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William Solecki, CUNY – Hunter College
EDF Workshop - Columbia University 15 October 2014
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Outline
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Smart Cities and Urban Environmental
Management
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Urbanization and Climate Change
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Big Data and the Response to Hurricane Sandy
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Urban Science (Urbanization
Science)
•
•
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Urban systems – energy, water, land use,
transportation, food
System dynamics – flows, inputs, stress,
resilience, transition, and transformation
Focus on speed, direction, volume
Taxi trips in an hour. Taxis are valuable
sensors for city life. In NYC, there are on
average 500,000 taxi trips each day.
Information associated with taxi trips thus
provides unprecedented insight into many
different aspects of a city life, from
economic activity and human behavior to
mobility patterns. This figure shows the
taxi trips in Manhattan on May 1 from 8
a.m. to 9 a.m. The blue dots correspond to
pickups and the orange ones correspond
to drop-offs. Note the absence of taxis
along 6th avenue, indicating that traffic
was blocked during this period. (Source:
An upcoming article titled “Visual
Exploration of Big Spatio-Temporal Urban
Data: A Study of New York City Taxi Trips,”
by Nivan Ferreira, Jorge Poco, Huy Vo,
Juliana Freire, and Claudio T. Silva. IEEE
Transactions on Visualization and
Computer Graphics (TVCG), 2013)
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Urban Science, Environment, and Big
Data
•
•
•
Resource use efficiency – smart buildings,
transportation, energy
Outside-of-the-box ideas – water supply as
energy source, solar energy; opportunities for
value capture
Emergency response and resilience – organize
Global Urbanization and
Climate Change
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Global Urbanization – 1960
Global Urbanization - 2011
Global Urbanization – 2025
IPCC – AR5
Climate Change -September 2013
Global Urbanization – Inland and
Coastal Locations 1970
Global Urbanization – Inland and
Coastal Locations 2011
Global Urbanization – Inland and
Coastal Locations 2025
Smart Cities and Extreme Weather
Events
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Emergency response and preparedness
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Disaster risk reduction
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Climate change adaptation
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Hurricane Sandy, 28 October 2012
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Source: NOAA
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Impacts and Associated
Vulnerabilities
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Urban Lifelines and Infrastructure System Failures
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Water Supply
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Electricity
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Transportation
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Gasoline Supply
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Pharmacy – Drug
Supply
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General Observations about
Impacts and Vulnerabilities
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Cascading system impacts
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Uneven geography – not all on the coast, but
most impactful on coast
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Role of ecosystem protection opportunities –
lost and found – e.g. wetlands
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Highly complex systems require significant
redundancy and context specific
vulnerabilities – e.g. health care system
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A lot more impact and vulnerability
20 work to
Tweets – Just Before to Just After
Sandy
http://www.fastcodesign.com/1671188/map-how-new-york-tweetedduring-hurricane-sandy
Hurricane Sandy-related tweets across the United States
Source: Shelton et al. 2014
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Social Media Check-Ins – Showing
Hurricane Sandy Outages
http://blog.gnip.com/tag/hurricane-sandy/
PlaNYC 2013 – Released 11 June
2013
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NYC Special Initiative for Rebuilding and Resiliency
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Addresses how to rebuild New York City to be
more resilient in the wake of Sandy but with a
long‐term focus on:
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1) how to rebuild locally; and
–
2) how to improve citywide infrastructure and
building resilience
A comprehensive report in June 2013 addresses
these challenges by investigating three key
questions:
–
What happened during and after Sandy and why?
Approximately
1,000,000
building and
related
structures in
New York City –
The City
maintains a GIS
parcel data base
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Future Climate Risk in New York City
Dynamic Context for Big
Data Application
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Released 11 June 2013;
available at CUNY Institute for
Sustainable Cities (CISC) website –
www.cunysustainablecities.org
Provides the updated
climate science
information and
foundation for PlaNYC
2013
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Extreme Events
2020s
Baseline
(19712000)
Number of
days/year with
maximum
temperature at or
above 90°F
Heat waves¹ ²
and cold
weather
events
Intense
Precipitation¹
2050s
Lowestimate
Middle
range
Highestimate
Lowestimate
Middle
range
Highestimate
18
24
26 to 31
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39 to 52
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Number of heat
waves/year
2
3
3 to 4
4
4
5 to 7
7
Average heat wave
duration (in days)
4
5
5 to 5
5
5
5 to 6
6
Number of
days/year with
minimum
temperature at or
below 32°F
72
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52 to 58
60
37
42 to 48
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Number of
days/year with
rainfall at or above
2 inches
3
3
3 to 4
5
3
4 to 4
5
¹Based on 35 GCMs and two Representative Concentration Pathways. Baseline data are from the NOAA NCDC USHCN, Version 2 (Menne et al., 2009). 30-year
mean values from model-based outcomes.
²Heat waves are defined as three more consecutive days with maximum temperatures at or above 90°F.
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Extreme Events
The NPCC developed qualitative projections where future changes are too
uncertain to provide local quantitative projections
Spatial Scale
of Projection
Direction of
Change by
2050s
Likelihood¹
Sources
North Atlantic
Basin
Unknown
--
--
Number of intense North Atlantic
hurricanes
Basin
Increase
More likely
than not
USGCRP, 2013; IPCC, 2012
Extreme hurricane North Atlantic
winds
Basin
Increase
More likely
than not
USGCRP, 2013; IPCC, 2012
Intense hurricane
precipitation
Increase
More likely
than not
USGCRP, 2013; IPCC, 2012
Tropical Cyclones
Total number
North Atlantic
Basin
Nor’easters
NYC area
Unknown
-IPCC 2012; Colle et al. 2013
Number of intense hurricanes in the North Atlantic Basin will more likely than not increase
¹ Probability of occurrence and likelihood defined as (IPCC, 2007): Virtually certain; >99% probability of occurrence, Extremely likely; >95% probability of
occurrence, Very likely; >90% probability of occurrence, Likely; >66% probability of occurrence, More likely than not; >50% probability of occurrence,
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About as likely as not; 33 to 66% probability of occurrence.
Source: PlaNYC 2013
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Source: PlaNYC 2013
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Source: PlaNYC 2013
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Source: PlaNYC 2013
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SREXa Climate Related Shifts in
Extreme Events - 1
a. IPCC Special Report on Extreme Events
SREX Climate Related Shifts in
Extreme Events - 2
SREX Climate Related Shifts in
Extreme Events - 3
Indicators and Monitoring
Smart Cities, Big Data, and
Climate Change Adaptation
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Monitoring for Extreme Events
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Flexible and mobile monitoring
Responsive to the structure and character of
the event – UHI, combined sewer outflow
Formal (including adjustment of existing
systems) and informal (community based)
monitoring
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Climate Risk, Extreme Events, and
Impacts as Indicators
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Acute - established
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Chronic - established
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New and Emerging Hazards
New York City Panel on Climate Change
Indicators Design and Process
Decision Criteria (to the extent
possible):
Process of Establishing Indicators:
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Scientifically defensible
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Link to conceptual framework
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Defined relationship to climate
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Scalable indicators
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Build on or augment existing
agency efforts
Current and leading indicators
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

