Statistics

Components to perform spatial statistics calculations.

These components require the CARTO Analytics Toolbox installed in the chosen connection to build the workflow.

Composite Score Supervised

Description

This component 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 input variables are dropped.

Inputs

Input table
Column with unique geographic identifier (spatial index)
Input variables
Response variables
Model regressor
Model options
Output formatting
R-squared threshold

Outputs

Output table

External links

BigQuery reference

Snowflake reference

Composite Score Unsupervised

Description

This component combines (spatial) input 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.

Inputs

Input table
Column with unique geographic identifier (spatial index)
Input variables
Scoring method
- Entropy
- Custom Weights:
  - When defining custom weights, you will find a button that controls the direction of the weight:
    By default, it is set as "Direct" (), which means that the variable's value will weight in to make the final score higher.
    If set to "Inverse" (), the variable's value will weight in to make the final score lower.
  - Scaling function:
    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 Function:
    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.
- First PC
  - Correlation variable: 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 threshold: the minimum absolute value of the correlation between each individual variable and the first principal component score.
Output formatting
- None
- Equal Intervals
- Jenks
- Quantiles
- Return Range

Outputs

Result table
Lookup table:only available if output formatting is Equal Intervals, Jenks, or Quantiles

External links

BigQuery reference

Snowflake reference

Cronbach Alpha Coefficient

Description

This component computes the Cronbach Alpha Coefficient, which can be used to measure internal consistency of different variables.

This component can be used to determine wether a combination of variables are consistent enough to be used to create a Composite Score using one of the components above.

Inputs

Input table
Input variables

Outputs

Output table

External links

BigQuery reference

Snowflake reference

Getis Ord

Description

This component computes the Getis-Ord Gi* statistic for each spatial index in the source table.

Inputs

Source table [Table]
Index column [Column]
Value column [Column]
Kernel function for spatial weights [Selection]
Size [Number]

Outputs

Result table [Table]

External links

Getis Ord Spacetime

Description

This component computes the spacetime Getis-Ord Gi* statistic for each spatial index and datetime timestamp in the source table.

Inputs

Source table [Table]
Index column [Column]
Date column [Column]
Value column [Column]
Kernel function for spatial weights [Selection]
Kernel function for temporal weights [Selection]
Size [Number]
Temporal bandwidth [Number]
Time interval [Selection]

Outputs

Result table [Table]

External links

GWR

Description

This component runs a spatial-index-based Geographically Weighted Regression (GWR) model.

Inputs

Input table [Table]
Index column [Column]
Feature variables [Column]
Target variable [Column]
K-ring size [Number]
Kernel function [Selection]
Fit intercept [Boolean]

Outputs

Result table [Table]

External links

BigQuery reference

Hotspot Analysis

This component requires the CARTO Analytics Toolbox installed in the chosen connection to build the workflow.

Description

This component is used to locate hotspot areas by calculating a combined Getis-Ord Gi* statistic using a uniform kernel over one or several variables.

Inputs

Source table [Table]
Index column [Column]
Input variables [Column]
Variable weights [String]
K-ring size [Number]
Significance level [Number]

Outputs

Result table [Table]

External links

BigQuery reference

Local Moran's I

Description

This component computes the local Moran's I statistic for each spatial index in the source table.

Inputs

Source table [Table]
Index column [Column]
Value column [Column]
Size [Number]
Decay function [Selection]
Permutations [Number]

Outputs

Result table [Table]

External links

Moran's I

Description

This component computes the Moran's I statistic for each spatial index in the source table.

Inputs

Source table [Table]
Index column [Column]
Value column [Column]
Size [Number]
Decay function [Selection]

Outputs

Result table [Table]

External links

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Last updated 16 days ago