Reimagining the role of data in government
•Download as PPTX, PDF•
1 like•1,389 views
Synopsis: With nearly 8.2 million residents and over 1.6 million fire, non-fire, and medical incidents per year, the Fire Department of New York (FDNY) is the largest municipal fire department in the United States. As a leading institution in the fire service, FDNY established its first Analytics Unit (AU) in June of 2013, charged with reimagining the role of data in operations and policy. Within the first year of operation, the AU has spearheaded 15 projects making New York City safer through machine learning , creating innovative use cases for a smarter operation, promoting public engagement through an open data initiative and building sustainable technical capacity for the fire service. Director of Analytics Jeff Chen will present an overview to analytics operations at FDNY and a strategic vision for data in government. Speaker: Jeff Chen, Director of Analytics FDNY Analytics Unit Management Initiatives Spearheads FDNY’s data science agenda for advancing policy and operational priorities. Develops strategies that combine sophisticated statistical research-driven approaches with operations-oriented solutions. Oversees Analytics Unit and its 20+ initiatives.
More Related Content
Similar to Reimagining the role of data in government
Similar to Reimagining the role of data in government (20)
More from AnalyticsWeek
Recently uploaded
Recently uploaded (20)
Reimagining the role of data in government
- 1. 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 Reimagining 01000100 01001110 01011001 the role 01000110 of 01000100 data 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 in 01000100 government 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 August 18, 2014 New York City Fire Department (FDNY) Jeff Chen, Director of Analytics
- 2. A Robust Operation for a Large City, 1865
- 3. A Robust Operation for a Large City, 2014 8.4 million 1.6 million $1.7 billion annual budget residents incidents UNITS LABOR FORCE F.D. N.Y. AMBULANCE FDNY 3,700 1,300,000 1,600 198 143 650 4 AMBULANCE 250 10 FIRE 10,000 490,000 EMS CIV
- 4. Analytics Activities Year 1 Public Sector Data Science Startup • Establish analytical capacity through data-driven use cases • Raise technical sophistication across agency Recruitment + Diversity FireCast FluCast Injury Analytics 0101010101 01010010 0101010 0101010 0101010 0101010 0101010 Open Data Initiative EMS Incident Forecast Σ Technical Training Σ Σ 20+ Projects
- 5. ROAD MAP FireCast Recruitment Science Open Data Initiative
- 6. FireCast
- 7. 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
- 8. 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
- 9. FDNY Tech Pre-March 2013 Overview
- 10. 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. Risk-Based Inspection System RBIS inspection application Portal Apps Operational and Analytical Reporting Data Share Handheld Fire Operations Fire Prevention Fire Investigation CBIDAS System Data Repository / Warehouse Fire Ops OLTP Inter-agency Interfaces Exchange critical building safety information with DOB, DEP and others Risk-based Repository BFP Inspection Consolidate all building and Tracking apps inspection information in accessible data repository / New Revenue Management system warehouse 10 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 01000110 01000100 01001110 01011001 FireCast Intra-agency and inter-agency Interfaces EAP eCIDS BFI COF NYFIRS FPIMS DataBridge DOB MapPLUTO DEP FDNY Tech: Current and Future Build Coordinated Building Inspection Data Analysis System (CBIDAS) Overview
- 11. Considerations for Inspection Activities 330,000 Overview # of buildings in inspection portfolio 10% Expected proportion that will be inspected per year 3x3 Each 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
- 12. FireCast A Data-Driven 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
- 13. Fire + Hot Spots 2002 to 2013 (500 foot grid cells) 00000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000 FDNYANALYTICS
- 14. Fire + Hot Spots Residential Fires (One + Two Family Homes) Commercial + High-rise fires (FireCast universe) FDNYANALYTICS
- 15. 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 Giorgi o’s Geography: Central Brooklyn FireCast 2.0: Consistent Risk Model Example building with risk score 99% Prob( | ) Probability of fire ignition given structural characteristics DEFINITION IMPACT • Impact (Avg # violations) • First 30 days = +19% • First 60 days = +10% EXAMPLE One Model for Citywide Use Structural
- 16. Benchmarking FireCast: 1.0, 2.0 % of Buildings with Structural Fire Incident that were 0.5% Pre-Arrival Coverage Rate: inspected within the 90 days before incident 1.9% 14.9% 16.5% Battalion 46 Citywide 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.
