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Study On Temporal Spatial Data Management And Analysis Key Techniques For Land Use Change

Posted on:2017-05-14Degree:DoctorType:Dissertation
Country:ChinaCandidate:Y B GaoFull Text:PDF
GTID:1108330482492546Subject:Agricultural information technology
Abstract/Summary:PDF Full Text Request
Since the first national land survey, the second national land survey, the vast amounts of land use data have accumulated in different land administration departments. With the more refined and deeper of applications of land management, land administration departments more and more emphasis on historical landuse data management and utilition. The departments urgently need to use temporal sequence of historical landuse data for a long time back tracing, landuse changes monitoring, anlysis of landuse trends and law, and landuse prediciton, in order to provide scientific basis to the landuse structure optimization, land use planning and other land management work. At present, the integrate storage, fast query and refined statistics of land-use data of different periods constitute one of the main bottlenecks whicn restrict the deep mining of historical landuse data.Thus, this paper studied the efficient organaziton and management of historical landuse data, and spatial variaotion statistics, to provide technical support for vertical, horizontal and multi-angle analysis and decision making.The paper presented an approach for integrated modeling and storage of spatial-temporal landuse data, a rapid indexing method and a statistical method for massive spatio-temporal landuse data. Also, by using spatio-temporal landuse data of typical region, this paper provided example analysis for the key technologies such as spatio-temporal data model, spatio-temporal data association, spatio temporal data indexing and temporal variation statistics. Finally, the paper developed a prototype system for spatio-temporal landuse data management and analysis. The main contents of this paper are as follows:(1) spatial-temporal data model for integrated landuse management:focusing on the problems of heterogeneous data of different periods, cascading changes of fregementary feature, linear features and polygon features, this paper studied mutli-base-state spatio-temporal data model based on events group. By recording change events and spatio-temporal topological relationships before and after feature change, adding base state in the key time point, introducing rebuilding event, and recording the spatio-temporal relationships before and after reconstruction, this model could support storage landuse data of first national land survey before reconstruction, and browsing and archiving of original data. Also, this model could solve the problem of the temporal chain breakup and the inheritance relationship absence, support parcel back tracing and rebuild of the status the historical moment, and reduce the redundant data storage.(2) Association technology of patial-temporal landuse data:firstly, the paper analyzes the technical difficulties of state selection and multi-base-state setting. The problems include:most land use data are snapshot data, incremental changes extraction is difficult, the incremental changes lack event description, and the temporal information is incomplete, and son on. Then focusing on the problems that this paper put forward the incremental changes extraction technology and patial-temporal landuse data association technology, to realize the automatic extraction of landuse data of different period and automatic building of spatio-temporal topogoical relationship. After that, the integerated storage database of landuse data of different period was built to save save human and material resources.(3) Spatio-temporal data quick indexing technology based on base state amendment model:Aiming at the large overhead problem in query on mass landuse data of long time span, this paper put forward temporal spatial indexing technology, which are hybrid multi-level temporal index partitions and HR tree. The method settle overhead problem in long time span mass on land use query. The proposed spatio-temporal indexing divide the spatial grid according to multi-level administrative, and divide each spatial grid into time grids by year in further. On this basis, build index following the R-tree, and then creat index tree by C-Liner splitting rule. And query, insert and delete opration on the index tree was realilized in this paper.(4) Optimized spatio-temporal statistics technology for landuse current situation and variation: Aiming at the of low precision and time comsuming problems of statistics under the situation of administrative changes and landuse type changes, this paper proposed ptimized spatio-temporal statistics method based on spatial-temproal graph theory model. Taking into account the influence of topology relationships among segmentation of linear features and polygon features on the area calculation, the area statistics algorithms based on set algebra is studied.The statistics of areas of landuse types were optimized by graph connectivity characteristics. SThe diagram of complex temporal network is reduced by graph multi-commodity flow uniqueness in order to reduce the time complexity of spatial overlay analysis. By considering the influence of the liner feature and fragment features on the area change statistics, the proposed method improves the accuracy of statistics, and is suitable for variation analysis of small regional.(5) Prototype system for spatio-temporal landuse data management and analysis:Based on the above studies, a prototype system is developed by C# and Arc engine 10.0, And built in SQL 2008 was adopted to manage the data. This system realized the functions of incremental change extraction, temporal topology automatic construction, polygon features back tracing, historic state reconstruction. Also, the system realized functions like index analysis of landuse change variation, dynamics evolution analysis based on animation, hotspot detection and so on. It plays as a practical tool for spatio-temporal analysis of landuse variation.
Keywords/Search Tags:Land Use Change, Spatial-temporal Data Model, Spatio-temporal Index, Spatio-temporal Data Statistics, Algebra of Sets, Graph Theory
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