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

1. 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 Workshop Spatial, Temporal and Multi-Dimensional Data Modelling and Analysis, Québec, October 2-3, 2003 Research funded by SSHRC, GEOIDE and NSERC

2. 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

3. 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’sprimitives: Before, After, During, Precede, etc. • Providing Rankoperators : 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

4. 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

5. 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

6. 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)

7. Complex Evolution Processes Personal Biography

8. 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

9. 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 • Processesuseaggregation(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)

10. Tentative Ontology of Lifelines and Trajectories

11. 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-specificsemantic relationships • Each lifeline isordering 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)

12. Application semantics Time line (elapsed time) Facts : events and episodes Historical ordering of facts Location of facts Database Modelling of Evolution in Trajectories 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 • Developing ageneric (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 Change motivating facts Enabling facts Target status

13. 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)

14. Temporal Operators on Two Time Intervals

15. Spatial Operators on Two Spatial Objects

16. Duration Operators Between Two Time Periods

17. Distance Operators Between Two Spatial Objects

18. Specifying target trajectory/fact Specifying spatial distance condition Specifying duration condition Specifying spatial location condition Specifying patterns of facts Specifying temporal conditions Specifying time ordering Specifying other status condition 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

19. 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 • Censoringrefers 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 • Consideringtime 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

20. Example of ST Query on Personal Trajectories • WithinQuebec 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).

21. 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?)

22. Probability for tenants to buy a house after their first child is born duresepis : duration of residential episode (years) distmove : distance between the tenant and the new home (km) sixties : first child birth was during the sixties seventies : first child birth was during the seventies eighties : first child birth was during the eighties

23. Example of Event-History Analysis Results How much stability in employment increases propensity to buy a home Rate of access to property ownership significantly increases through time - from the sixties to the eighties

24. 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 operatorswithin 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