Real-Time Emergency Response Optimization with SQL

Every second matters in emergency response. RisingWave, a PostgreSQL-compatible streaming database, continuously correlates dispatch IDs, unit GPS positions, incident event classifications, and hospital capacity feeds so that command centers can route the nearest appropriate resource to any incident—and detect when response time SLAs are at risk before a breach becomes a statistic.

Why Emergency Response Optimization Matters

Major cities handle tens of thousands of emergency calls per year. The difference between a good outcome and a preventable death often comes down to whether the nearest available unit was dispatched, or whether the closest trauma center had available capacity. Legacy CAD (computer-aided dispatch) systems update unit status in batch cycles, miss cross-agency resource visibility, and cannot predict SLA breaches before they happen.

Streaming SQL on a modern platform changes this by enabling:

• Live unit positioning: GPS pings from every emergency vehicle updated every 10–30 seconds, queryable with standard SQL.
• Predictive SLA alerts: when travel time estimates indicate a unit will arrive outside the target window, an escalation fires automatically.
• Incident clustering: multiple 911 calls for the same event are correlated within a time-space window to avoid duplicate dispatches and correctly size the initial response.
• Cross-agency visibility: fire, EMS, and police data streams merge into a unified operational picture without manual phone coordination.
• Post-incident analytics: the same materialized views that power live operations serve as the data source for after-action reports and planning models.

How Streaming SQL Solves This

RisingWave ingests Kafka topics from the CAD system, AVL (automatic vehicle location) feed, and hospital capacity API. SQL CREATE MATERIALIZED VIEW statements define the analytics; RisingWave maintains them incrementally as new events arrive. The PostgreSQL-compatible interface means any existing BI tool or CAD dashboard can query live response metrics without a custom integration layer.

Step-by-Step Tutorial

Step 1: Connect the Data Source

-- Incident feed from 911/CAD
CREATE SOURCE incident_stream (
    incident_id       VARCHAR,      -- e.g. 'INC-20240402-04471'
    zone_code         VARCHAR,      -- e.g. 'PRECINCT-12'
    call_time         TIMESTAMPTZ,
    event_class       VARCHAR,      -- 'FIRE','EMS','POLICE_PRIORITY1',...
    priority_level    SMALLINT,     -- 1 (highest) – 5
    lat               DOUBLE PRECISION,
    lon               DOUBLE PRECISION,
    status            VARCHAR       -- 'PENDING','DISPATCHED','ON_SCENE','CLEARED'
)
WITH (
    connector     = 'kafka',
    topic         = 'cad.incidents',
    properties.bootstrap.server = 'broker:9092',
    scan.startup.mode = 'latest'
)
FORMAT PLAIN ENCODE JSON;

-- Vehicle AVL (automatic vehicle location)
CREATE SOURCE unit_location_stream (
    unit_id           VARCHAR,      -- e.g. 'AMB-047'
    unit_type         VARCHAR,      -- 'AMBULANCE','FIRE_ENGINE','PATROL'
    zone_code         VARCHAR,
    ping_time         TIMESTAMPTZ,
    lat               DOUBLE PRECISION,
    lon               DOUBLE PRECISION,
    status            VARCHAR,      -- 'AVAILABLE','DISPATCHED','AT_SCENE','OUT_OF_SERVICE'
    speed_kmh         DOUBLE PRECISION
)
WITH (
    connector     = 'kafka',
    topic         = 'avl.units',
    properties.bootstrap.server = 'broker:9092',
    scan.startup.mode = 'latest'
)
FORMAT PLAIN ENCODE JSON;

-- Dispatch assignment log
CREATE SOURCE dispatch_log (
    dispatch_id       VARCHAR,
    incident_id       VARCHAR,
    unit_id           VARCHAR,
    dispatch_time     TIMESTAMPTZ,
    on_scene_time     TIMESTAMPTZ,
    clear_time        TIMESTAMPTZ,
    response_time_s   INTEGER
)
WITH (
    connector     = 'kafka',
    topic         = 'cad.dispatch.log',
    properties.bootstrap.server = 'broker:9092',
    scan.startup.mode = 'latest'
)
FORMAT PLAIN ENCODE JSON;

Step 2: Build the Core View

Aggregate response performance per zone and event class in rolling 15-minute windows:

