A data based model as a metropolitan management tool: The Bogotá-Sabana region case study in Colombia

Data mining techniques are useful tools for knowledge extraction in large databases. They are successfully applied in disciplines such as urban management and geography. The use of these techniques is suitable for the study of complex urban dynamics of post-industrial cities. The objective of this research is to empirically apply data mining techniques as a tool for metropolitan management. This paper presents a methodology developed to elucidate the dynamics of urban growth and its relationship with socioeconomic patterns in the Bogotá-Sabana region of Colombia. To analyze the evolution of growth in two time frames, various techniques are applied, including correlation, clustering, regionalization, and others. The results enable interesting patterns to be identified in urban development. A key aspect of these patterns is the high level of segregation and inequity that is apparent in the case study. Finally, the application opportunities of these tools in urban management are discussed.