An Improved Cellular Automata Urban Expansion Simulation Method

Abstract

The invention discloses a simulation method for urban expansion through modified cellular automaton. The method comprises the following steps of building an index system to evaluate urbanization development level and land utilization potential, and obtaining a land development potential map; based on the development potential map, sorting potential values of non-city picture elements from large to small, and recording the sorted values and corresponding picture element positions through an array; traversing each item of the array until finding the maximum value picture element meeting neighborhood influence, converting the picture element into an urban land type, and meanwhile, increasing the urban cellular quantity by one; updating the influences of all neighborhoods of the cellular, deleting the item from the array and breaking out of the loop, judging whether the cellular increase quantity meets the requirement or not, if yes, obtaining a simulation result, and if not, repeating the loop until the urban land increase quantity meets the requirement. The simulation method for urban expansion through the modified cellular automaton solves the difficult problem that the simulation result is not accurate due to nondeterminacy of a conversion threshold value of the traditional cellular automaton.

Description

technical field [0001] The invention belongs to the field of city planning, and in particular relates to an improved cellular automaton city expansion simulation method. Background technique [0002] The urban sprawl cellular automata model consists of a series of cells representing different land use types, and controls the conversion of non-urban land use to urban land use types through neighborhood states and conversion rules, thereby simulating the discrete spatiotemporal dynamics of urban sprawl Model. Using CA to simulate the evolution process of urban spatial structure is controlled by transformation rules. The conversion rule is the core of the cellular automata model, which determines the dynamic conversion process of the cellular automata. Taking the logistic-CA simulation of urban sprawl as an example, the final conversion rules are determined by many natural environment and socioeconomic factors that affect the evolution of urban spatial structure (topography, ...

AI Technical Summary

Problems solved by technology

If the threshold value is set too large, the number of urban cell growth simulated by CA will never reach the preset growth amount; if the threshold value is set too small, the number of cell iterations will be too small, resulting in a greatly reduced accuracy.

Therefore, there is an urgent need for a method to solve the problem that the final result is uncertain due to different thresholds.

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