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Outlier Analysis Worker

Processes outlier detection jobs to identify statistical outliers in spatial data.

Overview

The outlier analysis worker identifies features with values that are statistically unusual using z-score or MAD (Median Absolute Deviation) methods.

Job Type

outlier_analysis

Input Parameters

{
  "dataset_id": 123,
  "value_field": "income",
  "method": "zscore",
  "threshold": 2.0
}

Parameters

dataset_id (required): Source dataset ID
value_field (required): Numeric field to analyze
method (optional): "zscore" or "mad" (default: "zscore")
threshold (optional): Z-score threshold or MAD multiplier (default: 2.0)

Output

Creates a new dataset with outlier analysis results:

Original features marked as outliers
Outlier score (z-score or MAD score)
Outlier flag
Original attributes preserved

Methods

Z-Score Method

Calculates standardized z-scores:

Mean and standard deviation calculated
Z-score = (value - mean) / standard_deviation
Features with |z-score| > threshold are outliers

MAD Method

Uses Median Absolute Deviation:

Median calculated
MAD = median(|value - median|)
Modified z-score = 0.6745 * (value - median) / MAD
Features with |modified z-score| > threshold are outliers

Example

# Enqueue an outlier analysis job via API
curl -X POST "https://example.com/api/analysis/outlier_run.php" \
  -H "Content-Type: application/json" \
  -d '{
    "dataset_id": 123,
    "value_field": "income",
    "method": "zscore",
    "threshold": 2.0
  }'

Background Jobs

This analysis runs as a background job. The worker:

Fetches queued outlier_analysis jobs
Validates input parameters
Calculates statistics (mean/std or median/MAD)
Identifies outliers
Creates output dataset
Marks job as completed

Performance Considerations

Processing time depends on dataset size
Z-score method requires two passes (mean/std, then scoring)
MAD method is more robust to outliers in calculation
Consider filtering null values before analysis

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