Better Cities Through Data Axquisition and AnalysisHPC Saudi Arabia

KAUSTMarch 13, 2017

Dr. Steven E. Koonin, NYU CUSP DirectorNYU University Professor Stern School of BusinessTandon School of Engineering Schoolsteven.koonin@nyu.eduhttp://cusp.nyu.edu

Cities as a venue for data science

• Cities matter (most people live there)

• The data are plentiful and diverse

– Digitization (and opening) of records, proliferation of sensors

• There are things to do

– Develop technologies/methodologies to acquire, integrate, and analyze data

– Understand the “science of cities”

– Applications for government, citizens, private-sector

CUSP is Part of the NYC Applied Sciences Initiative

Mayoral Announcement April 23, 2012

Environment PeopleInfrastructure

What does it mean to instrument a city?

There’s a lot of data out there

Novel Technologies

• Visible, infrared and

spectral imagery

• RADAR, LIDAR

• Gravity and magnetic

• Seismic, acoustic

• Ionizing radiation,

biological, chemical

• …

Sensors

• Personal (location,

activity, physiological)

• Fixed in situ sensors

• Crowd sourcing

(mobile phones, …)

• Choke points (people,

vehicles)

Organic Data Flows

• Administrative records

(census, permits, …)

• Transactions (sales,

communications, …)

• Operational (traffic,

transit, utilities, health

system, …)

• Social media (Twitter,

Facebook, blogs, …)

Research strategy

• Focus on data

– Technology, science, applications

• Focus on New York City (mostly)

• “Own” the data

– Institutional familiarity, differential access, unique sensor systems

• Create facilities and capabilities

Unique

User

Facilities &

Major

Initiatives Data Facility Quantified Community Urban Observatory

Research

Groups &

Themes

Building Informatics Mobility Sounds of NYC Urban Energy

Projects ACCESS NYC Data Analysis Data Profiling and Integration Impacts of Urban Land Use

Interactive Exploration of NYC Bus Data Local Law 84 Analysis Local Law 87 Analysis

New York Open Government NYC Economic Map NYC Energy and Water Performance

Map NYPD Emergency Call Analysis Parks Quality Assessment Particulate Matter

Exposure Distribution Quantitative Analyses of Urban Topography Urban Waste Analytics

Water Street Data Aggregator 3D Urban GIS Interactive Visualization (Urbane)

Class of 2016 Capstones and more

Research structure

NYC DataBridge

Tweets!

❑New Yorkers tweet a lot:

2.6 Million Accounts2.37% of all tweets(NYU - 2011)

Geotagged Tweets By Eric Fisher - 2012

Lane et. al

The Quantified Community – Red HookIn partnership with

Quantified Community / Neighborhood Innovation Labs Network

Locations of phase I

sensor deployments:

5 <2 months,

15 <6 months

Pilot

sensor

Kontokosta et. al

Quantified Community: Red Hook, Brooklyn

3/21/2017

14

Toward a Real-Time Census of the City -Lower Manhattan WiFi pings

KONTOKOSTA 2016 - NOT FOR DISTRIBUTION

Source: Kontokosta and Johnson, forthcoming

N = 1,150

Mean = 219.5

s.d. = 101.7

020

40

60

80

Fre

qu

en

cy

0 200 400 600 800Weather Normalized Source EUI (kBtu/sq.ft./yr.)

Source: Local Law 84 Disclosure Data, Kontokosta 2013

Source Energy Use Intensity, Office Buildings, New York City

N = 7,505

Mean = 137.9

s.d. = 46.8

0

20

040

060

080

0

Fre

qu

en

cy

0 100 200 300 400Weather Normalized Source EUI (kBtu/sq.ft./yr.)

Source: Local Law 84 Energy Disclosure Data, Kontokosta 2013

Source Energy Use Intensity, Multi-Family Buildings, New York City

Building Energy Efficiency

Kontokosta 2013

Local Law 84 Benchmarking Data

Kontokosta, 2013

Taxis as Sensors for NYCTaxis are sensors that can provide unprecedented insight into city life: economic activity, human behavior, mobility patterns, …

“What is the average trip time from Midtown to the airports during weekdays?'’

“How the taxi fleet activity varies during weekdays?’’

“How was the taxi activity in Midtown affected during a presidential visit?'’

“How did the movement patterns change during Sandy?”

“Where are the popular night spots?”

May 1st – 7th

20113.6 Million Trips

Train Stations

Airports

Studying Taxi Patterns

CONFIDENTIAL: for intended recipient only 19June 27, 2016

Identify Traffic Conflicts

Using “Surrogates”

• “Surrogate” Safety Measures: Indicators that describe the scenarios in which a vehicle would collide with another vehicle if they did not change their current intentions.

