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The User-friendly Desktop Internet GIS (uDig) is both a GeoSpatial application and a platform through which developers can create new, derived applications. uDig is a core element in an internet aware Geographic Information System.
uDig has been developed with a strong emphasis on supporting the public standards being developed by the Open Geospatial Consortium
, and with a special focus on the Web Map Server and Web Feature Server standards.
We have developed and tested two measures of visual clutter: the Feature Congestion measure, and the Subband Entropy measure.
Feature Congestion measure: This measure of visual clutter is based on the common experience of going to put a note on a colleague's desk. If the desk is uncluttered, it's easy to find a place to put the note where we are confident our colleague will notice it. However, if the desk is cluttered, we tend not to be confident they will notice the note, and perhaps will leave the note on a chair so they will spot it.
This suggests that clutter is related to the difficulty in adding an attention-grabbing item to a display. Visual search models typically attempt to predict the difficulty of searching for a particular target among particular distractors. However, our Statistical Saliency Model can easily make the dual prediction of how difficult it would be to add an attention-grabbing item to a display, and what features that item should have in order to draw attention. Our Feature Congestion measure of visual clutter is based upon this model of visual search.
Subband Entropy measure: This measure of visual clutter is based upon the intuition that a scene or display is less cluttered the more "organized" it is, i.e. the more items "group" together perceptually, whether through use of similar colors, or alignment, or other tricks. A related question to ask is to what extent each part of the display or scene is predictable from the rest of the scene? How redundant is the visual information in the scene?
How We Watch the City: Popularity and Online Maps
Microsoft Research
Danyel Fisher
ABSTRACT
One way of conceptualizing physical spaces is to look at
where people notice, remember, or note them. Computer-
assisted methods give us new tools based on implicit, rather
than explicit, data about how users have examined and
travelled online through cities. “Hotmap” is a tool that
visualizes how people have used maps.live.com, an
interactive mapping service, looking at what parts of the
maps they find most compelling.
Journal of Planning Education and Research, Vol. 26, No. 4, 404-414 (2007)
DOI: 10.1177/0739456X06298820
© 2007 Association of Collegiate Schools of Planning
Exploring Changes in Income Clustering and Centralization during the 1990s
Casey J. Dawkins
Urban Affairs and Planning at Virginia Tech, Virginia Center for Housing Research
This article employs a new "spatial ordering index" to describe and explain changes in the degree of income clustering and centralization within U.S. metropolitan areas during the 1990s. The results suggest that while the spatial pattern of household income became more decentralized and less clustered during the 1990s, the patterns established as of 1990 were highly persistent over the decade. Factors associated with metropolitan area size and growth affected changes in both the degree of centralization and the degree of clustering. Although traditional determinants of suburbanization were associated with increases in income decentralization during the 1990s, densely developed cities with an increase in the percentage of white residents saw increases in income centralization during the decade. Furthermore, changes in the patterns observed were shaped by various policy influences, including the number of Low Income Housing Tax Credit units, urban containment policies, and the degree of local government fragmentation.
Key Words: economic segregation • spatial analysis • metropolitan governance • urban containment • growth management
MapBuilder is a powerful, standards compliant geographic mapping client which runs in a web browser.
Geotools is used by a number of projects including Web Feature Servers, Web Map Servers, and desktop applications, as is described on this page. Some screenshots of Geotools in action are also available.
Programmers wishing to use GeoTools in their own applications can get more information from the Use page and the User Guide. Developers wishing to extend the GeoTools library can get started on the Develop page and the Developer Guide.
GeoTools releases can be found on the downloads page. The Geotools code base is maintained in a subversion repository.
GeoServer is an Open Source server that connects your information to the Geospatial Web.
With GeoServer you can publish and edit data using open standards. Your information is made available in a large variety of formats as maps/images or actual geospatial data. GeoServer's transactional capabilities offer robust support for shared editing. GeoServer's focus is ease of use and support for standards, in order to serve as 'glue' for the geospatial web, connecting from legacy databases to many diverse clients.
GeoServer supports WFS-T and WMS open protocols from the OGC to produce JPEG, PNG, SVG, KML/KMZ, GML, PDF, Shapefiles and more. More information on specific features of GeoServer can be found here, and some samples of GeoServer in action are in the gallery.
GeoServer is built on Geotools, the same Java toolkit that udig uses. GeoServer is a truly open community, with a well documented and modular codebase, so don't hesitate to get involved.