Networks, Big Data and Statistical Physics: A killing combination

•

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This document discusses using statistical physics, network theory, and big data to study human mobility. It outlines challenges in obtaining and analyzing large-scale mobility data from sources like smartphones and social media. The author proposes applying network-based modeling approaches inspired by statistical mechanics to build null models that generate predictions about mobility flows. Comparing these predictions to real data allows identifying abnormal patterns and validating hypotheses about key drivers of human movement like distance, population density, and opportunities. The goal is to better understand human mobility and distinguish important factors from negligible ones in an unbiased way.

Education Technology Business

Statistical Physics, Network
theory & Big data
An approach to human mobility

Oleguer Sagarra
Dept. Física Fonamental,
University of Barcelona
1

A killing combination...
Statistical Physics
&
Big Data
“New Social Sciences”
2

Why?
We want to study Human Mobility…

Mobility has deep implications in many processes..
(contagion, spread of ideas...)
The development of GPS/mobile phone technologies
makes gathering data cheap and possible at large
scale.
3

What?
(Human) Mobility is a rather complex process…
Different scales (Micro/Meso/Macro)
Society is heterogeneous… (Humans are not
“monkeys”… in principle!)

But we are physicists! So we will try to
model it anyway…
4

But we don’t need
modelling…
“Computers are useless, they can only
give you answers…” (P. Picasso)
This talk is about questions rather…
“Models push the boundaries of our
understanding"
5

How?
Theoretical
Physics
Mathematics

Empirical
Real (big) Data

Network Science

6

The data... (has problems)
a) How to get it?
Private companies
(Social Media)

Citizens

7

Getting the data... Experiments
Smartphones give lots of “sensing opportunities”
Citizen science aims to involve people in data
collection, sharing and processing

BeePath: Experiments on
human mobility
http://bee-path.net
(Btw: Very interesting project, but don’t have time for it today)

8

Getting the data...
Social Media
b) Is it biased?
(Big data can also mean big errors)

9

Social media data
Social media data is geolocalized, we can extract
trajectories from it.
But ﬁrst, is the data representative from the population?

(We want info about people, not about “some people that tweet a lot”)

We can compare with the census…
Analysis must be done at user level!
10

The data... is geolocalized,
and (too) big!
c) Continuous vs discrete data
From points to a network?
(We want only the ﬂows: From where and to where people go, “on average”)

11

The network approach
Data
Filtering
Aggregation (grid)
Network

12

Network data

(We can now apply network metrics
and… data is normalized!)
Sagarra, O. Master Thesis. http://upcommons.upc.edu/pfc/handle/
2099.1/13134

13

Now we know how to deal
with the data...

We want to detect “abnormal” patterns...
What is chance, what is not?
What is important, what is not?
14

Modeling as a physicist…
Take all trivial elements out…
Keep just the “basic” factors in mobility
!

- Distance / Cost (a.k.a. laziness)
- Population density (a.k.a. opportunities)

(We look for causality, not correlation)
15

Macro/Meso level:
(urban/regional/national)

We need a general model for mobility networks…

Taking inspiration from Statistical Mechanics
and Network Theory, one can deﬁne ﬂexible
null models.

16

We need a null model for the
data...
Procedure:
1. Fix some hypothesis
“The population leaving or entering each cell is given”
!
(quite a lot of maths….)*

2. Generate predictions
“How do the ﬂows organize?”
!

3. Compare
Data vs Prediction
Sagarra, O. et altr. Phys. Rev. E 88, 062806 (2013)

17

Roadmap
Raw data
Experiments, Databases...

Prediction
(Product)

Data treatment tools

Statistical Validation
Hypothesis...
Modelling

(We are here)

Clean data
Null Model predictions

Data features
Visualizations
18

What’s the goal of all this?
Understand what drives human mobility
Discriminate important factors from negligible ones
(population density, distance, cost...)
Create tools to study data in an unbiased manner

19

osagarra@ub.edu
@usagarra

Thanks for your attention...

20

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Networks, Big Data and Statistical Physics: A killing combination

1. Statistical Physics, Network theory & Big data An approach to human mobility Oleguer Sagarra Dept. Física Fonamental, University of Barcelona 1

2. A killing combination... Statistical Physics & Big Data “New Social Sciences” 2

3. Why? We want to study Human Mobility… Mobility has deep implications in many processes.. (contagion, spread of ideas...) The development of GPS/mobile phone technologies makes gathering data cheap and possible at large scale. 3

4. What? (Human) Mobility is a rather complex process… Different scales (Micro/Meso/Macro) Society is heterogeneous… (Humans are not “monkeys”… in principle!) But we are physicists! So we will try to model it anyway… 4

5. But we don’t need modelling… “Computers are useless, they can only give you answers…” (P. Picasso) This talk is about questions rather… “Models push the boundaries of our understanding" 5

6. How? Theoretical Physics Mathematics Empirical Real (big) Data Network Science 6

7. The data... (has problems) a) How to get it? Private companies (Social Media) Citizens 7

8. Getting the data... Experiments Smartphones give lots of “sensing opportunities” Citizen science aims to involve people in data collection, sharing and processing BeePath: Experiments on human mobility http://bee-path.net (Btw: Very interesting project, but don’t have time for it today) 8

9. Getting the data... Social Media b) Is it biased? (Big data can also mean big errors) 9

10. Social media data Social media data is geolocalized, we can extract trajectories from it. But ﬁrst, is the data representative from the population? (We want info about people, not about “some people that tweet a lot”) We can compare with the census… Analysis must be done at user level! 10

11. The data... is geolocalized, and (too) big! c) Continuous vs discrete data From points to a network? (We want only the ﬂows: From where and to where people go, “on average”) 11

12. The network approach Data Filtering Aggregation (grid) Network 12

13. Network data (We can now apply network metrics and… data is normalized!) Sagarra, O. Master Thesis. http://upcommons.upc.edu/pfc/handle/ 2099.1/13134 13

14. Now we know how to deal with the data... We want to detect “abnormal” patterns... What is chance, what is not? What is important, what is not? 14

15. Modeling as a physicist… Take all trivial elements out… Keep just the “basic” factors in mobility ! - Distance / Cost (a.k.a. laziness) - Population density (a.k.a. opportunities) (We look for causality, not correlation) 15

16. Macro/Meso level: (urban/regional/national) We need a general model for mobility networks… Taking inspiration from Statistical Mechanics and Network Theory, one can deﬁne ﬂexible null models. 16

17. We need a null model for the data... Procedure: 1. Fix some hypothesis “The population leaving or entering each cell is given” ! (quite a lot of maths….)* 2. Generate predictions “How do the ﬂows organize?” ! 3. Compare Data vs Prediction Sagarra, O. et altr. Phys. Rev. E 88, 062806 (2013) 17

18. Roadmap Raw data Experiments, Databases... Prediction (Product) Data treatment tools Statistical Validation Hypothesis... Modelling (We are here) Clean data Null Model predictions Data features Visualizations 18

19. What’s the goal of all this? Understand what drives human mobility Discriminate important factors from negligible ones (population density, distance, cost...) Create tools to study data in an unbiased manner 19

20. osagarra@ub.edu @usagarra Thanks for your attention... 20

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