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Parcel scale or aggregation

4 messages · derek, Dexter Locke

Original
derek
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Hello, I am working on preparing data for to run in a spatial
autoregression model (probably SEM). You are one of the most well
grounded people I am aware of in this type of methodology. I am hoping you can
help me with a simple question.

My question is this: we have the data at the scale of the parcel (or
household) and there are a couple of hounded thousand records across the
size of a large metropolitan area. When feeding the data into the
model, is there
a reason/requirement to aggregate our data variable to some boundary scale
such as a city block? Or is it ok to keep it at the parcel scale? We are
interested in analyzing characteristics at e.g. household level similar to
a hedonic model.

Thanks for any feedback.
Dexter Locke
# 
Hello.

This is more of a research design question than a spatial analysis question.

If you research question pertains to parcels and you have parcel data, then
why aggregate?

Neighborhood-level attributes can be important. They can be included by
attributing parcels with their neighborhood characteristics, with random
effects at the neighborhood scale, among other techniques.

The chosen method depends on the research questions.

-Dexter
On Tue, Apr 21, 2020 at 9:34 AM John Morgan <jmorgan3 at uwf.edu> wrote:

            

      
      
      
    

        

      
      
    

        

  
  


  


      

    

    

      
      

        


  

        

      derek
    

    

      
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Dexter.

Your point that it is more of a research design question is well taken, 
but I figured if anyone would know it would be a group of folks using R 
for spatial analysis. The research question does pertain to the parcels 
and the explanatory variables will be computed at that scale. I just 
didn't know if that matter per say for functions like sarlm() or or 
gmerrorsar(). It sounds like not so much as they will be treat the same 
as rows in a data frame regardless of what scale they are at.

Thanks,

Derek

        
On 4/21/2020 9:59 AM, Dexter Locke wrote:

            

      
      
      
    

        

      
      
    

  
    

      
      
    

  


  


      

    

    

      
      

        


  

        

      Dexter Locke
    

    

      
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Yes, those functions should work provided your data are shaped correctly
(they sound like they are).

I imagine the results will be sensitive the chosen spatial weights matrix.
That choice indirectly gets to your point about aggregation. Some types of
weights may include spatial neighbors that might assimilate neighborhoods
or blocks.

Good luck!

-Dexter
http://dexterlocke.com/

        
On Tue, Apr 21, 2020 at 11:23 AM derek <jmorgan3 at uwf.edu> wrote: