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. 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 data transformation 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
Geographical multivariate flow data spatio-temporal autocorrelation analysis method based on cellular automaton