From individual spatio-temporal trajectories to spatial networks

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From individual spatio-temporal trajectories to spatial

networks

Maxime Lenormand

To cite this version:

Maxime Lenormand. From individual spatio-temporal trajectories to spatial networks. XTerM2019, 2019, Le Havre, France. �hal-02890716�

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Maxime Lenormand

UMR TETIS, Irstea, France

From individual spatio-temporal trajectories

to spatial networks

June 27, 2019

XTerM2019 | Le Havre, France

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Spatial network

Motivation

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Origin-Destination matrix 20 59 20 50 1 2 3 4 5 1 2 3 4 5 0 2 13 1 5 1 0 1 23 34 8 2 0 2 8 4 35 9 0 2 0 3 4 1 0 13 42 26 27 49 8

O

_i

Dj

T

_ij

Motivation

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Applications

Louf et al. (2015) Bertaud & Malpezzi (2003)

Lenormand et al. (2015)

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Applications

Balcan et al. (2009) Seasonal transmission potential and activity peaks of the new influenza.

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Applications

Wang et al. (2014) Encapsulating urban traffic rhythms into road networks.

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Applications

Lenormand et al. (2018) Multiscale socio-ecological networks in the age of information.

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How to estimate these flows?

Census & survey

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How to estimate these flows?

Census & survey

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How to estimate these flows?

Census & survey

Spatial interaction models

Individual geolocalized data

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How to estimate these flows?

Census & survey

Spatial interaction models

Individual geolocalized data

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"Reality"

Data

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Sampling framework

Time

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Song et al. (2010) Limits of predictability in human mobility. Science 327, 1018-1021. 52% 5% 6% 27% Home

Most frequented location between 7pm and 7am

Work

Most frequented location between 8am and 5pm on weekdays

Origin-Destination Matrix

Tij: number of individuals living in

cell i and working in cell j

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Spatial discretization

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Extracting most frequented locations

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The hours of activity are divided into two groups, daytime hours and nighttime hours. Only days of the week from Monday to Thursday are taken into account.

First filter: Consider only individuals "actives" during at least Nh hours

spread over at least Nd days.

For each hour of activity, the most frequently visited zone during this hour is identified.

For both groups of hours (daytime and nighttime), we identify the zone in which the user has been localized the highest number of hours. Second filter: Select only users whose fraction of hours spent at "home" and "work" are larger than a fraction of the total number of locations visited during nighttime and daytime, respectively.

Lenormand et al. (2016) Is spatial information in ICT data reliable? In proceedings of the 2016 Spatial

Accuracy Conference, 9-17, Montpellier, France. https://gitlab.com/maximelenormand/Most-frequented-locations

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an 0.2 0.4 0.6 0.8 0 2 4 6 8 10 12 δh Number of users (x 10 4) (a) Nh≥1 Nh≥5 Nh≥10 Nh≥15 Nh≥20 0.2 0.4 0.6 0.8 0.6 0.7 0.8 0.9 1.0 δh Fr action of intr a− zonal flo ws (b)

Extracting most frequented locations

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Cross-checking different sources

of mobility information

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Data

Lenormand et al. (2014) Cross-checking different sources of mobility information.PlosOne,9(8):e105407. Louail et al. (2017) Crowdsourcing the Robin Hood effect in cities. Applied Network Science 2, 11.

Madrid Barcelona 6 12 18 0 6 12 18 0 6 12 18 0 6 12 18 50 100 150 1.55 3.11 4.66 Time of Day (h) N um be r of P ho ne us er s (x 10 3) P er ce nt ag e of th e po pu la tio n Barcelona 6 12 18 0 6 12 18 0 6 12 18 0 6 12 18 1 3 5 0.03 0.09 0.16 Time of Day dh) N um be r of Tw itt er us er s dx 10 3) P er ce nt ag e of th e po pu la tio n Barcelona

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Pairwise OD comparison

● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 10−4 ₁₀−3 ₁₀−2 ₁₀−1 ₁₀0 10−4 10−3 10−2 10−1 100

Commuters (Mobile Phone)

C om m ut er s (T w itt er ) ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 10−4 ₁₀−3 ₁₀−2 ₁₀−1 10−4 10−3 10−2 10−1

Commuters (Mobile Phone)

