Recap

Cities are complex systems

Exhibit emergence, non-linear dynamics, feedback loops, etc.

Driven by micro-interactions

Cities are complex systems par excellence, more than the sum of their parts and developed through a multitude of individual and collective decisions from the bottom up to the top down (Batty et al., 2012).

Individual-level models (e.g. ABM) can capture these properties

This makes them essential for understanding urban dynamics and designing 'smart' cities

BUT: data to understand individual behaviour are hard to come by

The 'Big Data Revolution' might help...

Caveat

You will see lots of question marks in these slides.

This is because there is still a lot of discussion about the impact that 'big data' and 'smart cities' will have on societies.

The lecture is posing questions, rather than answers!

Outline

1. Abundance of Data
2. The Big Data "Revolution"
3. Big Data for Modelling Urban Phenomena
4. Smart Cities?
5. Smart Cities Examples
6. A Force for Good or Evil?
7. Conclusions

Data Collection: Background

1880 U.S. census took 8 years to tabulate!

1890 census used punch cards and tabulation machines (Batty et al. 2012)

Technology is making it possible to collect and analyse larger data sets

But still often need to rely on sampling for collection

Fortunately, random samples are often sufficielty representative

BUT: sampling breaks down when we want to identify small sub-categories

Better to have all data. With new technology, insight and methods, this is becomming possible for a range of fields.

N=all (Mayer-Schonberger and Cukier, 2013)

Abundance of Data

Quiz

What kinds of information about individuals can are being captured right now?

Abundance of Data

What data are being captured about you?

The Abundance of Data

In general, the amount of data being created by, and about, humans is proliferating.

90% of the world's data has been generated in the last two years ( Science Daily)

The amount of data is doubling every two years (EMC).

Total data will hit 8 zeta bytes by 2015 ( Silicon Angle).

If this were printed out on double-sided A4 sheets the pile of paper would stretch to the moon and back 10,000 times! (I've made this up so probably not one to quote, but you get the idea..).

World Information Capacity

Video draws interesting parallels with biological data processing capabilities. ( https://www.youtube.com/watch?v=iIKPjOuwqHo).

Outline

1. Abundance of Data
2. The Big Data "Revolution"
3. Big Data for Modelling Urban Phenomena
4. Smart Cities?
5. Smart Cities Examples
6. A Force for Good or Evil?
7. Conclusions

The Big Data "Revolution"

This abundance of new data is changing the way that we view the world.

In the physical sciences:

"Data intensive science" (e-science) (Gray, 2007)

Astronomy - Data are pooled in Virtual Observatories (VOs)

CERN Large Hadron Collider "CERN does not have the computing or financial resources to crunch all of the data on site" (CERN)

Planet Hunters (Wired, 2013).

The Big Data "Revolution"

In business

Loyalty cards etc - greater knowledge about customers

E.g. predicting pregnancy and telling your parents! (take with a pinch of salt)

Hidden value to 'secondary' / 'exhaust' data

Online browsing behaviour

Re-Captcha (digitising books)

Street view (Google self-driving car, improved mapping, wireless locations)

The Big Data "Revolution"

In medicine:

Do mobile phones cause cancer?

Mobile phone data (Danish Cancer Society)

N≈all - all cancer data and almost all mobile phone users (3.8 million person years)

If you want to know whether mobile phones cause cancer, have a look at the study: http://www.bmj.com/content/343/bmj.d6387

Abundance of patient data

When patients take multiple drugsm unexpected interactions can occur

Tatonetti et al. (2011) mine FDA adverse event reporting system (AERS) data.

Find unexpected relationships between combinations of drugs and glucose levels

Google Flu Trends (Nature, 2009)

Identify particular search words linked to emergence of a flu cluster

Able to predict new clusters rapidly (1 day vs. 1-2 weeks with traditional methods)

(More recent accuracy is questionable)

The Big Data "Revolution"

In the social sciences:

"Datafication" (Mayer-Schonberger and Cukier, 2013)

friends, favourite places, moods, thoughts

Location as data

(particularly relevant to geographers)

'Crisis' in the social sciences

(More on this later)

Data Collection

Collectors are everywhere!

E.g. Smart phone sensors

Accelorometer and gyroscope

GPS

Temperature, humidity, pressure

More on the way...

Air quality, UV light, earthquakes

Crowd-sourced data

Life tracking

'Big Data' Characteristics

Volume

There is lots of it!

E.g. Public transport journeys (Oyster): 3.5M daily tube journeys (not including busses, cycle hire, etc.). Mobile telephones: ~5Bn events daily.

But could be much smaller (N=all)

Variety

Extremely diverse sources and varied content within sources.

'Exhaust' data not created for a particular purpose (unlike traditional surveys)

Do the data tell us anything about the real world?

Low signal to noise ratio

Velocity

Data streams, rather than static databases

Often created (and necessarily analysed) in real time (c.f. climate models)

Big data quiz

Which of the following do you think could be classified as 'Big Data'? (Think about volume, variety, velocity)

Mobile telephone data

Census data

The Annual Business Survey

Open Street Map data

UK business tax returns

MasterCard purchases

Loyalty card data (e.g. Clubcard, Nectarcard)

The scorer of every recorded Premiership goal

A new research paradigm?

