Patterns of Police, Fire, and Ambulance Calls-for-Service: Scanning the Spatio-Temporal Intersection of Emergency Service Problems
Bibliographic record
Abstract
Abstract Independent analysis of police, fire, and ambulance calls for service demonstrates common patterns in emergency service activity. Targeted, place-focused interventions have been demonstrated to prevent future problems for emergency services. This research builds on these findings to examine the spatial and temporal intersection of police, fire, and ambulance incidents to explore the potential utility of enhanced collaboration between emergency-first responders. Using police and fire data from Surrey, BC, Canada, from 2011 to 2013, spatial and temporal patterns of police-, fire-, and ambulance-related incidents were examined. Initial analyses demonstrate that 36% of the City’s area experienced 72% of incidents responded to over this 3-year study period. Focusing on this high-volume area, the spatial and temporal intersection of these incident types was explored. Spatially, lattices of varying cell sizes (250 m, 500 m, and 1,000 m) were placed over the study area. Temporally, incident volume was examined across the entire 3-year study period, and at yearly and monthly intervals. Incidents were placed within these spatial and temporal frameworks and visual inspection was utilized to assess the convergence of service demand. Regardless of the cell grid size, police, fire, and ambulance incidents were spatially and temporally concentrated, with the top 10% of cells accounting for approximately 50% of all incidents across all services. Furthermore, there was considerable spatio-temporal convergence in cells which account for the top decile of call volume for all incident types. A 2 × 2 typology is proposed to classify locations (in this case grid cells) based on (1) the frequency at which they generate high demand for services (sporadic versus persistent), and (2) the combination of agencies required to respond to high demand problems (single versus convergent). The spatial and temporal convergence of emergency service problems observed in this study suggests that an interagency approach to problem identification will enhance problem analysis processes. Working in conjunction with established problem-focused intervention strategies (such as problem-oriented policing), the volume-service typology provides a framework that can contribute to the development of appropriate problem-responses. This, we hope, will support emerging efforts to increase the extent to which emergency-first responder agencies collaborate to maximize efficiency and effectiveness, and reduce harm.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".