Statistics
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Components to perform spatial statistics calculations.
Description
This component computes a clusterization based on the values of a feature over time (i.e. sales of different stores, temperature in different H3 cells, etc) and assigns a cluster number to each, using different methods to evaluate the values and their distribution over time.
Inputs
Input table: This component expects a table containing a column with at least a timestamp column, a value column and an index to be used as partition (usually an spatial index or a feature id)
Settings
Timestamp column: Select a column in the input that contains timestamps/dates.
Partition column: Each unique value on this column will be assigned a cluster.
Value column: Clusters will be calculated based on the value of this column along time
Number of clusters: Define the number of clusters to be generated.
Method:
Value
Profile
Outputs
Output table: This component will generate a table with the following columns:
Partitioning column with the same name as the given column from inputs.
Cluster (String): Contains the assigned cluster number.
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
Settings
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
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
Settings
Column with unique geographic identifier (spatial index)
Input variables
Scoring method
Entropy
Custom Weights:
Variables and weights:When defining custom weights, you will find a button that controls the direction of the weight:
When specifying custom weights, the component will internally apply a normalization so that the sum of all weights is equal to 1
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
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
Settings
Input variables
Outputs
Output table
External links
Description
Inputs
Source table [Table]: The source table. It must contain the index_column, the date_column, the value_column, a column named value_column_baseline with the values that should be used as a baseline to detect the anomalies and, when the distributional model parameter is set to ‘GAUSSIAN’ also its variance, value_column_baseline_sigma2.
Settings
Index column [Column]: The name of the column with the unique geographic identifier of the spatial index, either ‘H3’ or ‘QUADBIN’.
Date column [Column]: The name of the column with the date identifier.
Value column [Column]: The name of the column with the variable for which the space-time anomalies should be detected.
Time frequency [Selection]: The temporal frequency of the data selected from one of the following: ‘SECOND’, ‘MINUTE’, ‘HOUR’, ‘DAY’, ‘WEEK’, ‘MONTH’, ‘QUARTER’, ‘YEAR’.
Distributional model [Selection]: The distributional model of the data, either ‘POISSON’ or ‘GAUSSIAN’.
Estimation method [Selection]: The estimation method, either ‘EXPECTATION’ or ‘POPULATION’ for the expectation- and population-based methods respectively.
Prospective analysis [Boolean]: Option to specify if the analysis is retrospective or prospective. In a prospective analysis, only temporal zones that end with the last timestamp are considered and the interest lies in detecting new emerging anomalies. In a retrospective analysis instead, the space-time anomalies can happen at any point in time over all the past data (a temporal zone can end at any timestamp)
High-mean anomalies [Boolean]: Option to specify if the analysis is for detecting space-time zones higher or lower than the baseline.
K-ring size (advanced, optional) [Range]: The minimum and maximum k-ring size used to define the spatial zones.
Temporal window (advanced) [Range]: The minimum and maximum temporal window used to define the temporal zones.
Number of permutations (advanced) [Number]: The number of permutations used to derive the random replicas to test the anomaly statistical significance
Max results (advanced) [Number]: The maximum number of space-time zones returned
Outputs
Output table [Table]: The output table
External links
Inputs
Source table [Table]: The source table. It must contain the index_column, the date_column, the value_column, a column named value_column_baseline with the values that should be used as a baseline to detect the anomalies and, when the distributional model parameter is set to ‘GAUSSIAN’ also its variance, value_column_baseline_sigma2.
Settings
Index column [Column]: The name of the column with the unique geographic identifier of the spatial index, either ‘H3’ or ‘QUADBIN’.
Value column [Column]: The name of the column with the variable for which the spatial anomalies should be detected.
Distributional model [Selection]: The distributional model of the data, either ‘POISSON’ or ‘GAUSSIAN’.
Estimation method [Selection]: The estimation method, either ‘EXPECTATION’ or ‘POPULATION’ for the expectation- and population-based methods respectively.
High-mean anomalies [Boolean]: Option to specify if the analysis is for detecting space-time zones higher or lower than the baseline.
K-ring size (advanced, optional) [Range]: The minimum and maximum k-ring size used to define the spatial zones.
Number of permutations (advanced) [Number]: The number of permutations used to derive the random replicas to test the anomaly statistical significance
Max results (advanced) [Number]: The maximum number of space-time zones returned
Outputs
Output table [Table]: The output table
External links
Description
This component computes the Getis-Ord Gi* statistic for each spatial index in the source table.
Inputs
Source table [Table]
Settings
Index column [Column]
Value column [Column]
Kernel function for spatial weights [Selection]
Size [Number]
Outputs
Result table [Table]
External links
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
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
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
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
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
Description
Inputs
Settings
Index column: Select a column in the input that contains a spatial index (Quadbin or H3).
Date column: Select a column in the input that contains date/timestamp.
Gi Value: Select a column in the input that contains a Gi value generated by a Getis Ord Spacetime component.
P Value: Select a column in the input that contains a P value generated by a Getis Ord Spacetime component.
Threshold: Select the threshold of the P value for a location to be considered as hotspot/coldspot.
Algorithm: Select the algorithm to be used for the monotonic trend test:
Mann-Kendall (default)
Modified Mann-Kendall
Output
Output table: The component will return a table with the column that contains the index and the resulting columns from the procedure's execution.
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.
This component can be used to detect space-time regions where the variable of interest is higher (or lower) than its baseline value. It implements the developed in to detect space-time regions where the variable of interest is higher (or lower) than its baseline value.
This component can be used to detect spatial regions where the variable of interest is higher (or lower) than its baseline value. It implements the developed in to detect spatial regions where the variable of interest is higher (or lower) than its baseline value.
This component requires installed in the chosen connection to build the workflow.
This component takes the output of and classifies each location into specific types of hotspots or coldspots, based on patterns of spatial clustering and intensity trends over time.
Input table: This component expects to be connected to the output of a successfully executed node, or a table that contains that same result.
