# statistics

This module contains functions to perform spatial statistics calculations.

## P_VALUE

**Description**

This function computes the p-value (two-tails test) of a given z-score assuming the population follows a normal distribution where the mean is 0 and the standard deviation is 1. The z-score is a measure of how many standard deviations below or above the population mean a value is. It gives you an idea of how far from the mean a data point is. The p-value is the probability that a randomly sampled point has a value at least as extreme as the point whose z-score is being tested.

`z_score`

:`FLOAT`

**Return type**

`FLOAT`

**Example**

## CREATE_SPATIAL_COMPOSITE_SUPERVISED

**Description**

This procedure derives a spatial composite score as the residuals of a regression model which is used to detect areas of under- and over-prediction. The response variable should be measurable and correlated with the set of variables defining the score. For each data point. the residual is defined as the observed value minus the predicted value. Rows with a NULL value in any of the individual variables are dropped.

**Input parameters**

`input_query`

:`STRING`

the query to the data used to compute the spatial composite. It must contain all the individual variables that should be included in the computation of the composite as well as a unique geographic id for each row. A qualified table name can be given as well, e.g.`<database>.<schema>.<table>`

.`index_column`

:`STRING`

the name of the column with the unique geographic identifier.`output_table`

:`STRING`

the prefix for the output table. It should include database and schema, e.g.`<database>.<schema>.<output_table>`

.`options`

:`STRING`

containing a valid JSON with the different options. Valid options are described below.`model_options`

:`JSON`

string containing all the settings to be passed to the ML model function. These settings are:`input_label`

:`STRING`

name of the column to be used as a target to train the model and evaluate the predictions.`encoder`

:`JSON`

containing the name and parameters of the class from Snowpark ML to be used as an encoder, which will be applied to all the categorical features in your input query. It can be`NULL`

or omitted, but the function will return an error if there are categorical columns present. It can contain two different values:`class`

, a`STRING`

containing the fully qualified name of the Snowpark ML modeling class to be used in the step.`options`

, an optional`JSON`

dictionary containing the keyword arguments to be passed to the`class`

during initialization. Please check the Snowpark ML API reference to check which parameters can be passed to the class.

`scaler`

:`JSON`

containing the name and parameters of the class from Snowpark ML to be used as a scaler, which will be applied to all the input features (numerical or encoded categories) in your query. It can be`NULL`

or omitted to skip this step altogether. It can contain two different values:`class`

, a`STRING`

containing the fully qualified name of the Snowpark ML modeling class to be used in the step.`options`

, an optional`JSON`

dictionary containing the keyword arguments to be passed to the`class`

during initialization. Please check the Snowpark ML API reference to check which parameters can be passed to the class.

`regressor`

:`JSON`

containing the name and parameters of the class from Snowflake ML to be used as a regressor, and whose predictions will be used to generate the score index. It can contain two different values:`class`

, a`STRING`

containing the fully qualified name of the Snowpark ML modeling class to be used in the step.`options`

, an optional`JSON`

dictionary containing the keyword arguments to be passed to the`class`

during initialization. Please check the Snowpark ML API reference to check which parameters can be passed to the class.

`bucketize_method`

:`STRING`

the method used to discretize the spatial composite score. The default value is NULL, which will return a continuous variable. Possible options are:`EQUAL_INTERVALS_ZERO_CENTERED`

: the values of the spatial composite score are discretized into buckets of equal widths centered in zero. The lower and upper limits are derived from the outliers-removed maximum of the absolute values of the score.

`nbuckets`

:`INTEGER`

the number of buckets used when a bucketization method is specified. The default number of buckets is selected using Freedman and Diaconis’s (1981) rule. Ignored if`bucketize_method`

is not specified.`remove_outliers`

:`BOOL`

. When`bucketize_method`

is specified, if`remove_outliers`

is set to`true`

the buckets are derived from the oulier-removed data. The outliers are computed using Tukey’s fences k parameter for outlier detection. The default value is`true`

. Ignored if`bucketize_method`

is not specified.`r2_thr`

:`FLOAT`

the minimum allowed value for the R2 model score. If the R2 of the regression model is lower than this threshold this implies poor fitting and an error is raised. If it is NULL, a default value of 0.5 is used instead.

