Modelling propensity to move house after job change using event history analysis and GIS...

Modelling propensity to move house after job change using event history

analysis and GIS

Marie-Hélène Vandersmissen (CRAD, Laval University), Anne-Marie Séguin (INRS-UCS), Marius Thériault (CRAD,

Laval University) and Christophe Claramunt (Naval Academy Research Institute, France)

2nd MCRI/GEOIDE PROCESSUS Colloquium on the Foundations of Integrated Land-Use and Transportation Models

Toronto, June 12-15 2005

2nd MCRI/PROCESSUS Colloquium, Toronto, June 12-15, 2005

IntroductionIntroduction

Transportation land-use 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

Probabilistic discrete choice theory is becoming the central issue of urban and transport modelling research– Implemented using logistic and Cox regression techniques– Aimed at modelling individual’s and household’s behaviour

Need for spatio-temporal GIS for analysing urban and transport systems where – Uncertainties exist in the system (aggregation is not straightforward)

• Emergent behaviour occurs– 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


Our project in the MCRI programmeOur project in the MCRI programme


PurposePurpose

Emergent residential behaviours of individual actors in context of profound social changes in the work sphere

Long term-view in the analysis of the relationship between social changes in the work sphere and these behaviours

Socialchanges

Long-term dynamicsof residential location

behaviour

Travel behaviour


Objective and Research IssuesObjective and Research Issues

Estimate the propensity for professional workers to move house after a change of workplace– How many will move house during the

following job episode?

– For how long will they delay that decision?

– What are the factors significantly influencing that move house decision?


Data: The 1996 Retrospective Survey for Quebec City Survey collecting, in one interview, information about all

changes occurred over a long period of time, since their departure of the respondent’s parental home

Spatially stratified sample of two cohorts of professional workers– 418 respondents living in Quebec CMA in 1995– Two cohorts (mid-thirty and mid-forty)

• 224 women; 194 men• 112 women and 100 men in their mid-thirty• 112 women and 94 men in their mid-forty

– Reporting on significant events occurred during their life time describing

• Residential trajectory (every home occupied with their location)• Household trajectory (each change in the household’s

composition) • Professional trajectory (each change in employer, each workplace)

– Collecting dates of every change


Complex Evolution ProcessesComplex Evolution Processes

Leaving Parent's HomeTime Line

RESIDENTIALTRAJECTORY

CAREER TRAJECTORY

CO NSULTANTUNEMPLO YED

STUDENT

CO NSULTANT

PRO FESSIO NAL

TECHNICIAN

UNEMPLO YED

Survey date

CONSULTANT MOTHER Episode Lifeline

HOUSEHOLD TRAJECTORY

Fam ilySPO USE

SO N MO THERUNCLE

SPO USE

IN CO UPLESING LE

MARRIEDDIVO RCEDSING LE

M aritalstatus

Otherspersons

3

Occupation

CHALET

Secondaryhouse

TO W N HO USE

M ainhom e

RO O M STUDIO FLATRO O MAPARTMENTSTUDIO FLAT APARTMENT

Event

TECHNICIAN

Roomm ate

Roomm ate

3

97 242321171210

1 2664

2 221614

19

151311853 252018

Location

Personal BiographyComplex mix ofreal world phenomena

Change instatus

Leading toat least one episode

Combining factsdescribing a specific aspect of life

Set of related lifelinesusing application-specificsemantic relationships


Management of Evolution in TrajectoriesManagement of Evolution in Trajectories We developped a generic spatio-temporal data model to handle

historical orderings and querying patterns of facts in order to produce flat files needed for event-history analysis

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

Application semantics

Historical ordering of facts

Facts : events and episodes

Location of facts


Spatio-temporal Query of Patterns of Facts within TrajectoriesSpatio-temporal Query of Patterns of Facts within Trajectories

We developped a query interface combining georelational GIS capabilities and temporal historical ordering of facts using ODBC links

Specifying target factSpecifying target factSpecifying time orderingSpecifying time orderingSpecifying patterns of factsSpecifying patterns of factsSpecifying temporal conditionsSpecifying temporal conditionsSpecifying duration conditionSpecifying duration conditionSpecifying spatial location conditionSpecifying spatial location conditionSpecifying spatial distance conditionSpecifying spatial distance condition

