GeoDa, R and ArcGIS do spatial analysis

This week's assignment clearly illustrates that the tool is only as good as the user. While the spatial analysis capabilities of GeoDa, R and ArcGIS might be vast and varied, my lack of knowledge and understanding of the possibilities, limitations and, in general, the significance of geospatial statistical methods is the biggest hurdle.

With that understanding, I felt that would most identify (and find most useful) with the tool I've used the most: ArcGIS. However, without a solid understanding of how to map out spatial statistics I couldn't produce a mapped image of the Moran's I that I choose to calculate with each program. (And to my credit, I don't think this information can be mapped.) R and GeoDa were better tools for visualizing the statistical data particularly R. If the goal is to map out geospatial data, then ArcGIS would be preferable as color schemes, symbology and attributes can be easily manipulated. However in dealing with statistical results, R does a better job. GeoDa, I feel, tries to mediate between the two, but doesn't quite live up to the respective powers of the fancier programs. 

For this assignment I used each tool to create a spatial matrix of the k nearest neighbor variety and set k=1 and k=2. Then I calculated Moran's I and visualized the results the best I could in each respective program.

GeoDA

R

##print out from R moran.test

> moran.test(US$obama,nb2listw(US.knn1, style="W"))

        Moran's I test under randomisation

data:  US$obama 
weights: nb2listw(US.knn1, style = "W") 

Moran I statistic standard deviate = 3.2767, p-value =
0.0005252
alternative hypothesis: greater
sample estimates:
Moran I statistic       Expectation          Variance
       0.53038969       -0.02083333        0.02830000

> moran.test(US$obama,nb2listw(US.knn2, style="W"))

        Moran's I test under randomisation

data:  US$obama 
weights: nb2listw(US.knn2, style = "W") 

Moran I statistic standard deviate = 4.3288, p-value =
7.495e-06
alternative hypothesis: greater
sample estimates:
Moran I statistic       Expectation          Variance
       0.51891274       -0.02083333        0.01554667


ArcGIS

 ##printout from ArcGIS, no visualizations were created

Executing: SpatialAutocorrelation lower48 obama true "Get Spatial Weights From File" "Euclidean Distance" None # F:\R_work\week_8\knn1.swm 0 0 0
Start Time: Tue May 25 19:45:12 2010
Running script SpatialAutocorrelation...
WARNING 000916: The input feature class does not appear to contain projected data.

 Global Moran's I Summary
Moran's Index:   0.530499
Expected Index:  -0.020833
Variance:        0.027516
Z Score:         3.323665
p-value:         0.000888


Executing: SpatialAutocorrelation lower48 obama true "Get Spatial Weights From File" "Euclidean Distance" None # F:\R_work\week_8\knn2.swm 0 0 0
Start Time: Tue May 25 19:51:10 2010
Running script SpatialAutocorrelation...
WARNING 000916: The input feature class does not appear to contain projected data.

 Global Moran's I Summary
Moran's Index:   0.524584
Expected Index:  -0.020833
Variance:        0.015769
Z Score:         4.343416
p-value:         0.000014