**Return type**

The results are stored in the table named `output_table`

, which contains the following columns:

`index_column`

: the unique geographic identifier. The type of this column depends on the type of`index_column`

in`input_query`

.`spatial_score`

: the value of the composite score. The type of this column is`FLOAT`

if the score is not discretized and`INTEGER`

otherwise.

When the score is discretized by specifying the `bucketize_method`

parameter, the procedure also returns a lookup table named `<output_table>_lookup_table`

with the following columns:

`lower_bound`

:`FLOAT`

the lower bound of the bin.`upper_bound`

:`FLOAT`

the upper bound of the bin.`spatial_score`

:`INTEGER`

the value of the (discretized) composite score.

**Example**

## CREATE_SPATIAL_COMPOSITE_UNSUPERVISED

**Description**

This procedure combines (spatial) variables into a meaningful composite score. The composite score can be derived using different methods, scaling and aggregation functions and weights. Rows with a NULL value in any of the model predictors are dropped.

**Input parameters**

`input`

:`STRING`

the query to the data used to compute the spatial composite. It must contain all the individual variables that should be included in the computation of the composite as well as a unique geographic id for each row. A qualified table name can be given as well, e.g.`<database>.<schema>.<table>`

.`index_column`

:`STRING`

the name of the column with the unique geographic identifier.`output_table`

:`STRING`

the name for the output table. It should include database and schema, e.g.`<database>.<schema>.<output_table>_SCORE`

.`options`

:`STRING`

containing a valid JSON with the different options. Valid options are described below. If options is set to NULL then all options are set to default values, as specified in the table below.`scoring_method`

:`STRING`

Possible options are ENTROPY, CUSTOM_WEIGHTS, FIRST_PC. With the ENTROPY method the spatial composite is derived as the weighted sum of the proportion of the min-max scaled individual variables, where the weights are based on the entropy of the proportion of each variable. Only numerical variables are allowed. With the CUSTOM_WEIGHTS method, the spatial composite is computed by first scaling each individual variable and then aggregating them according to user-defined scaling and aggregation methods and individual weights. Depending on the scaling parameter, both numerical and ordinal variables are allowed (categorical and boolean variables need to be transformed to ordinal). With the FIRST_PC method, the spatial composite is derived from a Principal Component Analysis as the first principal component score. Only numerical variables are allowed.`weights`

:`ARRAY`

the (optional) weights for each variable used to compute the spatial composite when scoring_method is set to CUSTOM_WEIGHTS. If a different scoring method is selected, then this input parameter is ignored. If specified, the sum of the weights must be lower than 1. If no weights are specified, equal weights are assumed. If weights are specified only for some variables and the sum of weights is less than 1, the remainder is distributed equally between the remaining variables. If weights are specified for all the variables and the sum of weights is less than 1, the remainder is distributed equally between all the variables.`scaling`

:`STRING`

the user-defined scaling when the scoring_method is set to CUSTOM_WEIGHTS. Possible options are:MIN_MAX_SCALER: data is rescaled into the range [0,1] based on minimum and maximum values. Only numerical variables are allowed.

STANDARD_SCALER: data is rescaled by subtracting the mean value and dividing the result by the standard deviation. Only numerical variables are allowed.

RANKING: data is replaced by its percent rank, that is by values ranging from 0 lowest to 1. Both numerical and ordinal variables are allowed (categorical and boolean variables need to be transformed to ordinal).

DISTANCE_TO_TARGET_MIN(_MAX,_AVG): data is rescaled by dividing by the minimum, maximum, or mean of all the values. Only numerical variables are allowed.

PROPORTION: data is rescaled by dividing by the sum total of all the values. Only numerical variables are allowed.

`aggregation`

:`STRING`

the aggregation function used when the scoring_method is set to CUSTOM_WEIGHTS. Possible options are:LINEAR: the spatial composite is derived as the weighted sum of the scaled individual variables.

GEOMETRIC: the spatial composite is given by the product of the scaled individual variables, each to the power of its weight.