Rear-end conflict

Right-angle conflict

Time to Collision (TTC): The time required

for two vehicles to collide if they continue at

their present speed and on the same path.

Post-Encroachment Time (PET): The time

difference between the arrival of two

vehicles at the potential conflict point.

Potential conflict point

Kaan Ozbay et. al

CONFIDENTIAL: for intended recipient only 20June 27, 2016

Jay St Demo: Conflicts

Conflicts: TTC<1.5 seconds

Estimated Surrogates based on Automatic Tracking Results

Kaan Ozbay et. al

• Cyber-physical system for large-scale continuous monitoring of noise pollution

21

• Custom acoustic sensor based on MEMS microphone technology

22

Current SONYC deployment

3/21/2017

Current deployment

• Ten nodes deployed• ~100 by year’s end• 24/7 SPL data• Random sample of

audio snippets• Real-time telemetry

from all

• State-of-the-art machine listening technology for real-time sound source identification

25

Photo by Tyrone Turner/National Geographic

A single sensor with: synoptic non-permissive coverage, persistence, high granularity

Manhattan in the Thermal IR

199 Water StreetBuilt 1993 :: 998,000 sq ft

electricity, natural gas, steamLEED Certified

hyperspectral

infrared

visible

• Unique user facility for persistent and

synoptic observations of cities

• Advance “urban science” and applications to

enhance public well-being, city operations,

and future urban design

• Instrumentation to include both broad band

and hyper-spectral from visible to infrared

wavelengths

• Combine correlative data including

administrative records, in situ measurements,

topography, etc.

persistent synoptic granular

CUSP Urban

Observatory

The view from CUSP’s Urban Observatory in Brooklyn

Borough Block & Lots (BBL)

Standard UO view

colored by distance

Picture merges image captured from video, 3-D LIDAR map of NYC, PLUTO (Primary Land Use Tax Lot Output) database, and LL84 Energy Benchmarking data

Source: Dobler, et al. 30

Dynamics of the Urban Lightscape

• Each frame is “registered” to a common frame by spatial correlations

• 4,200 window apertures are identified by hand (out of approximately 20,000 windows in the scene)

• For each frame, the average brightness of each source is calculated in

3 bands (RGB)

• The brightness of a given source as a function of time is referred to

as its “light curve”

Dobler et al.;doi:10.1016/j.is.2015.06.002

Pulse of the City Lights

Proxies for energy consumption, occupation, sleep cycle, …

Sleep-wake rhythms

JAWBONE groundtruth validation

Detection of Soot Plumes

Plume identification and tracking:• denoise background subtracted image

• identify excess/deficit in luminosity space

• cross check object location in color space

• localization and probability weighted tracking of

centroids

Use cases:• Plume ID

• Plume rate, repeaters

• urban winds

• carbon vs steam emissions

• TOO (triggered) observations for composition

raw image

background subtracted

Source: Dobler, et al.

Hyperspectral Imaging of Manhattan Urban Lights

36

Short-term variability in thethermal IR

Reference image

~ 2 min later

Differenceimage

A cooling towerlit up.

Proxy for energyconsumption• Occupation ?• Activity ?

Temperature/ Emissivity Separation

T E

R

Materials

Reflections

Broadband Thermography

For building envelops

Chlorodifluoromethane (Freon 22)

Chlorodifluoromethane (Freon 22)

Chlorodifluoromethane (Freonne)

Thermal Image

• Freon 22 appears in absorptionand emission

• Ozone-destroying moleculebeing phased out in the US

Acetone Plume April 7, 2015 Midtown Manhattan

(1.5km distance) 4mx6m

Acetone is used extensively in dry cleaning

A proxy for economic activity?

Couture Cleaners679 Washington Street

Bringing multiple data sources together

Urbane: A 3D Framework to Support Data Driven Decision Making in Urban Development

Nivan Ferreira, Marcos Lage, Harish Doraiswamy, Huy Vo, Luc Wilson, Heidi Werner, Muchan Park, Claudio Silva

Urbane

• 3D based framework that enables a data-driven approach for decision making in urban development

– Interactive exploration of the data at multiple scales

– Test of “what-if” scenarios and compute impact of proposed changes

– Use of computer graphics techniques to achieve interactivity

– Designed in collaboration with architects

Huy Vo et. al

Urbane in action

CUSP EDUCATIONMS in Applied Urban Science and Informatics (MSAUSI)

MSAUSI - Urban Informatics Track (One Year)

PRE-FALL

Urban Computing

Skills Lab

City Challenge Week

FALL

Principles of Urban

Informatics

Civic Analytics &

Urban Intelligence

Select 2 from:

Urban Spatial

Analytics

Applied Data Science

Urban Decision

Models

SPRING

Machine Learning for

Cities

Urban Science

Intensive I: City

Operations & Applied

Informatics

Select 2 electives:

