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Geographical multivariate flow data spatio-temporal autocorrelation analysis method based on cellular automaton
Geographical multivariate flow data spatio-temporal autocorrelation analysis method based on cellular automaton
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机译:基于元胞自动机的地理多元流数据时空自相关分析方法
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#$%^&*AU2018101946A420190117.pdf#####ABSTRACT A geographical multivariate flow data spatio-temporal autocorrelation analysis method based on a cellular automaton is provided. The spatio-temporality and complexity of geographical data are expressed by adopting an improved cellular automaton dynamic model, and a transformation rule and spatial heterogeneity of asynchronous evolution of a cell (geographic region) are considered to analyze geographical multivariate flow data of a non-linear structure based on a complex network more accurately. A cellular automaton model parameter is obtained more accurately by analyzing a cellular unit and extracting a plurality of influence factors, and is accurate and high in efficiency; the transformation rule obtained by using an ANN algorithm is more dynamic than the fixed transformation rule of the whole model, more describable, and conforms to an actual cellular changing situation; the correlation between cells is expressed by Moran's I to reflect a spatio-temporal distribution situation of geographical data better and more clearly, thereby facilitating subsequent simulation and prediction of spatio-temporal data model , and achieving the simulation and prediction more accurately. Fig. 21/2 rule/transformation tie st function Cell attribute neighborhood ni oho celsae information state ifatiut geographical space Fig 1 land use data of hi storical years image data randomly sampled obtaining transformation rule | lasification training data based on ANN algorithm influence factor datatransformation rule data of distance at fdsac tatistical unit quantity of cell from city of cel fro - - - - (btaining through Cellular automatonfas downtown mirodneighborhood window) modelfas actual land use precision simulation result data assessment lan suang precision complies? yes spati-temoral Improved Moran's I -A ST I prediction result - data of future land use change Fig 2
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机译:#$%^&* AU2018101946A420190117.pdf #####抽象基于地理多元流数据的时空自相关分析方法提供了细胞自动机上的功能。地理数据的时空性和复杂性是通过采用改进的元胞自动机动力学模型和变换规则来表达细胞(地理区域)异步进化的空间异质性被认为是分析基于复杂网络的非线性结构的地理多元流数据更精确地。通过分析模型,可以更准确地获得元胞自动机模型参数蜂窝单元,提取多个影响因素,准确高效。使用ANN算法获得的变换规则比固定规则更动态整个模型的变换规则,更可描述,并符合实际的细胞形势变化;细胞之间的相关性由Moran的I表示,以反映地理数据的时空分布状况更好,更清晰,从而促进时空数据模型的后续模拟和预测,并实现模拟和预测更加准确。图21/2规则/转换领带功能单元格属性邻域ni ohoCelsae信息状态图地理空间图。1历史土地利用数据年图像数据随机采样获取变换规则|基于ANN算法的精细化训练数据影响因素数据转换规则fdsac统计单位数量的距离数据来自cel市的细胞数----(通过细胞自动机获取市中心mirodneighbourhood窗口)模型实际土地利用精度模拟结果数据评估蓝双精确符合?是空间改进的Moran's I -A ST I预测结果-未来数据土地利用变化图2
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