Aug 5, 2017 � This paper proposes several new computational methods for adaptive kernel estimation from spatial point pattern data.

Abstract Kernel smoothing of spatial point data can often be improved using an adaptive, spatially-varying bandwidth instead of a fixed bandwidth.

A key idea is that a variable-bandwidth kernel estimator for d-dimensional spatial data can be represented as a slice of a fixed-bandwidth kernel estimator�...

Kernel smoothing of spatial point data can often be improved using an adaptive, spatially varying bandwidth instead of a fixed bandwidth.

Kernel smoothing of spatial point data can often be improved using an adaptive, spatially varying bandwidth instead of a fixed bandwidth.

Jul 1, 2018 � Kernel smoothing of spatial point data can often be improved using an adaptive, spatially varying bandwidth instead of a fixed bandwidth.

The package uses an adaptive approach where the kernel bandwidth varies with each data point to estimate the expected number of points in an observation window�...

Aug 25, 2022 � This work presents an intensity estimation mechanism in which the spatial and temporal bandwidths change at each data point in a spatio-temporal point pattern.

Jul 19, 2017 � Brief details of topics related to the computation of adaptive kernel estimates and additional visualisation techniques are given in Section 8,�...

Implementation of the spatially adaptive kernel estimator relies on choice of a 'global bandwidth'. We derive the closed-form asymptotic bias for this�...