`correlation_var`

:`STRING`

when scoring_method is set to FIRST_PC, the spatial score will be positively correlated with the selected variable (i.e. the sign the spatial score is set such that the correlation between the selected variable and the first principal component score is positive).`correlation_thr`

:`FLOAT`

the minimum absolute value of the correlation between each individual variable and the first principal component score when scoring_method is set to FIRST_PC.`return_range`

:`ARRAY`

the user-defined normalization range of the spatial composite score, e.g [0.0,1.0]. Ignored if`bucketize_method`

is specified.`bucketize_method`

:`STRING`

the method used to discretize the spatial composite score. Possible options are:EQUAL_INTERVALS: the values of the spatial composite score are discretized into buckets of equal widths.

QUANTILES: the values of the spatial composite score are discretized into buckets based on quantiles.

JENKS: the values of the spatial composite score are discretized into buckets obtained using k-means clustering.

`nbuckets`

:`INTEGER`

the number of buckets used when a bucketization method is specified. When`bucketize_method`

is set to EQUAL_INTERVALS, if`nbuckets`

is NULL, the default number of buckets is selected using Freedman and Diaconis's (1981) rule. When`bucketize_method`

is set to JENKS or QUANTILES,`nbuckets`

cannot be NULL. When`bucketize_method`

is set to JENKS the maximum value is 100, aka the maximum number of clusters allowed by BigQuery with k-means clustering.`bucketize_random_state`

:`INTEGER`

the random state used to run the discretization when`bucketize_method`

is set to JENKS. If a different scoring method is selected, then this input parameter is ignored. A non-negative value must be specified. It defaults to 42.

**Return type**

The results are stored in the table named `<output_table>`

, which contains the following columns:

`index_column`

: the unique geographic identifier. The type of this column depends on the type of`index_column`

in`input`

.`spatial_score`

: the value of the composite score. The type of this column is`FLOAT`

if the score is not discretized and`INTEGER`

otherwise.

When the score is discretized by specifying the `bucketize_method`

parameter, the procedure also returns a lookup table named `<output_table>_LOOKUP_TABLE`

with the following columns:

`lower_bound`

:`FLOAT`

the lower bound of the bin.`upper_bound`

:`FLOAT`

the upper bound of the bin.`spatial_score`

:`INTEGER`

the value of the (discretized) composite score.

**Examples**

With the `ENTROPY`

method:

With the `CUSTOM_WEIGHTS`

method:

With the `FIRST_PC`

method:

## CRONBACH_ALPHA_COEFFICIENT

**Description**

This procedure computes the Cronbach’s alpha coefficient for a set of (spatial) variables. This coefficient can be used as a measure of internal consistency or reliability of the data, based on the strength of correlations between individual variables. Cronbach’s alpha reliability coefficient normally ranges between 0 and 1 but there is actually no lower limit to the coefficient. Higher alpha (closer to 1) vs lower alpha (closer to 0) means higher vs lower consistency, with usually 0.65 being the minimum acceptable value of internal consistency. Rows with a NULL value in any of the individual variables are dropped.

**Input parameters**

`input`

:`STRING`

the query to the data used to compute the coefficient. It must contain all the individual variables that should be included in the computation of the coefficient. A qualified table name can be given as well, e.g.`<database>.<schema>.<table>`

.`output_table`

:`STRING`

the name for the output table. It should include database and schema, e.g.`<database>.<schema>.<output_table>`

.

**Return type**

The output table with the following columns:

`cronbach_alpha_coef`

:`FLOAT`

the computed Cronbach Alpha coefficient.`k`

:`INTEGER`

the number of the individual variables used to compute the composite.`mean_var`

:`FLOAT`

the mean variance of all individual variables.`mean_cov`

:`FLOAT`

the mean inter-item covariance among all variable pairs.

**Example**

## GWR_GRID

**Description**

Geographically weighted regression (GWR) models local relationships between spatially varying predictors and an outcome of interest using a local least squares regression.

