An application of GIS and Bayesian network in studying spatial-causal relations between enterprises and environmental factors

Tiyan Shen; Xi Li; Maiqing Li

doi:10.1117/12.813141

10 November 2008 An application of GIS and Bayesian network in studying spatial-causal relations between enterprises and environmental factors

Tiyan Shen, Xi Li, Maiqing Li

Author Affiliations +

Proceedings Volume 7146, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Advanced Spatial Data Models and Analyses; 71461E (2008) https://doi.org/10.1117/12.813141
Event: Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Geo-Simulation and Virtual GIS Environments, 2008, Guangzhou, China

Abstract

The paper intends to employ Geographic Information System (GIS) and Bayesian Network to discover the spatial causality between enterprises and environmental factors in Beijing Metropolis. The census data of Beijing was spatialized by means of GIS in the beginning, and then the training data was made using density mapping technique. Base on the training data, the structure of a Bayesian Network was learnt with the help of Maximum Weight Spanning Tree. Eight direct relations were discussed in the end, of which, the most exciting discovery, "Enterprise-Run Society", as the symbol of the former planned economy, was emphasized in the spatial relations between heavy industry and schools. Though the final result is not so creative in economic perspective, it is of significance in technique view due to all discoveries were drawn from data, therefore leading to the realization of the importance of GIS and data mining to economic geography research.

Citation Download Citation

Tiyan Shen, Xi Li, and Maiqing Li "An application of GIS and Bayesian network in studying spatial-causal relations between enterprises and environmental factors", Proc. SPIE 7146, Geoinformatics 2008 and Joint Conference on GIS and Built Environment: Advanced Spatial Data Models and Analyses, 71461E (10 November 2008); https://doi.org/10.1117/12.813141

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