Data Science Elective

Domain Application

Elective

SUMMER

Data Governance,

Ethics, and Privacy

Urban Science

Intensive II: Practicum

Select 1 from:

Science of Cities

Research Seminar

Civic Technology

Strategy

Advance Topics in

Urban Informatics

Winter

Week

Spring Break

Data Dive

Informatics Core

Urban Core

Optional Programs

PRE-FALL FALL 2015 SPRING 2016 SUMMER 2016

Urban Computing Skills

Lab

4001 Civic Analytics &

Urban Intelligence

5006 Machine Learning

for Cities

1007 Data Governance

Ethics & Privacy

City Challenge Week5003 Principles of Urban

Informatics

9001 Urban Science

Intensive I: City

Operations & Applied

Informatics

6001 Science of Cities

Seminar

5004 Applied Data

Science

3007 Urban Spatial

Analytics

6004 Advanced Topics in

Informatics: Managing IT

5005 Urban Decision

Models

5008 Big Data

Management & Analysis

6004 Advanced Topics in

Informatics: Machine

Sensing & Learning

3007 Urban Spatial

Analytics

6003 Civic Technology

Strategy & Management

9002 Urban Science

Intensive II: Practicum

6008 Operations

Research for Cities

INFORMATICS CORE

URBAN CORE

ELECTIVES

CUSP 2015 - 2016 Classes Offered

Inaugural Class of 2014 ProfileInaugural Academic Year: September 2013 – July 2014

24 21% 27 36% 3.5Inaugural Class

(including 1 Adv. Cert.)

Selectivity Years

Average Age

Female Average

Undergraduate GPA

20 48% 9 4 28%Undergraduate

Disciplines

International Countries

Represented

Years Average

Work Experience

With Graduate Degree

Fall 2014 CohortClass of 2015 Profile – 2nd Cohort (64 students)

19 COUNTRIES 45% FEMALE 28 AVERAGE AGE

5YRS. AVG. WORK

EXPERIENCE32%GRAD

DEGREES completed/in-progress

6NYC

EMPLOYEES

Fall 2014 CohortClass of 2016 Profile – 3rd Cohort (82 students)

18 COUNTRIES 35% FEMALE 27 AVERAGE AGE

3.5 AVG. YRS. WORK

EXPERIENCE30% W/ GRAD

DEGREES 7

NYC

EMPLOYEES

CONFIDENTIAL: for intended recipient only 52June 27, 2016

Sample Capstone Projects 2016

Urban Science Intensives

- Capstones

Public/Nonprofit 40%

Technology 27%

Consulting 10%

Start Own Business

10%

Other 13%

Construction3% Educational

Services7%

Finance or Insurance

11%

Information32%

Management of Companies or Enterprises

3%

Other Services (Except Public Administration)

7%

Professional, Scientific or Technical Services

29%

Retail Trade4%

Transportation or Warehousing

4%

Talent Pipeline: CUSP Graduate Placement

• 17zuoye

• Accenture

• AECOM

• American Express

• Apple Inc.

• Bloomberg LP

• Booz Allen Hamilton

• Capital Energy Consultants

• Case Commons

• Cityzenith

• ClearGrid

• Coalition for Queens

• Collectivei

• CUNY Office of Research,

Evaluation, and Program Support

• Data & Society Fellow

• Datapolitan

• Descartes Labs

• DMI-Digital Management Inc.

• Enigma

• ENSTOA

• HR&A Advisors

• Huawei Technologies Co. Ltd.

• IBM Watson

• Memorial Sloan Kettering

• Motion Global

• New Lab

• New York City Department of City

Planning

New York City Department of

Education

• New York City Department of

Education

• New York City Economic

Development Corporation

• New York City Police Department

• New York Metropolitan

Transportation Authority

• New York State Office of the

Attorney General

• New York University Center for

Urban Science + Progress

• Phillips Innovation Group

• Prism Consultants

• Salzburg University of Applied

Science

• San Francisco Municipal

Transportation Agency

• simulmedia

• Taipei Cultural Center in New York

• Thinkful

• Two Sigma

• U.S. Department of the Treasury

Office of Financial Research

• Urban Compass

• 4 startups

Class of 2015 ▪ Cohort Size: 64 (includes part-time)

Placement rate: 86% (6 months after graduation)

Average salary: $94,000

Class of 2014 and 2015 Placements to Date

Class of 2014 ▪ Cohort Size: 23

Placement rate: 100%

Average salary: $90,000

Talent Pipeline: CUSP Graduates

Further points

• Urban ontology? Data standards?

• Data hoarding

• Who are the customers?

– City governments? Firms? Citizens?

• Citizen acceptance (Privacy? Control?)

• Putting the “citizen” in smart cities

Thank You

cusp.nyu.edu

NYUCUSP

@NYU-CUSP