## Summary

The SearchNeighborhoodStandard3D class can be used to define the three dimensional search neighborhood for the Empirical Bayesian Kriging 3D tool.

## Syntax

SearchNeighborhoodStandard3D ({radius}, {nbrMax}, {nbrMin}, {sectorType})

Parameter | Explanation | Data Type |

radius | The distance, in map units, specifying the length of the radius of the search neighborhood. | Double |

nbrMax | The maximum number of neighbors, within the search radius, to use when making the prediction. | Long |

nbrMin | The minimum number of neighbors, within the search radius, to use when making the prediction. | Long |

sectorType | The sector type of the search neighborhood. The search neighborhood can be divided into 1, 4, 6, 8, 12, or 20 sectors. Each sector type is based on a Platonic solid. - ONE_SECTOR —1 Sector (Sphere)
- FOUR_SECTORS —4 Sectors (Tetrahedron)
- SIX_SECTORS —6 Sectors (Cube)
- EIGHT_SECTORS —8 Sectors (Octahedron)
- TWELVE_SECTORS —12 Sectors (Dodecahedron)
- TWENTY_SECTORS —20 Sectors (Icosahedron)
| String |

## Properties

Property | Explanation | Data Type |

nbrMax (Read Only) | The maximum number of neighbors of the search neighborhood. | Long |

nbrMin (Read Only) | The minimum number of neighbors of the search neighborhood. | Long |

radius (Read Only) | The radius of the search neighborhood. | Double |

sectorType (Read Only) | The sector type of the search neighborhood. | String |

## Code sample

Use SearchNeighborhoodStandard3D with the Empirical Bayesian Kriging 3D tool to produce a geostatistical layer.

```
import arcpy
arcpy.ga.EmpiricalBayesianKriging3D("my3DLayer", "Shape.Z", "myValueField", "myGALayer", "METER", "",
"POWER", "NONE", 100, 1, 100, "NONE", "",
"NBRTYPE=Standard3D RADIUS=10000 NBR_MAX=15 NBR_MIN=10 SECTOR_TYPE=ONE_SECTOR",
"", "PREDICTION", 0.5, "EXCEED", "")
```

Use SearchNeighborhoodStandard3D with the Empirical Bayesian Kriging 3D tool to produce a geostatistical layer.

```
# Name: SearchNeighborhoodStandard3D_Example_02.py
# Description: Interpolates 3D points using a standard 3D neighborhood
# Requirements: Geostatistical Analyst Extension
# Author: Esri
# Import system modules
import arcpy
# Set local variables
in3DPoints = "C:/gapyexamples/input/my3DPoints.shp"
elevationField = "Shape.Z"
valueField = "myValueField"
outGALayer = "myGALayer"
elevationUnit = "METER"
measurementErrorField = "myMEField"
semivariogramModel = "LINEAR"
transformationType = "NONE"
subsetSize = 80
overlapFactor = 1.5
numSimulations = 200
trendRemoval = "FIRST"
elevInflationFactor = 20
radius = 10000
maxNeighbors = 15
minNeighbors = 10
sectorType = "FOUR_SECTORS"
searchNeighborhood = arcpy.SearchNeighborhoodStandard3D(radius, maxNeighbors, minNeighbors, sectorType)
outputElev = 1000
outputType = "PREDICTION"
# Check out the ArcGIS Geostatistical Analyst extension license
arcpy.CheckOutExtension("GeoStats")
# Execute Empirical Bayesian Kriging 3D
arcpy.ga.EmpiricalBayesianKriging3D(in3DPoints, elevationField, valueField, outGALayer, elevationUnit, myMEField,
semivariogramModel, transformationType, subsetSize, overlapFactor, numSimulations,
trendRemoval, elevInflationFactor, searchNeighborhood, outputElev, outputType)
```