GeoVISTA Studio : A Geocomputational Workbench

One barrier to the uptake of Geocomputation is that, unlike GIS, it has no system or toolbox that provides easy access to useful functionality. This paper describes an experimental environment, GeoVISTA Studio, that attempts to address this shortcoming. Studio is a Java-based, visual programming environment that allows for the rapid, programming free development of complex data exploration and knowledge construction applications to support geographic analysis. It achieves this by leveraging advances in geocomputation, software engineering, visualisation and machine learning. At the time of writing, Studio contains full 3D rendering capability and has the following functionality: interactive parallel coordinate plots, visual classifier, sophisticated colour selection (including Munsell colour-space), spreadsheet, statistics package, self-organising map (SOM) and learning vector quantisation. Through examples of Studio at work, this paper demonstrates the roles that geocomputation and visualization can play throughout the scientific cycle of knowledge creation, emphasising their supportive and mutually beneficial relationship. A brief overview of the design of Studio is also given. Results are presented to show practical benefits of a combined visual and geocomputational approach to analysing and understanding complex geospatial datasets.

[1]  George Henry Dunteman,et al.  Introduction To Multivariate Analysis , 1984 .

[2]  Jon Atli Benediktsson,et al.  Neural Network Approaches Versus Statistical Methods in Classification of Multisource Remote Sensing Data , 1989, 12th Canadian Symposium on Remote Sensing Geoscience and Remote Sensing Symposium,.

[3]  G. Lakoff.  Women, fire, and dangerous things : what categories reveal about the mind , 1989 .

[4]  W. B. Yates,et al.  Classification of remotely sensed data by an artificial neural network: issues related to training data characteristics , 1995 .

[5]  Mark Gahegan,et al.  Neural network architectures for the classification of temporal image sequences , 1996 .

[6]  Padhraic Smyth,et al.  From Data Mining to Knowledge Discovery: An Overview , 1996, Advances in Knowledge Discovery and Data Mining.

[7]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[8]  John Anderson,et al.  Exploring Coupled Atmosphere-Ocean Models Using Vis5D , 1996, Int. J. High Perform. Comput. Appl..

[9]  Hing-Yan Lee,et al.  Visualization Support for Data Mining , 1996, IEEE Expert.

[10]  Hans-Peter Kriegel,et al.  Visualization Techniques for Mining Large Databases: A Comparison , 1996, IEEE Trans. Knowl. Data Eng..

[11]  Alfred Inselberg,et al.  Multidimensional detective , 1997, Proceedings of VIZ '97: Visualization Conference, Information Visualization Symposium and Parallel Rendering Symposium.

[12]  Terry A. Slocum.  Thematic Cartography and Visualization , 1998 .

[13]  Alan M. MacEachren,et al.  Constructing knowledge from multivariate spatiotemporal data: integrating geographical visualization with knowledge discovery in database methods , 1999, Int. J. Geogr. Inf. Sci..

[14]  David A. Landgrebe,et al.  Information Extraction Principles and Methods for Multispectral and Hyperspectral Image Data , 1999 .

[15]  David M. Mark,et al.  Cognitive models of geographical space , 1999, Int. J. Geogr. Inf. Sci..

[16]  John F. Sowa,et al.  Knowledge representation: logical, philosophical, and computational foundations , 2000 .