Spatial Patterns and Processes in a Longitudinal Framework

This article explores the conceptual equivalence between hazard models applied to both temporal data and distance data by focusing on the `at-risk' concept, which is central to longitudinal models but has not received sufficient attention in the application of hazard models in spatial settings. A proper conceptualization in a spatial (distance) setting is based on distant-dependent Markovian transition probabilities describing the risk of switching between states. Such a conceptualization is possible for continuous spatial processes, as well as for point-generating processes leading to spatial point patterns. Hazard models for a series of scenarios simulating various point generation trajectories are compared. This process-oriented perspective is further augmented by explicitly accounting for temporal dimensions (speed) of point-generating processes.

[1]  James J. Heckman,et al.  Longitudinal Analysis of Labor Market Data: Social science duration analysis , 1985 .

[2]  S Reader,et al.  Using survival analysis to study spatial point patterns in geographical epidemiology. , 2000, Social science & medicine.

[3]  B. Waldorf,et al.  A parametric failure time model of international return migration , 1991 .

[4]  J. Odland Longitudinal Approaches to Analysing Migration Behaviour in the Context of Personal Histories , 1997 .

[5]  A. Esparza,et al.  BUSINESS SERVICES IN THE SPACE ECONOMY: A MODEL OF SPATIAL INTERACTION , 2005 .

[6]  Jerald F. Lawless,et al.  Statistical Models and Methods for Lifetime Data , 1983 .

[7]  B. Waldorf,et al.  Segregation and Residential Mobility of Vietnamese Immigrants in Brisbane, Australia , 1998 .

[8]  J. Hausman,et al.  Flexible parametric estimation of duration and competing risk models , 1990 .

[9]  James J. Heckman,et al.  Longitudinal Analysis of Labor Market Data , 1985 .

[10]  D.,et al.  Regression Models and Life-Tables , 2022 .

[11]  P. A. Pellegrini,et al.  Policy Coalitions in the U.S. Congress: A Spatial Duration Modeling Approach , 1999 .

[12]  I. James UNEMPLOYMENT DURATION: MODELLING AND ESTIMATION WITH PARTICULAR REFERENCE TO THE AUSTRALIAN LONGITUDINAL SURVEY , 1989 .

[13]  J. Keith Ord,et al.  Spatial Processes Models and Applications , 1981 .

[14]  N. Kiefer Economic Duration Data and Hazard Functions , 1988 .

[15]  G. Hosgood Markov models to estimate and describe survival time and experience in cohorts with high euthanasia frequency , 2002 .

[16]  Peter A. Rogerson,et al.  The Spatial Separation of Parents and Their Adult Children , 1993 .

[17]  Hans-Peter Blossfeld,et al.  Using Cox Models to Study Multiepisode Processes , 1989 .

[18]  Jürgen Symanzik,et al.  Statistical Analysis of Spatial Point Patterns , 2005, Technometrics.

[19]  Hjp Harry Timmermans,et al.  An Unconditional Competing Risk Hazard Model of Consumer Store-Choice Dynamics , 1996 .

[20]  James J. Heckman,et al.  The identifiability of the competing risks model , 1989 .

[21]  James J. Heckman,et al.  Longitudinal Analysis of Labor Market Data , 1985 .

[22]  E. Irwin,et al.  Endogenous Spatial Externalities: Empirical Evidence and Implications for the Evolution of Exurban Residential Land Use Patterns , 2004 .

[23]  James O. Huff,et al.  Geographic Regularities in Residential Search Behavior , 1986 .

[24]  W A Clark,et al.  Comparing Cross-Sectional and Longitudinal Analyses of Residential Mobility and Migration , 1992, Environment & planning A.

[25]  W. Narendranathan,et al.  Modelling the Probability of Leaving Unemployment: Competing Risks Models with Flexible Base‐Line Hazards , 1993 .

[26]  李幼升,et al.  Ph , 1989 .

[27]  Jonathan M. Thomas ON THE INTERPRETATION OF COVARIATE ESTIMATES IN INDEPENDENT COMPETING‐RISKS MODELS* , 1996 .

[28]  A. Esparza,et al.  The spatial extent of producer service markets: Hierarchical models of interaction revisited , 1996 .

[29]  M. Lee A two-state markov model , 1994 .