Geocoded data structures and their applications to Earth science investigations

A geocoded data structure is a means for digitally representing a geographically referenced map or image. The characteristics of representative cellular, linked, and hybrid geocoded data structures are reviewed. The data processing requirements of Earth science projects at the Goddard Space Flight Center and the basic tools of geographic data processing are described. Specific ways that new geocoded data structures can be used to adapt these tools to scientists' needs are presented. These include: expanding analysis and modeling capabilities; simplifying the merging of data sets from diverse sources; and saving computer storage space

Trends and concerns in digital cartography

CISRG discussion paper ;

Geographically Referenced Data for Social Science

An estimated 80% of all information has a spatial reference. Information about households as well as environmental data can be linked to precise locations in the real world. This offers benefits for combining different datasets via the spatial location and, furthermore, spatial indicators such as distance and accessibility can be included in analyses and models. HSpatial patterns of real-world social phenomena can be identified and described and possible interrelationships between datasets can be studied. Michael F. GOODCHILD, a Professor of Geography at the University of California, Santa Barbara and principal investigator at the Center for Spatially Integrated Social Science (CSISS), summarizes the growing significance of space, spatiality, location, and place in social science research as follows: "(...) for many social scientists, location is just another attribute in a table and not a very important one at that. After all, the processes that lead to social deprivation, crime, or family dysfunction are more or less the same everywhere, and, in the minds of social scientists, many other variables, such as education, unemployment, or age, are far more interesting as explanatory factors of social phenomena than geographic location. Geographers have been almost alone among social scientists in their concern for space; to economists, sociologists, political scientists, demographers, and anthropologists, space has been a minor issue and one that these disciplines have often been happy to leave to geographers. But that situation is changing, and many social scientists have begun to talk about a "spatial turn," a new interest in location, and a new "spatial social science" that crosses the traditional boundaries between disciplines. Interest is rising in GIS (Geographic Information Systems) and in what GIS makes possible: mapping, spatial analysis, and spatial modelling. At the same time, new tools are becoming available that give GIS users access to some of the big ideas of social science."

Applications of ISES for vegetation and land use

Remote sensing relative to applications involving vegetation cover and land use is reviewed to consider the potential benefits to the Earth Observing System (Eos) of a proposed Information Sciences Experiment System (ISES). The ISES concept has been proposed as an onboard experiment and computational resource to support advanced experiments and demonstrations in the information and earth sciences. Embedded in the concept is potential for relieving the data glut problem, enhancing capabilities to meet real-time needs of data users and in-situ researchers, and introducing emerging technology to Eos as the technology matures. These potential benefits are examined in the context of state-of-the-art research activities in image/data processing and management

Geographic Information Systems: The Developer\u27s Perspective

Geographic information systems, which manage data describing the surface of the earth, are becoming increasingly popular. This research details the current state of the art of geographic data processing in terms of the needs of the geographic information system developer. The research focuses chiefly on the geographic data model--the basic building block of the geographic information system. The two most popular models, tessellation and vector, are studied in detail, as well as a number of hybrid data models. In addition, geographic database management is discussed in terms of geographic data access and query processing. Finally, a pragmatic discussion of geographic information system design is presented covering such topics as distributed database considerations and artificial intelligence considerations

Collaborative Mapping of London Using Google Maps: The LondonProfiler

This paper begins by reviewing the ways in which the innovation of Google Maps has transformed our ability to reference and view geographically referenced data. We describe the ways in which the GMap Creator tool developed under the ESRC National Centre for E Social Science programme enables users to ‘mashup’ thematic choropleth maps using the Google API. We illustrate the application of GMap Creator using the example of www.londonprofiler.org, which makes it possible to view a range of health, education and other socioeconomic datasets against a backcloth of Google Maps data. Our conclusions address the ways in which Google Map mashups developed using GMap Creator facilitate online exploratory cartographic visualisation in a range of areas of policy concern