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

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
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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.
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