# SearchNeighborhoodStandard

## Summary

The SearchNeighborhoodStandard class can be used to define the search neighborhood for IDW, Local Polynomial Interpolation, and Radial Basis Functions.

## Syntax

**SearchNeighborhoodStandard ({majorSemiaxis}, {minorSemiaxis}, {angle}, {nbrMax}, {nbrMin}, {sectorType})**

Parameter | Explanation | Data Type |

majorSemiaxis (Optional) |
The distance, in map units, specifying the length of the major semi axis of the ellipse within which data is selected from. | Double |

minorSemiaxis (Optional) |
The distance, in map units, specifying the length of the minor semi axis of the ellipse within which data is selected from. | Double |

angle (Optional) |
The angle of the search ellipse. | Double |

nbrMax (Optional) |
Maximum number of neighbors, within the search ellipse, to use when making the prediction. | Long |

nbrMin (Optional) |
Minimum number of neighbors, within the search ellipse, to use when making the prediction. | Long |

sectorType (Optional) |
The searching ellipse can be divided into 1, 4, 4 with an offset of 45º, or 8 sectors. | String |

## Properties

Property | Explanation | Data Type |

angle (Read and Write) |
The angle of the search ellipse. | Double |

majorSemiaxis (Read and Write) |
The distance, in map units, specifying the length of the major semi axis of the ellipse within which data is selected. | Double |

minorSemiaxis (Read and Write) |
The distance, in map units, specifying the length of the minor semi axis of the ellipse within which data is selected. | Double |

nbrMax (Read and Write) |
Maximum number of neighbors, within the search ellipse, to use when making the prediction. | Long |

nbrMin (Read and Write) |
Minimum number of neighbors, within the search ellipse, to use when making the prediction. | Long |

nbrType (Read Only) |
The neighborhood type: Smooth or Standard. | String |

sectorType (Read and Write) |
The searching ellipse can be divided into 1, 4, 4 with an offset of 45º, or 8 sectors. | String |

## Code Sample

SearchNeighborhoodStandard with IDW to produce an output raster.

import arcpy arcpy.env.workspace = "C:/gapyexamples/data" arcpy.IDW_ga("ca_ozone_pts", "OZONE", "outIDW", "C:/gapyexamples/output/idwout", "2000", "2", arcpy.SearchNeighborhoodStandard(300000, 300000, 0, 15, 10, "ONE_SECTOR"), "")

SearchNeighborhoodStandard with IDW to produce an output raster.

# Name: InverseDistanceWeighting_Example_02.py # Description: Interpolate a series of point features onto a rectangular raster # using Inverse Distance Weighting (IDW). # Requirements: Geostatistical Analyst Extension # Author: ESRI # Import system modules import arcpy # Set environment settings arcpy.env.workspace = "C:/gapyexamples/data" # Set local variables inPointFeatures = "ca_ozone_pts.shp" zField = "OZONE" outLayer = "outIDW" outRaster = "C:/gapyexamples/output/idwout" cellSize = 2000.0 power = 2 # Set variables for search neighborhood majSemiaxis = 300000 minSemiaxis = 300000 angle = 0 maxNeighbors = 15 minNeighbors = 10 sectorType = "ONE_SECTOR" searchNeighbourhood = arcpy.SearchNeighborhoodStandard(majSemiaxis, minSemiaxis, angle, maxNeighbors, minNeighbors, sectorType) # Check out the ArcGIS Geostatistical Analyst extension license arcpy.CheckOutExtension("GeoStats") # Execute IDW arcpy.IDW_ga(inPointFeatures, zField, outLayer, outRaster, cellSize, power, searchNeighbourhood)