Spatial Data Mining Model For Land Use Zoning

A rapid development trend emerges in the domain of spatial information technology that contains the earth observation technology, database technology and network technology. Because Spatial information technology provide the convenient ways, in recent years, the land use database was established through actualize the agricultural land classification and gradation, land use investigation and other projects that get a lot of data and information. The complexity and volume of data overstep the analytical capacity of the human brain. How to mining the useful features and knowledge from the land use database become a bottleneck by frequent and quantitative method. From the spatial database, spatial data mining can extract the spatial patterns and characteristics, general relations of spatial and non spatial data, and other data features in common that hidden in the spatial database. As a part of data mining, spatial data mining is a hot issue for the scholars in China or abroad. Land use zoning is one of the core issues, as well as the popular of land use planning. But there is no systematic and intelligent method in land use zoning. It is urgent to develop a domain model of spatial data mining for the problems of land use zoning currently in the course of the accumulation of spatial data. This dissertation defines the research content and system of spatial data mining for land use zoning. Its theory and application is studied systematically.Based on the essentially of land use zoning research and the related research of land use zoning and spatial data mining, the theory and technology framework of spatial data mining for land information is established, and the theory and method of spatial data mining is put forward. Concretely, this dissertation defines the concept, features and content of spatial data mining for land use mining, from the aspect of the concept of land use zoning, spatial data mining. A whole architecture of spatial data mining is bring forward, including the data layer, knowledge level, mining layer and human-computer interaction layer. It is described that is basic steps and how to find the type of knowledge from land use database. Spatial relationship measurement methods that can be considered as space calculation model is a basic method of land use zoning. And then a domain knowledge-based model of land use zoning is designed through describe the issue and analysis the knowledge system of land use zoning.Formal concept analysis theory, also known as concept lattice theory is a powerful tool to analysis the course of from data to concept through the formal method of mathematics.This method is same to the course of data mining that can get the knowledge from large amounts of data. Therefore, the formal concept analysis theory is very suitable for data mining research. Because concept lattice is difficult to express the spatial problem, this dissertation studied the construction algorithm of multi-value context concept lattices. Based on the extention of formal concept analysis theory, a fuzzy concept lattice is proposed to mine the spatial association of knowledge. The incremental algorithm and drawing algorithm of Hasse is established. But for the characteristics of mass spatial data, this algorithm cannot be applicated efficiently. So the index tree is applied in this algorithm to solve the problem of complex spatialsystem. Beside, this dissertation presents a method of acquired for spatial association rules.Land use zoning is a method on divide the study area into a number of homogeneous areas, considering the impact factor of land quality and land use patterns including the physical, social, and economic factors comprehensively. Therefore, land use zoning is a very complex multi-objective optimization problem. The cluster analysis, a typical combinatorial optimization problem, can solve multi-objective optimization problem. Based on the traditional algorithm that over-reliance on data clustering prototype, this dissertation proposes a clustering model of chaos immune clonal selection algorithm (CICSA) based on the knowledge through the Logistic equation of chaos theory. This algorithm can integrate evolution search and random search, global search and local search. Through clonal selection operation on candidate solution, the global optimal solution is acquired quickly, rather than by the variance distribution of sample set. This model can transact data clustering problem with multi-prototype, and the information of classes can be gained automatically.A prototype system was developed based on the study in theory and technology of spatial data mining for land use zoning. This prototype system includes the following modules:data management module of land use, land-use knowledge mining module, land use zoning mining module, the system database management module and visualization modules. By means of prototype system development, the function of spatial data mining for land use zoning is defined, and specific process of land use zoning is explained furthermore. Yicheng, located in Hubei province of China, is an agriculture-based small city. This dissertation selects the land use database and other data, which is integrated by existing mathematical models. These data compose a new data set that can be mining by proposed algorithm. The fuzzy concept lattice is applied to acquire the spatial association rules of land use. This rules and other knowledge that come from domain is used to coding for antibody of CICSA, which is applied to the experiment of multi-objective land use zoning clustering. Experimental results show that spatial clustering for land use zoning based on the knowledge is an intelligent, efficient, accurate zoning tool.