Reimagining the role of data in government

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Reimagining the role of data in government

August 18, 2014

New York City Fire Department (FDNY)Jeff Chen, Director of Analytics

A Robust Operation for a Large City, 1865

FDNY

F.D. N.Y.AMBULANCE

3,700 1,300,000


UNITS LABOR FORCE

$1.7 billion annual budget residents incidents

8.4 million 1.6 million

1,600

143

198

650

4

AMBULANCE

10

250

FIR

E10,000 490,000

EM

SC

IV

Analytics Activities

Year 1Public Sector Data Science Startup

• Establish analytical capacity through data-driven use cases• Raise technical sophistication across agency

Recruitment + Diversity

FireCast

FluCast

Injury Analytics

01010101010101001001010100101010010101001010100101010

Open Data Initiative

EMS Incident Forecast

∑Technical Training

∑ ∑

20+ Projects

ROAD MAP

FireCast

Recruitment Science


FireCast

The Deutsche Bank Fire (2007)

• In 2007 the Deutsche Bank building was in the process of being remediated and demolished

• Fire engulfed the building killing two firefighters

Overview

Risk Mitigation Philosophy

Fires will happen. It’s just a matter of time. We need to get our units to inspect the

buildings of highest risk so they’re ready.

Overview

FDNY TechPre-March 2013

Overview

RBIS inspection

application

Portal Apps

Handheld

Operational and

Analytical Reporting

Data Share

FireOperations

Fire Prevention

FireInvestigation

CBIDAS SystemData Repository /

WarehouseFire Ops OLTP

Exchange critical building safety information with DOB, DEP and others

Risk-based

Repository

Risk-Based Inspection System

New Revenue Management

system

BFP Inspection Tracking

apps

Consolidate all building and inspection information in accessible data repository /

warehouse

Coordinated Building Inspection Data Analysis System (CBIDAS)Areas shaded in blue deployed city-wide as of 3/17/13; Areas shaded in green are slated to begin assessment, design, and development in 2013; Areas not shaded are slated for future enhancements. Inclusion of data from inter-agency interfaces will continue to expand thru the City Hall DEEP Initiative.

10

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FireCast

Intra-agencyand inter-agency Interfaces

EAPeCIDS

BFI

COF

NYFIRS

FPIMS

DataBridge

DOB

MapPLUTO

Inter-agencyInterfaces

DEP

FDNY Tech: Current and Future BuildCoordinated Building Inspection Data Analysis System (CBIDAS)

Overview

Considerations for Inspection Activities

330,000

Overview

# of buildings in inspection portfolio

10% Expected proportion that will be inspected per year

3x3Each company performs 3 hours of building inspections, 3 times per week

493,000 # of fire incidents per year(# of times an emergency could cut into building inspection time)

105 Heat index value at which inspection operations are suspended

FireCastA Data-Driven Predictive Risk Engine

Mar 2013:

1.0 Basic ris

k model

deployed ci

tywide Jul 2

013:

2.0 Revamped

model deployed In Testin

g:

3.0 Predictive Risk

Engine

Version 1.0

• Focus on platform development

• Predictive weights based on focus group surveys

• Right factors, wrong weights

FDNY’s Proof of Concept• Weights based on

statistical model• 13 building factors

Next Generation Model

• Thousands of variables • Interagency data feeds

FireCast Overview

00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000

Fire + Hot Spots2002 to 2013(500 foot grid cells)

FDNYANALYTICS

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00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000

Fire + Hot Spots

Residential Fires(One + Two Family Homes)

Commercial + High-rise fires(FireCast universe)

FDNYANALYTICS

FireCast 2.0

Building Class = Elevator Apartment Building with Semi-Fire Proof Store

Built: 1915

Partial Sprinkler

Street Frontage = 90ft

Retail Sq. Ft = 6300 sf

Previous Fires or Injury

Proximity = Attached

6 floors

2 buildings on tax lot,

Privately owned

Giorgio’s

Geography: Central Brooklyn

FireCast 2.0: Consistent Risk Model

Example building with risk score 99%

Prob( | )

Probability of fire ignition given structural characteristics

• Impact (Avg # violations)

• First 30 days = +19% • First 60 days = +10%

DEFINITION

IMPACT

EX

AM

PLE

One Model for Citywide Use

Structural

Benchmarking FireCast: 1.0, 2.0

Battalion 46 Citywide

0.5%

1.9%

14.9%

16.5%

Pre-Arrival Coverage Rate: % of Buildings with Structural Fire Incident that were

inspected within the 90 days before incident

FireCast 1.0

FireCast 2.0 (w/ TF)

[1] Battalion 46 was selected as an example for comparison to the citywide average. [2] TF indicates Task Force Deployment in which extra resources are allocated to risk inspection. [3] FireCast 3.0 is in testing. 25% represents the new acceptance criterion for deploying the new model based on simulation testing.

