The report is a summary of workshop presentations on global spatial data and information use conducted at CIESIN, Columbia University, in September 2004. Topical areas include technical data interoperability and science data integration.

de Sherbinin, A., D. Balk, K. Yager, M. Jaiteh, F. Pozzi, C. Giri, and A. Wannebo. 2002. Social Science Applications of Remote Sensing: A CIESIN Thematic Guide. Columbia University, N.Y. Available online at

This is an introductory remote sensing usage guide for social scientists. Key methodological concerns arise when integrating remote sensing data with socioeconomic data—with methods such as “gridding” socioeconomic data to better correspond with Earth science data or taking Earth science data and converting data to tabular formats that are useful for social scientists. The technical specifications of various remote sensing instruments are listed in a table together with descriptions of what the sensors detect. Challenges in applying remote sensing data in the social sciences include difficulties with scale, data integration, interdisciplinary research, and confidentiality.

Dilley, M., R.S. Chen, U. Deichmann, A.L. Lerner-Lam, and M. Arnold. 2005. Natural Disaster Hotspots: A Global Risk Analysis. Washington, DC: World Bank Group.

Natural Disaster Hotspots presents a global view of major natural disaster risk hotspots: areas at relatively high risk of loss from one or more natural hazards. It summarizes the results of an interdisciplinary analysis of the location and characteristics of hotspots for six natural hazards: earthquakes, volcanoes, landslides, floods, drought, and cyclones. Data on these hazards are combined with state-of-the-art data on the subnational distribution of population and economic output and past disaster losses to identify areas at relatively high risk from one or more hazards. (Annotation from

NRC (National Research Council). 1998. People and Pixels: Linking Remote Sensing and Social Science. Washington, DC: National Academy Press.

This report discusses the linkage between remote sensing and the social sciences, using examples from the Amazon, Thailand, and Guatemala, as well as an example of how data can be used in early famine warnings,

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