ST–ACTS: A Spatio-Temporal Activity Simulator

Győző Gidófalvi

Geomatic ApS

Center for Geoinformatik

Torben Bach Pedersen

Aalborg University

Presented by: Christian Thomsen (Aalborg University)

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Outline

Background and motivation

Important principles of social mobility

Real world data sources

ST-ACTS: simpersons and their activities Drawing demographic variables for simpersons Assigning simpersons to work places / schools Daily activity probabilities Activity simulation with spatio–temporal constraints

Temporal activity constraintActivity duration constraintMinimum elapsed time between activity repetition constraintMaximum distance constraintPhysical mobility constraint

Discrete event simulation

Evaluation of the simulation

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Background

Synthetic data is widely used in database research Moving Objects Database (MODB): represents and manages changes related to the movement of objectsExisting Moving Object Simulators (MOS) for MODBs:

GSTD (Generate Spatio–Temporal Data) [Theodoridis et. al ‘99]Object movement based on parameterized random functions

Extension of GSTD [Pfoser et. al, ’00]Control for change of direction + rectangular obstacles More realistic movements: preferred movement, group

movements and obstructed movement Network-based MOS [Brinkhoff ‘02]

Object movement is influenced by: 1) object attributes, 2) locations of other objects and the network capacity, and 3) locations of external objects that are independent of the network

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Background (cont.)

More Moving Object Simulators (MOS) for MODBs: Oproto [Saglio et. al, ‘99]

Moving or stationary objects of different type can attract and repulse eachother

GAMMA (Generating Artificial Modeless Movement by genetic–Algorithm) [Hu et. al, 05]

Based on sample activity trajectories, GAMMA can generate activity trajectories that contain real–life activity patterns, but

Generated activity trajectories are symbolic, as the input trajectories implicitly assume a location–dependent context

Representative sample is hard to otain

Time geography [4] is a conceptual basis/paradigm for human space–time behavior [Hägerstrand, ‘75]

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Motivation

Existing MOSs primarily model the physical aspects of mobility but neglect social and geo–demographical aspects of mobility:

1. objects (representing mobile users) move from one spatio–temporal location to another with the objective of performing a certain activity at the latter location

2. not all users are equally likely to perform a given activity 3. certain activities are performed at certain locations and times4. activities exhibit regularities that can be specific to a single user

or to groups of users

To development of adequate spatio–temporal data management and data mining techniques, a simulator is needed that effectively generates realistic spatio–temporal distribution of activities.

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Important Principles of Social Mobility

First Principle: People move from a given location to another location with an objective of performing some activity at the latter location.Second Principle: Not all people are equally likely to perform a given activity. The likelihood of performing an activity depends on the interest of a given person, which in turn depends on a number of demographic variables.Third Principle: The activities performed by a given person are highly context dependent:

current location of the person set of possible locations where a given activity can be performed the current time recent history of activities that the person has performed

Fourth Principle: The locations of facilities, where a givenactivity can be performed, are not randomly distributed.

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Real World Data Sources

Detailed geo–demographics about population: conzoom©

Grid-based, aggregated demographic information about the population of Denmark and the Nordic countries (more later)

29 segments of the population: conzoom© types

Information about businesses and facilities: bizmark™ 1-to-1 information about: location, business area size, number of

employees, business branch

Daily Movement Data: mobidk™ Home-to-work movement of the Danish population aggregated at the

parish-level

Related consumer surveys: GallupPC®

Answers of approximately 10000 subjects to questions about: demographics; interests in culture, hobbies, and sports; purchasing habits, and more…

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conzoom©

Grid-based population statistics: 100-meter grid cells are grouped into as clusters such that:

the clusters have a minimum number of persons and/or households in them to protect privacy

grid cells in a cluster are as homogeneous as possible in terms of a number of publicly available 1-to-1 information about properties

grid cells in a cluster are close geographically

Information (counts) are projected down to the cell-level

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conzoom© Types and Profile

Based on the statistics the population is segmented into 29 conzoom© types

For example a Cosmopolitans are more likely:

to be middle aged (30–59 years old), couples with children, who have a medium to long higher education, and hold higher level or top management positions in the financial or public sector

to live in larger cities in larger, multi–family houses that are either owned by them or are private rentals, and to have a better household economy than the average Dane (not shown)

Each grid cell is associated with one conzoom© type

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Drawing Demographic Variables for Simpersons

Assign demographic variable values for each simulated person in a cell, based on the counts for these variables in the cell.

