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Temporal GIS and Statistical Modelling of Personal Lifelines Marius Thériault, Christophe Claramunt, Anne-Marie Séguin and Paul Villeneuve July 2002 Spatial.

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Presentation on theme: "Temporal GIS and Statistical Modelling of Personal Lifelines Marius Thériault, Christophe Claramunt, Anne-Marie Séguin and Paul Villeneuve July 2002 Spatial."— Presentation transcript:

1 Temporal GIS and Statistical Modelling of Personal Lifelines Marius Thériault, Christophe Claramunt, Anne-Marie Séguin and Paul Villeneuve July 2002 Spatial Data Handling Ottawa Funded by the Canadian SSHRC, the Canadian NSERC, the Quebec Province’s FCAR and GEOIDE’s project SOC#8

2 Introduction  Urban modelling must consider decision-making behaviour of urban actors using disaggregate data  Activity location, home choice, commuting and travel decision  Household and professional profiles of persons  Probabilistic discrete-choice theory is becoming the central tenet of urban modelling research  Implemented using logistic and Cox regression techniques  Aimed at modelling individual’s and household’s behaviour  Needing dynamic spatial tools for analysing complex urban systems where  Uncertainties exist in the system (aggregation is not straightforward)  Decision rules for individuals and households can be intricate  System processes are path and location dependent - future system state depends partly on past and current states (thus needing event history analysis)

3 Purpose and Objectives  Purpose  Detect the unintentional consequences, at the macro scale (E.g. urban spread), of intentional actions and strategies occurring at the micro-scale (statistical aggregation)  Objective : develop a logical database model to handle personal biographies and to restructure individual lifelines data in a format suitable for statistical analysis  Needing to build a new spatio-temporal dataset (flat file) for any question at hand (data restructuring for statistical analysis)  GIS needed to study influence of neighbourhood on individual decisions and to summarise their combined effect on the evolution of the overall urban system

4 Why Studying Individual Biographies?  Focus of this work  Household, residential and professional history of citizens  Life course of most individuals  Is built around three interlocking series of events: - a household history; an occupational career; a residential trajectory  During the last decades, these trajectories generated patterns of events of increasing complexity: - more divorces - extension of contractual short-term employment - increasing geographical mobility, etc.  Within cities, these individual trajectories intersect and combine, yielding demographic and residential patterns –driving city evolution and transportation demand  Understanding processes by which personal biographies aggregate and evolve cannot be derived from censuses  They give only the barest spatio-temporal snapshot reports on complex situations and they do not relate facts

5 Example of an Individual’s Biography

6 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 status on more than one trajectory - or 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  They associate episodes (time periods with stable attributes) which intersect to depict global life status of the person  Hypothesis: their 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

7 Using Retrospective Surveys  Retrospective surveys  Provide detailed information about changes occurring during the life of the respondents  However, this spatio-temporal data must be properly structured and carefully analysed to reveal spatio-temporal structures and patterns  Advantages  Phenomena are measured for individuals (micro-level)  The follow-up cover long periods of time (E.g. since birth, marriage or departure from the parent’s home)  Information can be structured using lifelines and personal trajectories  Specific issues  Data reliability and questionnaire structure - respondents have to remember places and events that were happening many years earlier  However, sequence of events are more reliable than dates  Spatial and temporal data may be fuzzy  Need appropriate data modelling to handle historical sequences and to allow comprehensive time-based statistical analysis

8 The 1996 Retrospective Survey for Quebec City  In Quebec City, a retrospective survey collecting, in one interview, information about all changes occurred over a long period of time, since the departure of the parental home  A spatially stratified sample of two cohorts of professional workers  Sample of 418 respondents stratified by municipality, gender and age cohort (36-40 and 46-50).  Interviews realized at the respondent’s home, mean duration 1.5 hour  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 of every change (starting- and ending-time of each episode)

9 Spatio-Temporal Modelling of Biographies  Relate to the integration of time in GIS  Triad framework proposed by Peuquet (space - time - theme)  Integrate the notion of event-process and jointly-related entities described in Claramunt et al.  Main task  Design a relational database schema of individual’s trajectories and providing query mechanisms needed to restructure spatio- temporal data in a format suitable for statistical analysis (using GIS and DBMS)  Main characteristics  Entity-based implementation within RDBMS and GIS to describe individuals, events, processes, households, jobs, diploma, etc.  Building multi-dimensional sequences of events combining lifelines and trajectories  Providing flat files needed for statistical analysis of ad hoc queries

10 Modelling Life Trajectories  Specific conceptual modelling issue  How can we express the temporal structure of biography as an ordered sequence of intertwined statuses 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.) generating abstract temporal features (E.g. episodes, events)  Episodes are ordered along lifelines to form sequences of independent or joint evolution (linked trajectories or related individuals)  Trajectories hold sets of relationships  Aggregation (household made of persons), combination (mix of jobs held simultaneously), or collaboration (renting or buying a dwelling is using another type of entity and starts a new residential episode)

