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Dasymetric models increase the spatial resolution of population data by incorporating related ancillary data layers. The role of uncertainty in dasymetric modeling has not been fully addressed as of yet. Uncertainty is usually present because most population data are themselves uncertain, and/or the geographic processes that connect population and the ancillary data layers are not precisely known. A new dasymetric methodology - the Penalized Maximum Entropy Dasymetric Model (P-MEDM) - is presented that enables these sources of uncertainty to be represented and modeled. The P-MEDM propagates uncertainty through the model and yields fine-resolution population estimates with associated measures of uncertainty. This methodology contains a number of other benefits of theoretical and practical interest. In dasymetric modeling, researchers often struggle with identifying a relationship between population and ancillary data layers. The PEDM model simplifies this step by unifying how ancillary data are included. The P-MEDM also allows a rich array of data to be included, with disparate spatial resolutions, attribute resolutions, and uncertainties. While the P-MEDM does not necessarily produce more precise estimates than do existing approaches, it does help to unify how data enter the dasymetric model, it increases the types of data that may be used, and it allows geographers to characterize the quality of their dasymetric estimates. We present an application of the P-MEDM that includes household-level survey data combined with higher spatial resolution data such as from census tracts, block groups, and land cover classifications.

An exponentially growing volume of digital information makes extraction of relevant items increasingly difficult. This article documents the adoption of information visualization tools by researchers in the disciplines of geography, computer science, and information science to facilitate exploration of very large data archives. Graphic depiction of database content (or the database "semantics") can be based on a spatial or even a geographic metaphor. Such depictions, often called information spaces or information worlds, provide one example of "spatialization." Various forms of spatialized views are critiqued in this article. To date, systematic approaches to the creation of spatialized views have lacked solid theoretical foundations. Three spatial frames of reference are presented to formalize and visualize semantic spatialized views: geographic space, cognitive space, and Benediktine space. Application to an example of a very large online catalog (GEOREF) highlights the underlying assumptions of the space types and demonstrates what spatial properties are preserved for each proposed approach. Key Words: information retrieval, semantic information spaces, spatial metaphors, spatialization, visualization.The summation of human experience is being expanded at a prodigious rate, and the means we use for threading through the consequent maze to the momentarily important item is the same as was used in the days of squarerigged ships. (Bush 1945, 102) his statement, written half a century ago by the Director of the Office of Scientific Research and Development, has not lost relevance. On the contrary, the overwhelming volume of data makes it necessary to consider effective ways to locate, analyze, extract and digest information archived in proliferating digital repositories. This article explores the application of cognitive and spatial concepts to the exploration, navigation, and knowledge extraction of very large datasets, online archives, and digital libraries of geographic information, an issue that is critical to the development of geography in the Information Age. A broad range of research in geography can benefit from improved access to online information sources, especially areas that work with large data sets. As the volume of collected data increases, demands for improved access are also likely to increase. This research could be used to improve current digital information-seeking tools such as Yahoo, AltaVista, and other Internet search engines by including tools to provide an overview of available information, to discover relationships between items in a data archive, and to filter nonrelevant pieces of information. Timely and efficient access to relevant data facilitates knowledge acquisition, scientific endeavor, and research advance.The National Research Council's Committee on Human Factors (NRC 1995, 202) identifies three major concerns regarding information access and usability. First, inefficient extraction methods challenge access to a relevant subset of information, if many unwanted items are r...

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