statistics

This module contains functions to perform spatial statistics calculations.

P_VALUE

P_VALUE(z_score)

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: FLOAT64

Return type

FLOAT64

Example

carto-un

SELECT `carto-un`.carto.P_VALUE(u) as p_value
    FROM UNNEST([-2,-1,0,1,2]) u;

-- [ 0.04550012577451279, 0.31731052766472745, 0.999999999, 0.31731052766472745, 0.04550012577451279 ]

carto-un-eu

SELECT `carto-un-eu`.carto.P_VALUE(u) as p_value
    FROM UNNEST([-2,-1,0,1,2]) u;

-- [ 0.04550012577451279, 0.31731052766472745, 0.999999999, 0.31731052766472745, 0.04550012577451279 ]

manual

SELECT carto.P_VALUE(u) as p_value
    FROM UNNEST([-2,-1,0,1,2]) u;

-- [ 0.04550012577451279, 0.31731052766472745, 0.999999999, 0.31731052766472745, 0.04550012577451279 ]

KNN_TABLE

KNN_TABLE(input, output_table, geoid_col, geo_col, k)

Description

This procedure returns for each point the k-nearest neighbors of a given set of points.

• input: STRING the query to the data used to compute the KNN. A qualified table name can be given as well: <project-id>.<dataset-id>.<table-name>.

• output_table: STRING qualified name of the output table: <project-id>.<dataset-id>.<table-name>.

• geoid_col: STRING name of the column with unique ids.

• geo_col: STRING name of the column with the geometries.

• k: INT64 number of nearest neighbors (positive, typically small).

Output

The results are stored in the table named <output_table>, which contains the following columns:

• geo: GEOGRAPHY the geometry of the considered point.

• geo_knn: GEOGRAPHY the k-nearest neighbor point.

• geoid: STRING the unique identifier of the considered point.

• geoid_knn: STRING the unique identifier of the k-nearest neighbor.

• distance: FLOAT64 the k-nearest neighbor distance to the considered point.

• knn: INT64 the k-order (knn).

Example

carto-un

CALL `carto-un`.carto.KNN_TABLE(
    'myproject.mydataset.points',
    'myproject.mydataset.knn',
    'id',
    'geo',
    10
);

carto-un-eu

CALL `carto-un-eu`.carto.KNN_TABLE(
    'myproject.mydataset.points',
    'myproject.mydataset.knn',
    'id',
    'geo',
    10
);

manual

CALL carto.KNN_TABLE(
    'myproject.mydataset.points',
    'myproject.mydataset.knn',
    'id',
    'geo',
    10
);

KNN

KNN(points, k)

Description

This function returns for each point the k-nearest neighbors of a given set of points.

• points: ARRAY<STRUCT<geoid STRING, geo GEOGRAPHY>> input data with unique id and geography.

• k: INT64 number of nearest neighbors (positive, typically small).

Return type

ARRAY<STRUCT<geo GEOGRAPHY, geo_knn GEOGRAPHY, geoid STRING, geoid_knn STRING, distance FLOAT64, knn INT64>>

where:

• geo: the geometry of the considered point.

• geo_knn: the k-nearest neighbor point.

• geoid: the unique identifier of the considered point.

• geoid_knn: the unique identifier of the k-nearest neighbor.

• distance: the k-nearest neighbor distance to the considered point.

• knn: the k-order (knn).

Example

carto-un

SELECT *
FROM UNNEST((
    SELECT `carto-un`.carto.KNN(myarray, 10)
    FROM (
        SELECT ARRAY_AGG(STRUCT(format('%08x', uid),position_geom)) myarray
        FROM (
            SELECT ROW_NUMBER() OVER (ORDER BY hour) AS uid, position_geom
            FROM `bigquery-public-data.catalonian_mobile_coverage.mobile_data_2015_2017`
            WHERE date = '2017-12-31'
        )
    )
))
ORDER BY geoid;
--{
--  "geo": "POINT(2.82263 41.97118)",
--  "geo_knn": "POINT(2.8225 41.97117)",
--  "geoid": "00000001",
--  "geoid_knn": "00000624",
--  "distance": "10.804663098937658",
--  "knn": "1"
--},
--{
--  "geo": "POINT(2.82263 41.97118)",
--  "geo_knn": "POINT(2.823 41.9712)",
--  "geoid": "00000001",
--  "geoid_knn": "00000666",
--  "distance": "30.66917920746894",
--  "knn": "2"
--},
--{
--  "geo": "POINT(2.82263 41.97118)",
--  "geo_knn": "POINT(2.82298 41.9713)",
--  "geoid": "00000001",
--  "geoid_knn": "00000618",
--  "distance": "31.863463704968353",
--  "knn": "3"
--},
-- ...

