Bibliography of Resources—Integration of Disparate Data
Balk, D., and G. Yetman. 2004. The Global Distribution of Population: Evaluating the gains in resolution refinement. 10 February. CIESIN, Columbia University, N.Y. Available online at http://sedac.ciesin.columbia.edu/gpw/docs/gpw3_documentation_final.pdf.
Authors and researchers at the Center for International Earth Science Information Network (CIESIN) describe improvements to the Gridded Population of the World (GPW) data set and developments in rendering global population data sets at scales that can be used for broad-scale population-environment inquiries. The paper focuses mostly on the GPW and its improvements over the last 10+ years, particularly on increasing spatial resolution improvements. The authors note some barriers to improving data, including war, redistricting (former Soviet republics), and pricing policies for data. Overall, however, barriers to data collection and processing have decreased due to technological capacity and the greater interest in census taking and map making. These improvements will help with greater data integration.
CIESIN (Center for International Earth Science Information Network). 2005. Global Spatial Data and Information: Development, Dissemination, and Use. Report of a Workshop, 21-23 September. Columbia University, N.Y. Available online at http://sedac.ciesin.columbia.edu/GSDworkshop/GlobalDataWorkshop_report_web.pdf.
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 http://sedac.ciesin.columbia.edu/tg/guide_main.jsp.
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 http://publications.worldbank.org/.)
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,
climate modeling, and health applications. The report discusses some challenges in linking the two fields.
NRC. 2002. Down to Earth: Geographical Information for Sustainable Development in Africa. Washington, DC: National Academy Press.
This report summarizes the importance and applicability of geographic data for sustainable development. Geographic data describe spatial variations across the landscape at a variety of scales (local, national, global) and include such elements as climate, elevation, soil, vegetation, population, land use, and economic activity. The report draws on experiences in African countries and examines how future sources and applications of geographic data could provide reliable support to decision makers as they work toward sustainable development. The committee emphasizes the potential of new technologies, such as satellite remote sensing systems and geographic information systems (GIS), that have revolutionized data collection and analysis over the last decade. There is some discussion of data integration between the social sciences and satellite imagery.
Pelling, M., A. Maskrey, P. Ruiz, and L. Hall, eds. 2004. Reducing Disaster Risk: A Challenge for Development. New York, NY: United Nations Development Programme.
The Disaster Risk Index (DRI) measures the vulnerability of countries to three natural hazards (earthquakes, tropical cyclones, and floods), identifies the development factors that contribute to risk, and shows quantitatively how the effects of disaster can be either reduced or exacerbated by policy choices. There is little discussion of technical issues that arise from integrating disparate data types.
Rindfuss, R.R., S.J. Walsh, B.L. Turner, J. Fox, and V. Mishra. 2004. Developing a science of land change: Challenges and methodological issues. Proceedings of the National Academy of Sciences of the United States of America 101(39):13976-13981.
Land-change science (LCS) is hindered by a number of methodological and analytical difficulties that emerge from the integration of “space-time patterns” and “social-biophysical processes” and the different ways in which the two disciplines address them. Problems include aggregation and inference problems, land use pixel links, data and measurement, and remote sensing analysis. Examples are (1) linking land and pixels: how research is started, with land samples (parcels) or people—each starting point can lead to problems that have different solutions, (2) data quality
and validation: the validity and accuracy of the link between social science and land units and pixels, (3) spatial-temporal mismatch: matching spatial-temporal resolution of sensor systems with the resolutions of the social or biophysical data. Other issues addressed in this paper include classification and the use of ancillary data, spatial autocorrelation, and accuracy assessment of land change models.