This procedure performs a local least squares regression for every input cell. This approach was selected to improve computation time and efficiency. The number of models is controlled by the selected cell resolution, thus the user can increase or decrease the resolution of the cell index to perform more or less regressions. Note that you need to provide the cell ID (spatial index) for every location as input (see `cell_column`

parameter), i.e., the cell type and resolution are not passed explicitly, but rather the index has to be computed previously. Hence if you want to increase or decrease the resolution, you need to precompute the corresponding cell ID of every location (see H3 or Quadbin module).

In each regression, the data of the locations in each cell and those of the neighboring cells, defined by the `kring_distance`

parameter, will be taken into account. The data of the neighboring cells will be assigned a lower weight the further they are from the origin cell, following the function specified in the `kernel_function`

. For example, considering cell `i`

and `kring_distance`

of 1. Having `n`

locations located inside cell `i`

, and in the neigheboring cells [`n_1`

, `n_2`

, ..., `n_k`

], then the regression of the cell `i`

will have in total `n`

+ `n_1`

+ `n_2`

+ ... + `n_k`

points.

`input`

:`STRING`

the query to the input data. A qualified table name can be given as well:`<database-id>.<schema-id>.<table-name>`

.`features_columns`

:`ARRAY`

array of column names from`input_table`

to be used as features in the GWR.`label_column`

:`STRING`

name of the target variable column.`index_column`

:`STRING`

name of the column containing the cell ids.`kring_distance`

:`INT`

distance of the neighboring cells whose data will be included in the local regression of each cell.`kernel_function`

:`STRING`

kernel function to compute the spatial weights across the kring. Available functions are: 'uniform', 'triangular', 'quadratic', 'quartic' and 'gaussian'.`fit_intercept`

:`BOOLEAN`

whether to calculate the interception of the model or to force it to zero if, for example, the input data is already supposed to be centered. If NULL,`fit_intercept`

will be considered as`TRUE`

.`output_table`

:`STRING`

name of the output table. It should be a quoted qualified table with project and dataset:`<database-id>.<schema-id>.<table-name>`

.

**Output**

The output table will contain a column named either `H3`

(`STRING`

) OR `QUADBIN`

(`BIGINT`

) depending on the grid type, storing the unique geographic identifier of each grid cell, and a column for each feature column containing its corresponding coefficient estimate and one extra column for the intercept if `fit_intercept`

is set to `TRUE`

.

**Examples**

## GETIS_ORD_H3

**Description**

This procedure computes the Getis-Ord Gi* statistic for each row in the input table.

`input`

:`STRING`

the query to the data used to compute the coefficient. A qualified table name can be given as well:`<database-id>.<schema-id>.<table-name>`

.`output_table`

:`STRING`

qualified name of the output table:`<database-id>.<schema-id>.<table-name>`

.`index_column`

:`STRING`

name of the column with the H3 indexes.`value_column`

:`STRING`

name of the column with the values for each H3 cell.`size`

:`INT`

size of the H3 kring (distance from the origin). This defines the area around each index cell that will be taken into account to compute its Gi* statistic.`kernel`

:`STRING`

kernel function to compute the spatial weights across the kring. Available functions are: uniform, triangular, quadratic, quartic and gaussian.

The `index_column`

cannot contain NULL values, otherwise a `Invalid input origin`

error will be returned.

**Output**

The results are stored in the table named `<output_table>`

, which contains the following columns:

`h3`

:`STRING`

the H3 index.`gi`

:`FLOAT`

computed Gi* value.`p_value`

:`FLOAT`

computed P value.

**Example**

## GETIS_ORD_QUADBIN

**Description**

This procedure computes the Getis-Ord Gi* statistic for each row in the input table.

`input`

:`STRING`

the query to the data used to compute the coefficient. A qualified table name can be given as well:`<database-id>.<schema-id>.<table-name>`

.`output_table`

:`STRING`

qualified name of the output table:`<database-id>.<schema-id>.<table-name>`

.`index_column`

:`STRING`

name of the column with the Quadbin indexes.`value_column`

:`STRING`

name of the column with the values for each Quadbin cell.`size`

:`INT`

size of the Quadbin kring (distance from the origin). This defines the area around each index cell that will be taken into account to compute its Gi* statistic.`kernel`

:`STRING`

kernel function to compute the spatial weights across the kring. Available functions are: uniform, triangular, quadratic, quartic and gaussian.

