A Spatio-temporal Query Interface for Analysing Individual Biographies : Report on a Practical...

A Spatio-temporal Query Interface for Analysing Individual

Biographies : Report on a Practical Experience

Marius Thériault (CRAD, Laval University), Christophe Claramunt (French Naval Academy)

&Anne-Marie Séguin (INRS-UCS)

ISPRS WorkshopSpatial, Temporal and Multi-Dimensional Data Modelling and

Analysis,Québec, October 2-3, 2003

Research funded by SSHRC, GEOIDE and NSERC

ISPRS Workshop, October 2003

Introduction

Urban modelling must consider decision-making behaviour of urban actors using disaggregate data in order to relate Activity location, home choice, commuting and travel decision Household, individual and professional profiles of persons

Needing temporal GIS for analysing urban systems

because Uncertainties exist in the system (aggregation is not straightforward)

• Emergent behaviour is occurring Decision rules for individuals and households are intricate System processes are time-path and location dependent

• Future system state depends partly on past and current states


Issues of Modelling Evolution Paths Within GIS

However, current GIS database concepts are mostly static

Time is supported using Date formats and low-level operators

<, =, > and, eventually, Allen’s primitives

Enhancing ST operators to improve their semantic expressiveness Extending Allen’s primitives: Before, After, During, Precede, etc. Providing Rank operators : First, Second, Third, …, Last Introducing Duration operators : Shortest, …, Longest Set operators : All Before, All After, All During, All Shorter, etc. Database modelling approaches for analysing evolution paths (combine specific facts to define application dependent trajectories) Query interface for searching ordered patterns of facts

Select First Two Children Born Before their Parents Buy their Second Home

Integrated spatial, temporal and thematic query mechanisms within a unified language and/or interface


Context and Objectives of this Research

Context Develop GIS tools for analysing the unintentional consequences, at the

macro scale (E.g. urban spread), of intentional actions and strategies

occurring at the micro-scale (aggregation of individual decisions)

Provide GIS resources for studying influence of the neighbourhood on

individual decisions and to summarise their combined effect on the evolution

of the urban system

Objectives Develop a generic logical database model to handle evolution paths

(E.g. personal biographies) and a query interface combining

temporal, spatial and thematic criteria

Reshuffle ST data in order to describe specific evolution providing flat

files (one for each question at hand) suitable for statistical analysis

using statistical package like SPSS and SAS


Studying Individual Biographies

Focus of this application Household, residential and professional history of citizens

Life course of most individuals Is built around interlocking series of events

During the last decades, these trajectories generated patterns of events of

increasing complexity:

- more divorces,

- extension of contractual short-term employment

- increasing geographical mobility, telecommuting, etc.

Within cities, these individual trajectories intersect and combine, yielding

demographic and residential patterns – driving city evolution and

transportation demand

Understanding evolution processes within personal biographies cannot be

derived from censuses as they give only snapshot reports on complex

situations (aggregated data) and they do not relate successive facts


The 1996 Retrospective Survey for the Quebec Metro Area

Survey collecting, in one interview, information about all changes occurred over

a long period of time, since the departure of the respondent’s parental home

A spatially stratified sample of four cohorts of professional workers

Sample of 418 respondents stratified by municipality, gender and age (36-40 and 46-50)

Interviews realized at the respondent’s home, mean duration 1.5 hour (27,167 facts)

Three trajectories

Residential trajectory : every home occupied (three months or more) since the departure

of parent’s home, with their location (civic address) and other characteristics (tenure,

price, choice criteria, reasons to leave, etc.)

Household trajectory : each change in the composition of the respondent’s household

(arrival or departure of a spouse, birth, death, arrival of a child from an other household,

relatives, roommates, cotenants, etc.)

