Functions to convert between spatstats planar point pattern (ppp) format and sps SpatialPoints and SpatialPointsDataFrame as well as one-way conversion from SpatialGridDataFrame to ppp. S4-style as() coercion can be used as well.

as.ppp.SpatialPoints(X)
as.ppp.SpatialPointsDataFrame(X)
as.SpatialPoints.ppp(from)
as.SpatialPointsDataFrame.ppp(from)
as.SpatialGridDataFrame.ppp(from)

Arguments

from, X

object to coerce from

Methods

coerce

signature(from = "SpatialPoints", to = "ppp")

coerce

signature(from = "SpatialPointsDataFrame", to = "ppp")

coerce

signature(from = "ppp", to = "SpatialGridDataFrame")

coerce

signature(from = "ppp", to = "SpatialPointsDataFrame")

coerce

signature(from = "ppp", to = "SpatialPoints")

Details

The main conversion is between sps SpatialPoints/SpatialPointsDataFrame and spatstats ppp. Conversion between SpatialGridDataFrame and ppp should rarely be used; using as.owin.SpatialGridDataFrame is more transparent.

Note

The ppp format requires an observation window which is the sampling region. The sp formats contain no such information and by default the bounding box of the points is simply used. This is almost never the correct thing to do! Rather, information about the sampling region should be converted into spatstats owin format and assigned as the observation window. Usually conversion from ppp to sp format simply discards the owin. However, as.SpatialGridDataFrame.ppp actually first discards the points(!), second checks that the corresponding owin is in a grid format (matrix of TRUE/FALSE for inside/outside sampling region), and finally converts the TRUE/FALSE grid to a SpatialGridDataFrame.

Warning

In spatstat all spatial objects are assumed to be planar. This means that spatstat is not designed to work directly with geographic (longitude and latitude) coordinates. If a sp object is declared to have geographic (unprojected) coordinates maptools refuses to convert directly to spatstat format. Rather, these should be projected first using e.g. spTransform. If you know what you are doing, and really want to force coercion, you can overwrite the proj4string of the sp object with NA, proj4string(x) <- CRS(NA), which will fool the system to think that the data is in local planar coordinates. This is probably not a good idea!

Examples

run <- FALSE if (require(spatstat, quietly=TRUE)) run <- TRUE if (run) { ## Convert SpatialPointsDataFrame into a marked ppp data(meuse) coordinates(meuse) = ~x+y meuse_ppp <- as(meuse, "ppp") meuse_ppp # Window is the bounding rectangle }
#> Warning: some mark values are NA in the point pattern x
#> Marked planar point pattern: 155 points #> Mark variables: #> cadmium copper lead zinc elev dist om ffreq soil lime landuse dist.m #> window: rectangle = [178605, 181390] x [329714, 333611] units
if (run) { plot(meuse_ppp, which.marks = "zinc") }
if (run) { ## Convert SpatialPoints into an unmarked ppp meuse2 <- as(meuse, "SpatialPoints") as(meuse2, "ppp") }
#> Planar point pattern: 155 points #> window: rectangle = [178605, 181390] x [329714, 333611] units
if (run) { ## Get sampling region in grid format and assign it as observation window data(meuse.grid) gridded(meuse.grid) <- ~x+y mg_owin <- as(meuse.grid, "owin") Window(meuse_ppp) <- mg_owin meuse_ppp # Window is now a binary image mask (TRUE/FALSE grid) }
#> Warning: as.matrix.SpatialGridDataFrame uses first column; #> use subset or [] for other columns
#> Warning: some mark values are NA in the point pattern x
#> Marked planar point pattern: 155 points #> Mark variables: #> cadmium copper lead zinc elev dist om ffreq soil lime landuse dist.m #> window: binary image mask #> 104 x 78 pixel array (ny, nx) #> enclosing rectangle: [178440, 181560] x [329600, 333760] units
if (run) { plot(meuse_ppp, which.marks = "zinc") }
if (run) { ## Convert marked ppp back to SpatialPointsDataFrame rev_ppp_SPDF <- as.SpatialPointsDataFrame.ppp(meuse_ppp) summary(rev_ppp_SPDF) }
#> Object of class SpatialPointsDataFrame #> Coordinates: #> min max #> mx 178605 181390 #> my 329714 333611 #> Is projected: NA #> proj4string : [NA] #> Number of points: 155 #> Data attributes: #> marks.cadmium marks.copper marks.lead marks.zinc #> Min. : 0.200 Min. : 14.00 Min. : 37.0 Min. : 113.0 #> 1st Qu.: 0.800 1st Qu.: 23.00 1st Qu.: 72.5 1st Qu.: 198.0 #> Median : 2.100 Median : 31.00 Median :123.0 Median : 326.0 #> Mean : 3.246 Mean : 40.32 Mean :153.4 Mean : 469.7 #> 3rd Qu.: 3.850 3rd Qu.: 49.50 3rd Qu.:207.0 3rd Qu.: 674.5 #> Max. :18.100 Max. :128.00 Max. :654.0 Max. :1839.0 #> #> marks.elev marks.dist marks.om marks.ffreq marks.soil #> Min. : 5.180 Min. :0.00000 Min. : 1.000 1:84 1:97 #> 1st Qu.: 7.546 1st Qu.:0.07569 1st Qu.: 5.300 2:48 2:46 #> Median : 8.180 Median :0.21184 Median : 6.900 3:23 3:12 #> Mean : 8.165 Mean :0.24002 Mean : 7.478 #> 3rd Qu.: 8.955 3rd Qu.:0.36407 3rd Qu.: 9.000 #> Max. :10.520 Max. :0.88039 Max. :17.000 #> NA's :2 #> marks.lime marks.landuse marks.dist.m #> 0:111 W :50 Min. : 10.0 #> 1: 44 Ah :39 1st Qu.: 80.0 #> Am :22 Median : 270.0 #> Fw :10 Mean : 290.3 #> Ab : 8 3rd Qu.: 450.0 #> (Other):25 Max. :1000.0 #> NA's : 1
if (run) { ## Convert marked ppp back to SpatialPoints (discarding marks) rev_ppp_SP <- as.SpatialPoints.ppp(meuse_ppp) summary(rev_ppp_SP) }
#> Object of class SpatialPoints #> Coordinates: #> min max #> mx 178605 181390 #> my 329714 333611 #> Is projected: NA #> proj4string : [NA] #> Number of points: 155
if (run) { ## Convert marked ppp back to SpatialGridDataFrame (extracting the window grid) rev_ppp_SGDF <- as.SpatialGridDataFrame.ppp(meuse_ppp) summary(rev_ppp_SGDF) }
#> Object of class SpatialGridDataFrame #> Coordinates: #> min max #> [1,] 178440 181560 #> [2,] 329600 333760 #> Is projected: NA #> proj4string : [NA] #> Grid attributes: #> cellcentre.offset cellsize cells.dim #> 1 178460 40 78 #> 2 329620 40 104 #> Data attributes: #> mask #> Mode:logical #> TRUE:3103 #> NA's:5009