carto-un-eu

SELECT *
FROM UNNEST((
    SELECT `carto-un-eu`.carto.KNN(myarray, 10)
    FROM (
        SELECT ARRAY_AGG(STRUCT(format('%08x', uid),position_geom)) myarray
        FROM (
            SELECT ROW_NUMBER() OVER (ORDER BY hour) AS uid, position_geom
            FROM `bigquery-public-data.catalonian_mobile_coverage.mobile_data_2015_2017`
            WHERE date = '2017-12-31'
        )
    )
))
ORDER BY geoid;
--{
--  "geo": "POINT(2.82263 41.97118)",
--  "geo_knn": "POINT(2.8225 41.97117)",
--  "geoid": "00000001",
--  "geoid_knn": "00000624",
--  "distance": "10.804663098937658",
--  "knn": "1"
--},
--{
--  "geo": "POINT(2.82263 41.97118)",
--  "geo_knn": "POINT(2.823 41.9712)",
--  "geoid": "00000001",
--  "geoid_knn": "00000666",
--  "distance": "30.66917920746894",
--  "knn": "2"
--},
--{
--  "geo": "POINT(2.82263 41.97118)",
--  "geo_knn": "POINT(2.82298 41.9713)",
--  "geoid": "00000001",
--  "geoid_knn": "00000618",
--  "distance": "31.863463704968353",
--  "knn": "3"
--},
-- ...

manual

SELECT *
FROM UNNEST((
    SELECT carto.KNN(myarray, 10)
    FROM (
        SELECT ARRAY_AGG(STRUCT(format('%08x', uid),position_geom)) myarray
        FROM (
            SELECT ROW_NUMBER() OVER (ORDER BY hour) AS uid, position_geom
            FROM `bigquery-public-data.catalonian_mobile_coverage.mobile_data_2015_2017`
            WHERE date = '2017-12-31'
        )
    )
))
ORDER BY geoid;
--{
--  "geo": "POINT(2.82263 41.97118)",
--  "geo_knn": "POINT(2.8225 41.97117)",
--  "geoid": "00000001",
--  "geoid_knn": "00000624",
--  "distance": "10.804663098937658",
--  "knn": "1"
--},
--{
--  "geo": "POINT(2.82263 41.97118)",
--  "geo_knn": "POINT(2.823 41.9712)",
--  "geoid": "00000001",
--  "geoid_knn": "00000666",
--  "distance": "30.66917920746894",
--  "knn": "2"
--},
--{
--  "geo": "POINT(2.82263 41.97118)",
--  "geo_knn": "POINT(2.82298 41.9713)",
--  "geoid": "00000001",
--  "geoid_knn": "00000618",
--  "distance": "31.863463704968353",
--  "knn": "3"
--},
-- ...

LOF_TABLE

LOF_TABLE(src_fullname STRING, target_fullname STRING, geoid_column_name STRING, geo_column_name STRING, k INT64)

Description

This procedure computes the Local Outlier Factor for each point of a specified column and stores the result in an output table along with the other input columns.

• src_fullname: STRING The input table. A STRING of the form project-id.dataset-id.table-name is expected. The project-id can be omitted (in which case the default one will be used).

• target_fullname: STRING The resulting table where the LOF will be stored. A STRING of the form project-id.dataset-id.table-name is expected. The project-id can be omitted (in which case the default one will be used). The dataset must exist and the caller needs to have permissions to create a new table in it. The process will fail if the target table already exists.

• geoid_column_name: STRING The column name with a unique identifier for each point.

• geo_column_name: STRING The column name containing the points.

• lof_target_column_name: STRING The column name where the resulting Local Outlier Factor will be stored in the output table.

• k: INT64 Number of nearest neighbors (positive, typically small).

Output

The results are stored in the table named <output_table>, which contains the following columns:

• geo: GEOGRAPHY the geometry of the considered point.

• geoid: GEOGRAPHY the unique identifier of the considered point.

• lof: FLOAT64 the Local Outlier Factor score.