The `index_column`

cannot contain NULL values, otherwise the results will contain non valid results.

**Output**

The results are stored in the table named `<output_table>`

, which contains the following columns:

`quadbin`

:`BIGINT`

the QUADBIN index.`gi`

:`FLOAT`

computed Gi* value.`p_value`

:`FLOAT`

computed P value.

**Example**

## GETIS_ORD_SPACETIME_H3

**Description**

This procedure computes the space temporal Getis-Ord Gi* statistic for each H3 index and each datetime timestamp according to the method described in this paper. It extends the Getis-Ord Gi* function by including the time domain. The Getis-Ord Gi* statistic is a measure of spatial autocorrelation, which is the degree to which data values are clustered together in space and time. The statistic is computed as the sum of the values of the cells in the kring (distance from the origin, space and temporal) weighted by the kernel functions, minus the value of the origin cell, divided by the standard deviation of the values of the cells in the kring. The Getis-Ord Gi* statistic is calculated from minimum to maximum datetime with the step defined by the user, in the input array. The datetime timestamp is truncated to the provided level, for example day / hour / week etc. For each spatial index, the missing datetime timestamp, from minimum to maximum, are filled with the default value of 0. Any other imputation of the values should take place outside of the function prior to passing the input to the function. The p value is computed as the probability of observing a value as extreme as the observed value, assuming the null hypothesis that the values are randomly distributed in space and time. The p value is computed using a normal distribution approximation.

`input`

:`STRING`

the query to the data used to compute the coefficient. A qualified table name can be given as well:`<database-id>.<schema-id>.<table-name>`

.`output_table`

:`STRING`

qualified name of the output table:`<database-id>.<schema-id>.<table-name>`

.`index_column`

:`STRING`

name of the column with the H3 indexes.`date_column`

:`STRING`

name of the column with the date.`value_column`

:`STRING`

name of the column with the values for each H3 cell.`size`

:`INTEGER`

size of the H3 kring (distance from the origin). This defines the area around each index cell that will be taken into account to compute its Gi* statistic.`time_freq`

:`STRING`

The time interval - step to use for the time series. Available values are:`year`

,`quarter`

,`month`

,`week`

,`day`

,`hour`

,`minute`

,`second`

. It is the equivalent of the spatial index in the time domain.`time_bw`

:`INTEGER`

The bandwidth to use for the time series. This defines the number of adjacent observations in time domain to be considered. It is the equivalent of the H3 kring in the time domain.`kernel`

:`STRING`

kernel function to compute the spatial weights across the kring. Available functions are: uniform, triangular, quadratic, quartic and gaussian.`kernel_time`

:`STRING`

kernel function to compute the temporal weights within the time bandwidth. Available functions are: uniform, triangular, quadratic, quartic and gaussian.

The `index_column`

cannot contain NULL values, otherwise a `Invalid input origin`

error will be returned.

**Output**

The results are stored in the table named `<output_table>`

, which contains the following columns:

`h3`

:`STRING`

the H3 index.`date`

:`DATETIME`

`gi`

:`FLOAT`

computed Gi* value.`p_value`

:`FLOAT`

computed P value.

**Example**

## GETIS_ORD_SPACETIME_QUADBIN

**Description**

This procedure computes the space temporal Getis-Ord Gi* statistic for each Quadbin index and each datetime timestamp according to the method described in this paper. It extends the Getis-Ord Gi* function by including the time domain. The Getis-Ord Gi* statistic is a measure of spatial autocorrelation, which is the degree to which data values are clustered together in space and time. The statistic is computed as the sum of the values of the cells in the kring (distance from the origin, space and temporal) weighted by the kernel functions, minus the value of the origin cell, divided by the standard deviation of the values of the cells in the kring. The Getis-Ord Gi* statistic is calculated from minimum to maximum datetime with the step defined by the user, in the input array. The datetime timestamp is truncated to the provided level, for example day / hour / week etc. For each spatial index, the missing datetime timestamp, from minimum to maximum, are filled with the default value of 0. Any other imputation of the values should take place outside of the function prior to passing the input to the function. The p value is computed as the probability of observing a value as extreme as the observed value, assuming the null hypothesis that the values are randomly distributed in space and time. The p value is computed using a normal distribution approximation.

