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Big Data for Smart-Cities undergoing Climate Change

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William Solecki, CUNY Hunter College EDF Workshop - Columbia University 15 October 2014 * Big Data for Smart-Cities undergoing Climate Change – PowerPoint PPT presentation

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Title: Big Data for Smart-Cities undergoing Climate Change


1
Big Data for Smart-Cities undergoing Climate
Change
  • William Solecki, CUNY Hunter College
  • EDF Workshop - Columbia University 15 October
    2014

1
2
Outline
  • Smart Cities and Urban Environmental Management
  • Urbanization and Climate Change
  • Big Data and the Response to Hurricane Sandy

2
3
Urban Science (Urbanization Science)
  • Urban systems energy, water, land use,
    transportation, food
  • System dynamics flows, inputs, stress,
    resilience, transition, and transformation
  • Focus on speed, direction, volume
  • Dramatic growth of instrumentation, monitoring,
    data volume, and data structure capacity
  • Academic focus City as Laboratory
  • NYU Center for Urban Science Progress - CUSP
  • Santa Fe Institute
  • MIT, City Science

4
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)
4
5
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 and
    orchestrate precious resources and protect
    communities and infrastructure

6
Global Urbanization and Climate Change
6
7
Global Urbanization 1960
8
Global Urbanization - 2011
9
Global Urbanization 2025
10
IPCC AR5 Climate Change -September 2013
11
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12
Global Urbanization Inland and Coastal
Locations 1970
13
Global Urbanization Inland and Coastal
Locations 2011
14
Global Urbanization Inland and Coastal
Locations 2025
15
Smart Cities and Extreme Weather Events
  • Emergency response and preparedness
  • Disaster risk reduction
  • Climate change adaptation

15
16
Hurricane Sandy, 28 October 2012
16
Source NOAA
17
17
18
Impacts and Associated Vulnerabilities
18
19
Urban Lifelines and Infrastructure System Failures
  • Water Supply
  • Electricity
  • Transportation
  • Gasoline Supply
  • Pharmacy Drug Supply

19
20
General Observations about Impacts and
Vulnerabilities
  • Cascading system impacts
  • Uneven geography not all on the coast, but most
    impactful on coast
  • Role of ecosystem protection opportunities lost
    and found e.g. wetlands
  • Highly complex systems require significant
    redundancy and context specific vulnerabilities
    e.g. health care system
  • A lot more impact and vulnerability work to be
    done
  • Data rich assessment smart city context
    yielding critical data challenge is how to use
    it

20
21
Tweets Just Before to Just After Sandy
http//www.fastcodesign.com/1671188/map-how-new-yo
rk-tweeted-during-hurricane-sandy
22
Hurricane Sandy-related tweets across the United
States
Source Shelton et al. 2014
22
23
Social Media Check-Ins Showing Hurricane Sandy
Outages
http//blog.gnip.com/tag/hurricane-sandy/
24
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25
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26
PlaNYC 2013 Released 11 June 2013
26
27
NYC Special Initiative for Rebuilding and
Resiliency
  • Addresses how to rebuild New York City to be more
    resilient in the wake of Sandy but with a
    long-term focus on
  • 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?
  • What is the likely risk to NYC as the climate
    changes and the threat of future storms and
    severe weather increases?
  • What to do in the coastal neighborhoods and
    citywide infrastructure

28
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30
Approximately 1,000,000 building and related
structures in New York City The City maintains
a GIS parcel data base
30
31
Future Climate Risk in New York City
  • Dynamic Context for Big Data Application

