Parallel Cellular Automata Markov Model for Land Use Change Prediction over MapReduce Framework

Abstract

The Cellular Automata Markov model combines the cellular automata (CA) model’s ability to simulate the spatial variation of complex systems and the long-term prediction of the Markov model. In this research, we designed a parallel CA-Markov model based on the MapReduce framework. The model was divided into two main parts: A parallel Markov model based on MapReduce (Cloud-Markov), and comprehensive evaluation method of land-use changes based on cellular automata and MapReduce (Cloud-CELUC). Choosing Hangzhou as the study area and using Landsat remote-sensing images from 2006 and 2013 as the experiment data, we conducted three experiments to evaluate the parallel CA-Markov model on the Hadoop environment. Efficiency evaluations were conducted to compare Cloud-Markov and Cloud-CELUC with different numbers of data. The results showed that the accelerated ratios of Cloud-Markov and Cloud-CELUC were 3.43 and 1.86, respectively, compared with their serial algorithms. The validity test of the prediction algorithm was performed using the parallel CA-Markov model to simulate land-use changes in Hangzhou in 2013 and to analyze the relationship between the simulation results and the interpretation results of the remote-sensing images. The Kappa coefficients of construction land, natural-reserve land, and agricultural land were 0.86, 0.68, and 0.66, respectively, which demonstrates the validity of the parallel model. Hangzhou land-use changes in 2020 were predicted and analyzed. The results show that the central area of construction land is rapidly increasing due to a developed transportation system and is mainly transferred from agricultural land.

Introduction to Big Data Computing for Geospatial Applications

“…To optimize the performance of a parallel algorithm for geospatial processing, analysis, or modeling when using such general-purpose frameworks, the spatial characteristics of the data and algorithm must be considered for the algorithmic design [15,16]. The four papers by Jo et al [3], Zhao et al [4], Kang et al [5], and Safanelli et al [6] focus on parallel computing and highlight the adaption of existing computing frameworks for geospatial data preprocessing, parallel algorithm design, simulation modeling, and data analysis.…”

“…The CA-Markov model is one of the most widely used extended cellular automata (CA) models and has been used in the prediction and simulation of land-use changes [5]. As land-use change simulation and prediction involves massive amounts of data and calculations, many parallel CA algorithms have been designed to simulate urban growth based on various computing models, including central processing units (CPUs) and GPUs.…”

“…Alternatively, the MapReduce framework is capable of efficient parallel processing when coupled to the CA-Markov model; the key problem of segmentation and maintaining spatial connections remain unresolved. As such, Kang et al introduced a MapReduce-based solution to improve the parallel CA-Markov model for land-use-change prediction [5]. Results suggest that the parallel CA-Markov model not only solves the paradox that the traditional CA-Markov model cannot simultaneously achieve the integrity and segmentation for land-use change simulation and prediction but also achieves both efficiency and accuracy.…”

“…Parallelism can be achieved by innovatively utilizing advanced computing frameworks, such as MapReduce and Spark, for applications that include massive data sorting, computing, machine learning, and graph processing [20]. While this special issue highlighted advancements in geospatial big data preprocessing [3], land-use change prediction [5], and overlay analysis [4], more efforts should be devoted to identifying geospatial applications of great impact and benefiting from the integration of geospatial methods and parallelization in the big data era. Additionally, many existing geospatial big data applications simply inject spatial data types or functions inside existing big data systems (e.g., Hadoop) without much optimization [3].…”

A Cellular Automata Model for Integrated Simulation of Land Use and Transport Interactions

“…An indicative sample of the ongoing debate can be illustrated by Congalton and Green [53] advocating for its utility and Pontius and Millones [54] providing evidence of the problems and lack of accuracy of kappa when compared with other metrics derived from contingency matrices. Our decision to use kappa statistics derives from the possibility of establishing some degree of comparison with the overall performance of other CA models (for example, Kang et al [55], Grinblat, Gilichinsky and Benenson [56] and Petrov, Lavalle and Kasanko [57]).…”