Creating a Safety Visualization Dashboard for Construction Site Risk Analysis and Management
Kevin Matthews University of North Carolina Wilmington
Olivia Moss University of North Carolina Wilmington
Abstract Identifying, assessing, and prioritizing potential risks is a crucial aspect of success within any organization, but especially construction organizations. Without risk management, construction organizations could potentially incur losses greater than those caused directly by the risk itself. If safety observations are in an indistinguishable table of data, it can be hard to differentiate between the observations that require immediate attention and those that can be addressed at a later time with a lower priority.?We worked with an existing IS firm that offers a project information management system with various software products to help construction organizations manage and collect data. Within their scope of products, they handle a software we call SafetyLog which is a hosted SaaS (Software as a Service) application created to help clients effectively manage and report on their safety program. Currently, SafetyLog is storing more than a million records involving safety observations around a construction site that may be deemed risky to an organization. This data is stored but not thoroughly analyzed. Analyzing data associated with risk and observations can help mitigate future occurrences of unsafe events. By building a dashboard to easily analyze data at a glance, safety personnel can advise their colleagues of potential location of a more severe event to occur. Prevention and mitigation measures should be taken when there is an anomaly from past trends. For this data analytics study, we determined how the observation data SafetyLog collects can be leveraged for a construction site in a way that prevents and mitigates risk. The final product is a data visualization dashboard with features that allow a company to react to the available data regarding risk. The final paper will serve as a discussion of decisions and logical reasoning made to leverage the observation data into the visualization dashboard.