`input`

:`STRING`

the query to the data used to compute the coefficient. A qualified table name can be given as well:`<database-id>.<schema-id>.<table-name>`

.`output_table`

:`STRING`

qualified name of the output table:`<database-id>.<schema-id>.<table-name>`

.`index_column`

:`STRING`

name of the column with the Quadbin indexes.`date_column`

:`STRING`

name of the column with the date.`value_column`

:`STRING`

name of the column with the values for each Quadbin cell.`size`

:`INTEGER`

size of the Quadbin kring (distance from the origin). This defines the area around each index cell that will be taken into account to compute its Gi* statistic.`time_freq`

:`STRING`

The time interval - step to use for the time series. Available values are:`year`

,`quarter`

,`month`

,`week`

,`day`

,`hour`

,`minute`

,`second`

. It is the equivalent of the spatial index in the time domain.`time_bw`

:`INTEGER`

The bandwidth to use for the time series. This defines the number of adjacent observations in time domain to be considered. It is the equivalent of the Quadbin kring in the time domain.`kernel`

:`STRING`

kernel function to compute the spatial weights across the kring. Available functions are: uniform, triangular, quadratic, quartic and gaussian.`kernel_time`

:`STRING`

kernel function to compute the temporal weights within the time bandwidth. Available functions are: uniform, triangular, quadratic, quartic and gaussian.

The `index_column`

cannot contain NULL values, otherwise the results will contain non valid results.

**Output**

The results are stored in the table named `<output_table>`

, which contains the following columns:

`quadbin`

:`BIGINT`

the QUADBIN index.`date`

:`DATETIME`

`gi`

:`FLOAT`

computed Gi* value.`p_value`

:`FLOAT`

computed P value.

**Example**

## MORANS_I_H3

**Description**

This procedure computes the Moran's I spatial autocorrelation from the input table with H3 indexes.

`input`

:`STRING`

the query to the data used to compute the coefficient. A qualified table name can be given as well:`<database-id>.<schema-id>.<table-name>`

.`output_table`

:`STRING`

qualified name of the output table:`<database-id>.<schema-id>.<table-name>`

.`index_column`

:`STRING`

name of the column with the H3 indexes.`value_column`

:`STRING`

name of the column with the values for each H3 cell.`size`

:`INT`

size of the H3*k-ring*(distance from the origin). This defines the area around each index cell where the distance decay will be applied. If no neighboring cells are found, the weight of the corresponding index cell is set to zero.`decay`

:`STRING`

decay function to compute the distance decay. Available functions are: uniform, inverse, inverse_square and exponential.

The `index_column`

cannot contain NULL values, otherwise a `Invalid input origin`

error will be returned.

**Output**

The results are stored in the table named `<output_table>`

, which contains the following column:

`morans_i`

:`FLOAT`

Moran's I spatial autocorrelation.

If all cells have no neighbours, then the procedure will fail.

**Example**

## MORANS_I_QUADBIN

**Description**

This procedure computes the Moran's I spatial autocorrelation from the input table with Quadbin indexes.

`input`

:`STRING`

the query to the data used to compute the coefficient. A qualified table name can be given as well:`<database-id>.<schema-id>.<table-name>`

.`output_table`

:`STRING`

qualified name of the output table:`<database-id>.<schema-id>.<table-name>`

.`index_column`

:`STRING`

name of the column with the Quadbin indexes.`value_column`

:`STRING`

name of the column with the values for each Quadbin cell.`size`

:`INT`

size of the Quadbin*k-ring*(distance from the origin). This defines the area around each index cell where the distance decay will be applied. If no neighboring cells are found, the weight of the corresponding index cell is set to zero.`decay`

:`STRING`

decay function to compute the distance decay. Available functions are: uniform, inverse, inverse_square and exponential.

The `index_column`

cannot contain NULL values, otherwise the results will contain non valid results.

**Output**

The results are stored in the table named `<output_table>`

, which contains the following column:

`morans_i`

:`FLOAT`

Moran's I spatial autocorrelation.

