Label | Explanation | Data Type |

Input Hot Spot Result 1
| The first hot spot analysis result layer. | Feature Layer |

Input Hot Spot Result 2
| The second hot spot analysis result layer. | Feature Layer |

Output Features
| The output feature class that will contain the local measures of similarity and association. | Feature Class |

Number of Neighbors (Optional) | The number of neighbors around each feature that will be used for distance weighting. Distance weighting is one component of the overall similarity, and any features with matching significance levels within the neighborhood will be considered partial matches when calculating similarity and association. | Long |

Number of Permutations (Optional) | The number of permutations that will be used to estimate the expected similarity and kappa values. A larger number of simulations will increase the precision of the estimates but will also increase calculation time. - 99—The analysis will use 99 permutations.
- 199—The analysis will use 199 permutations.
- 499—The analysis will use 499 permutations. This is the default.
- 999—The analysis will use 999 permutations.
- 9999—The analysis will use 9,999 permutations.
| Long |

Similarity Weighting Method
(Optional) | Specifies how similarity weights between significance level categories will be defined. Similarity weights are numbers between 0 and 1 that define the categories of one result that are expected to match the categories of the other result. A value of 1 indicates that the categories will be considered exactly the same, and a value of 0 indicates that the categories will be considered completely different. Values between 0 and 1 indicate degrees of partial similarity between the categories. For example, 99% significant hot spots can be considered perfectly similar to other 99% hot spots, partially similar to 95% hot spots, and completely dissimilar to 99% cold spots. - Fuzzy weights—Similarity weights will be fuzzy (nonbinary) and determined by the closeness of significance levels. For example, 99% significant hot spots will be perfectly similar to other 99% significant hot spots (weight = 1), but they will be partially similar to 95% significant hot spots (weight=0.71) and 90% significant hot spots (weight = 0.55). The weight between 95% significant and 90% significant is 0.78. All hot spots will be completely dissimilar to all cold spots and nonsignificant features (weight = 0). This is the default.
- Exact significance level matching—Features must have the same significance level to be considered similar. For example, 99% significant hot spots will be considered completely dissimilar to 95% and 90% significant hot spots.
- Combine 90%, 95%, and 99% significant—Features that are 90%, 95%, and 99% significant hot spots will be considered perfectly similar to each other, and all features that are 90%, 95%, and 99% significant cold spots will be considered perfectly similar to each other. This option treats all features at or above 90% significance as being the same (statistically significant) and all features below 90% confidence as being the same (nonsignificant). This option is recommended when the hot spot analyses were performed at a 90% significance level.
- Combine 95% and 99% significant—Features that are 95% and 99% significant hot (or cold) spots will be considered perfectly similar, and features that are 95% and 99% significant cold spots will be considered perfectly similar. For example, 90% significant hot and cold spots will be considered completely dissimilar to higher significance levels. This option treats all features at or above 95% confidence as being the same (statistically significant) and all features below 95% confidence as being the same (nonsignificant). This option is recommended when the hot spot analyses were performed at a 95% significance level.
- Use only 99% significant—Only features that are 99% significant hot (or cold) spots will be considered perfectly similar to each other. This option treats all features below 99% significance as being nonsignificant. This option is recommended when the hot spot analyses were performed at a 99% significance level.
- Custom weights—Custom similarity weights provided in the Category Similarity Weights parameter will be used.
- Get weights from table—Similarity weights between significance levels will be defined by an input table. Provide the table in the Input Weight Tables parameter.
- Reverse hot and cold relationships—The default fuzzy weights will be used, but hot spots of the first hot spot result will be considered similar to the cold spots of the second hot spot result. For example, 99% significant hot spots in one result will be considered perfectly similar to 99% cold spots in the other result and partially similar to 95% and 90% cold spots in the other result. This option is recommended when the hot spot analysis variables have a negative relationship. For example, you can measure how closely hot spots of infant mortality correspond to cold spots of healthcare access.
| String |

Category Similarity Weights
(Optional) | The custom similarity weights between significance level categories. The weights are values between 0 and 1 and indicate how similar to consider the two categories. A value of 0 indicates the categories are completely dissimilar, a value of 1 indicates the values are perfectly similar, and values between 0 and 1 indicate the categories are partially similar. In the weight matrix pop-out, click a cell, type the weight value, and press Enter to apply the weight. | Value Table |

Input Weights Table
(Optional) | The table containing custom similarity weights for each combination of hot spot significance level categories. The table must contain CATEGORY1, CATEGORY2, and WEIGHT fields. Provide the significance level categories of the pair (the Gi_Bin field values of the input layers) in the category fields and the similarity weight between them in the weight field. If a combination is not provided in the table, the weight for the combination is assumed to be 0. | Table View |

Exclude Nonsignificant Features
(Optional) | Specifies whether pairs of features will be excluded from the comparisons if both hot spot results are nonsignificant. If excluded, conditional similarity and kappa values will be calculated that compare only the statistically significant hot and cold spots. Excluding features is recommended when you are interested only in whether the hot and cold spots of the input layers align, not whether the nonsignificant areas align, such as comparing whether hot and cold spots of median income correspond to hot and cold spots of food access. - Checked—Nonsignificant features will be excluded, and the comparisons will be conditional on statistically significant hot and cold spots.
- Unchecked—Nonsignificant features will be included. This is the default.
If any significance level categories are assigned a similarity weight of 1 to the nonsignificant category (indicating that the category will be treated the same as the nonsignificant category), features with that category will also be excluded from comparisons if they are paired with another nonsignificant feature. | Boolean |

### Derived Output

Label | Explanation | Data Type |

Global Similarity Value | The similarity value between the hot spot results. | Double |

Global Expected Similarity Value | The expected value of the similarity between the hot spot results. | Double |

Global Spatial Fuzzy Kappa | The spatially-adjusted fuzzy kappa value between the hot spot results. | Double |

Output Layer Group | A group layer of the output layers. | Group Layer |