cost. Using scenarios from a GIS-based model, I can generate different demand schedules, of which Map 2 is an example. First, we use a business-as-usual scenario to calibrate a BAU price vector. Then, changing certain constraint parameters in the GIS-based model, we run alternative scenarios which generate a different demand profile, and for which we generate their specific price vectors using the supply curve. Thus, the price impacts represent the percent change deviation from the BAU and the alternative scenario of which Map 1 is an example.
Hence, what I am interested in is to somehow evaluate the degree of correlation between the demand location and the price impact location. My questions are: What is the best way to go about this? Is it possible to do correlation analysis in QGIS? Or is best to run spatial regressions to test the fit between the price impact and demand layers?
I have looked at certain alternatives in QGIS. Specifically, I have managed to perform a Nearest Neighbour Analysis (NNA) to investigate the linkages between the location of the price impacts in Map 1 and the location of the demand in Map 2. However, I wonder if that is the 'best' route to go about.
Any advice and/or pointers would be very welcome.