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Adventures in Urban InformaticsUniversity of California
February, 2016
Dr. Steven E. Koonin, CUSP Director
steven.koonin@nyu.eduhttp://cusp.nyu.edu
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Big Cities + Big Data
All cities must be better for global issues
Individual cities need to be best for competitiveness in
talent, capital, Be efficient, resilient, sustainable
Address citizen quality of life, equity, engagement
The world is urbanizing
Cities are the loci of
consumption, economic
activity, and innovation
Cities are the cause of our problems
and the source of the solutions
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Big Cities + Big Data
Informatics
capabilities areexploding Storage, transmission,
analysis
Proliferation of staticand mobile sensors
Internet of things
Global network traffic, 30% CAGR
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JUST HOW DID A PHYSICIST WIND
UP IN THIS BUSINESS?
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Properly acquired, integrated, and analyzed, data can Take government beyond imperfect understanding
Better (and more efficient) operations, better planning, better policy
Improve governance and citizen engagement Enable the private sector to develop new services for citizens,
governments, firms
Enable a revolution in the social sciences
Environment
Meteorology, pollution,
noise, flora, fauna
People
Relationships, location,
economic /communications
activities, health, nutrition,opinions, organizations,
Infrastructure
Condition, operations
What does it mean to instrument a city?
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Urban Data Urban data have been collected for millenniastatistics (sttstks) n.1. The mathematics of the collection, organization, andinterpretation of numerical data, especially the analysis of populationcharacteristics by inference from sampling
From German Statistik,political science, from New Latin statisticus, of state affairs, fromItalian statista,person skilled in statecraft, from stato, state, from Old Italian, from Latinstatus,position, form of government.
Sparseness and quality have limited urban science difficult to usefully measure the urban system, test hypotheses
But new data technologies completely recast the study of cities digital records, sensors, computing power, analytical techniques
unprecedented granularity, variety, coverage, and timeliness
When you can measure what you are speaking about, and express it in numbers,
you know something about it; when you cannot express it in numbers, your knowledge
is of a meager and unsatisfactory kind. Lord Kelvin, 1883
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Organic data flows Administrative records (census, permits, )
Transactions (sales, communications, )
Operational (traffic, transit, utilities, health system, )
Twitter feeds, blog posts, Facebook,
Sensors Personal (location, activity, physiological)
Fixedin situ sensors
Crowd sourcing (mobile phones, )
Choke points (people, vehicles)
Opportunities for novel sensor technologies
Visible, infrared and spectral imagery RADAR, LIDAR
Gravity and magnetic
Seismic, acoustic
Ionizing radiation, biological, chemical
Urban Data Sources
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N = 1,150
Mean = 219.5
s.d. = 101.7
0
20
40
60
80
Fr
equency
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
200
400
600
80
0
Fr
equency
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
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Local Law 84 Benchmarking Data
Kontokosta, 2013
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Wang, P., Hunter, T., Bayen, A.M., Schechtner, K. & Gonzalez, M.C.
Understanding Road Usage Patterns in Urban Areas. Nature, Sci. Rep. 2, 1001; DOI:10.1038/srep01001(2012).
Cell Tower Records for Traffic Analysis
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Daily commute patterns
from phone records
Survey Chicago, Paris
Phone 4X104 in Paris
Model Chicago, Paris
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Taxis as Sensors for NYC
Taxis are sensorsthat 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?
