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. The creation of this package was supported by the Economic and Social Research Council (ESRC) through the Applied Quantitative Methods Network: Phase II, grant number ES/K006460/1.
|License:||GPL (>= 2)|
Guanpeng Dong, Richard Harris, Angelos Mimis <email@example.com>
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.
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