RELATIONAL DATA MODELING TO ENHANCE GIS-BASED VISUAL INFORMATION SYSTEMS

This paper uses a geographic information system (GIS) case study on the subject of poverty detection and visualization to illustrate how relational data modeling can be used to enhance the data set upon which the GIS is based. Regular expressions, followed by one-to-one joins performed directly within the GIS, significantly increase the richness of the data, and subsequently, the visual representations of those data. Correlations are drawn between poverty levels and other spatial attributes including population density, transportation infrastructure, and other potentially influential variables. Use of the methodology applied in this case study for business or governmental decision support is then discussed in order to illustrate the utility of the paper's topic to address a wide range of organizational problems and questions.