[R-sig-Geo] Moran's I based on ZIP Code data

[Jochen Albrecht]

Nikhil has a point here. The combined shapefile is > 1 GB.
As I am into working with national census datasets (and would eventually 
like to do the same for tracts and blocks), I created a neighbor list 
and then a weights list. The latter required using the zero.policy=TRUE 
switch as we are working with many islands that have no topological 
neighbors. I uploaded the two as R objects to 
http:giscience.hunter.cuny.edu/zcta/zcta.nb.R and 
http://giscience.hunter.cuny.edu/zcta/zcta.w.R.
Cheers,
    Jochen


Nikhil Kaza wrote:
> The national files for the zipcodes seems greyed out. I would caution 
> against creating nb lists for each state separate and then creating a 
> US wide neighbour list because, there will some zip codes in Alabama 
> who are neighbours to  zipcodes in GA, MS, TN. I would merge them 
> first into one big file and then construct the poly list.  you may run 
> into memory issues for this operation, depending on your set up.
>
>
> Nikhil Kaza
> Asst. Professor,
> City and Regional Planning
> University of North Carolina
>
> nikhil.list at gmail.com
>
> On Aug 20, 2010, at 1:06 PM, Sharon O'Donnell wrote:
>
>> Check out
>>
>> http://www2.census.gov/cgi-bin/shapefiles2009/national-files - left hand
>> side has national - level data.
>>
>> All 5 digit zipcode files are based on 2002 data but zipcode boundaries
>> change less frequently than tracts and blockgroups, there may be some 
>> issues
>> in correctly mapping out areas in high growth regions of the U.S. 
>> with new
>> zipcodes.
>>
>> Sharon
>>
>> On Fri, Aug 20, 2010 at 12:47 PM, Michael Haenlein
>> <haenlein at escpeurope.eu>wrote:
>>
>>> Thanks very much for your reply, Roger!
>>>
>>> I have downloaded the shape files from the US Census ZCTA webpage. 
>>> In case
>>> anyone else is interested in obtaining them the URL is:
>>> http://www.census.gov/geo/www/cob/z52000.html#shp
>>>
>>> I also managed to import those files into R and to convert them into
>>> a neighbour list:
>>>
>>> Alabama <-readShapePoly("c:/111/zt01_d00")
>>> Alaska <-readShapePoly("c:/111/zt02_d00")
>>> Arizona <-readShapePoly("c:/111/zt04_d00")
>>> ...
>>>
>>> Alabama.nb <- poly2nb(Alabama)
>>> Alaska.nb <- poly2nb(Alaska)
>>> Arizona.nb  <- poly2nb(Arizona)
>>> ...
>>>
>>> The problem is that instead of having one neighbour list I now have 
>>> 52 ones
>>> (one for each state).
>>> Is there a way to combine all of them into one large neighbour list 
>>> which I
>>> can then use as an input for my analysis?
>>>
>>>
>>>
>>>
>>> -----Original Message-----
>>> From: Roger Bivand [mailto:Roger.Bivand at nhh.no]
>>> Sent: Thursday, August 19, 2010 23:54
>>> To: Michael Haenlein
>>> Cc: r-sig-geo at stat.math.ethz.ch
>>> Subject: Re: [R-sig-Geo] Moran's I based on ZIP Code data
>>>
>>> On Thu, 19 Aug 2010, Michael Haenlein wrote:
>>>
>>> The first thing is to get the locations of the zip codes (about 
>>> 30,000?) -
>>> they are published as shapefiles by state (US Census ZCTA), so a 
>>> polygon
>>> representation is possible, but you could also look for a point
>>> representation. Next make a neighbour list (nb) object to the zip code
>>> entities for which you have observations. Then you could use 
>>> nb2blocknb()
>>> in
>>> spdep to "block up" observations where more than one belongs to the 
>>> same
>>> zip
>>> code, which effectively makes all the observations in a zip code
>>> neighbours,
>>> and adds all the observations in neighbouring zip codes too.
>>> It was written for housing data with only a postcode but no geocoded
>>> address.
>>>
>>> Hope this helps,
>>>
>>> Roger
>>>
>>>
>>> -- 
>>> Roger Bivand
>>> Economic Geography Section, Department of Economics, Norwegian 
>>> School of
>>> Economics and Business Administration, Helleveien 30, N-5045 Bergen,
>>> Norway.
>>> voice: +47 55 95 93 55; fax +47 55 95 95 43
>>> e-mail: Roger.Bivand at nhh.no
>>>
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>>>
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>>
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