Welcome to World Wind Geo: The Eclipse Geo browser. The Geospatial Web or Geoweb is a relatively new term that implies the merging of geographical (location-based) information with the abstract information that currently dominates the Internet. World Wind Geo is an experimental geo browser built on top of:
Opticks is an expandable remote sensing and imagery analysis software platform that is free and open source. If you've used other commercial tools like: ERDAS IMAGINE, RemoteView, ENVI, or SOCET GXP, then you need to give Opticks a try. Unlike other competing tools, you can add capability to Opticks by creating an extension. Opticks provides the most advanced extension capability of any other remote sensing tool on the market.
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L. Stenneth, O. Wolfson, P. Yu, and B. Xu. Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, page 54--63. New York, NY, USA, ACM, (2011)
E. Valle, H. Qasim, and I. Celino. Proceedings of 1st International Workshop on Pervasive Web Mapping, Geoprocessing and Services (WebMGS 2010), (2010)
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A. Geronimus, J. Bound, and L. Neidert. Journal of the American Statistical Association, 91 (434):
529--537(June 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..
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