[R-sig-Geo] best practice for reading large shapefiles?

Alex Mandel tech_dev at wildintellect.com
Wed Apr 27 03:16:56 CEST 2016

So the trick I use is to load vector data into PostGIS or Spatialite.
Then do basic spatial filtering with queries in those DB (SQL). Once
I've subset and manipulated what I want, either create a new Table or
View with the results. Then read those results in R.

The bottleneck you have is likely the reading of everything into memory
in R, which usually takes more memory than the original file size. So
changing sources won't help, only subsetting prior to loading will help.


On 04/26/2016 03:24 PM, Chris Reudenbach wrote:
> Vinh
> Even if it might be in this list OT, IMHO R is not the best tool for
> dealing with this amount of vector data. Actually I agree completely
> with Roger's remarks and corresponding to the "competent platform" you
> also may think about using software for big data...
> As Roger already has clarified: The recommendation what might be best
> depends highly  on your questions and issues or on the type of analysis
> you need to run and cannot be answered straightforward.
> I think Edzer can clarify up to which size sp object are still "usable",
> following my experience  i would guess something like 500K polygons 1M
> lines and up to 5M points but it is highly dependent on the number of
> attributes. So you are far beyond this.
> If you want to deal with this amount of spatial vector data using R, it
> is highly reasonable to have a look at one of the mature GIS packages
> like GRASS or QGIS. You can use them via their APIs.
> Nevertheless you easily can put it in postgres/postgis and perform all
> operations/analysis using the spatial capabilities and build in
> functions of postgis if you are an experienced PostGis user.
> cheers
> Chris
> Am 26.04.2016 um 22:33 schrieb Vinh Nguyen:
>> On Tue, Apr 26, 2016 at 1:12 PM, Roger Bivand <Roger.Bivand at nhh.no>
>> wrote:
>>> On Tue, 26 Apr 2016, Vinh Nguyen wrote:
>>>> Would loading the shapefile into postgresql first and then use readOGR
>>>> to read from postgres be a recommended approach?  That is, would the
>>>> bottleneck still occur?  Thank you.
>>> Most likely, as both use the respective OGR drivers. With data this
>>> size,
>>> you'll need a competent platform (probably Linux, say 128GB RAM) as
>>> everything is in memory. I find it hard to grasp what the point of doing
>>> this might be - visualization won't work as none of the considerable
>>> detail
>>> certainly in these files will be visible. Can you put the lot into an
>>> SQLite
>>> file and access the attributes as SQL queries? I don't see the
>>> analysis or
>>> statistics here.
>> - I can't tell from your response whether you are recommending PostGIS
>> is a recommended approach or not.  Could you clarify?
>> - I am working on a Windows server with 64gb ram, so not too weak,
>> especially for some files that are a few gb in size.  Again, not sure
>> if the job just halted or it's still running, but just rather slow.
>> I've killed it for now as the memory usage still has not grown after a
>> few hours.
>> - Yes, the shapes are quite granular and many in quantity.  The use
>> case was not to visualize them all at once.  Wanted a master file so
>> that when I get a data set of interest, I could intersect the two and
>> then subset the areas of interest (eg, within a state or county).
>> Then visualize/analyze from there.  The master shapefile was meant to
>> make it easy (reading in one file) as opposed to deciding which
>> shapefile to read in depending on the project.
>> - I just looked back at the 30 PLSS zip files, and they provide shapes
>> for 3 levels of granularity.  I went with the smallest.  I just
>> realized that the mid-size one would be sufficient for now, which
>> results in dbf=138mb and shp=501mb.  Attempting to read this in now (~
>> 30 minutes), which I assume will read in fine after some time.  Will
>> respond to this thread if this is not the case.
>> Thanks for responding Roger.
>> -- Vinh
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