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NFPA Technical Committee on

Fire Prevention Organization

and Deployment

Tempe, AZ

November 17-18, 2015

MINUTES

The meeting was called to order by Chair Farr on November 17, 2015 at 8:00 am.

Introduction of Attendees

Members Present:

Ronald Farr, UL, Chair

Ryan Depew, NFPA Staff Liaison

Michael Bodnar, Jensen Hughes

Keith Chambers, Chesterfield County Fire &

EMS

Lisa Cockerill, Region of Peel

James Dawson, Chesterfield County Fire &

EMS

Connie Forster, IAFC

Hugh Gibson, ISO

David Jacobowitz, NVFC

Brett Lacey, IFSTA

Patrick Landis, Amway

Randy Minaker, Port Coquitlam Fire &

Emergency Services

Laura Mueller, National League of Cities

James Munger, QDOT Engineering

Colleen Pennington, Inspection Reports on

Line

Eugene Pietzak, IAAI

Guy Santelli, WI Fire Inspectors Association

Kellie Sawyers, Oklahoma City FD

Lynn Schofield, NFPA Ed Section

Art Shaw, NAT&T, MI

Pamela Summers, Palm Beach County Fire

Rescue

Marcina Sunderhaus, AZ Fire Marshals

Association

Larry Willhite, Palm Beach County Fire

Rescue

Morgana Yahnke, CA Fire Chiefs

Association

Guests Present:

Nathaniel Lin, NFPA

Shayne Mintz, NFPA

Agnus Shaw

Approved the minutes from the February 10-11, 2015, Austin, TX meeting.

Chair Farr and Staff Liaison Depew made opening remarks and reviewed the purpose of

meeting.

Presentation from Nathaniel Lin, NFPA (attached)

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Task Groups working on drafting text.

Chapter 5 Community Risk Assessment

Jim Munger

Cina Sunderhaus

Gene Pietzak

Lynn Schofield

Chapter 6 Community Risk Plan Development

Lisa Cockerill, Chair

Connie Forster

Brett Lacey

Laura Mueller

Colleen Pennington

Arthur Shaw (Guest: Agnus Shaw)

Patrick Landis

Larry Willhite

Pam Summers

Chapter 7 Community Risk Plan Implementation and Evaluation

Robbie Dawson, Chair

Guy Santelli

Kellie Sawyers

Morgana Yanke

Hugh Gibson

David Jacobowitz

Randy Minaker

The tentative dates and locations for the next Committee Meetings are as follows:

1300 Draft Dev. Meeting - February 9-10, 2016 in Savannah, GA or Charleston, SC

1300 Draft Dev./1730 First Draft Meeting – August 30-September 1, 2016, Traverse

City, MI

The meeting adjourned on November 18, 2015 at 3:00 pm.

Respectfully Submitted,

Ryan Depew

Staff Liaison

Elephant in the room

November 18, 2015 | Nathaniel Lin, PhD: NFPA Data Analytics Strategy Lead

How to DEFINE community risks, hazards, CRA & CRR and to QUANTIFY them in NFPA 1300

Agenda 1.  Outstanding data related questions in 1300

– Definitions of CR, Hazards, CRA, CRR – Quantitative estimates of CR, Hazards, CRA, CRR

2.  Discuss a use case of CRA method – Courtesy of Chesterfield County VA Fire & EMS

3.  Highlight limitations & possible data analytics solutions 4.  Dream of a NFPA Analytics Sandbox for CRR 5.  Next steps

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Data in NFPA 1300 – Ch.5 & 6

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Data in NFPA 1300 – Chapter 7

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1.  Data characteristics: •  Types – Data for conditions (structure, demographics, operations, FP installation), impacts, CRR

remedial actions, other data such as insurance claims and outcomes from CRR actions •  Collection – Sources such as surveys, reports, reports and other media data, frequency and

coverage. •  Storage – Formats, historical, where, how •  Quality – QA and QC

2.  Data Analysis or Analytics: •  Risk Assessment

-  Probability of occurrence -  Magnitude of impacts

•  Risk Reduction -  From what level of risk before CRR actions to post level in terms of probability and impact

severity reduction

Community Risk Assessment & Reduction

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1.  Community Risk Assessment •  Probability – Qualitative or quantitative as in “1 in 10,000” chance or once in 5 years. •  Magnitude – Quantify respective levels

2.  Community Risk Reduction •  Reduction in probability – education, zoning, inspections •  Reduction in severity in terms of $, injuries, deaths, carbon budget by certain recommended remedial actions

Example of CRA & CRR Methodology

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$ $ % $ $ $ $ $ % $

Most remedial actions may reduce severity of impacts but not the probability of incidence. Exceptions may be CRR education, zoning and inspection frequency and regimen.

