I think a good choice would be Bivariate Moran's I or Bivariate Local Moran's I (also called Bivariate LISA for Local Indicators of Spatial Association), both of which can be implemented in GeoDa. The tutorial is "old", but except for some menu renaming is still pretty usefl, and of course the statistical concepts are still applicable.
The basics are as follows. Bivariate Moran's I is an extension of Univariate Moran's I, which is an index on the scale of -1 to 1 of the spatial autocorrelation of a variable. A phenomenon that is clustered will have a positive Moran's I, while a variable that is dispersed (e.g. a checkerboard pattern) will have a negative Moran's I. Moran's I close to 0 indicates spatial randomness. Global Moran's I (usually just referred to as Moran's I) calculates the measure over the entire study area, while Local Moran's I calculates it for each location based on its local neighborhood. For Local Moran's I, GeoDa produces maps (in a red-blue color scheme which has become the standard) of High values surrounded by High values, High-Low, Low-High, and Low-Low.
Bivariate Moran's I does something similar, except it compares a variable (in your example, INCRAT) at a location to the weighted average of another variable in the neighboring areas (say, ADJPCI). A positive Moran's I would indicate that INCRAT is spatially correlated with ADJPCI. Local Moran's I would show you maps of clusters of high and low values.
To access these functions in GeoDa, go to Space→Bivariate Moran's I or Space→Bivariate Local Moran's I. In both cases, you have to specify a weights matrix. When the Select Weights dialog comes up, click the button to Create new weights file. Specify your unique identifier (e.g. from your maps, I would guess the Tract ID). Choose between Contiguity Weight and Distance Weight. The easiest choice is Contiguity Weight, where contiguous polygons are weighted 1 and all others are weighted 0. You have to choose between rook or queen contiguity, i.e. are polygons contiguous only if their edges touch, or even if they only have one vertex in common?
For Moran's I, GeoDa will produce a scatterplot of your X variable against the spatially lagged Y variable, as well as the computed value in the range of -1 to 1.
For Local Moran's I, you have the option to produce the scatterplot, the cluster map (as shown above for the univariate case), and a significance map showing the significance level associated with each cluster.
Information on how to do this is contained in Chapter 21 of the tutorial linked above. Information on how to create the spatial weights matrix is in Chapter 15.