government-funded and approved agencies such as the Ordnance Survey and UK Hydrographic Office and Highways Agency collect data using our funds should make that data available for free
OpenLayers is a pure JavaScript library for displaying map data in most modern web browsers, with no server-side dependencies. OpenLayers implements a (still-developing) JavaScript API for building rich web-based geographic applications, similar to the Go
hare knowledge on geoinformation in Africa with as wide an audience as possible. This will include the sourcing and dissemination of research articles on different themes and focus areas, highlighting news items in the geoinformation industry and keeping
free geographical database of over eight million geographical names and consists of 6.3 million unique features available for download and accessible through a number of webservices
Spatial Data Integrator is the first Open Source ETL (Extract, Transform, Load) solution specialized in the manipulation of geographical information and supported by an editor.
PostGIS adds support for geographic objects to the PostgreSQL object-relational database. In effect, PostGIS "spatially enables" the PostgreSQL server, allowing it to be used as a backend spatial database for geographic information systems (GIS), much lik
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..