Correlation rather than causation

Don't need to understand why something is happening, only that it is happening.

e.g. Google Flu Trends

Data-driven rather than hypothesis-driven -- letting the data speak

"The End of Theory" (Anderson, 2008)

Lower sampling bias, can accept lower data accuracy

With so much data, accuracy is less important (?)

A Crisis for Social Scientists?

Savage and Burrows (2007) have commended on a "coming crisis" for empirical sociology.

The field has traditionally prided itself on developing statistical methods that can identify generalisable rules from small amounts of neat, well structured data.

These small surveys are potentially being superseded by massive "crowd-sourced" databases that monitor activity patterns

Outline

1. Abundance of Data
2. The Big Data "Revolution"
3. Big Data for Modelling Urban Phenomena
4. Smart Cities?
5. Smart Cities Examples
6. A Force for Good or Evil?
7. Conclusions

Urban Research at Leeds

Recent work recognises the complex characteristics of cities (e.g. Batty, 2005)

Strong potential of individual-level methods (e.g. ABM)

BUT data are often insufficient

Excellent residential (night-time) population data (from censuses etc.).

Limited ambient (day-time) population data.

Potential for 'big' data sources to shed light on daily urban dynamics

Importantly, there is the potential to learn about real behaviour

Example: Research with Twitter data

Surge in volume of contributions to social media

66% of online adults use social media (Smith, 2011)

100 million active Twitter accounts in 2011 (Twitter, 2011)

270,000 tweets per minute produced worldwide in 2012 (TechCrunch, 2012)

Accurate time stamps and (occasionally) spatial location

Combination of text, media, location, etc.

Potentially powerful source of data for urban dynamics

What's in a Tweet?

The Twitter Application Programming Interface (API) provides full (?) access to Twitter data

Leeds Twitter Data

Leeds, UK

Population 757,655 (in 2012)

Central shopping / leisure / commercial area

University and students

Social media data

Geo-located Twitter messages from 22 June 2011 to 14 April 2013

N=1,955,655 (after cleaning)

Two research directions:

1 - Simulating daily urban movements

2 - Populations at risk of street crime

1 - Simulating daily urban movements

Simulating daily urban movements

To begin with: Using twitter data to better understand how people use urban spaces

Stage 1: Extract prolific users

Simulating daily urban movements

Stage 2 - Generate a message density surface

Kernel density estimation

Highlight areas with high spatial message density for individual users

Simulating daily urban movements

Stage 3 - Identify areas of 'unusually' high density (anchor points)

GIS method used to identify peaks in digital elevation data

Use Landserf free software (Java) [11]

Anchor points are centres of peaks

Assume highest peak is 'home'

Towards a Model of Daily Urban Movements

We have:

Sufficient compute power to simulate millions of agents

Reliable methods to capture complex phenomena (e.g. ABM)

We need:

More information about behaviour!

But, maybe we now have sufficient data..

Social 'big' data might be the missing link

2 - Populations at risk of street crime

Are crime hot spots stable under the application of different population-at-risk measures?

Which areas have the highest crime rates when using both residential (census) and mobile (social media) population at risk data?

The Population-At-Risk in Crime Analysis

The population at risk is fundamental for crime rate calculations

CrimeRate = Crime Count / Population At Risk

No consensus on the best population-at-risk measure to use

“a valid rate ... should form a probability statement, and therefore should be based on the risk or target group appropriate for each specific crime category” (Boggs, 1965)

Usually default to residential population

Residential Population

OK for some crime types

E.g. Burglary

But not for many others. E.g.:

Assaults (Boivin 2013)

Robbery (Zhang et al. 2012)

Violent crime (Andresen 2006, 2011)

Why residential?

Data availability and resource constraints

More recent attempts to estimate the ambient population:

Proxies (Boggs, 1965). E.g. car park locations, pavement area, etc.

LandScan data (Andresen et al. 2012). Global estimates of average (24hr) population, resolution ~1km²

Social Media - Limitations

What are some of the limitations of research with social media?

Research with Social Media: Conclusions

Lots of limitations, but considerable potential:

Biased at present, but use of social media is rising

Important methodological progress

Best available data?

Considerable investment from the Government and Research Councils. E.g. the Consumer Data Research Centre at Leeds

Outline

1. Abundance of Data
2. The Big Data "Revolution"
3. Big Data for Modelling Urban Phenomena
4. Smart Cities?
5. Smart Cities Examples
6. A Force for Good or Evil?
7. Conclusions

Why 'Smart Cities'?

By 2030, the population living in UK cities is expected to rise from 79% (1950) to 92.2%

The Guardian, using World Resources Institute data)

Worldwide, the proportion is estimated to rise from 40% in 1990 to 70% by 2050

World Health Organisation

What are 'Smart Cities'?

We have seen how much (new) data are available

This is being used by businesses and by researchers

It could also be put to good use at improving the lives of people in cities

What are 'Smart Cities'?

Some definitions.

