Mexico City government has promoted central and historical areas by applying public policies intended to activate their economy. This is the case of the neighbourhood “La Condesa” located 4 km. from the Historical Downtown Area of Mexico City. Yet, beyond activating the economy, promoting massive tourism, leisure activities and business life, these policies have had questionable social implications. For example, valuing spaces for the benefit of the real estate investors that do not live in the areas and that promote non-permanent lodging; franchises that do not promote authentic services and products. Thus, it seems that the growth in economy with this approach is not compatible with human development, with the cultural benefit and the conservation of the green areas of the territory. This paper presents our approach for computing the index of quality of life considering quantitative and qualitative measures seeking to maximise a holistic return of investment. Our notion of return of investment is holistic because it considers both quantitative and qualitative variables calibrated to find and “optimum” of economic and well-being benefit. Our proposal combines different data collections provided by the Mexican National Institute of Statistics and Geography (INEGI) that feed a novel mathematical model proposed for computing determining the elasticity of the index of quality of life. Given the volume of data sets about Mexico City and its inhabitants it has been necessary to use adapted computational methods to model urban phenomena happening in the area “La Condesa” in Mexico City. We have applied data analytics computational techniques based on mathematical methods, statistics and knowledge discovery to find patterns within data sets that represent the behaviour of quality of life as a social phenomenon measure.

This paper demonstrates CURARE, an environment for curating and assisting data scientists to explore raw data collections. CURARE implements a data curation model used to store structural and quantitative metadata semi-automatically extracted. It provides associated functions for exploring these metadata. The demonstration proposed in this paper is devoted to evaluate and compare the effort invested by a data scientist when exploring data collections with and without CURARE assistance.