Specifying other status conditionSpecifying other status condition


Methodology: Event History AnalysisMethodology: Event History Analysis

Ordinary multiple regression is ill-suited to the analysis of biographies– Censoring: refers to the fact that the value of a

variable may be unknown at the time of survey – Considering time varying explanatory factors

• Need to consider time-varying information to study the effect of job change on house moving

Event history analysis can handle such a problem (survival tables and logistic regression)– The query interface enhance data restructuring

needed for this kind of statistical analysis


Event History Analysis (Cox Regression)Event History Analysis (Cox Regression)

Survival tables are using conditional probabilities to estimate the mean proportion of people experiencing some change in their life after a significant event occurs, computing the time delay after a specified enabling event

Specific conditions may influence propensity to change

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

Event History Analysis to model specific variations of the

probability of state transition through time for individuals considering independent variables describing their personal situation on other lifelines


)(*)(1)( MoveHomepropensityeNotMoveHomsurvivalMoveHomeyprobabilit

)(1)()( MoveHomeodds

MoveHomeoddsMoveHomepropensity

Xbcohortbagebgenderb neeeeMoveHomeodds 321)(

Probability to move home after a job change:Probability to move home after a job change:


Basic statisticsBasic statistics 380 respondents (on 418) had a change of job or workplace at

least once during during their career 411 respondents moved their home at least once after

departure from parent’s home 1056 changes of job or workplace within or towards the

Quebec CMA (321 persons)– 458 of those changes of workplace were followed by at least

one move house during the subsequent employment episode-stability of job and workplace

– 598 of those changes of workplace were not followed by any move house during the subsequent employment episode (231 persons)

Cohort Mid-Thirty Mid-Forty Gender Men Women Men Women

Moving House 122 117 97 122 Not Moving House 129 170 136 163

Number of pair of events (change of job-workplace versus moving house or not)


Basic variables for the Event History analysisBasic variables for the Event History analysis

Gender 1 (Male); 2 (Female) Cohort 1 (Mid-Thirty); 2 (Mid-Forty) ChWPL_Type 1 (New Job); 2 (Change of Workplace keeping the same job) ChWPL_Order Ordering of this change of work place among those of the same respondent (E.g. 2

means that it is the second change of workplace for this respondent) ChWPL_Age Age of the respondent when the change of workplace was occurring (Years) ChWPL_Marital Marital status when changing of workplace (1: Single; 2: Couple – marriage or free

union; 3: Separated, divorced or widow) ChWPL_Persons Total number of persons living in the household when changing of workplace ChWPL_Children Number of children living at home when changing of workplace Move_House The respondent was effectively moving house after the change of work place (1: Yes;

0: No) --- CENSORING VARIABLE Elapsed_Time Time elapsed between change of work place and moving house (Weeks) ----

DEPENDENT VARIABLE – time elapsed at the end of the new job episode if not moving house

MoveH_Marital Marital status when moving home (1: Single; 2: Couple – marriage or free union; 3: Separated, divorced or widow) – at end of new job episode if not moving home during that period

MoveH_Persons Total number of persons living in the household when moving home – at end of new job episode if not moving home during that period

MoveH_Children Number of children living at home when moving home – at end of new job episode if not moving home during that period

ChWPL_Neig Location of the new work place (1: city core; 2 old suburbs; 3: new suburbs; 4: urban fringe)


PJob_Neig Location of the previous job (1: city core; 2 old suburbs; 3: new suburbs; 4: urban fringe; 5: outside the Quebec CMA)

PJob_Durat Duration of the previous job episode (Years) PJob_Regime Employment regime at previous job location (1: Full time, >30 hours per week; 0: Part

time) PJob_Stability Perceived stability of employment at previous job location (1: Very stable; 2: Mostly

stable; 3: Mostly unstable; 4: Precarious) NJob_Regime Employment regime at new job location (1: Full time, >30 hours per week; 0: Part time) NJob_Stability Perceived stability of employment at new job location (1: Very stable; 2: Mostly stable;

