Editing Inverse distance weighting (section)

{{short description|Type of deterministic method for multivariate interpolation}}

[[File:Inverse Distance Weighting.png|thumb|400px|Inverse Distance Weighting as a sum of all weighting functions for each sample point. Each function has the value of one of the samples at its sample point and zero at every other sample point.]]

'''Inverse distance weighting''' ('''IDW''') is a type of [[Deterministic algorithm|deterministic method]] for [[multivariate interpolation]] with a known homogeneously scattered set of points. The assigned values to unknown points are calculated with a [[Weighted mean|weighted average]] of the values available at the known points. This method can also be used to create spatial weights matrices in [[spatial autocorrelation]] analyses (e.g. [[Moran's I|Moran's ''I'']]).<ref>{{Cite web |url=https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-statistics/spatial-autocorrelation.htm |title=Spatial Autocorrelation (Global Moran's I) (Spatial Statistics) |publisher=ESRI |website=ArcGIS Pro Documentation|access-date=13 September 2022}}</ref>

The name given to this type of method was motivated by the [[Weighted mean|weighted average]] applied, since it resorts to the inverse of the distance to each known point ("amount of proximity") when assigning weights.