Given a point pattern on a linear network, compute a kernel estimate of intensity, by solving the heat equation.
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Point pattern on a linear network (object of class
Smoothing bandwidth (standard deviation of the kernel)
in the same units as the spatial coordinates of
Arguments passed to
String specifying whether to compute the intensity values
at a fine grid of pixel locations on the network
Logical value indicating whether to compute a leave-one-out
estimator. Applicable only when
Optional. Numeric vector of weights associated with the
Optional. Spacing of the sampling points along the network.
A single number giving a distance value in the same units as
Optional. Time step in the heat equation solver. A single number.
Maximum number of iterations.
Logical value specifying whether the discrete approximation is required to be accurate along every segment of the network, no matter how short the segment is. See the section on Discretisation.
Logical value specifying whether to print progress reports.
densityHeat is generic.
This is the method for the class
"lpp" of points on a linear
Kernel smoothing is applied to the points of
using a kernel based on path distances in the network.
at="pixels" (the default),
the result is a pixel image on the linear network (class
"linim") which can be plotted.
at="points" the result is a numeric vector giving the
density estimates at the data points of
The smoothing operation is equivalent to the
“equal-split continuous” rule described in
Section 9.2.3 of Okabe and Sugihara (2012).
However, the actual computation is performed rapidly, by solving the classical
time-dependent heat equation on the network,
as described in McSwiggan et al (2016).
Computational time is short, but increases quadratically with
a leave-one-out estimate is computed at each data point
(that is, the estimate at each data point
x[i] is based
on all of the points except
using the truncated series approximation
of McSwiggan et al (2019).
at="pixels" (the default),
a pixel image on the linear network (object of class
at="points", a numeric vector with one entry for each point
sigma=Inf, the resulting density estimate is
constant over all locations,
and is equal to the average density of points per unit length.
(If the network is not connected, then this rule
is applied separately to each connected component of the network).
determine the discretisation of the network, according to
a set of rules. The argument
which rule will be applied.
are connected by several constraints; specifying one of these
arguments will affect the default values of the other two arguments.
finespacing specifies whether
a very fine spacing of sample points is required, in order to attain
finespacing=TRUE (the default), then the
sample point spacing
dx must not exceed
one-third of the length of the shortest
segment of the network. This ensures that the discrete approximation
is accurate along every segment, no matter how short the segment
is. However, this may not be feasible
if it implies a very large number of sample points, or a large number
of iterations: in such cases, the code may terminate with an error
about illegal values of
the sample point spacing
dx will be about one-half the width
of a pixel in the default pixellation of the window of
This is usually a much coarser resolution than the one
finespacing=TRUE. If it is too coarse,
the pixel resolution can be refined using
as.mask. For example,
dimyx=512 would specify a 512 x 512 pixel grid.
The default pixel resolution can be changed for the remainder of
the R session by
and Greg McSwiggan.
McSwiggan, G., Baddeley, A. and Nair, G. (2016) Kernel density estimation on a linear network. Scandinavian Journal of Statistics 44, 324–345.
McSwiggan, G., Baddeley, A. and Nair, G. (2019) Estimation of relative risk for events on a linear network. Statistics and Computing 30, 469–484.
Okabe, A. and Sugihara, K. (2012) Spatial analysis along networks. Wiley.
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