A spatio-temporal autocorrelation analysis method for geographic multi-stream data based on cellular automata

Abstract

The invention provides a spatio-temporal autocorrelation analysis method of geographic multi-stream data based on cellular automata, An improved cellular automata dynamic model is used to express thespatio-temporal characteristics and complexity of geographic data, and the spatial heterogeneity of cellular (geographic region) transformation rules and asynchronous evolution is considered, which can more accurately analyze the geographic multi-stream data with nonlinear structure based on complex network. By analyzing the cellular unit and extracting a plurality of influence factors, the cellular automata model parameters can be more accurately obtained, and the method has the advantages of high accuracy and efficiency. The transformation rules obtained by ANN algorithm are more dynamic than the fixed transformation rules of the whole model, which can describe and accord with the actual situation of cell transformation. According to Moran's I, the spatial-temporal distribution of geographic data is better and clearer, which makes it easier to simulate and predict the spatial-temporal data model, and the precision of simulation and prediction is higher.

Description

technical field [0001] The invention belongs to the statistical analysis method of geographic space, and specifically relates to the fields of cellular automata, geographic multivariate flow data processing, geographic spatial correlation analysis and the like. Background technique [0002] In recent years, with the continuous development and popularization of geographic information technology, remote sensing technology, Internet of Things, and mobile terminals, multivariate geographic data with spatial reference has undergone rapid evolution for decades, forming massive geographic multivariate stream data. These streaming data have the characteristics of sequential, massive, fast, and continuous arrival, and can be aggregated into a dynamic data set that grows infinitely over time. They can also be called spatio-temporal streaming data, such as traffic data, real-time temperature monitoring data, etc. . [0003] At present, the spatial autocorrelation analysis technology c...

AI Technical Summary

Problems solved by technology

However, from the research literature of scholars from various countries, it can be found that the model of geographic multivariate flow data is mainly based on time series model construction, which only considers temporal autocorrelation, and does not take into account geospatial heterogeneity and correlation between multivariate variables.

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