Traffic Impacts to Emergency Response

No increase in annual average police response times

This indicator measures annual average response times in seconds.Explore the data
seconds
Current
374seconds
Dec 2020 Target
Goal Period ended December 2020

            What is this indicator?

            This indicator measures the average emergency response time for police vehicles.

            Why is it important?

            Emergency response time is an essential component of a reliable public safety program. Residents, businesses and visitors rely on the Police and Fire Departments to respond to emergency calls in a timely manner. The City has a commitment to providing the community with the highest level of public safety.

            The Sustainable City Plan states a goal of creating a multi-modal transportation system that minimizes and, where possible, eliminates pollution and motor vehicle congestion while ensuring safe mobility and access for all without compromising our ability to protect public health and safety.

            How are we doing?

            In 2015, average emergency response time for police vehicles was 374 seconds, up from 319 seconds in 2010. The 2020 target is to maintain or improve response times.

            The variations in response time can have a myriad of causes.Some increase in delay can be attributed to increased congestion on roadways in the community as well as street calming measures such as speed bumps and traffic circles introduced to create a more walkable and safe community for pedestrians and bicyclists.

            How can we help?

            • Use public transportation, walk, or bike to help alleviate traffic congestion.
            • Learn more about the Santa Monica Police Department.
            • Learn more about emergency preparedness and the Community Emergency Response Team (CERT).

      Data Governance

      describes the quality of the data itself. Governance issues generally indicate that the data source is considered incomplete or unreliable.

      Model Health

      describes the quality of the predictive model. If the model health is poor, the trend prediction should not be trusted.

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