Task 2. Average Nearest Neighborhood Practical 4 Task 2. Average Nearest Neighborhood For the Average Nearest Neighborhood, the null hypothesis states that features are randomly distributed. Therefore low values of p indicate that the probability to have a cluster or disperse pattern is not the result of a random chance.

Task 4. Getis Order (Hot Spot Analysis) Practical 4 Task 4. Getis Order (Hot Spot Analysis) Gi statistic is a z score, so no further calculations are required. The resultant z-scores and p – values tell you where features with either high or low values cluster spatially. For statistically significant positive z-scores, the larger the z scores is, the more intense the clustering of high values (hot spot). For statistically significant negative z –scores, the smaller the z, the more intense the cluster of Low values (cold spot). P value indicates randomness of high or low values. A high z-score and small p-value for a feature indicates a spatial clustering of high values. A low negative z-score and small p-value indicates a spatial clustering of low values.

Task 4. Getis Order (Hot Spot Analysis) Practical 4 Task 4. Getis Order (Hot Spot Analysis) The local sum for a feature and its neighbours is compared proportionally to the sum of all features; when the local sum is very different from the expected local sum and when the difference is too large to be the result of a random chance, a statistically significant z score results.

Task 4. Getis Order (Hot Spot Analysis) Practical 4 Task 4. Getis Order (Hot Spot Analysis) Cold Spot (z<0) with only 35% confidence that this cluster is not the result of a random chance. Hot Spot (z>0) with almost 100% confidence that this cluster is not the result of a random chance.

Task 5. Local Morans I (Anselin) Practical 4 Task 5. Local Morans I (Anselin) Identifies spatial clusters of features with high or low values. The tool calculates a local Moran Index I, a z-score and a p-value and a code representing the cluster type. The z-score and p-values represent the statistical significance of the computed index values.

Practical 4 Task 5. Local Morans I (Anselin) Positive I value for I indicates that a feature has neighbouring features with similar high or low attributes values  this feature is part of a cluster. A negative value for I indicates that a feature has neighbouring features with dissimilar values this feature is an outlier.

Practical 4 H L Task 5. Local Morans I (Anselin) The p-value for the feature must be small enough for the cluster or outlier to be considered statistically significant (statistical significance is 95 %) HH  statistically significant cluster of high values. LL  statistically significant cluster of low values. HL  outlier surrounded by low values. LH  outlier in which a low value is surrounded by high values. H L I index