Study Of Spatial Data Quality Evaluation Based On Interval-valued Intuitionistic Fuzzy Sets

With the geographic information system had been used increasingly widely and spatial database of growing, more and more users concerned about the quality of data products. The core of data products is spatial data, and its quality will directly affect the reliability and practicability of the products, so how to make a comprehensive and effective scientific evaluation of spatial data quality is particularly important. This paper started from the uncertainty of spatial data and mainly aimed at the fuzzy uncertainty of spatial data. Based on the original traditional spatial data evaluation methods, it combined with the principle of interval intuitionistic fuzzy sets, it made a preliminary discussion and research about the spatial data quality evaluation and scientific analysis.It mainly studie dsthe following contents:1.It introduced the meaning of spatial data quality evaluation at present and summarized the dynamic and the status quo of the subject with domestic and foreign research. It summed up the deficiency existing in spatial data quality evaluation system.It put forward the method of this paper and summarized the structure of this article.2. This paper introduced the spatial data and spatial data uncertainty and summed up the uncertainty source of spatial data and the elements affecting the quality of spatial data. It summarized the main content of the spatial data quality evaluation and evaluation methods and analyzed the advantages and disadvantages of the existing method. Based on the shortage of the traditional evaluation method for the data quality on the fuzziness and referenced related literature, the paper put forward the spatial data quality evaluation based on interval intuitionistic fuzzy sets method.3. Combining with the instance and using intuitionistic fuzzy integrating evaluation method and group interval intuitionistic fuzzy multiple attribute evaluation method, the quality interval fuzzy sets of spatial data is evaluated. The results had been compared with the other results which were based on the traditional evaluation method, it showed the feasibility and scientific nature. Combining with the evaluation results, the deficiency of this method and the problem needed to solve in the future research were summarized.