Detection of spatial inconsistencies in land use data updating

Pao Yue-kong Library Electronic Theses Database

Detection of spatial inconsistencies in land use data updating

 

Author: Xiao, Shengjun
Title: Detection of spatial inconsistencies in land use data updating
Degree: M.Phil.
Year: 2013
Subject: Land use -- Data processing.
Information storage and retrieval systems -- Land use.
Geographic information systems.
Hong Kong Polytechnic University -- Dissertations
Department: Dept. of Land Surveying and Geo-Informatics
Pages: vii, 85 leaves : col. ill., maps ; 30 cm.
Language: English
InnoPac Record: http://library.polyu.edu.hk/record=b2652694
URI: http://theses.lib.polyu.edu.hk/handle/200/7223
Abstract: The purpose of this thesis is to develop a method for detecting spatial inconsistency in land use data updating based on the information of geometry, attribute and topological relationship of land use data. GIS data need to be updated regularly to maintain its timeliness, especially for land use data, which is very important when the strategy of land resource and local city planning is determined. However, during updating, errors or inconsistency may occur and influence the quality of land use data. Updating land use data needs a set of steps: surveying/mapping data, manual and automatic data interpretation and so on. Current updating methods cannot maintain the consistency and validity of data perfectly, which inevitably generate errors and inconsistency. Errors could be wrong value, such as wrong perimeter or area value, repeated key value, such as ID. In normal situation, errors could be detected easily, while data inconsistency is a special kind of error occurred in a potential way. Inconsistency is the error or illogical meaning implied by data. To detect data inconsistency, meaning and logic of data should be checked. Typical land use data inconsistency could be a conflict in attributes of two neighboring lands, such as limited height of buildings near airport land. Land use data was created under land classification standard formulated by the Government. Application of variant standards when checking data inconsistency increases the difficulties of detecting data inconsistency. If this is ignored, data quality will have problems. Moreover, inconsistency may accumulate during updating which cause further problems. Based on the study of land use data updating procedure, a new method is proposed to identify potential semantic inconsistency of data, which is ignored or hard to be detected in data quality control. It is useful for maintaining the quality of land use data after updating. Geometric, attribute and topological relationship information of land use data form the basis for analysis. Specific combination of these three aspects could be set as a rule to judge whether inconsistency occurs. Three sets of rules are extracted as references to identify inconsistency according to the types of inconsistency. Rules are designed and tested in a prototype system which is established via .NET Frame Work 2005 and C# language by using Arc Engine (AE9.2). The experiment land use data are obtained from Land and Resources Bureau of Beijing. The results show that detection rules proposed by the study detect 296 inconsistencies within the total of 1517 records, "the same class land merging problem" occurs most frequently, which is about 16.74% records in the experiment data. Careful verification suggests that all of these detected inconsistencies are true. The results of experiment indicate that detection methods proposed by this study are feasible and helpful for controlling the accuracy of the meaning of land use data, as well as the accuracy of data format and structure.

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