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May 1st
7th
2011
3.6 Million Trips
Train Stations
Airports
Studying Taxi Patterns
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Taxi Rides in Manhattan, October 28 November 3, 2012(Superstorm Sandy)
Juliana Freire, Claudio Silva, et al, NYUPoly
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Lottery Vis
Correlate sales with
Weather Sports team wins
Twitter mood
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From the Willis Tower, Chicago
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Photo by Tyrone Turner/National Geographic
Other synoptic modalities: Hyperspectral, RADAR, LIDAR,
Manhattan in the Thermal IR
199 Water Street
Built 1993 :: 998,000 sq ftelectricity, natural gas, steam
LEED Certified
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The view from CUSPs Urban Observatory in Brooklyn
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Borough Block & Lots (BBL)
Standard UO view
colored by distance
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Picture merges image captured from video, 3D LIDAR map of NYC, PLUTO
(Primary Land Use Tax Lot Output) database, and LL84 Energy Benchmarking data
Source: Dobler, et al. 21
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OBJECTIVES
Develop a fundamentally new modality forstudying the city from a distance
Identify aggregate patterns of light in thetime-dependent brightnesses of city lights
Leverage these patterns into foundationalcontributions to urban science and urbanfunctioning
Proof of Concept
IMPACT
Urban Science
Determine the underlying drivers of the pulse of the city
Understand the effects of perturbations
City Life
Monitor energy consumption by proxy using light patterns as a measure of buildingoccupancy
Evaluate the effects of disturbances (e.g., light/noise pollution) on public health
Camera: Point Grey Flea 3 USB ; 8.8 Mega-pixels ; Raw image output ; 25mm focal length lens
Observations:
1 image every 10 seconds from Oct 26 to Nov 16, 2013; 3 color images at 25MB each
Total data volume ~4.5TB; Custom data processing pipeline
City Lights Project
Dobler et al.;doi:10.1016/j.is.2015.06.002
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Institutional Review Board
approval of all projects involving
non-open data
Close oversight by CUSP ChiefData Officer
Limited # of pixels per window
(but atmosphere/instrument
effects typically dominate) Aggregate and de-identified
analysis only
Privacy Protections
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Dynamics of the Urban Landscape
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 toas its light curve
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Pulse of the City Lights
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Daytime Phenomenology
11:00 AM
11:01 AM
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Daytime Phenomenology: Subtle Variations
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Daytime Phenomenology: Subtle Variations (animation)
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Background subtraction: registration to reference image
form 10 absolute difference images from
surrounding frames
construct the minimum difference image pixel by
pixel
Subtracting the Cityraw image
background subtracted
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Crossbuilding view of a boiler plume
Such plumes may not be visible from street level.
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Plumes of Opportunity
Background subtraction: registration to reference image
form 10 absolute difference images from
surrounding frames
construct the minimum difference image pixel by
pixel
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
Upcoming use cases: plume rate
repeaters urban winds
carbon vs steam emissions
TOO (triggered) observations
raw image
background subtracted
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Thoughts on the big science questions Can we document the pulse of the city in its various dimensions?
Normal? Variability? Correlations? Response to perturbations? Predictability? Precursors?
How do the macro observables arise from micro behavior? Santa Fe scaling?
Physical structure of cities?
Decision rules in agentbased models
Role of geography? Culture? Policies?
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The CUSP PartnershipNational Laboratories
Lawrence Livermore
Los Alamos
Sandia
Brookhaven
Industrial Partners
IBM
Microsoft
Xerox
AECOM, Arup, IDEO
University Partners
NYU/ NYUPoly
University of Toronto
University of Warwick
CUNY IITBombay
Carnegie Mellon University
City & State Agency Partners
The City of New York
Metropolitan Transit Authority
Port Authority of NY & NJ
Buildings
City Planning
Citywide Administrative
Services
Design and Construction
Economic Development
Environmental Protection Finance
Fire Department
Health and Mental Hygiene
Information Technology
and Telecommunications
Parks and Recreation
Police Department
Sanitation Transportation
Cisco
Con Edison
Lutron
National Grid
Siemens
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Education Programs at CUSP
Master of Science in Applied UrbanScience & Informatics
F/T (One Year)
P/T (Two Year)
Civics Analytics Track
Advanced Certificate in Applied UrbanScience & Informatics
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CENTER FOR URBANSCIENCE+PROGRESS
Graduate Programs at CUSP Interdisciplinary and cutting edge approach that links data science,
statistics and analytics, and mathematics with complex urban
systems, urban management, and policy.
