Local Employment of City Staff

Measuring Distance City Employees Travel to Work in miles through December 2020

This goal is measured by tracking Distance City Employees Travel to Work in miles.Explore the data
miles
Current
miles
Dec 2020 Target
Goal Period ended December 2020

            What is this indicator?

            This indicator measures the average miles traveled by Santa Monica City employees from their home to City Hall.

            Why is it important?

            Shorter commutes enable a more sustainable lifestyle and a lower carbon footprint. This indicator relates to the jobs/housing balance, which indicates the ratio of available housing to available jobs in the community. When there are more jobs than available housing in employees' price ranges, the number of vehicle miles traveled to work increases as employees reside in more affordable areas outside of Santa Monica.

            How are we doing?

            In 2015, the average distance City employees traveled to work was 28 miles. The jobs-housing imbalance in Santa Monica has contributed to above-average travel times for City employees. This also contributes to increased carbon emissions.

            Santa Monica has a disproportionate ratio of approximately 1.6 jobs for every available unit of housing. While the high number of jobs spurs economic growth, this limited housing supply has driven prices up to unaffordable rates for the majority of employees working in Santa Monica, including City employees.

            How can we help?

            Unfortunately, housing prices are not likely to drop anytime soon. If you are one of the many employees commuting long distances to work each day, try carpooling with friends or coworkers or investing in a low-emissions or electric vehicle to lower your carbon footprint.

      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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