1. 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.

2. 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.

3. 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.

4. 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.

5. 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.

6. 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.

7. 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