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.
ZDNet's Dana Blankenhorn reports today on a new open source navigation project launched by European GPS company TomTom that adds additional functionality to navigational devices, regardless of the make or model. The OpenLR project aims to put navigation data on top of a GPS unit's existing database so drivers can access local traffic, weather, and other useful information as they travel.
After installing ArcGIS Server, there are a few things you need to do before you can start creating services and allowing client applications to access these services. The steps below provide a summary of the things you need to think about and do to get s
Open Source programs are applications of which you can access the source code. Listed here are available open source GIS-based applications you can download written for a variety of platforms and in various languages.
Commonly referred to as GRASS, this is free Geographic Information System (GIS) software used for geospatial data management and analysis, image processing, graphics/maps production, spatial modeling, and visualization. GRASS is currently used in academic and commercial settings around the world, as well as by many governmental agencies and environmental consulting companies. GRASS is an official project of the Open Source Geospatial Foundation.
ESRI's GIS (geographic information systems) mapping software helps you understand and visualize data to make decisions based on the best information and analysis.
A. Soheili, V. Kalogeraki, and D. Gunopulos. GIS '05: Proceedings of the 13th annual ACM international workshop on Geographic information systems, page 61--70. New York, NY, USA, ACM Press, (2005)
S. Ahern, M. Naaman, R. Nair, and J. Yang. JCDL '07: Proceedings of the 7th ACM/IEEE-CS joint conference on Digital libraries, page 1-10. New York, NY, USA, ACM, (2007)
E. Valle, H. Qasim, and I. Celino. Proceedings of 1st International Workshop on Pervasive Web Mapping, Geoprocessing and Services (WebMGS 2010), (2010)
J. Owens, M. Yuan, M. Wachowicz, V. Kantabutra, E. Jr., D. Ames, and A. Gangemi. National Endowment for the Humanities Workshop Visualizing the Past: Tools and Techniques for Understanding Historical Processes, University of Richmond, Virginia, USA, volume 188 of Frontiers in Artificial Intelligence and Applications, IOS Press, (2009)
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..