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Author Zuhdi, Ahmad ♦ Arymurthy, Aniati Murni ♦ Suhartanto, Heru
Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Transition Rule ♦ Data Mining Technique ♦ Cellular Automaton ♦ Geographic Spatiotemporal Dynamic Model ♦ Data Grid ♦ Time Series Data Grid ♦ Simulation Process ♦ Minimum Distance ♦ Entity Change ♦ Information Availability ♦ Clustering Technique ♦ Multiple Linear Regression Analysis ♦ Geographic Data ♦ Chosen Transition Rule ♦ Simulation Accuracy ♦ Error Measurement ♦ Geospatial Data ♦ New Unknown Grid ♦ Error Information ♦ New Data Grid
Abstract Geospatial data and information availability has been increasing rapidly and has provided users with knowledge on entities change and movement in a system. Cellular Geography model applies Cellular Automata on Geographic data by defining transition rules to the data grid. This paper presents the techniques for extracting transition rule(s) from time series data grids, using multiple linear regression analysis. Clustering technique is applied to minimize the number of transition rules, which can be offered and chosen to change a new unknown grid. Each centroid of a cluster is associated with a transition rule and a grid of data. The chosen transition rule is associated with grid that has a minimum distance to the new data grid to be simulated. Validation of the model can be provided either quantitatively through an error measurement or qualitatively by visualizing the result of the simulation process. The visualization can also be more informative by adding the error information. Increasing number of cluster may give possibility to improve the simulation accuracy.
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article