Geospatial data infrastructure: The development of metadata for geo-information in China
Stores of geoscience records are in constant flux. These stores are continually added to by new information, ideas and data, which are frequently revised. The geoscience record is in restrained by human thought and technology for handling information. Conventional methods strive, with limited success, to maintain geoscience records which are readily susceptible and renewable. The information system must adapt to the diversity of ideas and data in geoscience and their changes through time. In China, more than 400,000 types of important geological data are collected and produced in geological work during the last two decades, including oil, natural gas and marine data, mine exploration, geophysical, geochemical, remote sensing and important local geological survey and research reports. Numerous geospatial databases are formed and stored in National Geological Archives (NGA) with available formats of MapGIS, ArcGIS, ArcINFO, Metalfile, Raster, SQL Server, Access and JPEG. But there is no effective way to warrant that the quality of information is adequate in theory and practice for decision making. The need for fast, reliable, accurate and up-to-date information by providing the Geographic Information System (GIS) communities are becoming insistent for all geoinformation producers and users in China. Since 2010, a series of geoinformation projects have been carried out under the leadership of the Ministry of Land and Resources (MLR), including (1) Integration, update and maintenance of geoinformation databases; (2) Standards research on clusterization and industrialization of information services; (3) Platform construction of geological data sharing; (4) Construction of key borehole databases; (5) Product development of information services. "Nine-System" of the basic framework has been proposed for the development and improvement of the geospatial data infrastructure, which are focused on the construction of the cluster organization, cluster service, convergence, database, product, policy, technology, standard and infrastructure systems. The development of geoinformation stores and services put forward a need for Geospatial Data Infrastructure (GDI) in China. In this paper, some of the ideas envisaged into the development of metadata in China are discussed.
Geospatial Data Infrastructure : Concepts, Cases, and Good Practice
7 ANTICIPATING CULTURAL FACTORS OF GDI 8 THE FOUNDATION TECHNOLOGIES 9 GDI ARCHITECTURES 10 CONCEPTUAL TOOLS FOR SPECIFYING GEOSPATIAL DESCRIPTIONS 11 SPATIAL REFERENCING 12 PHOTOGRAMMETRY AND REMOTE SENSING IN SUPPORT OF GDI 13 ACCESS TO GDI AND THE FUNCTION OF VISUALIZATION TOOLS 14 HUMAN RESOURCES ISSUES IN THE EMERGING GDI ENVIRONMENT 15 FOUR CASES 16 ADVANCING THE GDI CONCEPT
The representation of geoscience information for data integration
In mineral exploration, resource assessment, or natural hazard assessment, many layers of geoscience maps such as lithology, structure, geophysics, geochemistry, hydrology, slope stability, mineral deposits, and preprocessed remotely sensed data can be used as evidence to delineate potential areas for further investigation. Today's PC-based data base management systems, statistical packages, spreadsheets, image processing systems, and geographical information systems provide almost unlimited capabilities of manipulating data. Generally such manipulations make a strategic separation of spatial and nonspatial attributes, which are conveniently linked in relational data bases. The first step in integration procedures usually consists of studying the individual charateristics of map features and interrelationships, and then representing them in numerical form (statistics) for finding the areas of high potential (or impact).Data representation is a transformation of our experience of the real world into a computational domain. As such, it must comply with models and rules to provide us with useful information. Quantitative representation of spatially distributed map patterns or phenomena plays a pivotal role in integration because it also determines the types of combination rules applied to them.Three representation methods—probability measures, Dempster-Shafer belief functions, and membership functions in fuzzy sets—and their corresponding estimation procedures are presented here with analyses of the implications and of the assumptions that are required in each approach to thematic mapping. Difficulties associated with the construction of probability measures, belief functions, and membership functions are also discussed; alternative procedures to overcome these difficulties are proposed. These proposed techniques are illustrated by using a simple, artificially constructed data set.