C om m ut er s (C en su s) ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 10−4 ₁₀−3 ₁₀−2 ₁₀−1 10−4 10−3 10−2 10−1 Commuters (Twitter) C om m ut er s (C en su s) 10−7₁₀−6₁₀−5₁₀−4₁₀−3₁₀−2₁₀−1 10−7 10−6 10−5 10−4 10−3 10−2 10−1 Commuters (Census) C om m ut er s (B B V A )

Lenormand et al. (2014) Cross-checking different sources of mobility information.PlosOne,9(8):e105407. Louail et al. (2017) Crowdsourcing the Robin Hood effect in cities. Applied Network Science 2, 11.

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Immigrant community integration

in world cities

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Data

Lamanna et al. (2018) Immigrant community integration in world cities.

Plos One 13, e0191612.

(a) (b) Vancouver San Francisco Los Angeles San Diego Caracas Bogota Lima Santiago Rio de Janeiro Sao Paulo Buenos Aires Guadalajara Mexico City Montreal Toronto Detroit Chicago London Dublin Manchester Paris Brussels Amsterdam Berlin Dallas Atlanta Phoenix Houston Miami Stockholm Saint Petersburg Moscow Istanbul Bangkok Kuala Lumpur Singapore Jakarta Bandung Tokyo Nagoya Osaka Seoul Manila Sydney {italian} {french} {spanish} (c) (d) (e) Milan Rome Madrid Barcelona Lisbon Boston New York Philadelphia Washington

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Spatial distribution of residence

Lamanna et al. (2018) Immigrant community integration in world cities.

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Multiscale socio-ecological networks

in the age of information

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Data

PLoS ONE 13, e0206672. Barcelona Cairngorms Carpathians Costa Vicentina Danube De Cirkel Dovre French Alps Kainuu Kiskunsag Loch Leven Sierra Nevada Stubai valleyTrnava

Vinschgau Warwickshire

16 case study sites

User Time Lon/Lat Photo

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Survey

PLoS ONE 13, e0206672.

90% of accuracy in the users' place of residence detection! 11% response rate Ar tisan, mer chant Intellectual pr of . Farmer Intermediate pr of . Student Other Housekeeper Retired Other 0 10 20 30 40 Percentage of users < 25 [25,40[ [40,60] > 60 0 10 20 30 40 50 Percentage of users Man Woman 0 20 40 60 80 Percentage of users Active Inactive

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Is spatial information in ICT data reliable?

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Data

300,000 mobile phone users' trajectories x 25 two-week periods

Identifying home-work locations from mobile phone activity

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50 100 150 200 250 300 0.65 0.70 0.75 0.80 0.85 0.90 0.95 Number of users (x 103₎ λc

OD comparison

Accuracy Conference, 9-17, Montpellier, France.

5 10 15 20 25 74 76 78 80 82 84 86 88 Nh Accur acy Home Work 1 2 3 4 5 6 7 8 9 10 11 12 12 11 10 9 8 7 6 5 4 3 2 1

Four weeks period

Four w eeks per iod −0.06 −0.04 −0.02 0.00 0.02 λc−λc Voronoi Grid 1km Grid 2km Grid 3km

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Good agreement between the different data sources. Uncertainty & accuracy are highly dependent of the spatial resolution and sample size.

More studies in this spirit need to be done to assess the biases and uncertainty associated with ICT data.

Take home messages...

It could be interesting to involve (more strongly and widely) the indiviual ICT data providers.

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an

References

Accuracy Conference, 9-17, Montpellier, France.

Toole et al. (2015) The path most traveled: Travel demand estimation using big data resources Transp. Res.

PartC: Emerging Technol. 58, PartB,162–177.

Caceres et al. (2007) Deriving origin-destination data from a mobile phone network. Intell. Transport Syst.I

ET1 1, 15–26.

Iqbal et al. (2014) Development of origin-destination matrices using mobile phone call data, Transp. Res. Part

C:EmergingTechnol. 40, 63–74.

Tizzoni et al. (2014) On the use of human mobility proxies for modeling epidemics. PLoS Comput. Biol. 10,

e1003716.

Barbosa-Filho et al. (2018) Human Mobility: Models and Applications. Physics Reports 734, 1-74.

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Acknowledgement

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