Abstract
This paper presents the implementation of a parallelization strategy using the OpenMP library, while developing a simulation tool based on a cellular automaton (CA) to run urban growth simulations. The characterization of an urban growth model CA is shown and it consists of a digitization process of the land use in order to get all the necessary elements for the CA to work. During the first simulation tests we noticed high processing times due to large quantity of calculations needed to perform one single simulation, in order to minimize this we implemented a parallelization strategy using the fork-join model in order to optimize the use of available hardware. The results obtained show a significant improvement in execution times in function of the number of available cores and map sizes, as a future work, it is planned to implement artificial neural networks in order to generate more complex urban growth scenarios.
Highlights
The evolution in the land use of the territory is a fundamental element in our society, since it manifests different variables that affect our daily life, for example, accessibility to different points of interest within the city, slopes of the land, etc
This growth occurred in a disorderly, that is, anarchic way under the protection of political leadership resulting in the city currently having more than 275 neighborhoods, most of them formed in common lands, ecological reserved areas, places without feasibility of utilities due to its topographic composition [8]
If we take into account that this calculation must be done for each pixel of the map, we find a problem of computational complexity ( ), this means, larger size of the input maps would increase the execution time of the simulation exponentially
Summary
The evolution in the land use of the territory is a fundamental element in our society, since it manifests different variables that affect our daily life, for example, accessibility to different points of interest within the city, slopes of the land, etc. With the use of these models it has been possible to generate territorial scenarios prospectively [5] To generate these scenarios, a characterization of a CA is needed, this has different components, such as the size of the study area, maps of urban uses, map scales, neighborhood radius, evolution rules, slopes, and others geographical factors [6]. The developing of a CA based simulation tool to generate territorial scenarios prospectively in order to implement future simulation techniques, bring us to address some challenges. One key calculus in the whole simulation process is, the transition potentials (TPs) of each cell in the map, these TPs show the probability of a cell to change from one state to another The amount of these TPs have a direct impact on the computation cost needed to perform the mathematical calculations.
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