New York City Used Relevant Data Mining to Reduce Incidents of Fire
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New York City Used Relevant Data Mining to Reduce Incidents of Fire

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The New York City Fire Department is applying data mining technique to assess fire risks in buildings.

The New York City Fire Department is applying data mining technique to assess fire risks in buildings.

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New York City Used Relevant Data Mining to Reduce Incidents of Fire Document Transcript

  • 1. New York City Used Relevant Data Mining to Reduce Incidents of Fire The New York City Fire Department is applying data mining technique to assess fire risks in buildings. Data mining techniques are helping the New York City Fire Department to assess fire risks in buildings, says a recent report. New York City has about a million buildings and each year about 3,000 of them experience a major fire. It became necessary for the department to be able to predict which of the city's buildings are at highest risk of catching fire. The Fire Department has begun running predictive computer models to pinpoint which buildings are in danger of burning down so that preventive measures can be taken well in advance. Identifying Fire Prone Buildings Analysts at the fire department say that there are some characteristics that help to identify fire-prone buildings: • The age of the building • The number and location of sprinklers • The Presence of Elevators • If, there are electrical issues • Building that are vacant or unguarded The department also says that buildings located in low-income areas of the city are at greater risk. Data Mining to Build Fire Risk Score The New York Fire Department compiled a prediction model based on 60 different factors to identify risk prone buildings using SAS statistical analysis and predictive modeling tools. The algorithm assigns each one of the city’s 330,000 inspectable buildings with a risk score. Based on the score, the fire officers go on weekly www.managedoutsource.com  1­800­670­2809
  • 2. inspections in order of priority. The process started last year and is expected to expand to 2,400 categories in the future. Prior to this, buildings were inspected on random basis, with schools and libraries given extra attention. Components of Typical Data Mining Software Data mining suites include a range of techniques and processes for data collection, data cleansing, data tagging, and data analysis. Data mining/extraction helps reveal patterns, identify key variables and relationships, and gain new insights for business decision-making. The components of SAS analytics, for instance, include the following: • Data/Text Mining – building descriptive and predictive models to deploy results throughout the enterprise • Data Visualization – to enhance analytic effectiveness with dynamic data visualization • Forecasting – predicting future outcomes based on past data • Model Management and Deployment – to reorganize the process of creating, managing and deploying analytical models • Optimization – applying techniques of operations research, scheduling and simulation to produce results • Quality Improvement – identify, monitor and measure quality processes over time. • Statistics – analysis of statistical data to drive fact-based decisions Data mining is a complex and costly procedure. However, managing disasters and risks has become much easier with analytic software. The best option for businesses that want to gain valuable insights from big data is to partner with an outsourcing company that can provide data mining services at affordable cost. www.managedoutsource.com  1­800­670­2809