FireCast 3.0

Building Class = Elevator Apartment Building with Semi-Fire Proof Store

Built: 1915

Partial Sprinkler

Street Frontage = 90ft

% Retail SF= 29.5%%Residential SF=

70.5%%Storage, %Factory, %Garage, %Office, %Other SF = 0%

Proximity = Attached

6 floors

2 buildings on tax lot,

Privately owned

Giorgio’s

Geography: Battalion 2

FireCast 3.0: Machine Learning Model

Structural

Behavioral Cues• Excessive Noise• Air Quality• Sidewalk

Condition• Electrical Issues• Rodents

• Lead • Seasonality• Sewer overflows• Heating problems• +other types

• Sidewalk cleanliness

• Loose Trash• Failure to

Maintain• Signage, Postings• Work without

permit• Illegal alteration

• Accumulation of combustible waste

• Missing certificates of fitness

• Cert. of Occupancy not reflective

• +other types

( )| Prob

One Risk Model per Incident Type for Each Battalion (49 models)

Violation Activity

DEFINITIONFORMULATION

Risk score: Depends

Width Dep

th

Previous Fires or Injury

Benchmarking FireCast: 1.0, 2.0, and 3.0

Battalion 46 Citywide

0.5%1.9%

14.9%16.5%

25.0% 25.0%

% of Buildings with Structural Fire Incident that were inspected within the 90 days before incident

FireCast 1.0

FireCast 2.0 (w/ TF)

FireCast 3.0 (Target)

[1] Battalion 46 was selected as an example for comparison to the citywide average. [2] TF indicates Task Force Deployment in which extra resources are allocated to risk inspection. [3] FireCast 3.0 is in testing. 25% represents the new acceptance criterion for deploying the new model based on simulation testing.

FireCast 3.0 aims to achieve a PACR of 1 in 4.

Risk Sequence

FireCast 3.0

7 E +0 2 3 E +0 2 7 E +0 2 1 E +0 2 6 E +0 2 6 E +0 2 7 E +0 2 7 E +0 2 7 E +0 2 7 E +0 2 7 E +0 2 8 E +0 2 1 E +0 2 7 E +0 2 6 E +0 2 6 E +0 2 4 E +0 2 3 E +0 2 4 E +0 2 5 E +0 2

















































































































































































































































































































































































































































7 E + 0 3 7 E + 0 3 9 E + 0 3 9 E + 0 3 8 E + 0 3 7 E + 0 3 8 E + 0 3 8 E + 0 3 7 E + 0 3 8 E + 0 3 9 E + 0 3 1 E + 0 4 1 E + 0 4 9 E + 0 3 8 E + 0 3 1 E + 0 4 9 E + 0 3 7 E + 0 3 1 E + 0 4 1 E + 0 4


















































































































































































































































































































































































































































Time

B

uild

ings

Recruitment Analytics

FDNY

F.D. N.Y.AMBULANCE

3,700 1,300,000


UNITS LABOR FORCE

$1.7 billion annual budget residents incidents

8.4 million 1.6 million

1,600

143

198

650

4

AMBULANCE

10

250

FIR

E10,000 490,000

EM

SC

IV

4 yeartest cycle

Test

Rank

Screen

Hire

Apply

145,000 Expressions of Interest

59,863 Candidate Applications

32,300 (54%)Filed w/o prior outreach

27,600 (46%)Filed w/ prior outreach

42,161 Test-Takers

(71% of applicants took the test)

7,200Considered

(17% of test-takes scoresufficiently high to be considered)

2,400Probies

(5.6% of applicants make it)

Find the next generation of the Bravest.

Digitize. Analyze. Optimize.

Paper FormsEntry errors and incompleteness


Recruitment Mobile Android App For more efficient and

accurate data collection


Analyze job competitiveness


Applicant Interest Segmentation

GROUP A

GROUP CGROUP B


Different behavior requires different recruitment tactics


Mentor-Mentee MatchingManual Matching: 30 – 60 days to complete

MENTORS MENTEES


Mentor-Mentee MatchingAlgorithm Matching: <5 minutes

MENTORS MENTEES

FDNY’s CAD systems chronicle the millions of moments

when New Yorkers are in the greatest need of help

F.D. N.Y.AMBULANCE

FDNYOpen Data

Next Generation Emergency

Services

Make it work for the user.

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Establish an Analytics Advisory Board

ITOwner

Haphazard Subset

Governance as a Community Process

Full Dataset

UnauthorizedXX

X

ProposedDataset

Analytics

Legal Exec

Public Beta Testers

Audit

BI The Public

privacy

ethicscommercial sensitivity

info trade

PIIadverse spillover

extra work

resourceslogistics

protectionefficiency

transparencyexcitement

innovation

security

liability

research

development

regulationpartnerships

misinterpretation

Governance + Ethics

Open Data

Query

Data Warehouse

F.D. N.Y.AMBULANCE

IncidentsThe

Public

[Your Name Here]

1. Hack and visualize the data to develop insight around incident patterns in New York City

2. Assess if there are any PII issues and security issues with the proposed data release

Get it Beta Tested

Get it Beta Tested

Research partnerships for

advanced prototyping

FDNY Prize for Advancement

of Machine Learning in the

Fire Service

Leverage open data to advance

data science capacity in the fire service

Years 2 + 3Center for Excellence

• Enhance FDNY’s public standing as a center for data and policy excellence in government through Open Data and innovative data science initiatives

• Grow sustainable analytics capacity for a resilient and innovative operation for New York City

Analytics Strategic Plan

Year 1Public Sector Data Science

Startup• Establish analytical capacity

through data-driven use cases• Raise technical sophistication

across agency

Recruitment + Diversity

FireCast

FluCast

Injury Analytics

01010101010101001001010100101010010101001010100101010


EMS Incident Forecast

∑Technical Training

∑ ∑

20+ Projects

01010101010101001001010100101010010101001010100101010

Open Data Initiative(Part Deux)

SustainableAnalyticsPlan

Analytics Skills Census

Data Science Curriculum

Analytics Forum

Hunch repository

Standards + Guidelines

Open Data Release

FDNY Data Science Prize

Hackathons

Academic Partnerships

Questions?

Jeff [email protected]

Reimagining the role of data in government

Leadership & Management

Transcript of Reimagining the role of data in government