Multilevel Modelling with Spatial Interaction Effects with Application to an Emerging Land Market in Beijing, China

This paper develops a methodology for extending multilevel modelling to incorporate spatial interaction effects. The motivation is that classic multilevel models are not specifically spatial. Lower level units may be nested into higher level ones based on a geographical hierarchy (or a membership st...

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Veröffentlicht in:PloS one Jg. 10; H. 6; S. e0130761
Hauptverfasser: Dong, Guanpeng, Harris, Richard, Jones, Kelvyn, Yu, Jianhui
Format: Journal Article
Sprache:Englisch
Veröffentlicht: United States Public Library of Science 18.06.2015
Public Library of Science (PLoS)
Schlagworte:
Autoregressive processes
Beijing
Commerce
Computer Simulation
Data processing
Dependence
Econometrics
Economic models
Geographic Information Systems
Geography
Group effects
Mathematical models
Modelling
Models, Economic
Models, Statistical
Monte Carlo Method
Monte Carlo methods
Monte Carlo simulation
Motivation
Multilevel
Multilevel Analysis
Parameter estimation
Performance assessment
Social sciences
Spatial Analysis
Spatial data
Beijing
Beijing China
New York
United Kingdom > UK
United States > US
China
ISSN:1932-6203, 1932-6203
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Zusammenfassung:This paper develops a methodology for extending multilevel modelling to incorporate spatial interaction effects. The motivation is that classic multilevel models are not specifically spatial. Lower level units may be nested into higher level ones based on a geographical hierarchy (or a membership structure--for example, census zones into regions) but the actual locations of the units and the distances between them are not directly considered: what matters is the groupings but not how close together any two units are within those groupings. As a consequence, spatial interaction effects are neither modelled nor measured, confounding group effects (understood as some sort of contextual effect that acts 'top down' upon members of a group) with proximity effects (some sort of joint dependency that emerges between neighbours). To deal with this, we incorporate spatial simultaneous autoregressive processes into both the outcome variable and the higher level residuals. To assess the performance of the proposed method and the classic multilevel model, a series of Monte Carlo simulations are conducted. The results show that the proposed method performs well in retrieving the true model parameters whereas the classic multilevel model provides biased and inefficient parameter estimation in the presence of spatial interactions. An important implication of the study is to be cautious of an apparent neighbourhood effect in terms of both its magnitude and statistical significance if spatial interaction effects at a lower level are suspected. Applying the new approach to a two-level land price data set for Beijing, China, we find significant spatial interactions at both the land parcel and district levels.
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Conceived and designed the experiments: GD RH. Performed the experiments: GD RH JY. Analyzed the data: GD JY. Contributed reagents/materials/analysis tools: GD RH KJ JY. Wrote the paper: GD RH KJ.
Competing Interests: The authors have declared that no competing interests exist.
ISSN:1932-6203
1932-6203
DOI:10.1371/journal.pone.0130761