Corecurriculum
Urban core Foundational understanding of the theories of urban planning andthe application of data-driven approaches to urban challenges.
Informatics core Fundamentals of data science/computer science, data management,
data mining, visualization, model selection, and machine learning
tools to urban problems and datasets.
Tracks UrbanInformatics For students who are looking for deep training in data science andinformatics as applied to cities.
Civic Analytics For students who will utilize analytics and data-driven decision-
making techniques to inform urban operations and policy decisions.
Length One Year Full-
Time Program
A research- and project-intensive environment
Two Year Part-
Time for
Working
Professionals
Evening courses with numerous opportunities for networking with
peers, faculty, and experts in the industry.
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CENTER FOR URBANSCIENCE+PROGRESS
MSAUSI - URBAN INFORMATICS TRACK (One Year)
PRE-FALLPRE-FALL
1001 Urban
Computing Skills Lab
1001 Urban
Computing Skills Lab
1000 City Challenge
Week
1000 City Challenge
Week
FALLFALL
5003 Principles of
Urban Informatics
5003 Principles of
Urban Informatics
4001 Computational
Urban Policy &
Planning
4001 Computational
Urban Policy &
Planning
Select 1 from:
7007 Urban Spatial
Analytics
9002 Urban Decision
Models
SPRINGSPRING
5006 Machine
Learning for Cities
5006 Machine
Learning for Cities
9001 Urban Science
Intensive I: City
Operations & Applied
Informatics
9001 Urban Science
Intensive I: City
Operations & Applied
Informatics
SUMMERSUMMER
1007 Data
Governance, Ethics,
and Privacy
9002 Urban Science
Intensive II: Practicum
9002 Urban Science
Intensive II: Practicum
Select 1 from:
6001 Science of Cities
Research Seminar
6003 Civic Technology
Strategy
Winter
Week
Winter
Week
Spring Break
Data Dive
Spring Break
Data Dive
Informatics Core
Urban Core
Optional Courses
5004 Applied DataScience
6004 Advanced Topics
in Urban Informatics
Year 1
Data Science Elective
Domain Application
Elective
Ad i i S Cl f 2014
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CENTER FOR URBANSCIENCE+PROGRESS
Admissions Summary, Class of 2014Inaugural 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 Cohort
2015 Class Highlights
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CENTER FOR URBANSCIENCE+PROGRESS
Fall 2014 Cohort2015 Class Highlights
19COUNTRIES
(111% ) 45%FEMALE
(275% ) 28AVERAGE
AGE (3% )
5YRS. AVG. WORK
EXPERIENCE
(25% )32%
GRAD
DEGREEScompleted/in-progress
66NYC
EMPLOYEES
F ll 2015 C h t
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Fall 2015 Cohort
NEW STUDENTS
August 2015
8787
F ll 2015 C h t
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Fall 2015 Cohort
NYC Employees
August 2015
1010
S d R h (GRA P 19 j )
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Student Research (GRA Program, 19 projects)
Aerial Analytics/Hyperspectral imagingBluetooth Tracking Researcher
Buildings Informatics Energy Index
Dynamics of the City Lights
Economic Impacts of Public Parks and Greenspaces
Efficiently Indexing the New York City Open Data For Spatial-
temporal-keyword QueriesEmotion Sensing
Garbage Identification
Machine Learning and Computational Statistics for NYPD
MTA Project
Parks Utilization and Attendance
Pedestrians and Vehicles: Interactions at Intersections
Quantified Community Research Initiative
Social Cities Initiative
SONYC
TaxiVis
Traffic Safety from Video Recordings
Understanding the spatial structure of crime
Urban ThermodynamicsUsing MTA bus-time data to determine traffic conditions
2015 Capstone Projects
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2015 Capstone Projects ACCESS NYCData Analysis