11 Database Modelling of Trajectories  Modelling concepts  Trajectories are combining events and episodes describing a multi-dimensional aspect of personal life  Each trajectory (E.g. household) groups a set of related lifelines (E.g. marital status, family composition)  Each lifeline describes a specific dimension of a trajectory, ordering episodes (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 a new episode (E.g. birth of a child in an household changes its composition)  Events and episodes form sequences ordered along lifelines (directional from past to future)

12 Quebec City - Trajectories and Lifelines  Our survey questionnaire leads us to define nine lifelines related to the three trajectories  The first lifeline is used to depict the Respondent life (from birth up to the interview)  Residential trajectory  Is simple; it’s a formed by one lifeline – Home tenure episodes and home related events (rent, buy, sell, etc.)  Household trajectory  Is more complex; it relates 3 types of lifelines – Marital status (including identification of successive spouses, union, separation) – Children (ordering events and episodes related to children : birth, adoption, departure after a divorce) – Household lifeline makes the synthesis of any family change  Professional trajectory  Is complex; it relates 4 types of lifelines – Educational (including degrees), Occupational (mix of independent or simultaneous jobs) and Work place histories – They combine to form the Professional lifeline

13 Spatio-temporal Database Modelling  The core of the spatio- temporal model is formed by an EPISODE table combining events and episodes ordered by the EpisodeSequences table.  The ontology of lifelines and trajectories is stored directly in the database, and each event and episode is typified  Episodes are related to the respondent, but also to any other acting individual in the household (E.g. linking the respondent to his/her spouse)  Every episode and event is related to space through the SPATIAL table providing locations managed by Spatialware functionalities (E.g. ODBC link with Access)

14 Modelling of Residential Trajectory  The Residential trajectory is made of only one lifeline describing the attributes and occupation modes of successive homes inhabited by the respondent during his/her adult life  Each home is located in space using street addresses and location is managed, for each episode, by the SPATIAL table  Integrity constrains are enforced  GIS operations are realized within MapInfo using ODBC technology with Spatialware features generating points in Map Views  The Home lifeline handle home tenure related events (Rent, Buy, Inhabit) and episodes (Tenant, Owner, Cotenant, etc.)

15 Modelling of Household Trajectory

16 Modelling of Professional Trajectory

17 Querying Using Temporal Sequence Views  These trajectories and lifelines are related into a unified database structure describing their successive temporal, spatial and thematic attributes  The relational model allows for building relationships across lifelines, events and trajectories using state transition views similar to the example shown here  These integrated views are later used to ease formulation of spatio- temporal query building ad hoc event history datasets needed for statistical analysis Episode – Event - Episode Event – Episode - Event

18 Linking to Event History Regression Analysis  Most of the phenomena discussed in this research may be thought as events  These events and their possible relationships are recorded using RDBMS  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)  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)  Our approach enable data restructuring that fulfil requirements for this kind of statistical analysis

19 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 in order 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?)

20 Cohorts: 1: respondents in their thirties 2: respondents in their forties Proportions of Tenants and Home-Owners Related to Time Elapsed Since Departure From Parent’s Home (survival rates) Time elapsed since departure from parent’s home (years) Proportion of tenants Proportion of home-owners Cohort

21 Cohorts: 1: respondents in their thirties 2: respondents in their forties Proportions of Tenants and Home-Owners Related to Respondent’s Age (survival rates) Respondent’s Age (years) Proportion of tenants Proportion of home-owners Cohort Respondent’s Age (years)

22 Spatio-temporal Analysis of Retrospective Data An Example  Study individual behaviour of persons making home-location choices under various conditions linked to their own personal history, considering the three trajectories  The retrospective survey and its implementation within a temporal GIS providing event-ordering functionalities will further our understanding of their strategies for moving through the city considering their own history, the impact of growing family, of changes in work place, their educational status, income, home price, stability in employment, etc.  The next slide shows a preliminary event-history model of the propensity of tenants to buy a house after the birth of their first child  It was realised with a very preliminary version of the spatio- temporal database, but can help understand the advantages of such an approach

23 Event History Analysis Results  What are the factors influencing the decision of tenants to buy a house (with some delay) after their first child is born?  Is there significant differences among persons? Yes (Chi Square)  Are their behaviour stable over time? No (Significant variations of propensity over time)  What are the factors influencing the propensity of changing status from tenant to home-owner? 1- Stability in employment, 2- Decade during which the child was born (time varying behaviour), 3- Willingness to move towards remote locations (related to house prices)

24 Example of Application in Excel (Activate the Spreadsheet to set parameters) Event History Statistical Model

25 Event History Analysis Results Rate of access to property ownership significantly increases through time - from the sixties to the eighties Stability in employment increases propensity to buy a home

26 Discussion and Conclusion  The proposed database modelling approach is classical  It uses entity-relationship principles, combined with geo-relational technology  With the exception of minor enhancements to existing methods, the purpose was not to contribute to the advance of STDB modelling  Contribution  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 within GIS database is a very important requirement for urban and transportation planning  Towards a generic spatio-temporal life trajectories ST schema  Multidisciplinary approach  Combining TGIS and event history analysis provide methodology well suited for behavioural modelling applications  We are now developing application-independent dialogs for querying this spatio-temporal structure and build projections yielding flat files needed for statistical analysis using SPSS, SAS or SPlus

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