31
32
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
32
33
Extreme Events
2020s 2020s 2020s 2050s 2050s 2050s
Baseline (1971-2000) Low-estimate Middle range High-estimate Low-estimate Middle range High-estimate
Heat waves¹ ² and cold weather events Number of days/year with maximum temperature at or above 90F 18 24 26 to 31 33 32 39 to 52 57
Heat waves¹ ² and cold weather events Number of heat waves/year 2 3 3 to 4 4 4 5 to 7 7
Heat waves¹ ² and cold weather events Average heat wave duration (in days) 4 5 5 to 5 5 5 5 to 6 6
Heat waves¹ ² and cold weather events Number of days/year with minimum temperature at or below 32F 72 50 52 to 58 60 37 42 to 48 52
Intense Precipitation¹ 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 90F.
33
34
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
Tropical Cyclones
Total number North Atlantic Basin Unknown -- --
Number of intense hurricanes North Atlantic Basin Increase More likely than not USGCRP, 2013 IPCC, 2012
Extreme hurricane winds North Atlantic Basin Increase More likely than not USGCRP, 2013 IPCC, 2012
Intense hurricane precipitation North Atlantic Basin Increase More likely than not USGCRP, 2013 IPCC, 2012
Noreasters 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 gt99
probability of occurrence, Extremely likely gt95
probability of occurrence, Very likely gt90
probability of occurrence, Likely gt66
probability of occurrence, More likely than not
gt50 probability of occurrence, About as likely
as not 33 to 66 probability of occurrence.
34
35
Source PlaNYC 2013
35
36
Source PlaNYC 2013
36
37
Source PlaNYC 2013
37
38
Source PlaNYC 2013
38
39
SREXa Climate Related Shifts in Extreme Events - 1
a. IPCC Special Report on Extreme Events
40
SREX Climate Related Shifts in Extreme Events - 2
41
SREX Climate Related Shifts in Extreme Events - 3
42
Indicators and Monitoring
  • Smart Cities, Big Data, and Climate Change
    Adaptation

42
43
Monitoring for Extreme Events
  • 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

43
44
Climate Risk, Extreme Events, and Impacts as
Indicators
  • Acute - established
  • Chronic - established
  • New and Emerging Hazards

45
New York City Panel on Climate Change
Indicators Design and Process
  • Decision Criteria (to the extent possible)
  • Scientifically defensible
  • Link to conceptual framework
  • Defined relationship to climate
  • Scalable indicators
  • Build on or augment existing agency efforts
  • Current and leading indicators
  • Process of Establishing Indicators
  • 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

46
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?
  • 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?
  • Climate system
  • Infrastructure systems
  • Social and public health systems
  • Adaptations
  • How useful - stakeholders policy level
  • Venue
  • What conceptual model of key components and
    systems should be used? A model will help
    identify key points of system structure,
    resilience, and transition (via a system level
    tipping point or threshold)

47
Tipping Points and Thresholds in Urban Systems-
Application 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) 
48
Conclusions Connections between Smart Cities and
Climate Change
  • Timing of impacts
  • Rate of change
  • Emergent vulnerabilities
  • Risk, uncertainties, cost curves
  • Actionable science relevant to engineering
    world
  • Uneven distribution of impacts and
    vulnerabilities
  • Urban system complexity opportunity and
    challenge
  • Defining indicators and monitoring schemes

48
49
Climate Adaptation Emerging Challenges and
Opportunities for Smart City Approaches and Big
Data Application
  • Baseline climate science data (and modeling if
    possible)
  • Rapid assessment strategy of impacts,
    vulnerabilities, opportunities for increased
    resiliency
  • Long term goal (e.g. resilience) as frame for
    action
  • Interagency cooperative (within govt. and across
    governments)
  • Integrate new risk and hazard measures (in
    conjunction with traditional measures e.g. 1
    maps)
  • Climate protection levels access codes,
    standards, and regulations, and monitoring and
    indicators for climate change robustness
  • System perspective for identifying tipping
    points/cascade impacts and vulnerabilities
  • Climate science data and mapping uncertainties
    (besides cost uncertainties)
  • Greater transparency of data analysis and data
    interpretation
  • Promote greater post extreme event learning
    pushing open the policy window

49
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Thank You. wsolecki_at_hunter.cuny.edu
50
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