Human mobility,urban structure analysis,and spatial community detection from mobile phone data

Song Gao

Email： [email protected]

University of California, Santa Barbara

Human mobility,

urban structure analysis,

and spatial community detection

from mobile phone data

http://stko.geog.ucsb.edu

Big Geo-Data Age

mailto:[email protected]

Open Questions 1). Does distance still constricts human mobility in geographic

space?

2). Whether the information communication technology (ICT)

increase or decline the probability of physical movements of

urban residents in daily life?

3). What is spatio-temporal patterns of phone call activities in

urban space? How to explore?

4). Does the phone call interaction follow Tobler’s first law

(TFL) of geography?

Background

Location Awareness Devices（Mobile Phone、GPS)

Large scale spatio-temporal datasets

Ratti, 2010 MIT SENSEable City Lab

Urban Computing

sense city dynamics to

enable a city-wide

computing as so to serve

people and cities.

Yu Zheng (2012),

Microsoft Research Asia

It is emerging as a concept where sensor,

device, person, vehicle, building, and street in

the urban areas can be used as components to

Background

Individual Level

Human mobility (Nature, Science, PNAS)

Trajectory data mining(ACM,IJGIS)

Community Detection(Complex Networks)

Background

Aggregate (Regional Level)

Dynamic urban landscape

Spatial interactions between sub-regions

Transportation demands estimation

Information, Communication, Technology & Space, Place & Social

Community Networks

Human

Mobility

Urban Structure

Space is opportunity,

Place is understood reality.

• Population distribution

• Movements

• Mobile landscape

• Functional region

• Flow

• ……

Data Descriptions Mon Tue Wed Thur Fri Sat Sun

11.19 10.89 10.92 10.70 11.01 9.82 9.44

Song Gao, April, 2013

approximate 10 million records a day

Human Mobility

Spatio-temporal patterns can be found with a

large amount of trajectories (X,Y, T)

GIS visualization and analysis applied to

represent and model individual dynamics

Human

Mobility


Geo-visualizing

Space-time path Frequency of

occurrence

Kang C., Gao S. et al. Analyzing

and Geo-visualizing Individual

Human Mobility Patterns Using

Mobile Call Records. 2010


Credit: Song

The variability of mobility in space-time

Regular

Irregular


The distribution of the ROG

covered with 869,992 mobile phone users.

Radius of gyration


Urban Structure

Aggregate approach (Hourly)

--Celli (volume00, volume01, volume02,…… volume23)

The scale of the urban area, may including the

city and some inner suburbs, to highlight

interesting metropolitan dynamics

Calculate the kernel density

Urban Structure


Spatio-temporal patterns

AM 03-04 AM 06-07 AM 09-10

PM 15-16 PM 18-19 PM 21-22


Mobile Landscape

LandScan Global Population Data

-- 1KM resolution


Correlation with population distribution

AM 06-07 AM 09-10

PM 15-16 PM 18-19 PM 21-22 Song Gao, April, 2013

Correlation with population distribution

r= 0.714 r= 0.697

r= 0.632 r= 0.748 r= 0.785

Spatial Interaction Network

Spatial Networks describe

the networks in which the

nodes are embedded in a

geographical space

Goal: to explore

telecommunication flow in

geographic space and to

understand how the spatial

context affect such

interactions

Community in spatial networks


Motivation Whether interaction structure, friendship likelihoods reveal

political boundaries, physical barriers, or social divide


Spatial effects on networks

(1) Spatial constraints on the distribution of

nodes embedded in geographical locations;

(2) Physical networks like roads and railways,

which are affected by spatial topology;

(3) Restrictions on long-distance links due to

economic costs.

Community in spatial networks


Two networks of spatial interactions

G_TeleFlow (V, E) be a weighted-undirected network graph

of phone call flows where Thiessen polygons of mobile

base stations are transformed into nodes (V) while

interactions among stations are represented by weighted

edges (E).

G_MoveFlow (V, E) be a weighted undirected network graph

of human movements and let Mijt represent the total

movement flow between cell i and cell j during time interval

t, including movement flows both from i to j and from j to i.


Distance Decay of Spatial Interactions

Cumulative probability

function of distance

distributions in two

interaction networks:

89.47% phone-call

interactions and 90.98%

movements occur across

distances less than 20 km


Distance Decay of Spatial Interactions

the power-law fit with a decay

parameter β=1.45

G_TeleFlow G_MoveFlow

dP

the power-law fit with a decay

parameter β=1.60


Community Detection Algorithm

The nodes of the network can be grouped into sets of nodes

so that each community is densely connected internally.

