Abstract
Lattice data have two different scale spatial properties: global depen- dence property and local fluctuation property. For lattice data space-time autoregressive modeling, to reduce influence of spatial fluctuation on prediction accuracy of neural networks, all regions are partitioned into several subareas by an improved k-means algorithm based on spatial contiguity relation. Some partition criteria are proposed to evaluate different partition schemes and the optimal scheme has the least spatial fluctuation and significant spatial dependent within each subarea. Each multi-layer perceptrons (MLPs) network is modeled respectively for each subarea, and the output nodes are the prediction values at time t of an attribute for all regions in a subarea, and the input nodes are observations before time t of this subarea itself and neighboring regions. As a case study, all local models are tested and compared with a single global MLPs network by one-step-ahead predicting of an epidemic dataset, and the results indicate that local NN model has better prediction performance than the global NN model.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Cressie, N.A.: Statistics for Spatial Data, 2nd edn. Wiley, New York (1993)
Tobler, W.: A Computer Movie Simulating Urban Growth in the Detroit Region. Economic Geography 46, 234–240 (1970)
Haining, R., Wise, S., Ma, J.: Designing and Implementing Software for Spatial Statistical Analysis in a GIS Environment. Journal of Geographical Systems 2, 257–286 (2000)
Anselin, L.: Spatial Econometrics: Methods and Models. Kluwer Academic Publishers, Dordrecht (1988)
Gilardi, N., Bengio, S.: Local Machine Learning Models for Spatial Data Analysis. Journal of Geographic Information and Decision Analysis 4(1), 11–28 (2000)
Anselin, L.: Local Indicators of Spatial Association–LISA. Geographical Analysis 27(2), 93–115 (1995)
Wise, S., Haining, R., Ma, J.: Regionalization Tools for the Exploratory Spatial Analysis of Health Data. In: Fisher, M., Getis, A. (eds.) Recent Developments in Spatial Analysis: Spatial Statistics, Behavioral Modeling, and Computational Intelligence. Springer, Heidelberg (1997)
Hu, T.M., Sung, S.Y.: Data Fusion in Radial Basis Function Networks for Spatial Regression. Neural Processing Letters 21, 81–93 (2005)
Anselin, L.: GeoDa 0.9 User’s Guide (2003), http://www.geoda.uiuc.edu
Zhang, G., Patuwo, B.E., Hu, M.Y.: Forecasting with Artificial Neural Networks: the State of the Art. International Journal of Forecasting 14, 35–62 (1998)
Fotheringham, A.S., O’Kelly, M.E.: Spatial Interaction Models: Formulations and Applications. Kluwer Academic Publishers, Dordrecht (1989)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wang, H., Wang, J., Liu, X. (2006). Local Neural Networks of Space-Time Predicting Modeling for Lattice Data in GIS. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3973. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760191_174
Download citation
DOI: https://doi.org/10.1007/11760191_174
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34482-7
Online ISBN: 978-3-540-34483-4
eBook Packages: Computer ScienceComputer Science (R0)