Study On Uncertainty Theory Of Spatial Positional Data

Uncertainty theory in GIS is one of the most difficult basic theories. The theory have a important action for confirming the quality standard of GIS data, evaluating and controlling the production quality of GIS, optimizing the distributing structure of space data, improving the arithmetic of GIS, decrease the blindness of GIS design and exploitation in GIS, and other study field in GIS.Spatial data is one of the fundamental parts of GIS. The quality of spatial data directly determines the fitness-for-use of GIS and affects the result of GIS application. Therefore, accuracy analysis and quality control of spatial data in GIS is regarded as one of the fundamental theoretical research issues internationally.Study of GIS spatial data uncertainty is mostly come down to positional and attribute uncertainty. It's content is full abroad..This paper is mostly discussed some spatial data uncertainties. This paper is obtained some study productions by mostlyaccording to the foundation of Probability and Statistics theory, and data processing theory of modern time surveying and mapping. That mostly studies and contributions described as following:(l)Large numbers of experimentations are done for AutoCAD and Maplnfoprocreant uncertainty in data transformation. The reason of to select them is thatAutoCAD and Maplnfo are popularly used.The study is indicated that procreant uncertainty cannot slight for dot, line andsurface elements after data transformation. The software may produce some errordue to the software use different arithmetic and choose different precision in lineand surface elements especially.Vector data error root in not only the precision of data that is saved ininterlocutory result, but also relate to mathematics model, truncation of data andintegral method in AutoCAD and Maplnfo process data transformation. If both software cannot find same geometry figure, they may use approximate figure, the replacement will make some error in data transformation.The paper bring forwards the methods for decrease data transformation error in data transformation. The methods possess directionally function for more study of data transformation.(2) The paper studies and analyses the uncertainty of spatial positional data.The paper discusses the method for using condition adjustment and condition adjustment with parameters to solve coordinates transformation parameters. The method can depress systematic error of digital map in some degree. Both methods are same to solve coordinates, but condition adjustment with parameters is more in reason.Digital map with cheapness and convenient is one of mostly methods for vector GIS spatial data. This paper discovers the error distribution for manual digitization not always normal but to be P-normal distribution, P-normal value is 1.5-1.6. This explains a lot of factors have complexity and syntheses in digitization. Error of digitization includes not only accident error, but also systematic error. Therefore, author brings forward just study random uncertainty is not integrated, the research is more important to systematic uncertainty.Positional error of digitization process is regarded a stationary random process for study the error and quality control. Variance-covariance function of data points can be given by random process theory. Measurement relativity of digitization points can be solved by the theory. This study has theoretical value and practicability, and makes an important action for line and surface elements of spatial data.(3) A theory system and a practical method are given for uncertainty of spatial data.This paper expatiates the concept and definition of uncertainty in GIS. The uncertainty is divided into random uncertainty synthetical uncertainty, the estimating methods is given respectively. Estimating systematic variance and believe coefficient is needed in GIS positional data.Quantificational index data is given for unknown distributive uncertainty. The index data has an important action for unknown distributive uncertainty. (4)A new method of describing error band is given according to mean square of a point. Excellence of inhere error band is keep in the method, and the computing formula is simple. The new error bands are similar with the old, and the action is same. The new error bands can be described by a uniform formula. This paper points out that the new error bands are dumbbell model in practice.Moreover, model uncertainty is not involved in studied error band that deals with data error of the line elements. The problem of synthetic quantification is brought forward for synthesize to take into account data and model uncertainty, and the method is given as well as the curve. The research indicates the method is feasible. (5) Estimating of uncertainty is studied for spatial positional data.A method of calculation is given for data of digital map used coordinates of the same name point on multiple-lift overlay to estimate uncertainty of the positional data. Main contents include using t test method of dual sub-sample array to do gross error test to manual digitizing data of two lift overlay, and is given the method of synthetic estimating variance of unit weight. The characteristic is to apply the principle of statistical test and statistical estimation, so its result is more reasonable and simple. The method improves on old method of estimating variance of unit weight. (6)The selective standard is given according to the rule of optimization forsystematic error and accident error. The standpoint of synthesize to take into account is brought forward. The method can decrease the influence of model error and data error to influence curve matching. The study develops the theory of uncertainty of spatial data.Because of digitization coordinates X and Y are observation value, so the coordinates contain systematic error and accident error. A formula of calculation to solve parameter of curve matching according to least squares collocation. The problem is solved by to introduce given covariance function in this paper.