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Agent-Based Simulation in Geospatial Analysis

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Progress in Geospatial Analysis

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Abstract

There is a wide array of simulation methods that mimic the mechanisms of human intelligence to achieve one or more objectives. Analytical simulation approaches basically use equations that explain data, while statistical ones work primarily with probabilities. An iterative combination of any or both of the above uses feedback options to answer problems which are too complex to be solved by one equation. Most of these equation-based mathematical models identify system variables, and evaluate or integrate sets of equations relating to these variables. A variant of such equation-based models are based on linear programming (Howitt 1995; Weinberg et al. 1993), and are potentially linked to geographical information science (GIS) information (Chuvieco 1993; Cromley and Hanink 1999; Longley et al. 1994). However, in practice there are limited levels of complexity that can be built into these models (Parker et al. 2003).

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  1. Division of Spatial Information Science, Graduate School of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan

    Kondwani Godwin Munthali

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Correspondence to Kondwani Godwin Munthali .

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  1. Graduate School of Life and Environmenta, Division of Spatial Information Science, University of Tsukuba, Tennodai 1-1-1, Tsukuba, 305-8572, Ibaraki, Japan

    Yuji Murayama

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Munthali, K.G. (2012). Agent-Based Simulation in Geospatial Analysis. In: Murayama, Y. (eds) Progress in Geospatial Analysis. Springer, Tokyo. https://doi.org/10.1007/978-4-431-54000-7_10

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