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

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

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)

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

Global Urbanization and Climate Change 6

Global Urbanization – 1960

Global Urbanization

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 Emergency response and preparedness Disaster risk reduction Climate change adaptation 15

Source: NOAA 16 Hurricane Sandy, 28 October 2012

17

Impacts and Associated Vulnerabilities 18

Urban Lifelines and Infrastructure System Failures Water Supply Electricity Transportation Gasoline Supply Pharmacy – Drug Supply 19

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

Tweets – Just Before to Just After Sandy during-hurricane-sandy

22 Source: Shelton et al Hurricane Sandy-related tweets across the United States

Social Media Check-Ins – Showing Hurricane Sandy Outages

PlaNYC 2013 – Released 11 June

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

30 Approximately 1,000,000 building and related structures in New York City – The City maintains a GIS parcel data base

Future Climate Risk in New York City Dynamic Context for Big Data Application 31

Released 11 June 2013; available at CUNY Institute for Sustainable Cities (CISC) website – Provides the updated climate science information and foundation for PlaNYC

Extreme Events s2050s Baseline ( ) 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 90°F to to 5257 Number of heat waves/year 233 to 4445 to 77 Average heat wave duration (in days) 455 to 5555 to 66 Number of days/year with minimum temperature at or below 32°F to to 4852 Intense Precipitation¹ Number of days/year with rainfall at or above 2 inches 333 to 4534 to 45 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.

Extreme Events 34 Spatial Scale of Projection Direction of Change by 2050s Likelihood¹Sources Tropical Cyclones Total numberNorth Atlantic Basin Unknown-- Number of intense hurricanes North Atlantic Basin IncreaseMore likely than not USGCRP, 2013; IPCC, 2012 Extreme hurricane winds North Atlantic Basin IncreaseMore likely than not USGCRP, 2013; IPCC, 2012 Intense hurricane precipitation North Atlantic Basin IncreaseMore likely than not USGCRP, 2013; IPCC, 2012 Nor’eastersNYC areaUnknown--IPCC 2012; Colle et al The NPCC developed qualitative projections where future changes are too uncertain to provide local quantitative projections 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, About as likely as not; 33 to 66% probability of occurrence. Number of intense hurricanes in the North Atlantic Basin will more likely than not increase

35 Source: PlaNYC 2013

36 Source: PlaNYC 2013

37 Source: PlaNYC 2013

38 Source: PlaNYC 2013

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 42

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

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

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 New York City Panel on Climate Change Indicators Design and Process 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

CONFIDENTIAL 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)

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, 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 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

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

50 Thank You.