3: Mostly unstable; 4: Precarious) PHome_Tenure Tenure of previous home (1: owner; 2: tenant; 3: co-tenant) NHome_Tenure Tenure of new home if any; otherwise previous tenure (1: owner; 2: tenant; 3: co-

tenant) PHome_Durat Duration of previous residential episode (Years) PHome_Neig Location of the previous home (1: city core; 2 old suburbs; 3: new suburbs; 4: urban

fringe; 5: outside the Quebec CMA) NHome_Neig Location of the new home if any; otherwise location of the old one (1: city core; 2 old

suburbs; 3: new suburbs; 4: urban fringe) MoveH_Dist Euclidean Distance between the old and the new residential locations (Km) PHomeNJob_Dist Euclidean Distance between the previous home and the new job locations (Km) NHomeNJob_Dist Euclidean Distance between the new home (if any; otherwise previous home) and the

new job locations (Km) PJobNJob_Dist Euclidean Distance between the old and the new job locations (Km)


Descriptive StatisticsDescriptive Statistics

Change of workplace order in the respondent career

Change Frequency % Cumulative %2 183 17,3 17,33 197 18,7 36,04 192 18,2 54,25 144 13,6 67,86 105 9,9 77,77 83 7,9 85,68 55 5,2 90,89 36 3,4 94,2

10 25 2,4 96,611 16 1,5 98,112 9 0,9 99,013 6 0,6 99,514 3 0,3 99,815 1 0,1 99,916 1 0,1 100

Total 1056 100


Location of the previous job episode (PJE) workplace

Frequency % Cumulative %City Core 316 29,9 29,9Old Suburbs 386 36,6 66,5New Suburbs 39 3,7 70,2Urban Fringe 9 0,9 71,0Outside the Quebec CMA 306 29,0 100Total 1056 100

Location of the new workplace (NWP)

Frequency % Cum. %City Core 449 42,5 42,5Old Suburbs 515 48,8 91,3New Suburbs 76 7,2 98,5Urban Fringe 16 1,5 100Total 1056 100,0


Change of home Neighbourhood

Frequency % Cum. %From or To Outside of the Quebec CMA 100 9,47 9,4697Core 223 21,12 30,5871Core --> Suburbs 41 3,883 34,4697Suburbs --> Core 29 2,746 37,2159Old Suburbs 484 45,83 83,0492Old Suburbs --> New Suburbs 26 2,462 85,5114New Suburbs --> Old Suburbs 14 1,326 86,8371New Subs + Fringe 139 13,16 100Total 1056 100

Change of tenure

Frequency % Cum. %Owner --> Owner 431 40,8 40,8Owner --> Tenant 31 2,9 43,8Tenant --> Owner 125 11,8 55,6Tenant --> Tenant 396 37,5 93,1Co-tenant --> Tenant 12 1,1 94,2Being Co-tenant 61 5,8 100Total 1056 100,0


Empirical Results: 1. Cross-tablesEmpirical Results: 1. Cross-tables

Gender

WomanMan

%

100

90

80

70

60

50

40

30

20

10

0

Move home

1

0

X2: 1,281ddl:1P: 0,258

Cohort

Mid_FortyMid-thirty

%

100

90

80

70

60

50

40

30

20

10

0

Move home

1

0

X2: 0,495ddl:1P: 0,482


Marital Status when CWP occurs

Separated, divorced,

Married or free unio

Single

%

100

90

80

70

60

50

40

30

20

10

0

Move home

1

0

X2: 19,192ddl:2P< 0,000C= 0,134

Number of children living at home when CWP occurs

43210

%

100

90

80

70

60

50

40

30

20

10

0

Move home

1

0

X2: 89,601ddl:4P< 0,000C= 0,280

2nd MCRI/PROCESSUS Colloquium, Toronto, June 12-15, 2005Type of Neighbourhood during PHE

Outside the Q

uebec C

Urban Fringe

New Suburbs

Old Suburbs

City Core

%

100

90

80

70

60

50

40

30

20

10

0

Move home

1

0

X2: 131,327ddl: 4P< 0,000C= 0,333

Tenure during the Previous Home Episode (PHE)

