The 3rd ISPRS Workshop on Dynamic and Multi-Dimensional GIS & the 10th Annual Conference of CPGIS on Geoinformatics

Chen, Jun

Bibliographic data

Monograph

Persistent identifier:: 856566209

Author:: Chen, Jun

Title:: The 3rd ISPRS Workshop on Dynamic and Multi-Dimensional GIS & the 10th Annual Conference of CPGIS on Geoinformatics

Sub title:: May 23 - 25, 2001, Bangkok, Thailand

Scope:: VI, 434 Seiten

Year of publication:: 2001

Place of publication:: Pathumthani, Thailand

Publisher of the original:: AIT

Identifier (digital):: 856566209

Illustration:: Illustrationen, Diagramme, Karten

Language:: English

Usage licence:: Attribution 4.0 International (CC BY 4.0)

Publisher of the digital copy:: Technische Informationsbibliothek Hannover

Place of publication of the digital copy:: Hannover

Year of publication of the original:: 2016

Document type:: Monograph

Collection:: Earth sciences

Chapter

Title:: THE RESEARCH OF THE INFINITELY VARIABLE MAP SCALE IN GIS. Yifan LI, Shaopeng SUN

Document type:: Monograph

Structure type:: Chapter

ISPRS, Vol.34, Part 2W2, “Dynamic and Multi-Dimensional GIS”, Bangkok, May 23-25, 2001 160 oriented multi-value spatial data model, using multi-dimension analytical approach, we analyze, compare and extract the hidden information from spatial data, and to realize the integration of data oriented and model oriented analytical approaches. Data warehouse means the database can incessantly update data. Data warehouse is a new technology used in data storage, management and processing from 90’s. It is a management supported, decision-making, theme oriented, changed by time and long lasting data collection. The main task of data warehouse is make procession of standardization, filter, matching, refining, mark time, guaranteed quality for source data of different types, different structures, different storage formats, different contents in distributed databases which located in different places and companies. Then integrates, divides, summarize, aggregate, forecast, deduct, translate, transform, and image these data of distributed database which have different features and different format based on need of special task. At last, we make modeling, aggregation, adjusting, affirmation for data warehouse and build structure query function, and etc. Spatial data warehouse includes the abstraction of real world, the storage management and data reorganization of collected data, and the tool of data-mining, extraction, and analysis based on spatial data warehouse. In general, spatial data warehouse is composed by four parts: data source, database, spatial data warehouse data storage system and spatial data warehouse analytical tools. Spatial data warehouse is a special form of data warehouse; it is one of the key technologies of Web-GIS. This paper mainly studies some key technologies to build infinitely variable map scale oriented spatial data warehouse, and the function of spatial data index in it. 2.3 Storage and Management of Internet-based Distributed Geographic Data Warehouse Mass, multi-source and multi-dimension spatial data brings forward a new problem about the management and application of spatial data. Traditional database has too many problems to deal with integration spatial data in decision, theme, time variation and permanence. Facing multiple, heterogeneous features and spatio-temporal features of spatial data, spatial data warehouse provides efficient approach to manage them. Spatial data warehouse technology of Digital Earth provides firm basis for building of macro-sophisticated decision support system. We can simplify the design and development of system in logic and application by using it to build subsystems or functional system in Digital Earth, and make it very clear in logic. Therefore, we can provide feasible data management scheme for system application in Digital Earth. 2.4 Seamless Integration of Multi-source Data (SIMS) The rapid development and extensive application of GIS accumulate mass data source. They were stored into different data format, and these differences make it very inconvenient to use the data. Seamless Integration of Multi-source Data technology realizes a special data access mechanism. It is not only provides the ability to access different format data directly, but also make GIS software has composite analytical function in different data source. SIMS is an advanced spatial data integrate technology that do not need data format transform, and access multi-format data directly. SIMS features are listed below: • Direct accessing of multi-format data. This is the basic function of SIMS. Because of avoiding transformation of data format, it is so convenient to use different format data source. • Format independent data integration. When GIS user using data, they may not care the storage format of data, realize the format independent data integration. • Position independent data integration. If we use large relational database (e.p. Oracle and SQL Server) to store spatial data, the data can be stored in Net Server, even Web Server. If we use file to store spatial data, the data usually located in local server. Accessing data through SIMS, we may not care not only the storage format of data, but also the storage position of data. User can operate remote data conveniently as local data. • Composite analysis of multi-source data. SIMS also allows composite spatial analysis of multi-source data. For example, user can overlap a land using data set of Arc/Info Coverage format on an administrative divisions data set stored in SDE. 2.5 Spatial Database Index In digital Earth, mass geographic data change with time, and appear diversity in spatio-temporal feature. The information generalization of infinitely variable map scale spatial data also make these mass data appear diversity with the continuous change of scale. Especially in infinitely variable map scale oriented spatial data warehouse, we much maintain the union of all scales and all themes data set. Thus, there are mass data. Traditional spatial data index is based on the static index of hierarchical model, and these indexes are all derived from traditional database index. This method can only improve the efficiency of data searching. Thus, we must use new approach to build spatial data index. This new approach should have dynamic and complete features. Dynamic means we can auto generate index for time dimension, geographic dimension and theme dimension based on the change of granularity in spatial data warehouse. Complete is to build special spatial relations of spatial data based on spatial data index, it mainly include topological relationship, geometrical relationship and ordinal relation. Dynamic spatial data index provides better support for online analysis and datamining, especially provides a new approach to information generalization of infinitely variable map scale. 2.6 Spatial Data Engine Because Digital Earth mainly related to multi-dimension spatio-temporal data, the traditional data warehouse and datamining technology cannot satisfy this need. We should organize and manage data unitedly through spatial data engine based on spatial database, data warehouse and datamining to provides high performed searching method and improves analyze and aid decision ability. Spatial data engine can support spatial data warehouse in spatial data organization and data searching. Through research of spatial data engine, we can improve searching speed to mass spatial information, optimize searching result in infinitely variable map scale information generalization, and return searching result to end user through a friendly interface. Concept model of infinitely variable map scale oriented spatial data warehouse is described in figure 1.

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