Journal of Geographic Information System, 2013, 5, 548-558 Published Online December 2013 (http://www.scirp.org/journal/jgis) http://dx.doi.org/10.4236/jgis.2013.56052 Open Access JGIS Using GIS Data to Build Informed Virtual Geographic Environments (IVGE) Mehdi Mekni Department of Math Science and Technology, University of Minnesota, Crookston, USA Email: mmekni@umn.edu Received April 5, 2013; revised May 5, 2013; accepted May 12, 2013 Copyright © 2013 Mehdi Mekni. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. ABSTRACT In this paper, we propose a novel approach to automatically building Informed Virtual Geographic Environments (IVGE) using data provided by Geographic Information Systems (GIS). The obtained IVGE provides 2D and 3D geo- graphic information for visualization and simulation purposes. Conventional VGE approaches are generally built upon a grid-based representation, raising the well-known problems of the lack of accuracy of the localized data and the diffi- culty to merge data with multiple semantics. On the contrary, our approach uses a topological model and provides an exact representation of GIS data, allowing an accurate geometrical exploitation. Moreover, our model can merge se- mantic information, even if spatially overlapping. In addition, the proposed IVGE contains spatial information which can be enhanced thanks to a geometric abstraction method. We illustrate this model with an application which auto- matically extracts the required data from standard GIS files and allows a user to navigate and retrieve information from the computed IVGE. Keywords: Geographic Information System (GIS); Informed Virtual Geographic Environments (IVGE); Multi-Agent Geo-Simulation (MAGS) 1. Introduction Modern geography techniques play an irreplaceable role in exploring temporal-spatial patterns and dynamic proc- esses, and understanding the relationships between geo- graphic features, objects, and actors in the real world. Studying these relationships is fundamental and compre- hensive geographical analysis often requires the integra- tion of different disciplines at various scales. By using a variety of different techniques, it is possible to produce virtual reality representations, integrated models, simula- tions, forecasts and evaluations of the geographic envi- ronment. Therefore, the Virtual Geographic Environment (VGE) can be a useful tool for understanding the evolu- tionary process, temporal-spatial patterns, driving mecha- nisms, and the direction of succession in the real envi- ronment. Current VGE techniques, including three-di- mensional (3D) techniques, have extended the potential applications of geographic modeling. The ability of VGE to depict past, present, and future geographic environ- ments has been of particular importance [1]. Traditional methods for extracting and expressing spa- tial information can be modified to better suit develop- ment in the field of geographic modeling [2]. Through using geographic analytical modeling and visualization techniques, VGE can be used to perform geographic analysis, simulate geographic phenomena, represent and predict changes in the geographic environment, and eva- luate the influence of human activities on the environ- ment [3]. By using VGE to share ideas, people can coop- erate and coordinate their work on geographic objects and phenomena, resulting in more advanced methods of designing and transforming the world (Figure 1). VGE will become increasingly important in geography in the future [2]. In this paper, we propose a novel approach to model- ing VGE which deals with all these constraints. Our ap- proach provides an exact representation of the geo- graphic environment using vector data and including elevation. This representation is organized as a topologi- cal graph, enhanced with data integrating both quantita- tive (geometry) and qualitative information (zones such as roads and buildings). Moreover, our approach is di- rected to efficient spatial reasoning like path planning for crowd simulation (Figure 2). Hence, we also address the performance issue when exploiting such environments.