Example

carto-un

CALL `carto-un`.carto.LOF_TABLE(
  'bigquery-public-data.new_york_subway.stations',
  'myproject.mydataset.my_output_table',
  'station_id',
  'station_geom',
  'lof',
  10
);
-- The table `'myproject.mydataset.my_output_table` will be created
-- with an extra column containing the `lof` value.

carto-un-eu

CALL `carto-un-eu`.carto.LOF_TABLE(
  'bigquery-public-data.new_york_subway.stations',
  'myproject.mydataset.my_output_table',
  'station_id',
  'station_geom',
  'lof',
  10
);
-- The table `'myproject.mydataset.my_output_table` will be created
-- with an extra column containing the `lof` value.

manual

CALL carto.LOF_TABLE(
  'bigquery-public-data.new_york_subway.stations',
  'myproject.mydataset.my_output_table',
  'station_id',
  'station_geom',
  'lof',
  10
);
-- The table `'myproject.mydataset.my_output_table` will be created
-- with an extra column containing the `lof` value.

LOF

LOF(points, k)

Description

This function computes the Local Outlier Factor of each point of a given set of points.

• points: ARRAY<STRUCT<geoid STRING, geo GEOGRAPHY>> input data points with unique id and geography.

• k: INT64 number of nearest neighbors (positive, typically small).

Return type

ARRAY<STRUCT<geo GEOGRAPHY, geoid GEOGRAPHY, lof FLOAT64>>

where:

• geo: the geometry of the considered point.

• geoid: the unique identifier of the considered point.

• lof: the Local Outlier Factor score.

Example

carto-un

SELECT *
FROM UNNEST((
    SELECT `carto-un`.carto.LOF(myarray, 10)
    FROM (
        SELECT ARRAY_AGG(STRUCT(format('%08x', uid),position_geom)) myarray
        FROM (
            SELECT ROW_NUMBER() OVER (ORDER BY hour) AS uid, position_geom
            FROM `bigquery-public-data.catalonian_mobile_coverage.mobile_data_2015_2017`
            WHERE date = '2017-12-31'
        )
    )
))
ORDER BY geoid;
-- {"geo": POINT(2.82263 41.97118), "geoid": "00000001", "lof": 1.3217599116891428}
-- {"geo": POINT(2.35705 41.49786), "geoid": "00000002", "lof": 1.235551000737416}
-- {"geo": POINT(2.13967 41.3838), "geoid": "00000003", "lof": 1.1305674032876687}
-- ...

carto-un-eu

SELECT *
FROM UNNEST((
    SELECT `carto-un-eu`.carto.LOF(myarray, 10)
    FROM (
        SELECT ARRAY_AGG(STRUCT(format('%08x', uid),position_geom)) myarray
        FROM (
            SELECT ROW_NUMBER() OVER (ORDER BY hour) AS uid, position_geom
            FROM `bigquery-public-data.catalonian_mobile_coverage.mobile_data_2015_2017`
            WHERE date = '2017-12-31'
        )
    )
))
ORDER BY geoid;
-- {"geo": POINT(2.82263 41.97118), "geoid": "00000001", "lof": 1.3217599116891428}
-- {"geo": POINT(2.35705 41.49786), "geoid": "00000002", "lof": 1.235551000737416}
-- {"geo": POINT(2.13967 41.3838), "geoid": "00000003", "lof": 1.1305674032876687}
-- ...

manual

SELECT *
FROM UNNEST((
    SELECT carto.LOF(myarray, 10)
    FROM (
        SELECT ARRAY_AGG(STRUCT(format('%08x', uid),position_geom)) myarray
        FROM (
            SELECT ROW_NUMBER() OVER (ORDER BY hour) AS uid, position_geom
            FROM `bigquery-public-data.catalonian_mobile_coverage.mobile_data_2015_2017`
            WHERE date = '2017-12-31'
        )
    )
))
ORDER BY geoid;
-- {"geo": POINT(2.82263 41.97118), "geoid": "00000001", "lof": 1.3217599116891428}
-- {"geo": POINT(2.35705 41.49786), "geoid": "00000002", "lof": 1.235551000737416}
-- {"geo": POINT(2.13967 41.3838), "geoid": "00000003", "lof": 1.1305674032876687}
-- ...

GFUN_TABLE

GFUN_TABLE(input, output_table, geo_col)

Description

This function computes the G-function of a given set of points.