Draw from a distribution without replacement

Problem: demographic variables are highly correlated

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Skew Distributions Based on Previous Draws

1.Draw (without replacement) the age variable 2.Given the outcome, skew the distribution of education variable based on the correlation between age and education3.Keep on drawing variables (without replacement) from skewed distributions until all variables have values assigned

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Assigning Simpersons to Work Places / Schools

Three types of simpersons: retired, worker, and student Retired simpersons do not have mandatory activities (work) hence

only move to perform leisure activities (including shopping ) Worker simpersons are probabilistically assigned to businesses /

work places based on the business branch the simperson works in, the size of the business, and daily movement data (mobidk™)

Students are probabilistically assigned to local educational institutions matching their age and obeying some public statistics about education

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Assigning Simpersons to Work Places (cont.)

Likelihood of work parish given a home parish: mobidk™

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Assigning Simpersons to Work Places (cont.)

Likelihood of work place based on business size: bizmark™

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Assigning Simpersons to Work Places (cont.)

Combined likelihood of work places: mobidk™ + bizmark™

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Daily Activity Probabilities

Gallup survey subjects answer questions of the form: Do you perform activity a n times during a period Δt?

Subjects are linked to a particular conzoom© type c based on their answers to questions about demographics. Daily Activity Probabilities (DAP):

The likelihood that c will perform a in any given day is: P(a|c) = n / day(Δt), where day(Δt) is the number of days during Δt

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Activity Simulation with Spatio–Temporal Constraints through User Defined Parameters

Temporal Activity Constraint (TAC): Certain activities are more likely to be performed during specific periods than

others User defined parameter specifying the likelihood of performing activity a for a

simperson group g at every hour of the day h: P(a|g,h)

Activity Duration Constraint (ADC): Not all activities take the same amount of time: work (μδoccupied(a), σδoccupied(a)) are user defined parameter that specify by a normal

distribution for the duration of each activity

Minimum Elapsed Time Between Activity Repetition Constraint (METC):

It is unlikely that an activity is repeated one-after-the-other within a short period

Store recent activities of simpersons and only allow repetition of an activity after δelapsed(a) time has passed

δelapsed(a) is a user defined parameter that is specified by a normal distribution for each activity

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Activity Simulation with Spatio–Temporal Constraints through User Defined Parameters (cont.)

Maximum Distance Constraint (MDC): For most activities there is a maximum distance a person is willing

to travel A simpersons only performs an activity a if there is a suitable

facility within Dmax(a) of the current location of the simperson Dmax(a) is a user defined parameter that is specified by a normal

distribution for each activity

Physical Mobility Constraint: It takes time to move from one location to another The speed (in km/h) at which simpersons cover a distance d

between two locations of consecutive activities is probabilistically drawn from a normal distribution: speed(d) = max(5,N(3d, d2))

speed(d) assigns lower speeds to shorter distances and higher speeds to longer distances -> captures common modes of transportation

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Discrete Event Simulation

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Evaluation of the Simulation

ST-ACTS has been implemented and can be downloaded as a MATLAB toolbox: http://www.geomatic.dk/research/ST-ACTS Experiments performed on: Windows XP on a 3.6GHz Pentium 4 processor with 1.5 GB main memoryExperiments show that ST-ACTS is effective, scalable, and

characteristics of the generated data correspond to the model parameters

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Aknowledgements

Thanks to Geomatic ApS, The Gallup Organization, and Thomas Nielsen from the Danish Center for Forest, Landscape and Planning for making the data sources available for research purposes.

Thanks for the help from co–workers, Susanne Caroc, Esben Taudorf, Jesper Christiansen, and Lau Kingo Marcussen.

Finally, thanks to Christian Thomsen, a fellow PhD student and friend, who was kind enough to give this presentation in my absence.

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Thank you for your attention!