Label | Explanation | Data Type |

Input Feature Class | The feature class for which cluster and outlier analysis will be performed. | Feature Layer |

Input Field | The numeric field to be evaluated. | Field |

Output Feature Class | The output feature class to receive the results fields. | Feature Class |

Conceptualization of Spatial Relationships | Specifies how spatial relationships among features are defined. - Inverse distance—Nearby neighboring features have a larger influence on the computations for a target feature than features that are far away.
- Inverse distance squared—Same as Inverse distance except that the slope is sharper, so influence drops off more quickly, and only a target feature's closest neighbors will exert substantial influence on computations for that feature.
- Fixed distance band—Each feature is analyzed within the context of neighboring features. Neighboring features inside the specified critical distance (Distance Band or Threshold Distance) receive a weight of one and exert influence on computations for the target feature. Neighboring features outside the critical distance receive a weight of zero and have no influence on a target feature's computations.
- Zone of indifference—Features within the specified critical distance (Distance Band or Threshold Distance) of a target feature receive a weight of one and influence computations for that feature. Once the critical distance is exceeded, weights (and the influence a neighboring feature has on target feature computations) diminish with distance.
- K nearest neighbors—The closest k features are included in the analysis. The number of neighbors (k) is specified by the Number of Neighbors parameter.
- Contiguity edges only—Only neighboring polygon features that share a boundary or overlap will influence computations for the target polygon feature.
- Contiguity edges corners—Polygon features that share a boundary, share a node, or overlap will influence computations for the target polygon feature.
- Get spatial weights from file—Spatial relationships are defined by a specified spatial weights file. The path to the spatial weights file is specified by the Weights Matrix File parameter.
| String |

Distance Method | Specifies how distances are calculated from each feature to neighboring features. - Euclidean—The straight-line distance between two points (as the crow flies)
- Manhattan—The distance between two points measured along axes at right angles (city block); calculated by summing the (absolute) difference between the x- and y-coordinates
| String |

Standardization | Row standardization is recommended whenever the distribution of your features is potentially biased due to sampling design or an imposed aggregation scheme. - None—No standardization of spatial weights is applied.
- Row—Spatial weights are standardized; each weight is divided by its row sum (the sum of the weights of all neighboring features).
| String |

Distance Band or Threshold Distance (Optional) | Specifies a cutoff distance for Inverse Distance and Fixed Distance options. Features outside the specified cutoff for a target feature are ignored in analyses for that feature. However, for Zone of Indifference, the influence of features outside the given distance is reduced with distance, while those inside the distance threshold are equally considered. The distance value entered should match that of the output coordinate system. For the Inverse Distance conceptualizations of spatial relationships, a value of 0 indicates that no threshold distance is applied; when this parameter is left blank, a default threshold value is computed and applied. This default value is the Euclidean distance that ensures every feature has at least one neighbor. This parameter has no effect when Polygon Contiguity or Get Spatial Weights From File spatial conceptualizations are selected. | Double |

Weights Matrix File (Optional) | The path to a file containing weights that define spatial, and potentially temporal, relationships among features. | File |

Apply False Discovery Rate (FDR) Correction
(Optional) | Specifies whether statistical significance will be assessed with or without FDR correction. - Checked—Statistical significance will be based on the False Discovery Rate correction for a 95 percent confidence level.
- Unchecked—Features with p-values less than 0.05 will appear in the COType field reflecting statistically significant clusters or outliers at a 95 percent confidence level. This is the default.
| Boolean |

Number of Permutations
(Optional) | The number of random permutations for the calculation of pseudo p-values. The default number of permutations is 499. If you choose 0 permutations, the standard p-value is calculated. - 0—Permutations are not used and a standard p-value is calculated.
- 99—With 99 permutations, the smallest possible pseudo p-value is 0.01 and all other pseudo p-values will be multiples of this value.
- 199—With 199 permutations, the smallest possible pseudo p-value is 0.005 and all other possible pseudo p-values will be multiples of this value.
- 499—With 499 permutations, the smallest possible pseudo p-value is 0.002 and all other pseudo p-values will be multiples of this value.
- 999—With 999 permutations, the smallest possible pseudo p-value is 0.001 and all other pseudo p-values will be multiples of this value.
- 9999—With 9999 permutations, the smallest possible pseudo p-value is 0.0001 and all other pseudo p-values will be multiples of this value.
| Long |

Number of Neighbors
(Optional) | The number of neighbors to include in the analysis. | Long |

### Derived Output

Label | Explanation | Data Type |

Index Field Name | The index field name. | Field |

ZScore Field Name | The z-score field name. | Field |

Probability Field | The probability field name. | Field |

Cluster Outlier Type | The cluster/outlier field name. | Field |

Source ID | The source ID field name. | Field |