Co-tenantTenantOw ner

%100

90

80

70

60

50

40

30

20

10

0

Move home

1

0

X2=152,63ddl: 2P< 0,000C= 0,355

Empirical Results: 2. Event History AnalysisEmpirical Results: 2. Event History Analysis

Tests of Model coeff.X2: 845,29Df: 22Sig.: 0,000

Variables B SE Wald Sig Exp(B)Gender (0=Woman;1=Man) 0,307 0,102 9,105 0,003 1,359 +Change of Home Neighbourhood (0= From or To Ouside of the Qc CMA) 49,077 0,000 1= Core -0,955 0,175 29,844 0,000 0,385 - 2= Core Suburbs -0,163 0,204 0,641 0,423 0,849 3= Suburbs Core -0,349 0,225 2,409 0,121 0,705 4= Old Suburbs -0,855 0,155 30,418 0,000 0,425 - 5= Old Suburbs New Suburbs -0,391 0,259 2,279 0,131 0,676 6= New Suburbs Old Suburbs -0,247 0,329 0,563 0,453 0,781 7= New Suburbs + Fringe -0,785 0,240 10,673 0,001 0,456 -Age -0,005 0,011 0,221 0,638 0,995Previous Job Duration 0,015 0,018 0,643 0,422 1,015Tenure (0= Owner Owner) 0,000 1= Owner Tenant 1,004 0,261 14,799 0,000 2,728 + 2= Tenant Owner 0,721 0,186 15,107 0,000 2,057 + 3= Tenant Tenant 0,612 0,175 12,221 0,000 1,845 + 4= Co-tenant Tenant 1,096 0,346 10,063 0,002 2,993 + 5= Staying Co-tenant 0,467 0,272 2,940 0,086 1,595Previous Home Duration -0,363 0,025 213,393 0,000 0,695 -Number of Children at home when CWP -0,204 0,098 4,327 0,038 0,815 -Distance New Home®New Job/Previous Home®New Job 0,052 0,019 7,284 0,007 1,053 +Employment Regime at New Job Location (0= Part Time; 1= Full Time) 0,016 0,154 0,010 0,920 1,016Perceived Stability of Employment at New Job (0= Very Stable) 0,894 0,827 1= Mostly Stable 0,071 0,114 0,386 0,535 1,073 2= Mostly Unstable 0,138 0,156 0,784 0,376 1,148 3= Precarious 0,058 0,223 0,067 0,796 1,059


For how long will they delay that decision?For how long will they delay that decision?

One Minus Survival Function

at mean of covariates

Elapsed time between CWP and MH (Weeks) - censoring at end of

120010008006004002000-200

One

Min

us C

um S

urvi

val

1,2

1,0

,8

,6

,4

,2

0,0

-,2


One Minus Survival Function

for patterns 1 - 2

Elapsed time between CWP and M H (Weeks) - censoring at end of

120010008006004002000-200

On

e M

inu

s C

um

Su

rviv

al

1,2

1,0

,8

,6

,4

,2

0,0

-,2

Gender

Woman

Man


Discussion and ConclusionDiscussion and Conclusion Results given by Event History Analysis:

– How many will move house during the following job episode?

• On 418 respondents, 271 moved home after a job change (64,8%)

– For how long will they delay that decision?

• Probability of changing home after a job change =0,2 after ~2 years

– What are the factors significantly influencing that move house decision?• Tenure

– Co-tenant Tenant +

– Owner Tenant +

– Tenant Owner +

– Tenant Tenant +

• Gender (man) +• Increased Distance home job +• Number of Children -• Previous home duration -• Change of Home Neighbourood

– New Suburbs + Fringe -

– Old Suburbs -

– Core -


Retrospective Survey– Inaccuracy of responses (limitations of human memory

with elapsed time)– Memory distorsions (individual’s account of the event)– But people tend to remember major events (year of

residential move, job change)– Results reflect situation in 80’s and 90’s

To the best of our knowledge, this type of application is original (residential move after a job change

– Positive contribution to transportation land-use modelling (Quebec)

– The query interface could be also used to analyse patterns of activity/travel decision coming from our panel surveys (Quebec & Toronto) and OD surveys

– Next stage: Elaborate separate models for owners and tenants

Modelling propensity to move house after job change using event history analysis and GIS...

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