Patterns of Human Mobility in the Metropolitan Area of Rio de Janeiro

Support: Institutions:

Region under study

The data are about the calls made in phone area 21, that corresponds approximately to the Metropolian Area of Rio de Janeiro.

http://pt.wikipedia.org/wiki/Regi%C3%A3o_Metropolitana_do_Rio_de_Janeiro

Space-time Distribution

6:00

18:00

12:00

24:00

0,E+00

2,E+04

4,E+04

6,E+04

8,E+04

1,E+05

1,E+05

Cen

tro

Bo

tafo

go

Bar

ra d

a Ti

juca

Lap

a

São

Cri

stó

vão

Ipan

ema

Leb

lon

Co

pac

aban

a

Inh

oaí

ba

Bo

nsu

cess

o

Pad

re M

igu

el

Sen

ado

r C

amar

á

Rea

len

go

Pac

iên

cia

Inh

aúm

a

Cid

ade

No

va

Gu

arat

iba

Mad

ure

ira

Mar

acan

ã

San

ta C

ruz

Tiju

ca

Pra

ça d

a B

and

eir

a

Pen

ha

Está

cio

Jard

im B

otâ

nic

o

San

tíss

imo

Gam

bo

a

Cat

ete

Rio

Co

mp

rid

o

Ram

os

Gáv

ea

Ben

fica

Pra

ça S

eca

San

to C

rist

o

Saú

de

Rec

reio

Cid

ade

Un

iver

sitá

r

Iraj

á

Ric

ard

o d

e…

Gra

jaú

An

chie

ta

Cam

po

Gra

nd

e

Gló

ria

Osv

ald

o C

ruz

Ilha

do

…

Sep

eti

ba

Méi

er

São

Co

nra

do

Gar

dên

ia A

zul

Vaz

Lo

bo

Cat

um

bi

Jard

im S

ula

cap

Origin-Destination Matrix

0,0%

0,1%

0,2%

0,3%

0,4%

0,5%

jan-14 fev-14 mar-14 abr-14 mai-14 jun-14 jul-14 ago-14 set-14 out-14 nov-14 dez-14

ZONA SUL x CENTRO

CARNAVAL SEM. SANTA TIRADENTES

CORPUS CHRISTI

SEG TER QUA QUI SEX SAB DOM

CONSCIÊNCIA NEGRA

NATAL

home-work Patterns of Mobility

0,0%

0,2%

0,4%

0,6%

0,8%

1,0%

1,2%

1,4%

jan-14 fev-14 mar-14 abr-14 mai-14 jun-14 jul-14 ago-14 set-14 out-14 nov-14 dez-14

ZONA SUL-TERESÓPOLIS

SEG TER QUA QUI SEX SÁB DOM

CARNAVAL SEM. SANTA TIRADENTES

home-leisure Patterns of Mobility

BRT Transcarioca

1,00%

1,20%

1,40%

1,60%

1,80%

2,00%

2,20%

2,40%

01/01/14 01/02/14 01/03/14 01/04/14 01/05/14 01/06/14 01/07/14 01/08/14 01/09/14 01/10/14 01/11/14 01/12/14

MADUREIRA BANGU MOV. AVG. 30 DAYS MADUREIRA MOV AVG. 30 DAYS BANGU

Reveillon 2014 Copacabana

0

20000

40000

60000

80000

100000

120000

Reveillon 2014 Copacabana

Tourism to Búzios Aug – Sep/2014

0

200

400

600

800

1000

1200

1400

1600

1800

2000

0

100

200

300

400

500

600

700

800

900

1000

20

14

-08

-01

20

14

-08

-02

20

14

-08

-03

20

14

-08

-04

20

14

-08

-05

20

14

-08

-06

20

14

-08

-07

20

14

-08

-08

20

14

-08

-09

20

14

-08

-10

20

14

-08

-11

20

14

-08

-12

20

14

-08

-13

20

14

-08

-14

20

14

-08

-15

20

14

-08

-16

20

14

-08

-17

20

14

-08

-18

20

14

-08

-19

20

14

-08

-20

20

14

-08

-21

20

14

-08

-22

20

14

-08

-23

20

14

-08

-24

20

14

-08

-25

20

14

-08

-26

20

14

-08

-27

20

14

-08

-28

20

14

-08

-29

20

14

-08

-30

20

14

-08

-31

20

14

-09

-01

20

14

-09

-02

20

14

-09

-03

20

14

-09

-04

20

14

-09

-05

20

14

-09

-06

20

14

-09

-07

20

14

-09

-08

20

14

-09

-09

20

14

-09

-10

20

14

-09

-11

20

14

-09

-12

20

14

-09

-13

20

14

-09

-14

20

14

-09

-15

20

14

-09

-16

20

14

-09

-17

20

14

-09

-18

20

14

-09

-19

20

14

-09

-20

20

14

-09

-21

20

14

-09

-22

20

14

-09

-23

20

14

-09

-24

20

14

-09

-25

20

14

-09

-26

20

14

-09

-27

20

14

-09

-28

20

14

-09

-29

20

14

-09

-30

Usuários por Dia: Agosto - Setembro

21 OUTROS 22

Floating Population in Police Districts

00

100

200

300

400

500

600

1 5 4 18 17 6 16 10 29 14 15 19 23 13 9 12 22 41 42 21 27 44 33 20 25 7 35 26 24 30 40 37 36 38 28 32 39 34 45 31 43 11

Tho

usa

nd

s o

f p

eo

ple

Police Station ID

Floating Population in Police Districts Residents

Floating Population

CONNECTOR

ATTRACTOR

PERIPHERAL

LOCAL

doi:10.1016/j.trc.2015.04.022

16

doi:10.1016/j.trc.2015.04.022

Challenge – Combining CDR data with other sources

buses and subway

Images from public cameras

Georeferenced messages

Privacy Issues

• “Unique in the Crowd: The privacy bounds of human mobility”

Montjoye, Hidalgo, Verleysen & Blondel. Nature, 2013. http://www.nature.com/articles/srep01376 • “Urban Sensing Using Mobile Phones Network Data: A

Survey of Research”. F Calabrese, L Ferrari, V Blondel. ACM Computing Surveys, 2014. http://arxiv.org/abs/1502.03406

http://research.microsoft.com/pubs/211950/UrbanComputing-zheng-tist2014.pdf

Obrigado!

Alexandre Evsukoff(Coppe)

Moacyr A. H. B. da Silva

(FGV/EMAp)

Antonio C. S. Branco

(FGV/EMAp)

Júlio Cesar Chaves (FGV/Coppe)

Carlos André R. Pinheiro

(KU Lueven)

Nuno Crokidakis (UFF)

Marta Gonzalez (MIT)

Serdar Colak (MIT)

Yingxiang Yang (MIT)