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

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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
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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

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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
• lt, , gt and, eventually, Allens primitives
• Enhancing ST operators to improve their semantic
  expressiveness
• Extending Allens 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

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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

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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

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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 respondents
  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 respondents home,
  mean duration 1.5 hour (27,167 facts)
• Three trajectories
• Residential trajectory every home occupied
  (three months or more) since the departure of
  parents 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 respondents 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)

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Complex Evolution Processes
Personal Biography
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Changes in Personal Life

• An individuals 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

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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)

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Tentative Ontology of Lifelines and Trajectories
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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)

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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 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

Change motivating facts
Enabling facts
Target status
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Enhancing Expression of ST Relationships

• Time ordering should use time stamps
  (chronological), historical (topological
  firstlast) and/or duration (shortestlongest)
  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)

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Temporal Operators on Two Time Intervals
Commutative Allens operators are identified with grey tones Operational definition
a) Comparison between the time limits of two time intervals (periods or instants) Extended from Allen a) Comparison between the time limits of two time intervals (periods or instants) Extended from Allen a) Comparison between the time limits of two time intervals (periods or instants) Extended from Allen
yes T Equal U (T U) Ù (T U)
no T MeetBeg U T U
no T MeetEnd U U MeetBeg T
yes T Touch U (T MeetBeg U) Ú (T MeetEnd U)
no T During U (T gt U) Ù (T lt U)
no T Start U (T U) Ù (T lt U)
no T Finish U (T gt U) Ù (T U)
no T Inside U (T During U) Ú (T Start U) Ú (T Finish U)
no T Contain U U Inside T
no T CoverBeg U T lt U lt T lt U
no T CoverEnd U U CoverBeg T
yes T Overlap U ((T CoverBeg U) Ú (T CoverEnd U)) Ù (T Contain U)
no T Before U T lt U
no T After U U Before T
yes T Disjoint U (T Before U) Ú (T After U)
yes T Outside U (T Disjoint U) Ú (T Touch U)
yes T Intersect U (T Disjoint U)
no T Anterior U (T Before U) Ú (T TMeetBeg U)
no T Posterior U (T After U) Ú (T MeetEnd U)
no T Precede U (T Before U) Ú (T MeetBeg U) Ú (T CoverBeg U)
no T Succeed U (T After U) Ú (T MeetEnd U) Ú (T CoverEnd U)
no T Bound U ((T Start U) Ú (T Finish U)) Ù (T Inside U)
no T Initiate U (T Start U) Ù (T CoverEnd U)
no T Terminate U (T Finish U) Ù (T CoverBeg U)
no T Begin U (T Initiate U) Ú (T Equal U)
no T End U (T Terminate U) Ú (T Equal U)
b) Comparison between the durations of two time intervals (periods or instants) b) Comparison between the durations of two time intervals (periods or instants) 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) lt (U-U)
no T Longer U (T-T) gt (U-U)
no T ShorterEquiv U (T Shorter U) Ú (T Equivalent U)
no T LongerEquiv U (T Longer U) Ú (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 Temporal operands (T and U) are delimited by their beginning (T and U) and ending (T and U) time stamps Temporal operands (T and U) are delimited by their beginning (T and U) and ending (T and U) time stamps
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Spatial Operators on Two Spatial Objects
Commutative Clementinis primitive operators are identified with grey tones Operational definition
yes E Equal F (E Ç F E È F) Ù (dE Ç dF dE È dF)
yes E Touch F (E Ç F Æ) Ù (dE Ç dF ¹ Æ)
no E Inside F (E Ç F E) Ù ( E Ç F ¹ Æ)
no E Contain F F Inside E
yes E Overlap F (E Ç F ¹ E) Ù (E Ç F ¹ F) Ù (E Ç F ¹ Æ)
yes E Disjoint F E Ç F Æ
yes E Outside F (E Disjoint F) Ú (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 (dE and dF) Spatial operands (E and F) are formed by their interiors (E and F) and boundaries (dE and dF) Spatial operands (E and F) are formed by their interiors (E and F) and boundaries (dE and dF)
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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 TIf (T Contain U) then U UIf (T CoverBeg U) then T UIf (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 Temporal operands (T and U) are delimited by their beginning (T and U) and ending (T and U) time stamps Temporal operands (T and U) are delimited by their beginning (T and U) and ending (T and U) time stamps Temporal operands (T and U) are delimited by their beginning (T and U) and ending (T and U) time stamps
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Distance Operators Between Two Spatial Objects
Commutative Distance operators Operational definition Euclidean distances Exceptions
yes E DisCtrs F Length (Line (Eclong Eclat Fclong Fclat))
yes E Distance F Length (Shortest Line (dElong dElat dFlong dFlat)) If (E Outside F) then null
no E DistInside F Length (Shortest Line (dElong dElat dFlong dFlat)) If (E Inside F) then null
no E DistContain F Length (Shortest Line (dElong dElat dFlong dFlat)) If (E Contain F) then null
Spatial operands (E and F) are defined by their respective boundaries (dE and dF) and centre points (Ec and Fc) Spatial operands (E and F) are defined by their respective boundaries (dE and dF) and centre points (Ec and Fc) Spatial operands (E and F) are defined by their respective boundaries (dE and dF) and centre points (Ec and Fc) Spatial operands (E and F) are defined by their respective boundaries (dE and dF) and centre points (Ec and Fc)
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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

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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

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Example of ST Query on Personal Trajectories

• Within Quebec Metro Area, considering only facts
  at a distance gt 500 metres from respondents
  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).

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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?)

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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
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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
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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
  Allens and Clementinis

• 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