CREATE MATERIALIZED VIEW response_performance_15m AS
SELECT
    i.zone_code,
    i.event_class,
    window_start,
    window_end,
    COUNT(DISTINCT d.dispatch_id)                        AS total_dispatches,
    AVG(d.response_time_s)                               AS avg_response_time_s,
    PERCENTILE_CONT(0.9) WITHIN GROUP (ORDER BY d.response_time_s)
                                                         AS p90_response_time_s,
    COUNT(*) FILTER (WHERE d.response_time_s > 480
                       AND i.priority_level <= 2)        AS p1_p2_sla_breaches,
    COUNT(*) FILTER (WHERE d.on_scene_time IS NULL
                       AND i.call_time < NOW() - INTERVAL '8 MINUTES'
                       AND i.priority_level <= 2)        AS pending_sla_at_risk
FROM TUMBLE(incident_stream i, call_time, INTERVAL '15 MINUTES')
JOIN dispatch_log d ON i.incident_id = d.incident_id
GROUP BY i.zone_code, i.event_class, window_start, window_end;

Build a live unit availability summary per zone:

CREATE MATERIALIZED VIEW unit_availability_live AS
SELECT
    zone_code,
    unit_type,
    COUNT(*) FILTER (WHERE status = 'AVAILABLE')       AS available_units,
    COUNT(*) FILTER (WHERE status = 'DISPATCHED')      AS dispatched_units,
    COUNT(*) FILTER (WHERE status = 'AT_SCENE')        AS on_scene_units,
    COUNT(*) FILTER (WHERE status = 'OUT_OF_SERVICE')  AS oos_units,
    COUNT(*)                                           AS total_units
FROM (
    SELECT DISTINCT ON (unit_id)
        unit_id, unit_type, zone_code, status
    FROM unit_location_stream
    ORDER BY unit_id, ping_time DESC
) latest_status
GROUP BY zone_code, unit_type;

Step 3: Alerts and Downstream Integration

CREATE MATERIALIZED VIEW alerts AS
-- Zone resource shortage
SELECT
    zone_code,
    unit_type,
    NOW()                   AS alert_time,
    'LOW_UNIT_AVAILABILITY' AS alert_type,
    available_units,
    total_units,
    NULL::INTEGER           AS sla_breach_count
FROM unit_availability_live
WHERE available_units = 0 AND total_units > 0
UNION ALL
-- SLA breach escalation
SELECT
    zone_code,
    event_class,
    window_start,
    'SLA_BREACH_RISK',
    pending_sla_at_risk,
    total_dispatches,
    p1_p2_sla_breaches
FROM response_performance_15m
WHERE pending_sla_at_risk > 0 OR p1_p2_sla_breaches > 0;

CREATE SINK emergency_alerts_sink
FROM alerts
WITH (
    connector  = 'kafka',
    topic      = 'cad.alerts',
    properties.bootstrap.server = 'broker:9092'
)
FORMAT PLAIN ENCODE JSON;

Comparison Table

FAQ

Q: Can we join incident data with a hospital capacity feed that comes from a REST API rather than Kafka?

Yes. RisingWave supports an HTTP source connector that polls a REST endpoint on a configurable interval and makes the result available as a table. You can join this reference data with your streaming incident and dispatch tables in a materialized view.

Q: How do we prevent the unit availability view from showing stale GPS pings if a unit goes offline?

Add a freshness filter to the DISTINCT ON subquery:

WHERE ping_time > NOW() - INTERVAL '5 MINUTES'

Units that have not reported in 5 minutes are excluded from the availability count, and a separate alert can fire for stale AVL pings.

Q: Is it possible to use RisingWave results to automatically trigger dispatch recommendations?

RisingWave generates alerts and recommendations via its Kafka sink. A lightweight microservice subscribed to the alerts topic can apply domain rules (unit type matching, distance estimation) and push recommended dispatches to the CAD system's API. RisingWave handles the analytics layer; the microservice handles the actuation.

Key Takeaways

• Streaming SQL over incident, AVL, and dispatch feeds enables live response-time monitoring that traditional CAD systems cannot achieve.
• Zone-level resource availability views fire alerts when all units of a given type are unavailable, enabling pre-emptive mutual aid requests.
• P90 response-time views computed continuously in SQL replace nightly batch reports without additional ETL infrastructure.
• The PostgreSQL-compatible interface integrates with any existing CAD dashboard or BI tool without custom connectors.

• RisingWave Documentation
• Ingesting from Kafka
• Streaming Joins Reference