# pixellate.ppp

##### Convert Point Pattern to Pixel Image

Converts a point pattern to a pixel image. The value in each pixel is the number of points falling in that pixel, and is typically either 0 or 1.

##### Usage

```
# S3 method for ppp
pixellate(x, W=NULL, …, weights = NULL,
padzero=FALSE, fractional=FALSE, preserve=FALSE)
```# S3 method for ppp
as.im(X, …)

##### Arguments

- x,X
Point pattern (object of class

`"ppp"`

).- …
Arguments passed to

`as.mask`

to determine the pixel resolution- W
Optional window mask (object of class

`"owin"`

) determining the pixel raster.- weights
Optional vector of weights associated with the points.

- padzero
Logical value indicating whether to set pixel values to zero outside the window.

- fractional,preserve
Logical values determining the type of discretisation. See Details.

##### Details

The functions `pixellate.ppp`

and `as.im.ppp`

convert a spatial point pattern `x`

into a pixel
image, by counting the number of points (or the total weight of
points) falling in each pixel.

Calling `as.im.ppp`

is equivalent to
calling `pixellate.ppp`

with its default arguments.
Note that `pixellate.ppp`

is more general than `as.im.ppp`

(it has additional arguments for greater flexibility).

The functions `as.im.ppp`

and `pixellate.ppp`

are methods for the generic functions `as.im`

and `pixellate`

respectively,
for the class of point patterns.

The pixel raster (in which points are counted) is determined
by the argument `W`

if it is present (for `pixellate.ppp`

only).
In this case `W`

should be a binary mask (a window object of
class `"owin"`

with type `"mask"`

).
Otherwise the pixel raster is determined by
extracting the window containing `x`

and converting it to a
binary pixel mask using `as.mask`

. The arguments
`…`

are passed to `as.mask`

to
control the pixel resolution.

If `weights`

is `NULL`

, then for each pixel
in the mask, the algorithm counts how many points in `x`

fall
in the pixel. This count is usually either 0 (for a pixel with no data
points in it) or 1 (for a pixel containing one data point) but may be
greater than 1. The result is an image with these counts as its pixel values.

If `weights`

is given, it should be a numeric vector of the same
length as the number of points in `x`

. For each pixel, the
algorithm finds the total weight associated with points in `x`

that fall
in the given pixel. The result is an image with these total weights
as its pixel values.

By default (if `zeropad=FALSE`

) the resulting pixel image has the same
spatial domain as the window of the point pattern `x`

. If
`zeropad=TRUE`

then the resulting pixel image has a rectangular
domain; pixels outside the original window are assigned the value zero.

The discretisation procedure is controlled by the arguments
`fractional`

and `preserve`

.

The argument

`fractional`

specifies how data points are mapped to pixels. If`fractional=FALSE`

(the default), each data point is allocated to the nearest pixel centre. If`fractional=TRUE`

, each data point is allocated with fractional weight to four pixel centres (the corners of a rectangle containing the data point).The argument

`preserve`

specifies what to do with pixels lying near the boundary of the window, if the window is not a rectangle. If`preserve=FALSE`

(the default), any contributions that are attributed to pixel centres lying outside the window are reset to zero. If`preserve=TRUE`

, any such contributions are shifted to the nearest pixel lying inside the window, so that the total mass is preserved.

##### Value

A pixel image (object of class `"im"`

).

##### See Also

##### Examples

```
# NOT RUN {
data(humberside)
plot(pixellate(humberside))
plot(pixellate(humberside, fractional=TRUE))
# }
```

*Documentation reproduced from package spatstat, version 1.49-0, License: GPL (>= 2)*