Professional trajectory : each change in employer, each work place, with their

characteristics (including secondary jobs, education and unemployment episodes)

Collecting dates and location of every change (starting- and ending-time of episode)


Complex Evolution Processes

Leaving Parent's HomeTime Line

RESIDENTIALTRAJECTORY

CAREER TRAJECTORY

CO NSULTANTUNEM PLO YED

STUDENT

CO NSULTANT

PRO FESSIO NAL

TECHNICIAN

UNEM PLO YED

Survey date

CONSULTANT MOTHER Episode Lifeline

HOUSEHOLD TRAJECTORY

Fam ilySPO USE

SO N M O THERUNCLE

SPO USE

IN CO UPLESING LE

M ARRIEDDIVO RCEDSING LE

M aritalstatus

Otherspersons

3

Occupation

CHALET

Secondaryhouse

TO W N HO USE

M ainhom e

RO O M STUDIO FLATRO O MAPARTM ENTSTUDIO FLAT APARTM ENT

Event

TECHNICIAN

Roomm ate

Roomm ate

3

97 242321171210

1 2664

2 221614

19

151311853 252018

Location

Personal Biography


Changes in Personal Life

An individual’s history is altered When an event occurs modifying at least one important aspect of his personal status

(marital, family, job, home, education, income, etc.)

Such an event may alter simultaneously statuses on more than one trajectory - or

may have effect on several individuals in the family

Some events (E.g. new born baby) can be anticipated and may potentially lead to

prior adjustment (actions linked to expectation)

Effects can also be delayed (after the enabling event occurs)

Life trajectories show interlocked evolution Behaviour based on personal values, beliefs and strategy

Facts report events and episodes (time periods with stable attributes) which intersect

to depict global life status of the person along lifelines

Hypothesis: facts ordering builds logical sequences (evolution patterns)

related to life cycles (E.g. young couples, retired persons, etc.)

Studying these patterns is more relevant to urban studies than knowing the exact

timing of events for each individual


Issues in Modelling Life Trajectories

How can we express the temporal structure of biography as an ordered sequence of intertwined statuses (episodes) and events, using database modelling concepts, while retaining its behavioural meaning?

Personal biographies are a complex mix of real world phenomena (E.g. persons, dwellings, etc.) described using facts (E.g. episodes, events) Facts are ordered along lifelines to form sequences of independent or

joint evolution (linked trajectories or related individuals) processes

Processes use aggregation (household made of persons), combination (mix of jobs held simultaneously), and collaboration (renting or buying a dwelling is using another type of entity and starts a new residential episode)


Tentative Ontology of Lifelines and Trajectories

+build trajectory()

-name

Trajectory

+get facts()

-contributing fact classes

Lifestate

+beginning() : Date+ending() : Date

Lifeline

+MBR()

The Geographical Space

datumprojectionunits

Spatial Reference System

* 1

origingranularityunits

Temporal Reference System

-details

Fact

+get distance()+get orientation()

-name-longitude : float-latitude : float

Place

* *-geometry

Geographical Feature

location relationship()distance relationship()orientation relationship()topology relationship()

operation

Spatial Relationships

* *

Event Episode

+get duration()+get age()

-begin : Date-end : Date

Time Period

* *+get chronological order()+get duration order()+get historical order()

-ordering type

Temporal Ordering

+get facts()

-pattern type

Pattern of Facts

Change Stable State

+select trajectories()

-owner name : wchar_t

Individual Biography

The History

-anterior facts-posterior facts

Historical OrderingChronological Ordering

*

*

historical relationship()chronological relationship()duration relationship()

operation

Temporal Relationships

* *

+get facts()

-birthdate : Date-survey time : Date

Individual Lifeline

1 1

get age() : float

chronological ordering setduration ordering sethistorical ordering set

Pattern Relationships

*

*get age() : float

chronological ordering setduration ordering sethistorical ordering set

Sequence Relationships

H

B

C

DE

A

F

G

7

3

4

8

5

2

1

4

a

c

g

d

f

b

Time-varying Attributes


Database Modelling Concepts for Trajectories

A lifelines is combining facts (events and episodes) describing a

specific aspect of personal life (E.g. employment) A trajectory (E.g. household) combines a set of related lifelines

(E.g. marital status, family composition) using application-specific

semantic relationships Each lifeline is ordering facts (periods of time) during which a given

status was stable (E.g. single or married). When an event occurs, there is some change in status, leading to at

least one new episode (E.g. birth of a child in an household changes

its composition); this defines evolution patterns Lifelines define multi-dimensional networks of evolution paths

(directional from past to future) Finally, each fact could be located in space (using a list of locations)


