Nominations for Global Citizen Award of the GSDI Association

The GSDI Association desires to receive nominations to recognize globally an individual who has provided exemplary thought leadership and substantive worldwide contributions in (1) promoting informed and responsible use of geographic information and geospatial technologies for the benefit of society and/or (2) fostering spatial data infrastructure developments that support sustainable social, economic, and environmental systems integrated from local to global scales.

Eligibility: The candidate pool includes all persons meeting the criteria and nominations may come from any source.

Process: Nominations are accepted prior to each major global conference of the GSDI Association. The nominations letter should describe explicitly and in detail how the nominee meets the criteria. No specified format is required. Please send your letters of nomination on or before 21 May 2010 to Harlan J. Onsrud, Executive Director, GSDI Association at onsrud@spatial.maine.edu with a copy to Abbas Rajabifard, President, GSDI Association at abbas.r@unimelb.edu.au. Expect a confirmation of receipt within two days.

Selection: The sole recipient, if any, is selected by past recipients of the award and the GSDI Association executive committee which consists of the past president, president, and president-elect. The goal is to honor persons who contribute laudably as citizens of the world both for their profession and society.

Award: A plaque is awarded at the global GSDI conference, the recipient is invited to present a vision speech to the plenary audience, the recipient is permanently recognized on the Association’s Global Citizen Award web site, and the recipient receives a lifetime membership in the International Geospatial Society. The recipient must be present at the ceremony to accept the award or no award shall be presented.

Automatic Generation of the Axial Lines of Urban Environments to Capture What We Perceive

International Journal of Geographical Information Science, Volume 24, Issue 4 April 2010 , pages 545 – 558

Bin Jiang; Xintao Liu

“Based on the concepts of isovists and medial axes, we developed a set of algorithms that can automatically generate axial lines for representing individual linearly stretched parts of open space of an urban environment. Open space is the space between buildings where people can freely move around. The generation of the axial lines has been a key aspect of space syntax research, conventionally relying on hand-drawn axial lines of an urban environment, often called axial map, for urban morphological analysis. Although various attempts have been made towards an automatic solution, few of them can produce the axial map that consists of the least number of longest visibility lines, and none of them really works for different urban environments. Our algorithms provide a better solution than existing ones. Throughout this article, we have also argued and demonstrated that the axial lines constitute a true skeleton, superior to medial axes, in capturing what we perceive about the urban environment.”

Geostatistical Smoothing of Areal Data: Mapping Employment Density with Factorial Kriging

Geographical Analysis, Volume 42 Issue 1  (January 2010) p 99-117

Nicholas N. Nagle

“This article summarizes area-to-point (ATP) factorial kriging that allows the smoothing of aggregate, areal data into a continuous spatial surface. Unlike some other smoothing methods, ATP factorial kriging does not suppose that all of the data within an area are located at a centroid or other arbitrary point. Also, unlike some other smoothing methods, factorial kriging allows the user to utilize an autocovariance function to control the smoothness of the output. This is beneficial because the covariance function is a physically meaningful statement of spatial relationship, which is not the case when other spatial kernel functions are used for smoothing. Given a known covariance function, factorial kriging gives the smooth surface that is best in terms of minimizing the expected mean squared prediction error. I present an application of the factorial kriging methodology for visualizing the structure of employment density in the Denver metropolitan area.”