- 17. 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 Giorgi o’s Geography: Battalion 2 FireCast 3.0: Machine Learning Model Structural Prob( | ) DEFINITION FORMULATION 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 One Risk Model per Incident Type for Each Battalion (49 models) Violation Activity Risk score: Depends Width Previous Fires or Injury
- 18. Benchmarking FireCast: 1.0, 2.0, and 3.0 0.5% FireCast 3.0 aims to achieve a PACR of 1 in 4. % of Buildings with Structural Fire Incident that were inspected within the 90 days before incident 25.0% 25.0% 1.9% 14.9% 16.5% Battalion 46 Citywide 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.
- 19. Risk Sequence FireCast 3.0 Time Buildings
- 21. A Robust Operation for a Large City, 2014 8.4 million 1.6 million $1.7 billion annual budget residents incidents UNITS LABOR FORCE F.D. N.Y. AMBULANCE FDNY 3,700 1,300,000 1,600 198 143 650 4 AMBULANCE 250 10 FIRE 10,000 490,000 EMS CIV
- 22. Test 4 year test cycle Rank Screen Hire Apply
- 23. 145,000 Expressions of Interest
- 24. 59,863 Candidate Applications 32,300 (54%) Filed w/o prior outreach 27,600 (46%) Filed w/ prior outreach
- 25. 42,161 Test-Takers (71% of applicants took the test)
- 26. 7,200 Considered (17% of test-takes score sufficiently high to be considered)
- 27. 2,400 Probies (5.6% of applicants make it)
- 28. Find the next generation of the Bravest.
- 30. Paper Forms Entry errors and incompleteness Digitize. Analyze. Optimize.
- 31. Recruitment Mobile Android App For more efficient and accurate data collection Digitize. Analyze. Optimize.
- 32. Analyze job competitiveness Digitize. Analyze. Optimize.
- 33. Applicant Interest Segmentation GROUP A GROUP B GROUP C Digitize. Analyze. Optimize. Different behavior requires different recruitment tactics
- 34. Digitize. Analyze. Optimize. Mentor-Mentee Matching Manual Matching: 30 – 60 days to complete MENTORS MENTEES
- 35. Digitize. Analyze. Optimize. Mentor-Mentee Matching Algorithm Matching: <5 minutes MENTORS MENTEES
- 37. FDNY’s CAD systems chronicle the millions of moments when New Yorkers are in the greatest need of help
- 38. F.D. N.Y. AMBULANCE FDNY Open Data Next Generation Emergency Services
- 39. 0101010101 01010010 0101010 0101010 0101010 0101010 0101010 Make it work for the user.
- 40. Establish an Analytics Advisory Board
- 41. Governance as a Community Process ITOwner Haphazard Subset Full Dataset XUnauthorized X X Proposed Dataset Analytics Legal Exec Public Beta Testers Audit BI The Public
- 42. Governance + Ethics ethics commercial sensitivity privacy protection info trade PII adverse spillover extra work development innovation resources logistics efficiency transparency excitement security liability research regulation partnerships misinterpretation
- 43. Open Data Query Data Warehouse Incidents F.D. N.Y. AMBULANCE The Public [Your Name Here] Get it Beta Tested 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
- 44. Get it Beta Tested
- 46. Analytics Strategic Plan Years 2 + 3 Center 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 Year 1 Public Sector Data Science Startup • Establish analytical capacity through data-driven use cases • Raise technical sophistication across agency Recruitment + Diversity FireCast FluCast Injury Analytics 0101010101 01010010 0101010 0101010 0101010 0101010 0101010 Open Data Initiative EMS Incident Forecast Σ Technical Training Σ Σ 20+ Projects 0101010101 01010010 0101010 0101010 0101010 0101010 0101010 Open Data Initiative (Part Deux) Sustainable Analytics Plan Analytics Skills Census Data Science Curriculum Analytics Forum Hunch repository Standards + Guidelines Open Data Release FDNY Data Science Prize Hackathons Academic Partnerships
- 47. Questions? Jeff Chen Jeff.Chen@fdny.nyc.gov
Editor's Notes
- It’s an exciting time to be in government Data revolution to make the notion of smart government a reality Before I get into that, I just wanted to give a little background: econometrician (large scale projects), moved to mayor’s office of oeprations during bloomberg, now founding director of analytics at FDNY Opportunity to push a dept from the 1990’s to the 2020’s in a relatively short period of time But we haven’t always been an agency that is all about the latest tech
- The department has always been a robust operation for a large city It was established as the new FDNY in 1865 Of course, back then, the notion of machine readable open data would have been absurd. There was not a 911 system or 311 call taking system, computer dispatch, fire engines were pulled by horses. But of course, over the last 149 years a few things have changed.