Database Modelling of Evolution in Trajectories

Generic part of the ST data model

Respondents

PK,FK1,FK2,U1 RespondId

I1 GenderI1 Cohort

Facts

PK FactId

FK2 OwnerIdFK3,I2 SpatialIdFK1,I1 LifeStateIdI3 PeriodBegI4 PeriodEndI5 ObsTime

belongs to

HistoricalOrdering

PK HistoryId

FK1,I1 FBeforeIdFK2,I2 FAfterId

is before

is after

ActingIndividuals

PK ActingId

FK2,I2 PersonIdFK1,I1 FactId

is involved in

Individuals

PK PersonId

I1 NameI1 SurNameI1 Gender

isis

LifeStates

PK LifeStateId

U2,U1 LifeStateNameU2,U1 Episode

belongs to

TrajectoryStates

PK LifeDimId

FK1,I1 LifeStateIdFK2,I2 TrajectId

uses

Trajectories

PK TrajectId

U2,U1 TrajectName

defines

Spatial

PK SpatialId

I1 LongitudeI2 Latitude

is located at

MapInfo_MapCatalog

I1 SpatialTypeU1 TableNameI1 CoordinateSystemI1 SymbolI1 XColumnNameI1 YColumnName

Link to Spatialw

are

FactOwners

PK OwnerId

I1 BirthDateI1 Survey Time

is

Developing a generic (application-independent) spatio-temporal data model to handle historical orderings and querying patterns of facts in order to produce flat files needed for event-history analysis

Application semantics

Facts : events and episodes

Historical ordering of facts

Location of facts

Modelling the probability of a status change considering the context : Cox regression combines survival tables and logistic regression

A target changed status is modelled using a set of change enabling facts, some change motivating facts and a target changed status

For example the propensity for couple of tenants (enabling facts) to buy their first house (target status : home owner) after the birth of their second child if they hold a stable job (motivating facts)

Time elapsed after enabling facts and/or motivating facts and local context are relevant

Time line (elapsed time)

Change motivating facts

Enabling facts Target status


Enhancing Expression of ST Relationships

Time ordering should use time stamps (chronological), historical (topological – first…last) and/or duration (shortest…longest) criteria

Semantics of trajectories are application dependent and should be modelled accordingly, as well as explicitly handled during the query

Query mechanisms should be provided to search patterns of facts (E.g. second child birth after longest unemployment episode) eventually using time buffers (delayed and anticipated actions)

Operation of the interface should be close to natural language and should maximize semantic expressiveness

Spatial and temporal operators should be integrated and handled together within a query interface/language combining filters (selecting facts used to build ad hoc lifelines) and criteria (selecting specific facts)


Temporal Operators on Two Time IntervalsCommutative Allen’s operators are identified with grey tones Operational definition

a) Comparison between the time limits of two time intervals (periods or instants) – Extended from Allenyes T Equal U (T’ = U’) (T” = U”)no T MeetBeg U T” = U’no T MeetEnd U U MeetBeg Tyes T Touch U (T MeetBeg U) (T MeetEnd U)no T During U (T’ > U’) (T” < U”)no T Start U (T’ = U’) (T” < U”)no T Finish U (T’ > U’) (T” = U”)no T Inside U (T During U) (T Start U) (T Finish U)no T Contain U U Inside Tno T CoverBeg U T’ < U’ < T” < U”no T CoverEnd U U CoverBeg Tyes T Overlap U ((T CoverBeg U) (T CoverEnd U)) ~(T Contain U)no T Before U T” < U’no T After U U Before Tyes T Disjoint U (T Before U) (T After U)yes T Outside U (T Disjoint U) (T Touch U)yes T Intersect U ~(T Disjoint U)no T Anterior U (T Before U) (T TMeetBeg U)no T Posterior U (T After U) (T MeetEnd U)no T Precede U (T Before U) (T MeetBeg U) (T CoverBeg U)no T Succeed U (T After U) (T MeetEnd U) (T CoverEnd U)no T Bound U ((T Start U) (T Finish U)) (T Inside U)no T Initiate U (T Start U) T CoverEnd Uno T Terminate U (T Finish U) T CoverBeg Uno T Begin U (T Initiate U) (T Equal U)no T End U (T Terminate U) (T Equal U)

b) Comparison between the durations of two time intervals (periods or instants)yes T Equivalent U (T”-T’) = (U”-U’)no T Shorter U (T”-T’) < (U”-U’)no T Longer U (T”-T’) > (U”-U’)no T ShorterEquiv U (T Shorter U) (T Equivalent U)no T LongerEquiv U (T Longer U) (T Equivalent U)yes T Different U ~(T Equivalent U)

