Construction and Application of Bayesian Network Model for Spatial Data Mining

The advent of spatial information technologies, such as GIS, GPS and remote sensing, have great enhanced our capabilities to collect and capture spatial data. How to discover potentially useful information and knowledge from massive amounts of spatial data is becoming a crucial project for spatial analysis and spatial decision making. Bayesian networks have a powerful ability for reasoning and semantic representation, which combined with qualitative analysis and quantitative analysis, with prior knowledge and observed data, and provides an effective way to spatial data mining. This paper focuses on construction and learning a Bayesian network model for spatial data mining. Firstly, we introduce the theory of spatial data mining and discuss the characteristics of Bayesian networks. A framework and process of spatial data mining is proposed. Then we construct a Bayesian network model for spatial data mining with the given dataset. The experimental results demonstrate the feasibility and practical of the proposed approach to spatial data mining. Finally, we draw a conclusion and show further avenues for research.