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.biv...@nhh.no]
Sent: Thursday, August 19, 2010 23:54
To: Michael Haenlein
Cc: r-sig-geo@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.biv...@nhh.no
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