• input: STRING the query to the data used to compute the G-Function. A qualified table name can be given as well: <project-id>.<dataset-id>.<table-name>.

• output_table: STRING qualified name of the output table: <project-id>.<dataset-id>.<table-name>.

• geo_col: STRING name of the column with the geometries.

Output

The results are stored in the table named <output_table>, which contains the following columns:

• distance: FLOAT64 the nearest neighbors distances.

• gfun_G: FLOAT64 the empirical G evaluated for each distance in the support.

• gfun_ev: FLOAT64 the theoretical Poisson G evaluated for each distance in the support.

Example

carto-un

CALL `carto-un`.carto.GFUN_TABLE(
    'myproject.mydataset.points',
    'myproject.mydataset.knn',
    'geo'
);

carto-un-eu

CALL `carto-un-eu`.carto.GFUN_TABLE(
    'myproject.mydataset.points',
    'myproject.mydataset.knn',
    'geo'
);

manual

CALL carto.GFUN_TABLE(
    'myproject.mydataset.points',
    'myproject.mydataset.knn',
    'geo'
);

GFUN

GFUN(points)

Description

This function computes the G-function of a given set of points.

• points: ARRAY<GEOGRAPHY> input data points.

Return type

ARRAY<STRUCT<distance FLOAT64, gfun_G FLOAT64, gfun_ev FLOAT64>>

where:

• distance: the nearest neighbors distances.

• gfun_G: the empirical G evaluated for each distance in the support.

• gfun_ev: the theoretical Poisson G evaluated for each distance in the support.

Example

carto-un

SELECT *
FROM UNNEST((
    SELECT `carto-un`.carto.GFUN(myarray)
    FROM (
        SELECT ARRAY_AGG(position_geom) myarray
        FROM `bigquery-public-data.catalonian_mobile_coverage.mobile_data_2015_2017`
        WHERE date = '2017-12-31'
    )
))
ORDER BY distance;
--{
--  "distance": "38.599968853183",
--  "gfun_G": "0.4319167389418907",
--  "gfun_ev": "4.037383876246414E-4"
--},
--{
--  "distance": "77.199937706366",
--  "gfun_G": "0.5771899392888118",
--  "gfun_ev": "0.0016139757856029613"
--},
--{
--  "distance": "115.799906559549",
--  "gfun_G": "0.6522116218560278",
--  "gfun_ev": "0.003627782844736638"
--},
-- ...

carto-un-eu

SELECT *
FROM UNNEST((
    SELECT `carto-un-eu`.carto.GFUN(myarray)
    FROM (
        SELECT ARRAY_AGG(position_geom) myarray
        FROM `bigquery-public-data.catalonian_mobile_coverage.mobile_data_2015_2017`
        WHERE date = '2017-12-31'
    )
))
ORDER BY distance;
--{
--  "distance": "38.599968853183",
--  "gfun_G": "0.4319167389418907",
--  "gfun_ev": "4.037383876246414E-4"
--},
--{
--  "distance": "77.199937706366",
--  "gfun_G": "0.5771899392888118",
--  "gfun_ev": "0.0016139757856029613"
--},
--{
--  "distance": "115.799906559549",
--  "gfun_G": "0.6522116218560278",
--  "gfun_ev": "0.003627782844736638"
--},
-- ...

manual

SELECT *
FROM UNNEST((
    SELECT carto.GFUN(myarray)
    FROM (
        SELECT ARRAY_AGG(position_geom) myarray
        FROM `bigquery-public-data.catalonian_mobile_coverage.mobile_data_2015_2017`
        WHERE date = '2017-12-31'
    )
))
ORDER BY distance;
--{
--  "distance": "38.599968853183",
--  "gfun_G": "0.4319167389418907",
--  "gfun_ev": "4.037383876246414E-4"
--},
--{
--  "distance": "77.199937706366",
--  "gfun_G": "0.5771899392888118",
--  "gfun_ev": "0.0016139757856029613"
--},
--{
--  "distance": "115.799906559549",
--  "gfun_G": "0.6522116218560278",
--  "gfun_ev": "0.003627782844736638"
--},
-- ...

CREATE_SPATIAL_COMPOSITE_SUPERVISED

CREATE_SPATIAL_COMPOSITE_SUPERVISED(input_query, index_column, output_prefix, options)

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. 'project-id.dataset-id.table-name'.

