Public Safety

Measuring the total number of high hazard inspections in 2017

This measurement tracks high hazard inspections in ten categories (places of assembly, schools and day care facilities, medical care, apartments & hotels, commercial/mercantile, industrial, manufacturing, warehouse, special events and other).Explore the data
inspections
Final
inspections
Dec 2017 Target
Goal Period ended December 2017

        Fire Prevention

        The Fire District’s Prevention Bureau is a comprehensive risk and hazard reduction program, implementing strategies that protect natural resources, secure the economic vitality of the community and improve the quality of life for its citizens. A team of full-time and part-time Fire Prevention Specialists inspect local businesses, places of public assembly (such as churches), and schools to ensure they are compliant with the Fire Code, and operating safely for employees, patrons and the community. This provides for the economic well-being of our businesses and continuity of employment as well as preventing fires, industrial accidents and hazardous materials releases.

        The graph below depicts the Prevention Bureau's progress on High Hazard Business Inspections. A high hazard facility is a building or outdoor area in which any of the following criteria are met in the normal course of business: manufacturing, production, or storage processes that are specifically regulated by the Fire Code (examples include storage of combustible products over 12' high, storage or use of flammable or hazardous materials, dust producing operations, welding, paint or flammable finish spraying, and most automobile repair work); K-12 schools, apartments, hospitals, jails, theaters, restaurants, hotels, and any other facilities where large numbers and/or concentrations of people are; and critical infrastructure and/or economic value facilities (examples include utility buildings and locaitons, large retail stores, and businesses that employ a large number of people).

      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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