This is the current Landsat circa 1990/2000 coverage available. Click on the map above to zoom in/out, or 'Select Image' to open a new window (requires javascript) to view or download the Landsat scene.
CartoWeb is a comprehensive and ready-to-use Web-GIS (Geographical Information System) as well as a convenient framework for building advanced and customized applications.
Slashdot: "Giant Insect Invades Germany" hehe... Slashdot: "microsoft europe strenuously denied it was a bug from their code, 'ours are a few metres smaller.'"
J. Owens, M. Yuan, M. Wachowicz, V. Kantabutra, E. Jr., D. Ames, и A. Gangemi. National Endowment for the Humanities Workshop Visualizing the Past: Tools and Techniques for Understanding Historical Processes, University of Richmond, Virginia, USA, том 188 из Frontiers in Artificial Intelligence and Applications, IOS Press, (2009)
A. Soheili, V. Kalogeraki, и D. Gunopulos. GIS '05: Proceedings of the 13th annual ACM international workshop on Geographic information systems, стр. 61--70. New York, NY, USA, ACM Press, (2005)
S. Ahern, M. Naaman, R. Nair, и J. Yang. JCDL '07: Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries, стр. 1-10. New York, NY, USA, ACM, (2007)
A. Geronimus, J. Bound, и L. Neidert. Journal of the American Statistical Association, 91 (434):
529--537(июня 1996)Investigators of social differentials in health outcomes commonly augment incomplete microdata by appending socioeconomic characteristics of residential areas (such as median income in a zip code) to proxy for individual characteristics. But little empirical attention has been paid to how well this aggregate information serves as a proxy for the individual characteristics of interest. We build on recent work addressing the biases inherent in proxies and consider two health-related examples within a statistical framework that illuminates the nature and sources of biases. Data from the Panel Study of Income Dynamics and the National Maternal and Infant Health Survey are linked to census data. We assess the validity of using the aggregate census information as a proxy for individual information when estimating main effects and when controlling for potential confounding between socioeconomic and sociodemographic factors in measures of general health status and infant mortality. We find a general, but not universal, tendency for aggregate proxies to exaggerate the effects of micro-level variables and to do more poorly than micro-level variables at controlling for confounding. The magnitude and direction of these biases vary across samples, however. Our statistical framework and empirical findings suggest the difficulties in and limits to interpreting proxies derived from aggregate census data as if they were micro-level variables. The statistical framework that we outline for our study of health outcomes should be generally applicable to other situations where researchers have merged aggregate data with microdata samples..