- Chen: Main point: FireCast 2.0 is the proof of concept, a way of showing that big data can work for a field operations-oriented agency
- Roth: Main point: []
- Roth: Main point: []
- Roth: Main point: []
- Roth: Main point: []
- Roth: Main point: []
- Roth: Main point: []
- Chen: Main point: But before building a model, we can use the data to develop an appreciation for historical fire risk In this map, we divided NYC into a grids tabulating number of fires per grid area between 2002 and 2013 There are indeed hotspots of activity in Bronx, Manhattan and Central Brooklyn
- Chen: Main point: FDNY’s data is a wealth of codified digital knowledge and from it we may be able to identify consistent sources of fire risk. If we map out where structural fires have occurred between 2003 and 2012, we find that incident patterns are quite different depending on building. Whereas residential fires in one and two family homes are spread between Brooklyn and Queens, the FireCast system is focused
- Chen: Main point: Thus, for the proof of concept model, research focused on identifying consistent sources of risk, which are readily available through structural characteristics Formally, we defined risk as the probability of fire ignition given structural characteristics. As such, if we look at one example of a risky building, such a building would be an elevator apartment constructed early in the 20th century with some retail space, 6 floors, located in central brooklyn among other factors. In total, we had a model with 13 variables that subdivide into 60 levels FireCast 2.0 was deployed in July 2013 and within the first 30 days, we saw an increase in per building violation volume of 19%, which fell to 10% by day 60. As we later found, the worst of the consistently risky buildings are really concentrated in the top 5% or 15,000 buildings
- Chen: Main point: The new FireCast system incorporates so much more digital signal and does so by custom tailoring 49 models for each battalion and each type of incident FireCast 3.0 astronomically increases the data being consumed by the department. Version 2 was based on 21 million data elements and 13 risk factors. Version 3 is developed on 49.5 billion data elements, looking at 7,500 potential risk factors. We find that about 950 factors In total, we have over 950 different factors that feed the model. Structural characteristics now capture more functional use and structural characteristics Behavioral information such as complaints are called in on a daily basis– about 5000 complaints citywide per day for excessive noise, cleanliness, air quality, housing problems In addition, violation activity from 11 different agencies now will help prioritize our risk inspections
- Main point: The data inputs are quite dynamic. This image represents our riskDNA or risk sequences for just one of FDNY’s battalions. Each row is a building and each column is a period of time. Red indicates high risk rank and blue means low risk rank. We find that some buildings are just consistently risky, others jump around, and some are almost never risky
- Every
- Jeff C To put the job application pattern in context, we used network analysis to examine the relationship between exam applications. Each point represents a job, the size of the point indicates number of applications in the job, the lines represent applications to two jobs, the thickness of the line represents the volume and the proximity of the points indicates relatedness. There is some similarity in job interest
- Jeff C We find that white applicants have a relatively small network core meaning that the bulk of the applications were to a select few jobs whereas black applicants applied to a greater diversity of jobs. White applicants had a consistent set of jobs that were applied to in addition to FF Hispanic applicants had a slightly more diverse set of interests and Black applicants more consistently applied to a wide range of exams We also analyzed if people who were reached during recruitment season were more inclined to only applied to the FF exam. Overall, there is a 24% higher chance that people with EOIs would only apply for the FF exam. But, the chances vary across ethnicities
- Chaz Using a streamgraph approach, discussion volume were plotted To examine the discussion volume, initial sessions covered a greater breadth of topics. Over time, analysis indicates that subsequent discussions tended towards a select number of key topics, namely the Academy
- Chaz Using a streamgraph approach, discussion volume were plotted To examine the discussion volume, initial sessions covered a greater breadth of topics. Over time, analysis indicates that subsequent discussions tended towards a select number of key topics, namely the Academy
- Chen: Main point: FireCast 2.0 is the proof of concept, a way of showing that big data can work for a field operations-oriented agency
- How can FDNY provide or use data to give everyone that spring in their step, improve their understanding about the urban environment around them, and develop new tools for the betterment of society? How do we make sure that the data release is successful and meaningful? Between the signing of the Open Data Law in 2012 and the increased use of open source and open data, FDNY’s open data mantra has evolved to one simple phrase “Make it work for the user.”
- Talk about the ropeadope of initiatives Get everyone to talk about the pressing issues upfront, get it out of the way, get them familiar before proceeding