Hierarchical Spatial Autoregressive Model (HSAR)

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Description

Implements a Hierarchical Spatial Simultaneous Autoregressive Model (HSAR) or a multi-scale spatial econometrics model, with inference in a Bayesian setting using Markov chain Monte Carlo (MCMC) simulation. The appraoch is developed for modelling geographic data with a hierarchical/nested structure, for example, houses nesting into districts or fine-grained spatial units into more aggregated units. The HSAR model brings together the spatial econometrics and multilevel models and thus suitable for a simultaneous capturing the potential spatial dependence (autocorrelations) at each level of the data hierarchy arising from geographical proximity effect and the contextual effect (or group dependence effect) from higher-level units upon lower-level units.

Details

Package: HSAR
Type: Package
Version: 0.4.0
Date: 2016-05-20
License: GPL (>= 2)

Author(s)

Guanpeng Dong, Richard Harris, Angelos Mimis <mimis@panteion.gr>

References

Anselin, L. (1988). Spatial Econometrics: Methods and Models. Dordrecht: Kluwer Academic Publishers.

Goldstein, H. (2003). Multilevel Statistical Methods, 3rd ed. London: Arnold.

LeSage, J. P., and R. K. Pace. (2009). Introduction to Spatial Econometrics. Boca Raton, FL: CRC Press/Taylor & Francis

Dong, G. and Harris, R. 2015. Spatial Autoregressive Models for Geographically Hierarchical Data Structures. Geographical Analysis, 47:173-191.