• index_column: STRING the name of the column with the unique geographic identifier.

• output_prefix: STRING the prefix for the output table. It should include project and dataset, e.g. 'project-id.dataset-id.table-name'.

• options: STRING containing a valid JSON with the different options. Valid options are described below.

  • model_transform: STRING containing the TRANSFORM clause in a BigQuery ML CREATE MODEL statement. If NULL no TRANSFORM clause is applied.

  • model_options: JSON with the different options allowed by BigQuery ML CREATE MODEL statement for regression models. Any model is allowed as long as it can deal with numerical inputs for the response variable. At least the INPUT_LABEL_COLS and MODEL_TYPE parameters must be specified. By default, data will not be split into train and test (DATA_SPLIT_METHOD = 'NO_SPLIT'). Hyperparameter tuning is not currently supported.

  • r2_thr: FLOAT64 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 a warning is raised. The default value is 0.5.

  • bucketize_method: STRING the method used to discretize the spatial composite score. The default value is NULL. 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: INT64 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. For large inputs, setting this option to TRUE might cause a Query exceeds CPU resources error. Ignored if bucketize_method is not specified.

Return type

The results are stored in the table named <output_prefix>, 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 FLOAT64 if the score is not discretized and INT64 otherwise.

When the score is discretized by specifying the bucketize_method parameter, the procedure also returns a lookup table named <output_prefix>_lookup_table with the following columns:

• lower_bound: FLOAT64 the lower bound of the bin.

• upper_bound: FLOAT64 the upper bound of the bin.

• spatial_score: INT64 the value of the (discretized) composite score.

Example

carto-un

CALL `carto-un`.carto.CREATE_SPATIAL_COMPOSITE_SUPERVISED(
    'SELECT * FROM `cartobq.docs.spatial_scoring_input`',
    'geoid',
    '<project-id>.<dataset-id>.<table-name>',
    '''{
        "model_transform":[
            "revenue_change",
            "fempop_15_44, public_transport, education, pois, urbanity"
        ],
        "model_options":{
            "MODEL_TYPE":"LINEAR_REG",
            "INPUT_LABEL_COLS":["revenue_change"],
            "DATA_SPLIT_METHOD":"no_split",
            "OPTIMIZE_STRATEGY":"NORMAL_EQUATION",
            "CATEGORY_ENCODING_METHOD":"ONE_HOT_ENCODING"
        },
        "r2_thr":0.4,
        "bucketize_method":"EQUAL_INTERVALS_ZERO_CENTERED",
        "nbuckets":5
    }
    '''
)
-- Table `<my-project>.<my-dataset>.<table-name>` will be created

carto-un-eu

CALL `carto-un-eu`.carto.CREATE_SPATIAL_COMPOSITE_SUPERVISED(
    'SELECT * FROM `cartobq.docs.spatial_scoring_input`',
    'geoid',
    '<project-id>.<dataset-id>.<table-name>',
    '''{
        "model_transform":[
            "revenue_change",
            "fempop_15_44, public_transport, education, pois, urbanity"
        ],
        "model_options":{
            "MODEL_TYPE":"LINEAR_REG",
            "INPUT_LABEL_COLS":["revenue_change"],
            "DATA_SPLIT_METHOD":"no_split",
            "OPTIMIZE_STRATEGY":"NORMAL_EQUATION",
            "CATEGORY_ENCODING_METHOD":"ONE_HOT_ENCODING"
        },
        "r2_thr":0.4,
        "bucketize_method":"EQUAL_INTERVALS_ZERO_CENTERED",
        "nbuckets":5
    }
    '''
)
-- Table `<my-project>.<my-dataset>.<table-name>` will be created

manual

CALL carto.CREATE_SPATIAL_COMPOSITE_SUPERVISED(
    'SELECT * FROM `cartobq.docs.spatial_scoring_input`',
    'geoid',
    '<project-id>.<dataset-id>.<table-name>',
    '''{
        "model_transform":[
            "revenue_change",
            "fempop_15_44, public_transport, education, pois, urbanity"
        ],
        "model_options":{
            "MODEL_TYPE":"LINEAR_REG",
            "INPUT_LABEL_COLS":["revenue_change"],
            "DATA_SPLIT_METHOD":"no_split",
            "OPTIMIZE_STRATEGY":"NORMAL_EQUATION",
            "CATEGORY_ENCODING_METHOD":"ONE_HOT_ENCODING"
        },
        "r2_thr":0.4,
        "bucketize_method":"EQUAL_INTERVALS_ZERO_CENTERED",
        "nbuckets":5
    }
    '''
)
-- Table `<my-project>.<my-dataset>.<table-name>` will be created

CREATE_SPATIAL_COMPOSITE_UNSUPERVISED

CREATE_SPATIAL_COMPOSITE_UNSUPERVISED(input_query, index_column, output_prefix, options)

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_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. 'project-id.dataset-id.table-name'.

