The Research And Application Of Aggregation And Generalization In Spatial Data Based On Geocoding Technique

With the development of computer technology and the maturation of satellite and aerial technology, GIS has been widely used in various fields. Spatial data becomes more and more popular in computer data management system. How to effectively use and manage these spatial data to support spatial analysis and decision-making is an urgent research tasks.Spatial Data Warehouse is a powerful tool to solve this problem. Spatial data warehouse is a special form of the data warehouse and it's a combination of data warehouse technology and GIS. It is based on some certain subject content and aggregated different data sources with integrated data structure. Compared with the spatial database, spatial data warehouse can take advantage of using multi-dimensional analysis methods to extract the information hidden in the data in order to achieve the use of decision analysis support by analyzing and comparing data in many different aspects.This paper mainly studies the following two basic issues in spatial data warehouse building:The first is spatial data aggregation based on geocoding. Data aggregation is the first step in building a spatial data warehouse and data quality directly affects the correctness of the decision-making system. Geocoding is a method to unifying the heterogeneous spatial data sources. Because the address in China is far more complicated than other country, foreign geocoding standard is not suitable in China. Based on analyzing the characteristics of a variety of addresses, this paper proposed a new address classification and geocoding method which combine the benefits of MIS and GIS. Use this method to build the data model can manage spatial data better.The second is spatial data generalization. Heterogeneous, multi-source spatial data generalization can be transformed into the issue of data similarity ratios. This paper analyzes the Levenshtein algorithm which is common to solve this problem. To fit the practical application of project, the algorithm has been improved. These improvements can improve the efficiency and accuracy of the algorithm.At last, based on the research above, the paper take Shanghai Resident Population System as an example showing us how to use the new method and algorithm. Practice has proved that the algorithm proposed in this paper has some guiding significance and practical value in spatial data warehouse building.