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Geographic Network Automata for Representing Complex Evolving Spatial Systems

Published: 06 November 2018 Publication History

Abstract

Almost all spatial systems can be modelled as networks. Typically, networks representations of real systems are static, useful for generating descriptive network measures. However, recent interest lies in the representation and analysis of evolving spatial networks that can facilitate the examination of the close coupling between network structure and network dynamics. Therefore, this study proposes a novel spatial modelling framework, Geographic Network Automata (GNA), to represent evolving spatial networks as a function of iteratively applied transition rules. The framework is demonstrated using an adapted and spatially explicit version of Conway's Game of Life. The simulated spatial network structures are quantified using network measures. The presented GNA model framework is general and flexible so that different types of geospatial phenomena can be modelled, providing valuable insights into the link between network structure, dynamics, and evolution.

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    cover image ACM Conferences
    GeoSim '18: Proceedings of the 1st ACM SIGSPATIAL International Workshop on GeoSpatial Simulation
    November 2018
    14 pages
    ISBN:9781450360432
    DOI:10.1145/3284038
    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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    Publication History

    Published: 06 November 2018

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    Author Tags

    1. Complex Systems
    2. Geographic Automata Systems
    3. Geographic Information Science
    4. Geographic Network Automata
    5. Network Science

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