• index_column: STRING the name of the column with the unique geographic identifier.

• output_prefix: STRING the prefix for the output table. It should include project and dataset, e.g. 'project-id.dataset-id.table-name'.

• 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: STRUCT the (optional) weights for each variable used to compute the spatial composite when scoring_method is set to CUSTOM_WEIGHTS, passed as {"name":value, …}. 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: FLOAT64 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<FLOAT64> 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: INT64 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.

Option	ENTROPY	CUSTOM_WEIGHTS	FIRST_PC	Valid options	Default value
scoring_method	Optional	Optional	Optional	ENTROPY, CUSTOM_WEIGHTS, FIRST_PC	ENTROPY
weights	Ignored	Optional	Ignored	{"name":value…}	NULL
scaling	Ignored	Optional	Ignored	MIN_MAX_SCALER, STANDARD_SCALER, RANKING, DISTANCE_TO_TARGET_MIN, DISTANCE_TO_TARGET_MAX, DISTANCE_TO_TARGET_AVG, PROPORTION	MIN_MAX_SCALER
aggregation	Ignored	Optional	Ignored	LINEAR, GEOMETRIC	LINEAR
correlation_var	Ignored	Optional	Mandatory	-	NULL
correlation_thr	Ignored	Optional	Optional	-	NULL
return_range	Optional	Optional	Optional	-	NULL
bucketize_method	Optional	Optional	Optional	EQUAL_INTERVALS, QUANTILES, JENKS	NULL
nbuckets	Optional	Optional	Optional	-	When bucketize_method is set to EQUAL_INTERVALS is selected using Freedman and Diaconis’s (1981) rule

Return type

The results are stored in the table named <output_prefix>, 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 FLOAT64 if the score is not discretized and INT64 otherwise.

When the score is discretized by specifying the bucketize_method parameter, the procedure also returns a lookup table named <output_prefix>_lookup_table with the following columns:

• lower_bound: FLOAT64 the lower bound of the bin.

• upper_bound: FLOAT64 the upper bound of the bin.

• spatial_score: INT64 the value of the (discretized) composite score.

Examples

With the ENTROPY method:

carto-un

CALL `carto-un`.carto.CREATE_SPATIAL_COMPOSITE_UNSUPERVISED(
    'SELECT * EXCEPT(geom, revenue_change, urbanity, urbanity_ordinal) FROM `cartobq.docs.spatial_scoring_input`',
    'geoid',
    '<project-id>.<dataset-id>.<table-name>',
    '''{
        "scoring_method":"ENTROPY",
        "return_range":[0.0,1.0]
    }
    '''
)
-- Table `<my-project>.<my-dataset>.<table-name>` will be created

carto-un-eu

CALL `carto-un-eu`.carto.CREATE_SPATIAL_COMPOSITE_UNSUPERVISED(
    'SELECT * EXCEPT(geom, revenue_change, urbanity, urbanity_ordinal) FROM `cartobq.docs.spatial_scoring_input`',
    'geoid',
    '<project-id>.<dataset-id>.<table-name>',
    '''{
        "scoring_method":"ENTROPY",
        "return_range":[0.0,1.0]
    }
    '''
)
-- Table `<my-project>.<my-dataset>.<table-name>` will be created

manual

CALL carto.CREATE_SPATIAL_COMPOSITE_UNSUPERVISED(
    'SELECT * EXCEPT(geom, revenue_change, urbanity, urbanity_ordinal) FROM `cartobq.docs.spatial_scoring_input`',
    'geoid',
    '<project-id>.<dataset-id>.<table-name>',
    '''{
        "scoring_method":"ENTROPY",
        "return_range":[0.0,1.0]
    }
    '''
)
-- Table `<my-project>.<my-dataset>.<table-name>` will be created