Analysis of Citibike Data and Modeling of TimeDependent OriginDestination Matrices
Building & Sustainability Informatics
BusVis: Interactive Exploration of NYC Bus Data
Crime and Policing Analytics in New York City Digital Equality: Sensing, citizen science, data analytics &
visualization
From Light Variability to Energy Consumption
LearnrA Seamless Education Volunteering Platform New York City Economic Map
New York Open Government
Parks Quality Assessment
Quantifying Particulate Matter ExposureDistribution in NYC Quantitative Analyses of Urban Topography
Urban Waste Analytics
Using Social Media to Predict Urban Transportation
Al i P fil
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CENTER FOR URBANSCIENCE+PROGRESS
Alumni Profiles
Advance your CareerAliya Merali (B.S. Physics)Director of Learning and Access, Coalition For Queens
Become a Data Scientist
Warren Reed (B.S. Chemical Engineering)Data Scientist, Office Of Financial Research
Career ChangerAlex Chohlas-Wood (B.A. Studio Arts)
Director of Research & Evaluation, NYPD
CUSP Facilities/Capabilities
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CUSP Facilities/Capabilities
Under Development
Data facility
Quantified Community
SONYC project
Urban Observatory
Data Facility
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Data Facility
Overview
Omnivorous ingestion to a repository for NYCrelated data
Objective and Goals Make data interoperable, with proper multilayered access protocols
Data
Data from City agencies on operations, schedules, maps, etc.
Working with the Mayors Office of Analytics
Start with the open datasets
Includes proprietary data, social media data, CUSPgenerated data
CUSP Chief Data Officer oversees ethical, legal, and social issues
https://datahub.cusp.nyu.edu/dataset
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NYC DataBridge
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Privacy, Big Data, and the Public Good:
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The book identifies ways in which vast new sets of data on human beings can
be collected, integrated, and analyzed to improve urban systems and quality of
life while protecting confidentiality. Sponsored by CUSP, the American Statistical
Association, its Privacy and Confidentiality subcommittee, and the ResearchData Centre of the German Federal Employment Agency.
Editors: Julia Lane, American Institutes for Research; Victoria Stodden, Columbia;
Stefan Bender, The German Federal Employment Agency; Helen Nissenbaum, NYU
Chapter AuthorsSteve Koonin, CUSP; Frauke Kreuter, U-MD and Richard Peng, Johns Hopkins; Alessandro Acquisti, Carnegie
Mellon University; Robert Goerge, UChicago; Helen Nissenbaum, NYU; Kathy Strandberg, NYU;
Paul Ohm, Colorado; Victoria Stodden, Columbia; Alan Karr, National Institute of Statistical Sciences andJerry Reiter, Duke University; John Wilbanks, Sage Bionetworks/Kauffman Foundation;
Cynthia Dwork, Microsoft; Alexander Pentland, et al., MIT; Carl Landwehr, George Washington
University; Peter Elias, University of Warwick.
Privacy, Big Data, and the Public Good:
Frameworks for Engagement
The Quantified Community
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The CUSP Quantified Community (QC) will be
a fully instrumented urban neighborhood that uses
an integrated, expandable sensor network and
citizen engagement to support the measurement,integration, and analysis of neighborhood
conditions.
Through an informatics overlay, data on physical
and environmental conditions and use patterns will
be processed in real-time to maximize
operational efficiencies, improve quality of life
for residents and visitors, and drive evidence-
based planning.
Kontokosta, et al.
