Research On Qualitative Evaluation And Quantification Methods Of Spatial Data Quality Based On Vague Sets

With the continuous development of modern science and technology progress,as well as the increasingly growing database,the quality of spatial data is concerned by more and more spatial data users.As a kind of data product,its quality directly determines the effectiveness of decision-making to a large extent,weigh heavily affect the reliability of analyzing results and the application of goal realizing,so,it is vitally fundamental to research how to assess the comprehensive spatial data quality with all-round?scientific?effective standards.At present scholars have been done a great quantity theory research towards this problem and yielded fruitful results.but the achievements mainly focus on quantitative evaluation,research results towards qualitative evaluation are lacking.Due to the complexity and uncertainty of the spatial data,and ambiguity of human thinking,people recognize that they can not describe the attributes and attribute weights with accurate values when they carry on the comprehensive spatial data evaluation,and they use linguistic evaluation instead frequently,such as “excellent”?“good”?“good”?“medium”?“poor” and so on.so,spatial data quality assessors often face the attribute values and attribute weights that part or all given by linguistic evaluation vocabularies.this kind of spatial data evaluation that include language information take full account of uncertainty of the data information,also make up for the shortcomings that generated in quantitative evaluation process,such as extreme,and so the evaluation results can be more object and real.In order to control and weaken the uncertainty and fuzziness of spatial data quality linguistic evaluation,and make the evaluation results quantitative and scientific,Vague sets theory is adopted in this paper to quantify the qualitative evaluation values,researched several quantitative evaluation methods that based on score function theory and distance comparison theory,and research results show that the evaluation results based on Vague sets can reflect the uncertainty and fuzziness of assessor about the recognition of spatial data quality information,can reflect the real quality more objectively,this paper mainly carried out the following research work.(1)Summarize and introduce the research status and main research results ofspatial data quality evaluation theory,the research status of Vague sets theory.(2)Rearch the comprehensive evaluation methods of spatial data quality that the attribute values given by language evaluation vocabulary and the attribute weights given by real numbers.according to attribute values given by certain language or uncertain language,research the correspondent single-evaluation methods and the group-evaluation methods.analysis the case in the end.(3)Rearch the comprehensive evaluation methods of spatial data quality that the attribute values given by language evaluation vocabulary and the attribute weights unknown.mainly research the thinking of determining spatial data attribute weights,according to attribute values given by certain language or uncertain language,research the correspondent single-evaluation methods and the group-evaluation methods,analysis the case in the end.(4)Rearch the comprehensive evaluation methods of spatial data quality that attribute values and attribute weights all given by certain language.study the correspondent evaluation methods basis of quantify the data attribute valus and map attribute weights,analysis the case in the end.(5)Combined with the example analysis,use C++ programing language,and realize the automation of assessment of spatial data,to make the process more effective.The research of qualitative evaluation and its quantification that based on Vague sets is an important breakthrough to the current spatial data quality evaluation theory,because it make up for the shortcomings to a certain extent such as extreme and so on that generated in quantatitive evaluation process,and in account of the uncertainty and fuzziness at the same time,so the research results are scientific and reasonable.