Evolutionary and classification methods for local labor markets delineation

Abstract

This paper proposes some new evolutionary and classification methods for the delineation of local labor markets (LLMs) in areas where there are a large number of small localities with little labor interaction. The evolutionary methods presented here, based on previous works of Florez-Revuelta et al. (Int J Autom Comput 5:10–21, 2008a; PPSN X, LNCS 5199:1011–1020, 2008b) and Martinez-Bernabeu et al. (Expert Syst Appl 39:6754–6766, 2012), decrease their computational times (up to a 99 %) without deteriorating the quality and robustness of the solutions. Also, in this work we avoid geographical contiguity constraints because such restrictions might reduce the realism of the process. Another contribution of this paper is related to the location of new services—hospitals, schools, employment centers, etc.—taking into account the labor mobility patterns. In this context, we present a cluster partitioning of k-means procedure, which captures the common aspects of all the potential solutions of these evolutionary algorithms and allows us to rank the LLMs foci, understood as the main centers of activity of the markets. The performance of the algorithms is analyzed through a real commuting dataset of the region of Aragon (Spain).