I have two data sets:
a) a set of polygons with areal data variables.
b) point measures of various co-variates of interest (note I do not necessarily have a point for each polygon)
How do I correctly analyse this data set for correlations between an areal variable (dependent variable) and point measures (independent variable)?
Unsure what techniques I need to use. Working in R.
Edit on 7 July 2014:
What I'm really wondering is - if you have areal data i.e. some variable that depends on an area and not points - so something related to population density for example - and you want to analyze that versus a point measurement - rainfall at specific measurement points for example. What is the correct/best way to do this ? I've thought about it since and I see two approaches: 1) Krige the point data and get an average value for each polygon (I presume by picking multiple sample points within the polygon and getting an average) - then build polygon based models using the kriged estimates as covariates. 2) Treat the areal data as point estimates of a field taking the centroid of each polygon as the location for each point estimate, then build a geostatistical model using all data as point estimates. Both these approaches strike me as "incorrect" - what I'm asking really is which is least "incorrect" ?