If all cells have no neighbours, then the procedure will fail.

**Example**

## LOCAL_MORANS_I_H3

**Description**

This procedure computes the local Moran's I spatial autocorrelation from the input table with H3 indexes. It outputs the H3 `index`

, local Moran's I spatial autocorrelation `value`

, simulated p value `psim`

, Conditional randomization null - expectation `EIc`

, Conditional randomization null - variance `VIc`

, Total randomization null - expectation `EI`

, Total randomization null - variance `VI`

, and the `quad`

HH=1, LL=2, LH=3, HL=4.

`input`

:`STRING`

the query to the data used to compute the coefficient. A qualified table name can be given as well:`<database-id>.<schema-id>.<table-name>`

.`output_table`

:`STRING`

qualified name of the output table:`<database-id>.<schema-id>.<table-name>`

.`index_column`

:`STRING`

name of the column with the H3 indexes.`value_column`

:`STRING`

name of the column with the values for each H3 cell.`size`

:`INTEGER`

size of the H3*k-ring*(distance from the origin). This defines the area around each index cell where the distance decay will be applied. If no neighboring cells are found, the weight of the corresponding index cell is set to zero.`decay`

:`STRING`

decay function to compute the distance decay. Available functions are: uniform, inverse, inverse_square and exponential.`permutations`

:`INTEGER`

number of permutations for the estimation of p-value.

`index_column`

cannot contain NULL values, otherwise a `Invalid input origin`

error will be returned.

**Output**

The results are stored in the table named `<output_table>`

, which contains the following columns:

`h3`

:`STRING`

the H3 index.`value`

:`FLOAT`

local Moran's I spatial autocorrelation.`psim`

:`FLOAT`

simulated p value.`EIc`

:`FLOAT`

conditional randomization null - expectation.`VIc`

:`FLOAT`

conditional randomization null - variance.`EI`

:`FLOAT`

total randomization null - expectation.`VI`

:`FLOAT`

total randomization null - variance.`quad`

:`INTEGER`

HH=1, LL=2, LH=3, HL=4.

**Example**

## LOCAL_MORANS_I_QUADBIN

**Description**

This procedure computes the local Moran's I spatial autocorrelation from the input table with Quadbin indexes. It outputs the Quadbin `index`

, local Moran's I spatial autocorrelation `value`

, simulated p value `psim`

, Conditional randomization null - expectation `EIc`

, Conditional randomization null - variance `VIc`

, Total randomization null - expectation `EI`

, Total randomization null - variance `VI`

, and the `quad`

HH=1, LL=2, LH=3, HL=4.

`input`

:`STRING`

the query to the data used to compute the coefficient. A qualified table name can be given as well:`<database-id>.<schema-id>.<table-name>`

.`output_table`

:`STRING`

qualified name of the output table:`<database-id>.<schema-id>.<table-name>`

.`index_column`

:`STRING`

name of the column with the Quadbin indexes.`value_column`

:`STRING`

name of the column with the values for each Quadbin cell.`size`

:`INTEGER`

size of the Quadbin*k-ring*(distance from the origin). This defines the area around each index cell where the distance decay will be applied. If no neighboring cells are found, the weight of the corresponding index cell is set to zero.`decay`

:`STRING`

decay function to compute the distance decay. Available functions are: uniform, inverse, inverse_square and exponential.`permutations`

:`INTEGER`

number of permutations for the estimation of p-value.

The `index_column`

cannot contain NULL values, otherwise the results will contain non valid results.

**Output**

The results are stored in the table named `<output_table>`

, which contains the following columns:

`quadbin`

:`BIGINT`

the QUADBIN index.`value`

:`FLOAT`

local Moran's I spatial autocorrelation.`psim`

:`FLOAT`

simulated p value.`EIc`

:`FLOAT`

conditional randomization null - expectation.`VIc`

:`FLOAT`

conditional randomization null - variance.`EI`

:`FLOAT`

total randomization null - expectation.`VI`

:`FLOAT`

total randomization null - variance.`quad`

:`INTEGER`

HH=1, LL=2, LH=3, HL=4.

**Example**

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