Hot Spot Time Series Worker

Processes hot spot time series analysis jobs to analyze temporal patterns in hot spots.

Overview

The hot spot time series worker performs hot spot analysis across multiple time periods to identify temporal patterns in spatial clustering.

Job Type

hotspot_timeseries

Input Parameters

{
  "dataset_id": 123,
  "value_field": "population",
  "time_field": "date",
  "time_periods": ["2020", "2021", "2022"],
  "neighbor_type": "distance",
  "distance": 1000
}

Parameters

  • dataset_id (required): Source dataset ID

  • value_field (required): Numeric field to analyze

  • time_field (required): Field containing time period identifiers

  • time_periods (required): Array of time period values to analyze

  • neighbor_type (optional): “distance” or “knn” (default: “distance”)

  • distance (required if neighbor_type=”distance”): Distance threshold

  • k_neighbors (required if neighbor_type=”knn”): Number of nearest neighbors

Output

Creates a new dataset with time series hot spot results:

  • Hot spot analysis for each time period

  • Temporal patterns in clustering

  • Time period identifiers

  • Gi* z-scores and p-values for each period

Algorithm

The worker:

  1. Filters data by time period

  2. Performs hot spot analysis for each period

  3. Combines results with time period information

  4. Stores results in output table

Example

# Enqueue a hot spot time series job via API
curl -X POST "https://example.com/api/hotspot_timeseries_run.php" \
  -H "Content-Type: application/json" \
  -d '{
    "dataset_id": 123,
    "value_field": "population",
    "time_field": "year",
    "time_periods": ["2020", "2021", "2022"],
    "neighbor_type": "distance",
    "distance": 1000
  }'

Background Jobs

This analysis runs as a background job. The worker:

  1. Fetches queued hotspot_timeseries jobs

  2. Validates input parameters

  3. Performs hot spot analysis for each time period

  4. Combines results

  5. Creates output dataset

  6. Marks job as completed

Performance Considerations

  • Processing time depends on dataset size and number of time periods

  • Each time period requires separate hot spot analysis

  • Consider limiting number of time periods for large datasets

  • Results can be large for many time periods