Smart city is a term that gets together in an integrated way those initiatives oriented at improving the quality of life, sustainability and efficient management of services while innovating in relation to the materials, resources and models used and using technology in an intensive manner. (CTECNO 2012)

[A city] that uses information and communications technologies to make the critical infrastructure components and services ... more aware, interactive and efficient (Belissent 2010)

A city [is] 'smart' when investments ... fuel sustainable economic growth and a high quality of life, with a wise management of natural resources, through participatory government. (Caragliu et al. 2009)

... the urban center of the future, made safe, secure, environmentally green, and efficient because all structures ... are designed, constructed and maintained making use of advanced, integrated materials, sensors, electronics, and networks which are interfaced with computerized systems comprised of databases, tracking, and decision-making algorithms. (Bowerman et al. 2000)

What are common to all of these definitions?

What are 'Smart Cities'?

'Smart Cities' are characterised by:

Abundance of data

Technologies for interpretation and adding value

Simulation

Visualisation

Animation

etc.

Mechanisms for feedback and control

Lots of examples to follow...

Outline

1. Abundance of Data
2. The Big Data "Revolution"
3. Big Data for Modelling Urban Phenomena
4. Smart Cities?
5. Smart Cities Examples
6. A Force for Good or Evil?
7. Conclusions

Intelligent Rubbish

Finding new ways to understand urban dynamics ...

(example from the Trash Truck project)

Intelligent Traffic Systems

Stockholm becomes 'Green Capital'

Part of this thanks to intelligent traffic systems designed with IBM:

To help Stockholm overcome its traffic congestion problems, IBM helped them develop a road charging system that covers a 24-square-kilometer area of the inner city with 18 barrier-free control points equipped with cameras and a mix of payment channels. This project resulted in a 50 percent drop in morning traffic waiting time, an increase of 60,000 passengers per day in public transportation ridership and an overall improved quality of life for the residents of Stockholm.

Intelligent Traffic Systems

Or you could go one step further...

City Dashboards - London

A way to collect and present data from a variety of different sources

Become an integral part of urban planning and management?

Support a feedback loop; people make decisions based on dynamic, real-time data.

Empowering citizens with knowledge about where they live?

City Dashboards - Leeds

City Dashboards - Singapore

Or, if you are MIT, the dashboard looks more like this...

A project as part of the MIT Senseable Cities Laboratory.

Efficient Parcel Delivery

Not all projects are still in planning, some are a reality.

UPS adopted a new route-finding system called ORION (On-Road Integration Optimisation and Navigation)

Massive data-crunching algorithm (all trucks have sensors)

Optimises parcel routes (even trying to avoid left turns)

Some numbers ( Wired magazine):

85 million miles saved

15 trillion trillion (15,000,000,000,000,000,000,000,000) possible routes for a driver with 25 packages

$30 million saved per year if each driver travels one fewer mile each day.

Outline

1. Abundance of Data
2. The Big Data "Revolution"
3. Big Data for Modelling Urban Phenomena
4. Smart Cities?
5. Smart Cities Examples
6. A Force for Good or Evil?
7. Conclusions

Ethical Implications

What are some of the ethical implications associated with the use of Big Data?

E.g. think about: surveillance, informed consent, privacy

A force for good or for evil?

Some people are very optimistic about the possibilities offered by 'Smart Cities' (e.g. Batty, 2012).

Opportunities

A deeper understanding of how urban systems function (at least in the short term).

Manage disruption / emergencies - understand points of failure

Improve quality of life

Manage burgeoning urban populations

Democratisation of urban management (through public data)

A force for good or for evil?

But others are less optimistic (e.g. Galdon-Clavell, 2013).

Risks

How to align 'smart cities' with

informed consent

privacy and data protection

dual use

non-discrimination

Risks of abuse (big brother)

E.g. "Surveillance ... has challenged and undermined the right of all humans to "remain unobserved and unmolested" in their thoughts, personal environments and communications." (The Guardian, 2013).

Market driven, anti-democratic

This all sounds awesome, but have you spotted the flaw in the plan? In such a smart city, the control systems would all be programmed, installed and managed by IBM and CISCO. These private companies would have huge, billion-dollar contracts to manage the biggest cities in the world. When Ops Centres around the world are eventually automated, IBM's software will effectively become your digital mayor - or tyrant.
... IBM would become a de facto member of the government. After all, politicians might tell IBM how they want a city to be run, but it's IBM's implementation that ultimately matters. A new law might decree that smart cars travelling in smart cities must be limited to 30 mph - but what if IBM disagrees, or says the system doesn't have that capability, or simply takes six months to implement the change?(Anthony, 2012).

Outline

1. Abundance of Data
2. The Big Data "Revolution"
3. Big Data for Modelling Urban Phenomena
4. Smart Cities?
5. Smart Cities Examples
6. A Force for Good or Evil?
7. Conclusions

Conclusions

Abundance of new data, already being exploited by businesses

Potentially use these data to better understand urban dynamics and human behaviour

This can, and should, inform our models

Lots of examples of new 'smart cities' initiatives

ABM will form a significant part of these initiatives

Ultimately improve quality of life (?)

Big questions around data protection, privacy, ethics and surveillance.