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Start with the questions to be
addressed by indicators
Identify stakeholders in diverse
institutions
Engage stakeholders (producers and
users) from development to
implementation to evaluation
Prototype indicators to establish
priorities for implementation
New indicators will be assessed and
tested on an ongoing basis
Evaluate the system
NPCC Indicators and Monitoring – Other Concerns


What are the key indicator questions – specifically what role and purpose should the indicator
serve? Sample questions include…

How do we know that climate is changing and how is the climate projected to change in the
future?

What important climate impacts and opportunities are occurring or are predicted to occur in the
future?

How are we preparing for rapid change or extreme events related to climate?

How are measures of adaption over longer time frames?
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What are our fundamental vulnerabilities and resiliencies to climate variability and change?
What are key components and systems for which indicators and measures are necessary?
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Climate system
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Infrastructure systems

Social and public health systems
CONFIDENTIAL
Tipping Points and Thresholds in Urban SystemsApplication for Climate Change Indicators and Monitoring
An New York State
Metropolitan
Transportation
Authority employee
fills an "AquaDam,"
placed across the Long
Island Rail Road tracks
at New York City's
Penn Station, on
Saturday, August 27,
2011. The temporary
barrier was installed to
help keep flood waters
xstirred up by
Hurricane Irene out of
Penn Station's tunnels.
(AP Photo/NY
Metropolitan
Transportation Authority,
John Kettel)
Conclusions: Connections between Smart Cities
and Climate Change
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Timing of impacts
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Rate of change
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Emergent vulnerabilities
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Risk, uncertainties, cost curves
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Actionable science – relevant to
engineering world
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Climate Adaptation Emerging Challenges and Opportunities for
Smart City Approaches and Big Data Application
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Baseline climate science
data (and modeling if
possible)
Rapid assessment strategy
of impacts, vulnerabilities,
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Thank You. [email protected]
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