The Quantified Community
Understanding the Patterns of Urban Life
Buildings
Resource consumption;
Infrastructure
Solid waste, storm-water
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p ;
indoor air quality;
productivity, health
measures
People
Behavior; mobility;
health; activity; social
networks, metagenomics
Environment
carbon emissions; air
pollution and particulates;
noise; climate
,
management, power
generation/distribution
Safety and SecurityNetwork Security,
Situational Awareness,
Emergency Management
Integration, Event
Forecasting
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Sound of New York City
Cyberphysical systemfor largescale, continuousmonitoring of noise pollution
Custom acoustic sensor(~$100/unit), dB measurement
accurate to city agency standards (+/2 dB)
Stateoftheart machine listeningtechnology forautomatic sound source identification in realtime
Also includes citizen science & data visualizationcomponents
54
J. Bello, C. Mydlarz, J. Salamon
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Cyberphysical system for largescale
continuous monitoring of noise pollution
55
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Custom acoustic sensor based on MEMS
microphone technology
56
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Stateoftheart machine listening technology
for realtime sound source identification
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Urban Observatory
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Provisioned urban vantage point(s) include Downtown Brooklyn
Midtown Manhattan
Suite of boresighted instruments Photometric and colorimetric optical imaging
Broadband IR imaging (SWIR, MWIR, and thermal)
Hyperspectral imaging (trace gases)
LIDAR (building motions, pollution)
RADAR (building /street vibrations, building motion, traffic flow)
Correlative data on the urban scenes Meteorology (temperature, winds, visibility)
Scene geometry (distances, directions, identities of features visible)
Parcel and land use data, building characteristics and activities,building utility consumptions, and real estate valuation data
In situpollution data and location/nature of major sources
In situvehicle and pedestrian traffic for the streets visible
Demographic and economic data
Capability to archive, process, and analyze data acquired
Image processing chains Data warehouse, GIS, Visualization tools
Software and procedures to enhance privacy protection
Personnel and funding to create and operate the above
Urban Observatory
Hyperspectral Imaging of Manhattan Bridge Lights
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Source: Dobler, et al.
Hyperspectral Imaging of Manhattan Bridge Lights
59
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Persistent LWIR Imaging of Manhattan CUSP + Aerospace Corporation
April 615 from HobokenWest Side from the Battery to ~59 Street
128 spectral channels covering 7.6 13.6 m
Plume detection and molecular ID
61
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Northern portion of the view from
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Northern portion of the view from
Hoboken
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GBSS set
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GBSS set
up
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Shortterm
variability in the
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y
thermal IR
Reference image
~ 1 min later
Difference
image
Shortterm
variability in the
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y
thermal IR due
to a cooling tower
lighting up.
Temperature/ Emissivity SeparationR
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T EMaterials
Reflections
BroadbandThermography
For building envelops
Thermal ImageSpectral analysisshows diverse,
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Ammonia
Difluoroethane (Freon)
episodic plumes
A typical spectral fit to pixels with NH
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A typical spectral fit to pixels with NH3
TStatistic for fit to each
of
700 compounds in the
library;NH3is a hit with t~10
8
NH3spectral template
Data fit with template
Residual to fit
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CO2at 200 C
Controled Release Diflouroethane
North of Chelsea piers
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One can at each of three locations
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Number of Captures per Molecule in 9 days
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p p y
ID Count of ID
Ammonia 9,816
Chlorodifluoromethane 1,722
Carbon Dioxide (HITRAN 200C) 1,051
Difluoromethane 412
Carbon Dioxide (HITRAN 300C) 211
1,1,1,2Tetrafluoroethane 197
Pentafluoroethane 190
Carbon Dioxide (HITRAN 100C) 162
Methane 83
Acetyl iodide 65Sulfur dioxide 57
Methane (HITRAN 5C) 39
Chlorofluoromethane 36
2(Diisopropylamino)ethanol 29
Cyclohexanol 29Carbon Dioxide (HITRAN 50C) 28
Other components 416
TOTAL 14,543
Acetone Plume
April 7, 2015 Midtown Manhattan
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p ,
(1.5km distance) 4mx6m
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Acetone is used extensively
in dry cleaning
Couture Cleaners
679 Washington Street
Whats success after 5 years?
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Define and elaborate Urban Science
A vibrant worldclass center pursuing suchNucleate an NYU/NYUPoly community
Implement CUSP facilities
Projects that impact the City and its CitizensCUSP established as a trusted partner to NYC
Support public understanding and engagement Train several hundred people in this new field
Commercialization of CUSP technologies
Bring new tools to the social sciences Begin to franchise the brand globally
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Thank You
cusp.nyu.edu
NYUCUSP@NYUCUSP