A Spatio-Temporal Mixture Model for Point Processes with Application to Ambulance Demand
Bibliographic record
Abstract
We introduce a parsimonious Bayesian approach for modeling spatio-temporal point processes. We model a time series of spatial densities by finite mixture models. The mixture component distributions are assumed common to all time periods while the mixture weights evolve over time. This allows efficient estimation of the underlying spatial structure, yet enough flexibility to capture dynamics over time. We include several extensions to this model. First, we introduce constraints on the mixture weights to capture temporal patterns such as diurnal cycles and seasonality. Second, location-specific temporal dynamics are modeled by applying a separate autoregressive prior on each mixture weight. While estimation may be performed using a fixed number of mixture components, we also extend to estimate the number of components using birth-and-death Markov chain Monte Carlo. Finally, we tailor the estimation to a specific spatial boundary. To illustrate the proposed models, and highlight the impact and utility of each extension, we estimate spatio-temporal ambulance demand in Toronto, Canada at fine time and location scales; such estimates are critical for fleet management and deployment. Our method effectively and efficiently
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".