Temporal operands (T and U) are delimited by their beginning (T’ and U’) and ending (T” and U”) time stamps


Spatial Operators on Two Spatial Objects

Commutative

Clementini’s primitive operators are identified with grey tones

Operational definition

yes E Equal F (E° F° = E° F°) (E F = E F)

yes E Touch F (E° F° = ) (E F )

no E Inside F (E F = E) E° F )

no E Contain F F Inside E

yes E Overlap F (E F E) (E F F) (E° F° )

yes E Disjoint F E F =

yes E Outside F (E Disjoint F) (E Touch F)

yes E Intersect F ~(E Disjoint F)

Spatial operands (E and F) are formed by their interiors (E° and F°) and boundaries (E and F)


Duration Operators Between Two Time Periods

Commutative Duration operators

Operational definition Exceptions

yes T DSpan U Maximum (T”,U”) – Minimum (T’,U’)

yes T DMerge U Maximum (T”,U”) – Minimum (T’,U’) If (T Disjoint U) then 0

yes T DCommon U If (T Inside U) then T” – T’If (T Contain U) then U” – U’

If (T CoverBeg U) then T” – U’If (T CoverEnd U) then U” – T’,

If (T Equal U) then T” – T’

If (T Outside U) then 0

yes T Distance U If (T Before U) then U’ – T” else T’ – U” If ~(T Disjoint U) then 0

no T DBefore U U’ – T” If ~(T Before U) then 0

no T DAfter U T’ – U” If ~(T After U) then 0

no T DAnterior U U’ – T’ If ~(T Anterior U) then 0

no T DPosterior U T” – U” If ~(T Posterior U) then 0

Temporal operands (T and U) are delimited by their beginning (T’ and U’) and ending (T” and U”) time stamps


Distance Operators Between Two Spatial Objects

Commutative Distance operators

Operational definitionEuclidean distances

Exceptions

yes E DisCtrs F Length (Line (Eclong: E

clat; F

clong: F

clat))

yes E Distance F Length (Shortest Line (Elong:Elat; Flong:Flat)) If ~(E Outside F) then null

no E DistInside F Length (Shortest Line (Elong:Elat; Flong:Flat)) If ~(E Inside F) then null

no E DistContain F Length (Shortest Line (Elong:Elat; Flong:Flat)) If ~(E Contain F) then null

Spatial operands (E and F) are defined by their respective boundaries (E and F) and centre points (Ec and Fc)


Spatio-temporal Query of Patterns of Facts within Trajectories We developed a

query interface combining georelational GIS capabilities and temporal/historical ordering of facts (including search of patterns) using ODBC links

Specifying target trajectory/factSpecifying target trajectory/factSpecifying time orderingSpecifying time orderingSpecifying patterns of factsSpecifying patterns of factsSpecifying temporal conditionsSpecifying temporal conditionsSpecifying duration conditionSpecifying duration condition

Specifying spatial location conditionSpecifying spatial location conditionSpecifying spatial distance conditionSpecifying spatial distance condition

Specifying other status conditionSpecifying other status condition


Linking to Event History Regression Analysis

Evolution phenomena are related to facts giving evidence of change These facts and their possible relationships are recorded using relational databases

We want to submit to statistical analysis these data and expressions based on them in

order to build event history models

Ordinary multiple regression is ill-suited to the analysis of biographies, because of

two peculiarities: censoring and time-varying explanatory variables

Censoring refers to the fact that the value of a variable may be unknown at the time

of survey, generally because the event did not occur (E.g. duration of marriage for a person

who never divorce) – computation of divorce rate should consider censoring

Considering time varying explanatory factors To study the effect of the family composition on residential location choice, one needs to

consider time-varying information

A bio-statistical method called event history regression analysis can handle such a

problem (it combines survival tables and logistic regression)

The query interface enhance data restructuring needed for this kind of statistical

analysis


Example of ST Query on Personal Trajectories

Within Quebec Metro Area, considering only facts at a distance >= 500 metres from respondent’s first owned home (filtering), retain all first three children (before any fourth – censoring) arrival or birth events provided their ending time was not during (Disjoint) the first tenant episode and they where separated by more than 2 months from at least one (Any) job episode (criteria). Selected facts’ periods are extended by 60 days before and 30 days after the actual time stamps (time buffering).


