# Calc Kernel Map

## Description

This functor calculates kernel density of an event map representing a point pattern.

## Inputs

Name | Type | Description |
---|---|---|

Events | Map Type | Map showing the events location. Events are represented by non null cells, indicating the number of occurrences on the cell. Cell values must be positive integer; otherwise an error will be reported. |

Bandwidth | Real Value Type | The bandwidth is the radius (in meters) of a disc centered on each cell within which events will contribute to the Kernel estimate. |

## Optional Inputs

Name | Type | Description | Default Value |
---|---|---|---|

Mask | Map Type | A map used to mask the distance calculation on its null cell areas. Data cell type of this map must be "Signed 32 Bit Integer" or an error will be reported. | None |

Null Value | Integer Value Type | The bandwidth is the radius (in meters) of a disc centered on each cell within which events will contribute to the Kernel estimate. | -9999 |

## Output

Name | Type | Description |
---|---|---|

Kernel Density Map | Map Type | Output map showing the kernel density estimate for each cell. |

## Group

## Notes

Typically, the objective of point pattern analysis is to identify the spatial distribution of the events in a region, in particular cluster patterns. In this case, visual inspection of the events may be tricky, as superimposition of points may confuse the identification of high density areas. Kernel density estimation produces continuous estimates of the spatial intensity of a point pattern (Silverman, 1986), hence it allows exploration of the spatial distribution of points and identification of hotspots. Basically, it is a non parametric statistical method that returns event density weighted by a kernel function that is based on the distance between each one of the events, located within a radius, and the center of the cell.

The effect of increasing the radius (bandwidth) is to stretch the region around the cluster center, in a manner that for large radii the density will appear flat and local features will be obscured; on the other hand, if the radius is small, density will tend to show local patterns of hotspots.

The technical aspects of the algorithm that calculates kernel density was extracted from Bailey & Gatrell (1995) and uses the quartic Kernel function.

### ReferĂȘncias

Bailey, T. and Gatrell, A., 1995: Interactive Spatial Data Analysis. Longman, Harlow.

Silverman, W., 1986: Density estimation. Chapman and Hall, London.

## Internal Name

CalcKernelMap