RE: st: spatgsa and converting coordinates from shp file

[Joost Bruijsten <joostbruijsten@hotmail.com>]

Thank you for your answer.

If I understand correctly, you wrote the spmap and spatgsa command Mr. Pisati? Do you consider taking the mean of the X en Y coordinates, which the shp2dta command create, as a good way to provide the different municipality their own two 'unique' coordinates?

Best regards,

Joost

> Date: Sat, 19 Jun 2010 10:45:03 +0200
> From: maurizio.pisati@unimib.it
> To: statalist@hsphsun2.harvard.edu
> Subject: Re: st: spatgsa and converting coordinates from shp file
> 
> The bandwidth for computing Moran's I should be set to any value that 
> makes sense to your research problem. In your case, coordinates appear 
> to be expressed in meters, so that option -band(0 10991) means that you 
> are defining the neighbors of every municipality j as the set of 
> municipalities whose centers are within a radius of 11 kilometers from 
> the center of j. If this definition doesn't suit your needs, than you 
> can extend the bandwidth the way you deem more appropriate to your 
> research problem. Likewise, choosing a binary or a non-binary spatial 
> weight matrix -- standardized or not -- depends on the way you want to 
> define spatial contiguity between municipalities (see related literature 
> for more info on spatial weight matrices).
> Best wishes,
> Maurizio Pisati
> 
> 
> 
> 
> 
> 
> Il 19/06/10 10.06, Joost Bruijsten ha scritto:
>>
>> Dear Statalisters,
>>
>> my problem is about Moran's I (command: spatgsa) en the Weights matrix (spatwmat).
>>
>> I use Stata/SE 11.0 for Windows (32-bit).
>> Born 21 October 2009
>>
>> First of all, I am analyzing the urban structure of the Veneto region in Italy using commuting flows.
>> I want to use Moran's I to see if employment is concentrated. Employment is defined in my study as the total number of working persons that commute into a region or municipality.
>>
>> Using shp files (from istat.it/ambiente/cartografia) I made the map of Veneto and the provinces and municipalities (using spmap).
>>
>> Then I took the coordinates that the spmap command uses (using shp2dta) of every municipality and used the mean of the x and y coordinates for every municipality in a new dataset with the total number of ingoing commuters into a municipality. Beneath you see an example I used.
>>
>>
>>
>> destinationprovince destinationmunicipality totalpersons x y
>>
>> 23 3 892 1630494 5007893
>>
>> 23 8 686 1633549 5013109
>>
>> 23 9 538 1641141 5030001
>>
>> 23 10 489 1648422 5032311
>>
>> 23 19 2463 1649542 5041687
>>
>> 23 20 1882 1652473 5044176
>>
>> 23 25 8405 1660123 5045447
>>
>> 23 29 471 1662310 5047371
>>
>> 23 44 15613 1667065 5051641
>>
>> 23 48 1870 1668117 5052971
>>
>> 23 72 1834 1668861 5053562
>>
>> 23 85 676 1670068 5056730
>>
>> 23 95 1607 1681036 5057435
>>
>> I performed the commands for spatwmat and spatgsa (see beneath), although I changed the band width from a very high number (35000+) to the largest minimum distance the spatwmat command gave:
>>
>> spatwmat, name(test7) xcoord(x) ycoord(y) band(0 10991)
>>
>> My first question is: What is the best way with these sort of coordinates to set the band width? Or is there a way to convert these coordinates to real latitude and longitude variables? The last would actually be my preferred solution.
>>
>> I then used this command to calculate Moran's I:
>>
>> spatgsa totalpersons, weights(test7) moran
>>
>> This gave me a result, however I do not know whether these are good given the problems. Below you see the output.
>>
>> My second question is: should I standardize the spatwmat command, or use binary? I could not find in the help files what these do, in 'real' explanation.
>>
>> Thanks for your consideration,
>>
>>
>> Joost Bruijsten
>>   		 	   		
>>
>>
>> . spatwmat, name(test7) xcoord(x) ycoord(y) band(0 10991)
>>
>>
>> The following matrix has been created:
>>
>> 1. Inverse distance weights matrix test7
>>     Dimension: 13x13
>>     Distance band: 0<  d<= 10991
>>     Friction parameter: 1
>>     Minimum distance: 950.2
>>     1st quartile distance: 11229.2
>>     Median distance: 21674.2
>>     3rd quartile distance: 36380.2
>>     Maximum distance: 70773.6
>>     Largest minimum distance: 10990.63
>>     Smallest maximum distance: 38792.54
>>
>>
>>
>> .
>> .
>> . spatgsa aantalpersonen, weights(test7) moran
>>
>>
>> Measures of global spatial autocorrelation
>>
>>
>> Weights matrix
>> --------------------------------------------------------------
>> Name: test7
>> Type: Distance-based (inverse distance)
>> Distance band: 0.0<  d<= 10991.0
>> Row-standardized: No
>> --------------------------------------------------------------
>>
>> Moran's I
>> --------------------------------------------------------------
>>            Variables |    I      E(I)   sd(I)     z    p-value*
>> --------------------+-----------------------------------------
>>       aantalpersonen | -0.189  -0.083   0.211  -0.499   0.309
>> --------------------------------------------------------------
>> *1-tail test
>>
>>   		 	   		
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