Data Index Method Oriented To Urbanization Data Integration

With the rapid development of urbanization in China, the amount of valuable data captured in different systems and different structures grows noticeably. At the same time, technical progress makes it possible to establish link among these different systems and different structures. As a result, the wish to monitor changes in urban areas and exchange, share these data arises and becomes more and more important. The combination of data done on different levels that we subsume under the term data integration requires resolving heterogeneities, which still poses research questions.The dissertation aims to establish indexes for the stored multi-source data, which aid to query useful information rapidly within urbanization integration system and realize the visualization of query results on the map rapidly, etc. Finally, an antitype system is developed to evaluate and test the method proposed by the dissertation. The main research programs and results derived are as follows:(1) The indexes for urbanization data is constructed based on the analysis of present index structures. The corresponding metadata table is constructed as non-spatial data stored in the binary format. The metadata information are utilized to classify the data and to establish the classified index respectively for the attribute data. An R tree index is established for the spatial data, and a kind of hierarchy data index based on administrative region is put forward to deal with overlay problem of R tree in the dissertation.(2) This dissertation analyses the two ways of relations establishment and combined query between spatial data and non-spatial data. The first one is to put these kinds in the same database with the relation established before data loading. Once data are changed, the relations will be changed subsequently which causes trouble in updating and modification the relations. The second one is that utilizing objects properties (i.e. address number. geographic number.) of spatial data to establish relation with non-spatial data. The drawback of former is that the relation should be established beforehand, and of later is that the query of information is limited. As the non-spatial data of urbanization are mainly available in the format of files and the direct information of relations are lacking, it is not suitable to adopt these two ways to establish the relations.(3) The dissertation researches into the rapid map visualization of non-spatial attribute data associated with location (e.g. place names, river names) in urbanization system. In general, the present approach of visualization is like that:Firstly, place names are compared and found according to attribute table, secondly find the corresponding records, finally visualize place names on the map. However, this method is not suitable for our situation, as urban place names lack corresponding information. It is urgent to adopt a new method to deal with the problem.(4) A kind of hierarchy structure-the hierarchy index tree, which is constructed mainly based on area name, area number and area MBR, is proposed to realize combined queries. The nodes of the index tree are corresponded with MBR, establishing the relation of the spatial data and non-spatial data. The approach could expand query range and search more multi-source data information rapidly.(5) An effective approach based on MBR is proposed for non-spatial attribute data associated with location to be visualized on the map rapidly. The approach mainly contains three steps, firstly the corresponding number is found by area name, secondly, the node containing smallest area is found by searching in the index tree, finally visualizing on the map by MBR content of the node.(6) A prototype system on data indexes establishment, data combined query and rapid visualization of query results is developed using.Net, Arc Engine and Microsoft SQL Server 2005 to examine the methods and algorithms presented in this dissertation. Experimental results show evidence of the reasonability and practicability of the index tree proposed by this paper, which not only could implement combined query, but also could implement rapid visualization for query results of non-spatial data with locations.So a conclusion is given without doubt that the hierarchy index tree structure is feasible and can greatly search more information in multi-source data. Urban data should be integrated to establish urbanization monitoring system, one of the keys is to query data and visualize the results on the map rapidly. The study of hiberarchy index tree reaches expectant goal and can provides a novel idea for urban data integration.