A Cellular Automata Model of Spatio-Temporal Distribution of Species

We present a new two-level numerical model describing the evolution of transportation network. Two separate but mutually interacting sub-systems are investigated: a starving environment and the network. We assume that the slow modes of the environment growth can be modeled with classical cellular automata (CA) approach. The fast modes representing the transportation network, we approximate by the graph of cellular automata (GCA). This allows the simulation of transportation systems over larger spatio-temporal scales and scrutinizing global interactions between the network and the environment. We show that the model can mimic the realistic evolution of complex river systems. We also demonstrate how the model can simulate a reverse situation. We conclude that the paradigm of this model can be extended further to a general framework, approximating many realistic multiscale transportation systems in diverse fields such as geology, biology and medicine.

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A mechanistic model of the S-shaped population growth

10.1101/012104 ◽

2014 ◽

Author(s):

Lev V. Kalmykov

Keyword(s):

Cellular Automata ◽

Population Growth ◽

Interspecific Competition ◽

Competitive Exclusion ◽

Mechanistic Model ◽

Main Idea ◽

Exclusion Principle ◽

Cellular Automata Model ◽

Competitive Exclusion Principle ◽

Spatio Temporal

The main idea of this note is to show the most basic and purely mechanistic model of population growth, which has been used by us to create models of interspecific competition for verification of the competitive exclusion principle (1, 2). Our logical deterministic individual-based cellular automata model demonstrates a spatio-temporal mechanism of the S-shaped population growth.

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Spatio-temporal organization of vehicles in a cellular automata model of traffic with `slow-to-start' rule

Journal of Physics A General Physics ◽

10.1088/0305-4470/32/18/303 ◽

1999 ◽

Vol 32 (18) ◽

pp. 3229-3252 ◽

Cited By ~ 15

Author(s):

Debashish Chowdhury ◽

Ludger Santen ◽

Andreas Schadschneider ◽

Shishir Sinha ◽

Abhay Pasupathy

Keyword(s):

Cellular Automata ◽

Temporal Organization ◽

Cellular Automata Model ◽

Spatio Temporal

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A novel cellular automata model integrated with deep learning for dynamic spatio-temporal land use change simulation

Computers & Geosciences ◽

10.1016/j.cageo.2020.104430 ◽

2020 ◽

Vol 137 ◽

pp. 104430 ◽

Cited By ~ 5

Author(s):

Weiran Xing ◽

Yuehui Qian ◽

Xuefeng Guan ◽

Tingting Yang ◽

Huayi Wu

Keyword(s):

Land Use ◽

Deep Learning ◽

Cellular Automata ◽

Land Use Change ◽

Cellular Automata Model ◽

Spatio Temporal

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CELLULAR AUTOMATA FOR MODELLING SHELL PIGMENTATION OF MOLLUSCS

Journal of Biological System ◽

10.1142/s0218339095000897 ◽

1995 ◽

Vol 03 (04) ◽

pp. 999-1011 ◽

Cited By ~ 2

Author(s):

MARIO MARKUS ◽

INGO KUSCH

Keyword(s):

Cellular Automata ◽

Cellular Automaton ◽

Error Propagation ◽

Travelling Waves ◽

Temporal Distribution ◽

Propagation Rate ◽

Spatio Temporal ◽

Pattern Generators ◽

Turing Structures ◽

Class Iv

A minimum cellular automaton, carrying precise biophysical significance in each rule, is presented to model pigmentation patterns on molluscan shells. We find the following types of modes: self-organisation into stationary (Turing) structures, travelling waves, chaos, and so-called class IV behaviour. The latter consists of a disordered spatio-temporal distribution of periodic and chaotic patches; it differs from chaos in that it has no well-defined error propagation rate. The calculations of the modes agree well with observations in natural shells. In particular, our results suggest evidence in nature for class IV behaviour, a mode that had so far been reported only as the result of simulations. Moreover, we show that patchiness results in a class IV mode from the same algorithm that renders chaos and periodicity; thus, there is no need to invoke two competing pattern generators, as in previous approaches.

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