Event History Analysis

Survival tables are using conditional probabilities to estimate the mean proportion

of people experiencing some change in their life after a significant event occurs (E.g.

proportion of tenants buying a home after the arrival of the second child), computing

the time delay after a specified enabling event (E.g. time to divorce after marriage)

However, these probabilities are not exactly the same for everyone because specific

conditions may influence propensity to change

Finding those specific factors that condition individual propensity to do something

requires a combination of survival tables and logistic regression to estimate the

marginal effect of other personal attributes on the probability that an event occurs

The purpose of Event History Analysis (also called Cox Regression) is to model

specific variations of the probability of state transition through time for individuals

considering independent (even time-varying) variables describing their personal

situation on other lifelines (E.g. What is the marginal effect of a 6-month

unemployment period occurred less than five years ago, on the propensity to buy a

home after the second child is born? Is their a significant effect? Is this effect stable

over time and space?)


Probability for tenants to buy a house after their first child is born

)(*)(1)( BuypropensityTenantsurvivalBuyHouseyprobabilit

eightiesseventiessixtiesdistmoveduresepis eeeeeBuyodds 267.0574.1115.2007.0137.0)(

Modelling propensity of tenants for buying a home after the first child is born

units quantitiescoefficient effectDuration of residential episode in the new house(proxy for employment status and stability) years 5 0,137 1,98377

Distance between the place where the child was born and the new home location km 5 0,007 1,03562(proxy of willingness to move far away to enhance market opportunities)

Decade during with the child was born 1960-69 0 -2,115 1(retain only one choice) 1970-79 0 -1,574 1

1980-89 1 -0,267 0,765671990-96 0 0 1

Time elapsed after the first child is born years 2 Odds ratio 1,57302

Survival table cumulative proportion 30,0%

Marginal probability 61,1%

Probability to buy a house 18,3%

Survival Functions of Tenants After the First Child is Born (Cumulative proportions)

00.10.20.30.40.5

0.60.70.80.9

1

0 5 10 15 20

Years after the birth of the first child

Cu

mu

lati

ve

pro

po

rtio

nTenant

Home owner

duresepis : duration of residential episode (years)distmove : distance between the tenant and the new home (km)sixties : first child birth was during the sixtiesseventies : first child birth was during the seventieseighties : first child birth was during the eighties

)(1)()( Buyodds

BuyoddsBuypropensity


Example of Event-History Analysis Results

Propensity of Tenants to Buy a Home Conditional to Decade and Elapsed Time after Birth of the First Child

0

0.2

0.4

0.6

0.8

1

0 5 10 15 20

Years after birth of first child

Prob

abili

ty o

f bei

ng h

ome

owne

r

1960-69

1970-79

1980-89

1990-95

Propensity of Tenants to Buy a Home Conditional to Employment Status and Elapsed Time after

Birth of the First Child

0

0.2

0.4

0.6

0.8

1

0 5 10 15 20

Years after birth of first child

Pro

babi

lity

of b

eing

ho

me

owne

r Unstable

Stable

Very stableRate of access to property ownership significantly increases

through time - from the sixties to the eighties

How much stability in employment increases

propensity to buy a home


Discussion and Conclusion

The modelling approach and the query interface

Use standard entity-relationship principles, combined with geo-relational

technology

Encapsulate application-semantics within the database structure allowing for

the development of a generic query interface

Provide means for combining facts (events and episodes), locations, timings,

lifelines and trajectories within a unified framework allowing for exploration of

patterns of facts and evolution networks

Integrates spatial, temporal and thematic operators within a unified dialog

Provide original temporal rank and set operators + Allen’s and Clementini’s

Conclusion

To the best of our knowledge, this type of application for the spatial monitoring

of changes in population behaviour is original

Keeping track of dynamics using GIS has a strong potential to enhance urban

and transportation planning

A Spatio-temporal Query Interface for Analysing Individual Biographies : Report on a Practical...

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