Modularity maximization

Minimum-cut method

Hierarchical clustering

Girvan–Newman algorithm

Clique based methods


Modularity is defined as the sum of differences

between the fraction of edges falling within

communities and the expected value of the

same quantity under the random null model.

Incorporating Gravity Model

(Gao et al. 2013, Transactions in GIS)

The fraction format of gravity-modularity for detecting communities:

Community detection results of networks of

call interaction (G_TeleFlow)

Day Node Edge Number Avg

Size

Modularity

Monday 609 41960 10 61 0.528

Tuesday 608 40902 10 61 0.533

Wednesday 609 40649 10 61 0.538

Thursday 609 56070 8 76 0.405

Friday 608 54091 8 76 0.422

Saturday 605 48673 8 75 0.438

Sunday 607 46506 8 75 0.446


Urban Community detection results

MAXID-----: 616

NUMNODES--: 609

NUMEDGES--: 41960

TOTALWT---: 934561

NUMGROUPS-: 10

MINSIZE---: 27

MEANSIZE--: 60.9

MAXSIZE---: 120

MAXQ------: 0.527837

STEP------: 599

Examples of differentiated geographical context of

isolated regions in spatial communities



Cell A locates in the overpass

intersection of ring highway and

the airport expressway which is

near a large residential suburb

area of this city, and a high

volume of call interaction make it

merged to the northern spatial

community (yellow) of official

cells.



Cell B has been grouped into the same

distant community on Monday,

Thursday and Friday, whereas it

aggregates into nearby spatial adjacent

community on weekends.

It corresponds to a set of governmental

buildings which has strong connections

with eastern cells (green) of central

business district on weekdays.



Cell C has a strong link to the southern

cells (red) during the whole week and

they are assigned to the same community.

Cell C locates nearby a industrial place

which covers a wood processing plant,

food brewery, and wholesale market.

There may be business communications

that make these cells aggregated into the

same community.



Cell D covers a local famous farm and

implies a business connection to the city

community. In order to identify whether

physical movements also exist between

these spatially separated cells, we will

refer to the partition results of the

network of movements.

G_TeleFlow

G_MoveFlow

Relation between Telecommunication

and Movement

ICT & Mobility：

-替代(Substitution)

-增强(Stimulation)

-缓和(Modificaiton)

a causal relationship?

Mon Tue Wed Thur Fri Sat Sun

R2 0.857 0.852 0.852 0.848 0.852 0.857 0.865

Correlation coefficients between phone call interaction and movements


Conclusion and Discussion 1). Does distance still constricts human mobility in geographic

space? -- Yes, it is.

2). Does the information communication technology (ICT)

increase or decline the probability of physical movements of

urban residents in daily life?

– Statistically yes, but not sure whether causally

3). What is spatio-temporal patterns of phone call activities in

urban space? How to explore? -- Dynamic mobile landscape

4). Does the phone call interaction follow Tobler’s first law

(TFL) of geography? -- To some degree, Yes

5). A combined qualitative-quantitative framework to identify

phone-call interaction patterns in spatial networks

Gao et al. 2013 Discovering spatial interaction communities from mobile phone data.

Transactions in GIS 17(3)

Gao et al. 2013 Understanding urban traffic flow characteristics: A rethinking of

betweenness centrality. Environment and Planning B: Planning and Design 40(1)

Kang et al. 2013 Inferring properties and revealing geographical impacts of inter-city

mobile communication network of China using a subnet data set. International Journal

of Geographical Information Science 27(3)

Kang et al. 2012 Towards Estimating Urban Population Distributions from Mobile Call

Data. Journal of Urban Technology 19(4)

Kang et al. 2012 Intra-urban human mobility patterns: An urban morphology

perspective. Physica A: Statistical Mechanics and its Applications 391(4)

Liu Y et al. 2012 Understanding intra-urban trip patterns from taxi trajectory data.

Journal of Geographical Systems 14(4)

Liu Y et al. 2012 Urban land uses and traffic ‘source-sink areas’: Evidence from GPS-

enabled taxi data in Shanghai. Landscape and Urban Planning 106

References

Yuan et al. 2012 Correlating mobile phone usage and travel behavior – a case study of

Harbin, China. Computers, Environment and Urban Systems 36(2)

Walsh et al. 2011 Spatial structure and dynamics of urban communities.

Ratti et al. 2010 Redrawing the map of Great Britain from a network of human

interactions. Plos One 5(12)

Guo, D. 2009 Flow Mapping and Multivariate Visualization of Large Spatial

Interaction Data", IEEE Transactions on Visualization and Computer Graphics, 15(6)

Human mobility,urban structure analysis,and spatial community detection from mobile phone data

Technology

spatial networks song

methods song gao

song gao email

resolution song gao

social divide song gao

radius of gyration song

urban space

spatial community detection