Background: Need for data

ABM is a fantastic tool for exploring complex systems

But we are often let down by data

Limitted information about daily behaviour

Difficult / impossible to properly validate individual-level models

'Big Data' might offer a solution

The Big Data "Revolution"

There is an abundance of new data

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).

It 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

Example: Research with Twitter

Aim: can we use twitter to better understand routine activities?

Obviuos relevance to crime research

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.

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

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'

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

Messiness / Noise

Not gathered using systematic / statistically guided methodology

Omissions are commonplace

No systematic corrections

Generalisability

The digital divide

E.g. higher socioeconomic status groups are over represented in Twitter and Flickr (Li et al., 2013)

Bias

Over-representation of the most prolific users

Or certain activities

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

Ethical Implications

Risks

How to align 'Big Data' 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).

Conclusions

Abundance of new data

Business already exploiting new sources

Potentially use these data to better understand society

.. and to inform crime models

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