Delineating zones to increase geographical detail in individual response data files: An application to the Spanish 2011 Census of population

Abstract

Due to confidentiality considerations, the microdata available from the 2011 Spanish Census have been codified at a provincial (NUTS 3) level except when the municipal (LAU 2) population exceeds 20,000 inhabitants (a requirement that is met by less than 5% of all municipalities). For the remainder of the municipalities within a given province, information is only provided for their classification in wide population intervals. These limitations, hampering territorially-focused socio-economic analyses, and more specifically, those related to the labour market, are observed in many other countries. This article proposes and demonstrates an automatic procedure aimed at delineating a set of areas that meet such population requirements and that may be used to re-codify the geographic reference in these cases, thereby increasing the territorial detail at which individual information is available. The method aggregates municipalities into clusters based on the optimisation of a relevant objective function subject to a number of statistical constraints, and is implemented using evolutionary computation techniques. Clusters are defined to fit outer boundaries at the level of labour market areas.

A definition of relevant functional regions for international comparisons: The case of Central Europe

“…The overall review of 50 years of development in functional regions and functional regional taxonomy is summarised by . Recently, the use of evolutionary or genetic algorithms spread into functional regional taxonomy (Alonso et al, 2015;Flórez-Revuelta et al, 2008;Martínez-Bernabeu & Casado-Díaz, 2016;Martínez-Bernabeu et al, 2012). Though rule-based approaches are considered to be the most successful , some authors (e.g., Cörvers et al, 2009;Mitchell & Watts, 2010) have recently once again focused their attention on the general methods of numerical taxonomy as originally proposed by Brown and Holmes (1971), Masser and Brown (1975), Scheurwater (1978, 1980) and Baumann et al (1983).…”

“…Graph theoretical methods as proposed by Nystuen and Dacey () and Holmes and Haggett () were used, for instance, by Karlsson and Olsson (). Recently, the use of evolutionary or genetic algorithms spread into functional regional taxonomy (Alonso et al., ; Flórez‐Revuelta et al., ; Martínez‐Bernabeu & Casado‐Díaz, ; Martínez‐Bernabeu et al., ). These heuristic methods use genetic concepts such as selection, mutation and crossover and optimise so‐called fitness function and provide us with a number of variant solutions to the large regionalisation problems (Klapka & Halás, ).…”