3.2 Local Geary
Local geary is a type of LISA that focuses on squared differences/dissimilarity. A small value of the local geary statistics suggests positive spatial autocorrelation, whereas large values suggest negative spatial autocorrelation. For more details, please read: http://geodacenter.github.io/workbook/6b_local_adv/lab6b.html#local-geary
local_geary()
local_geary() is a PostgreSQL WINDOW function. Please call it with an OVER clause.
Synopsis
Short version:
Full version:
Arguments
Name | Type | Description |
val | numeric | the numeric column that contains the values for LISA statistics |
weights | bytea | the bytea column that stores the spatial weights information |
permutations | integer | the number of permutations for the LISA computation. Default: 999. |
permutation_method | character varying | the permutation method used for the LISA computation. Options are 'complete', 'lookup'. Default: 'lookup'. |
significance_cutoff | float | the cutoff value for significance p-values to filter not-significant clusters. Default: 0.05. |
cpu_threads | integer | the number of CPU threads used for parallel LISA computation. Default: 6. |
seed | integer | the seed for random number generator used in LISA statistics. Default: 123456789. |
Return
Type | Description |
float[] | an array contains 3 values, which are {'lisa value', 'pseudo-p value' and 'cluster indicator'} |
Examples
Apply local geary statistics on the variable "hr60" (homicide rate 1960 in natregimes dataset) using queen contiguity weights "queen_w":
Please see chapter 'Contiguity Based Weights' for how to create a Queen contiguity weights.
One can specify the arguments of local geary using the full version of local_geary() function. For example, apply local geary statistics using 9,999 permutations, significance cutoff value 0.01:
Cluster Indicators
The predefined values of the cluster indicators of local moran are:
Cluster indicator value | Description | Color |
0 | Not significant | #eeeeee |
1 | High-High | #b2182b |
2 | Low-Low | #ef8a62 |
3 | Other Positive | #fddbc7 |
4 | Negative | #67adc7 |
5 | Undefined Value | #464646 |
6 | Isolated | #999999 |
One can extract the cluster indicators and make a local geary cluster map using the 'Color" values in the table above:
Or by extracting the pseudo-p values to make a significance map:
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