%

$

Community Risk Reduction – Use Case

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$ $ % $ $ $ $ $ % $

Prob

abili

ty o

f Inc

iden

ce

Severity of Impacts

Limitations & Solutions

•  Subjective & qualitative à Objective & quantitative •  Lacks risk assessment model à Build two predictive

models – Propensity of Incidence & Impact Severity •  Difficult to account for combinations of all possible

conditions à Model training & validation with historical data

•  Subjectively assigned effectiveness of remedial actions à Reliable what-if simulator powered by validated models quantified with potential remedial actions

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Advanced Predictive Analytics 101

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1.  Basics: •  Model Types – Two types of potential models

•  Probability Prediction – Use either Logistic Regression or Decision Tree Models •  Impact Severity Prediction – Use Multivariate Regression or Regression Tree Models

•  Data – Historical data on everything that may have effects on the outcome and the historical data on the outcome

•  Quality – Better data means better results but even partial and less than pristine data may yield usable insights

2.  Training and Validation: •  Model Training – Use data from previous years to predict subsequent year outcomes •  Model Validation – Apply trained models to un-touched data to validate predictions with real

outcomes •  Model Coverage – Train models with data from diverse conditions to expand coverage

Applications of Advanced Data Analytics •  Community Risk Assessment – Predict & designate

buildings and neighborhoods with high propensity of fires with injuries and fatalities & high impacts

•  CRR Plan Development – Provide risk/benefits analysis for what-if scenarios of remedial actions and plan

•  CRR Plan Implementation & Evaluation – Evaluate relative causal effects of conditions and actions and support post-hoc test and learn exercises with scientifically de-biased samples

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NEED QUALITY & INTEGRATED DATA

Analytics Needs Integrated Data •  Data from census, government agencies, weather services, traffic

patterns, home owners/consumers, insurance, manufacturers, builders, data vendors (Acxiom, Experian, IXI, Claritas etc)

•  Open & Linked Data Movement

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LINKED OPEN DATA - No Large Companies or Consumer Data!

What Prevents Data Sharing •  Consumers:

–  Fear of privacy intrusions –  Compromised physical safety –  Identity theft –  Loss of rights & control

•  Government Agencies: –  Departmental silos –  Lack of funds –  Lack of public trust

•  Industries: –  Loss of customers to competitors –  Loss of “secret sauce” –  Uncertainties over the benefits and risks of sharing data –  Who to ensure continuous proper data standards and safeguards

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Partner With

Consumer Privacy Advocates

Government Agencies

Equipment Manufacturers Fire Services

NFPA ANALYTICS SANDBOX

Advantages of an NFPA Analytics Sandbox •  Allows Data Sharing in a limited & secured setting •  Leverages Proven Process in NFPA Codes & Standards – provides the

tools needed to support the C&S process •  Achieves Consensus among parties of disparate interests and concerns •  Engenders Cross-Interests Innovations within the sandbox e.g., fire

services, insurance, ITM business, property owners & politicians etc. •  Rewards Participants with new solutions, business opportunities and

tangible benefits e.g., consumer credit scores using consumer credit & transaction data

•  Supports all FP communities & makes available all essential tools & data

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Next Steps? •  Situation assessment – Your Comments & Feedback •  Set up the NFPA CRR Analytics Sandbox •  Determine critical factors

–  Participants – Besides Chesterfield County? –  Process of identifying & soliciting best practices –  Result dissemination & vetting –  Alignment with NFPA 1300 and other related C&S

•  Anything else?

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Thank You

nlin@nfpa.org

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