With the CUSTOM_WEIGHTS method:

carto-un

CALL `carto-un`.carto.CREATE_SPATIAL_COMPOSITE_UNSUPERVISED(
    'SELECT * EXCEPT(geom, revenue_change, urbanity) FROM `cartobq.docs.spatial_scoring_input`',
    'geoid',
    '<project-id>.<dataset-id>.<table-name>',
    '''{
        "scoring_method":"CUSTOM_WEIGHTS",
        "weights":{"fempop_15_44":0.2,"education":0.1,"urbanity_ordinal":0.1,"pois":0.1},
        "scaling":"RANKING",
        "aggregation":"LINEAR",
        "bucketize_method":"JENKS"
    }
    '''
 )
-- Table `<my-project>.<my-dataset>.<table-name>` will be created

carto-un-eu

CALL `carto-un-eu`.carto.CREATE_SPATIAL_COMPOSITE_UNSUPERVISED(
    'SELECT * EXCEPT(geom, revenue_change, urbanity) FROM `cartobq.docs.spatial_scoring_input`',
    'geoid',
    '<project-id>.<dataset-id>.<table-name>',
    '''{
        "scoring_method":"CUSTOM_WEIGHTS",
        "weights":{"fempop_15_44":0.2,"education":0.1,"urbanity_ordinal":0.1,"pois":0.1},
        "scaling":"RANKING",
        "aggregation":"LINEAR",
        "bucketize_method":"JENKS"
    }
    '''
 )
-- Table `<my-project>.<my-dataset>.<table-name>` will be created

manual

CALL carto.CREATE_SPATIAL_COMPOSITE_UNSUPERVISED(
    'SELECT * EXCEPT(geom, revenue_change, urbanity) FROM `cartobq.docs.spatial_scoring_input`',
    'geoid',
    '<project-id>.<dataset-id>.<table-name>',
    '''{
        "scoring_method":"CUSTOM_WEIGHTS",
        "weights":{"fempop_15_44":0.2,"education":0.1,"urbanity_ordinal":0.1,"pois":0.1},
        "scaling":"RANKING",
        "aggregation":"LINEAR",
        "bucketize_method":"JENKS"
    }
    '''
 )
-- Table `<my-project>.<my-dataset>.<table-name>` will be created

With the FIRST_PC method:

carto-un

CALL `carto-un`.carto.CREATE_SPATIAL_COMPOSITE_UNSUPERVISED(
    'SELECT * EXCEPT(geom, revenue_change, urbanity, urbanity_ordinal) FROM `cartobq.docs.spatial_scoring_input`',
    'geoid',
    '<project-id>.<dataset-id>.<table-name>',
    '''{
        "scoring_method":"FIRST_PC",
        "correlation_var":"fempop_15_44",
        "correlation_thr":0.6,
        "bucketize_method":"QUANTILES"
    }
    '''
)
-- Table `<my-project>.<my-dataset>.<table-name>` will be created

carto-un-eu

CALL `carto-un-eu`.carto.CREATE_SPATIAL_COMPOSITE_UNSUPERVISED(
    'SELECT * EXCEPT(geom, revenue_change, urbanity, urbanity_ordinal) FROM `cartobq.docs.spatial_scoring_input`',
    'geoid',
    '<project-id>.<dataset-id>.<table-name>',
    '''{
        "scoring_method":"FIRST_PC",
        "correlation_var":"fempop_15_44",
        "correlation_thr":0.6,
        "bucketize_method":"QUANTILES"
    }
    '''
)
-- Table `<my-project>.<my-dataset>.<table-name>` will be created

manual

CALL carto.CREATE_SPATIAL_COMPOSITE_UNSUPERVISED(
    'SELECT * EXCEPT(geom, revenue_change, urbanity, urbanity_ordinal) FROM `cartobq.docs.spatial_scoring_input`',
    'geoid',
    '<project-id>.<dataset-id>.<table-name>',
    '''{
        "scoring_method":"FIRST_PC",
        "correlation_var":"fempop_15_44",
        "correlation_thr":0.6,
        "bucketize_method":"QUANTILES"
    }
    '''
)
-- Table `<my-project>.<my-dataset>.<table-name>` will be created

CRONBACH_ALPHA_COEFFICIENT

CRONBACH_ALPHA_COEFFICIENT(input_query, output_prefix)

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