A model driven framework for geographic knowledge discovery

Geographic knowledge discovery (GKD) is the process of extracting information and knowledge from massive georeferenced databases. Usually the process is accomplished by two different systems, the Geographic Information Systems (GIS) and the data mining engines. However, the development of those systems is a complex task due to it does not follow a systematic, integrated and standard methodology. To overcome these pitfalls, in this paper, we propose a modeling framework that addresses the development of the different parts of a multilayer GKD process. The main advantages of our framework are that: (i) it reduces the design effort, (ii) it improves quality systems obtained, (iii) it is independent of platforms, (iv) it facilitates the use of data mining techniques on geo-referenced data, and finally, (v) it ameliorates the communication between different users.

Intra-population variation and geographic correlation in Canthon humectus hidalgoensis using FTIR-ATR spectroscopy

In some cases external morphology is not sufficient to discern between populations of a species, as occurs in the dung beetle Canthon humectus hidalgoensis Bates; and much less to determine phenotypic distances between them. FTIR-ATR spectroscopy show several advantages over other identification techniques (e.g. morphological, genetic, and cuticular hydrocarbons analysis) due to the non-invasive manner of the sample preparation, the relative speed of sample analysis and the low-cost of this technology. The infrared spectrum obtained is recognized to give a unique ‘fingerprint’ because vibrational spectra are specific and unique to the molecular nature of the sample. In our study, results showed that proteins, amino acids and aromatic ethers of insect exocuticle have promising discriminative power to discern between different populations of C. h. hidalgoensis. Furthermore, the correlation between geographic distances between populations and the chemical distances obtained by proteins + amino acids + aromatic ethers was statistically significant, showing that the spectral and spatial information available of the taxa together with appropriated chemometric methods may help to a better understanding of the identity, structure, dynamics and diversity of insect populations.