On Fri, 20 Aug 2010, Michael Haenlein 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?
Combine the imported SpatialPolygonsDataFrames, possibly after filtering
out the ones with no hits, and possibly after checking for ZIP uniqueness.
If not unique (if a ZIP crosses a state boundary or if the input maps
assign a single ZIP code to multiple Polygons objects) use
unionSpatialPolygons in maptools (usual warning about gpclib) or
gUnionCascade() in rgeos. If you can install rgeos, you can also speed up
the poly2nb() step for the whole map by providing a GEOS-generated list of
candidate neighbours.
It is feasible, but perhaps tedious - there are a lot of polygons!
Good luck!
Roger
-----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
--
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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